Added Groq streaming support and optimizations - clean version

.github/workflows/sync-to-space.yml

@@ -0,0 +1,17 @@
+name: Sync to Hugging Face Space
+on:
+  push:
+    branches: [main]
+
+jobs:
+  sync-to-space:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+      - name: Push to Space
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: |
+          git push https://USER:[email protected]/spaces/USER/SPACE_NAME main

.gitignore

@@ -0,0 +1,91 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+env/
+ENV/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Logs
+*.log
+logs/
+log/
+
+# Local development
+.env
+.env.local
+.env.*.local
+
+# Data
+data/
+*.db
+*.sqlite3
+
+# Model files
+*.pt
+*.pth
+*.ckpt
+*.bin
+*.onnx
+
+# Temporary files
+.DS_Store
+Thumbs.db
+*.tmp
+*.bak
+*.swp
+*~
+
+# Distribution
+dist/
+build/
+*.egg-info/
+
+# Documentation
+docs/_build/
+site/
+
+# Testing
+.coverage
+htmlcov/
+.pytest_cache/
+.tox/
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+*.ipynb
+
+# Project specific
+outputs/
+results/
+experiments/
+checkpoints/

Dockerfile

@@ -0,0 +1,52 @@
+FROM python:3.10-slim
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    DEBIAN_FRONTEND=noninteractive \
+    REQUESTS_TIMEOUT=30 \
+    PYTHONPATH=/app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    git \
+    dnsutils \
+    iputils-ping \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set working directory
+WORKDIR /app
+
+# Set up DNS configuration with multiple DNS servers for redundancy
+RUN echo "nameserver 8.8.8.8" > /etc/resolv.conf && \
+    echo "nameserver 8.8.4.4" >> /etc/resolv.conf && \
+    echo "nameserver 1.1.1.1" >> /etc/resolv.conf && \
+    echo "options timeout:1 attempts:5" >> /etc/resolv.conf
+
+# Copy requirements first to leverage Docker cache
+COPY requirements.txt .
+
+# Install Python dependencies with retry mechanism and explicit Gradio upgrade
+RUN pip install --no-cache-dir -r requirements.txt || \
+    (sleep 5 && pip install --no-cache-dir -r requirements.txt) || \
+    (sleep 10 && pip install --no-cache-dir -r requirements.txt) && \
+    pip install --no-cache-dir gradio==4.44.1
+
+# Copy application code
+COPY . .
+
+# Expose port
+EXPOSE 7860
+
+# Add network verification script
+RUN echo '#!/bin/sh\n\
+ping -c 1 huggingface.co || ping -c 1 8.8.8.8\n\
+' > /healthcheck.sh && chmod +x /healthcheck.sh
+
+# Healthcheck with more lenient settings
+HEALTHCHECK --interval=30s --timeout=10s --start-period=30s --retries=5 \
+    CMD /healthcheck.sh || exit 1
+
+# Command to run the application
+CMD ["python", "app.py"]

README.md

@@ -0,0 +1,113 @@
+---
+title: Advanced Reasoning System 🤖
+emoji: 🤖
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.16.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# Advanced Reasoning System 🤖
+
+A sophisticated multi-agent reasoning system that combines multiple strategies for advanced problem-solving.
+
+## Features
+
+- 🧠 Multiple Reasoning Strategies:
+  - Chain of Thought
+  - Tree of Thoughts
+  - Meta Learning
+  - Local LLM Integration
+  - Recursive Reasoning
+  - Analogical Reasoning
+
+- 🤝 Multi-Agent System:
+  - Dynamic Team Formation
+  - Cross-Team Collaboration
+  - Resource Management
+  - Task Orchestration
+
+- 🔄 Adaptive Learning:
+  - Performance Tracking
+  - Strategy Weight Adjustment
+  - Pattern Recognition
+  - Meta-Learning Integration
+
+## Quick Start
+
+1. **Environment Setup**:
+   ```bash
+   python -m venv venv
+   source venv/bin/activate  # On Windows: .\venv\Scripts\activate
+   pip install -r requirements.txt
+   ```
+
+2. **Configuration**:
+   ```bash
+   cp .env.example .env
+   # Edit .env with your settings
+   ```
+
+3. **Run the Application**:
+   ```bash
+   python app.py
+   ```
+
+## Docker Support
+
+Build and run with Docker:
+
+```bash
+docker build -t advanced-reasoning .
+docker run -p 7860:7860 advanced-reasoning
+```
+
+## API Endpoints
+
+- `/`: Main interface
+- `/health`: Health check endpoint
+- `/api/process_query`: Process queries via API
+
+## Components
+
+1. **Reasoning Engine**:
+   - Unified reasoning combining multiple strategies
+   - Dynamic strategy selection
+   - Result synthesis
+
+2. **Team Management**:
+   - Specialized teams (Coders, Business, Research, Trading)
+   - Cross-team collaboration
+   - Resource sharing
+
+3. **Orchestration**:
+   - Task planning and decomposition
+   - Resource allocation
+   - Performance monitoring
+
+## Contributing
+
+1. Fork the repository
+2. Create your feature branch
+3. Commit your changes
+4. Push to the branch
+5. Create a Pull Request
+
+## License
+
+MIT License - see LICENSE file for details
+
+## Files
+- `app.py`: Main application with Gradio interface and API integration
+- `requirements.txt`: Project dependencies
+- `.env.example`: Example environment variables (for reference)
+
+## Dependencies
+- gradio==4.16.0
+- requests==2.31.0
+
+---
+Created with ❤️ using Gradio and Hugging Face

agentic_system.py

@@ -0,0 +1,558 @@
+"""
+Advanced Agentic System
+----------------------
+A sophisticated multi-agent system with:
+
+Core Components:
+1. Agent Management
+2. Task Execution
+3. Learning & Adaptation
+4. Communication
+5. Resource Management
+
+Advanced Features:
+1. Self-Improvement
+2. Multi-Agent Coordination
+3. Dynamic Role Assignment
+4. Emergent Behavior
+"""
+
+import logging
+from typing import Dict, Any, List, Optional, Union, TypeVar
+from dataclasses import dataclass, field
+from enum import Enum
+import json
+import asyncio
+from datetime import datetime
+import uuid
+from concurrent.futures import ThreadPoolExecutor
+import numpy as np
+from collections import defaultdict
+
+from orchestrator import (
+    AgentOrchestrator,
+    AgentRole,
+    AgentState,
+    TaskPriority,
+    Task
+)
+from reasoning import UnifiedReasoningEngine as ReasoningEngine, StrategyType as ReasoningMode
+from reasoning.meta_learning import MetaLearningStrategy
+
+class AgentCapability(Enum):
+    """Core capabilities of agents."""
+    REASONING = "reasoning"
+    LEARNING = "learning"
+    EXECUTION = "execution"
+    COORDINATION = "coordination"
+    MONITORING = "monitoring"
+
+class AgentPersonality(Enum):
+    """Different personality types for agents."""
+    ANALYTICAL = "analytical"
+    CREATIVE = "creative"
+    CAUTIOUS = "cautious"
+    PROACTIVE = "proactive"
+    ADAPTIVE = "adaptive"
+
+@dataclass
+class AgentProfile:
+    """Profile defining an agent's characteristics."""
+    id: str
+    name: str
+    role: AgentRole
+    capabilities: List[AgentCapability]
+    personality: AgentPersonality
+    expertise_areas: List[str]
+    learning_rate: float
+    risk_tolerance: float
+    created_at: datetime
+    metadata: Dict[str, Any]
+
+class Agent:
+    """Advanced autonomous agent with learning capabilities."""
+    
+    def __init__(
+        self,
+        profile: AgentProfile,
+        reasoning_engine: ReasoningEngine,
+        meta_learning: MetaLearningStrategy,
+        config: Dict[str, Any] = None
+    ):
+        self.profile = profile
+        self.reasoning_engine = reasoning_engine
+        self.meta_learning = meta_learning
+        self.config = config or {}
+        
+        # State management
+        self.state = AgentState.IDLE
+        self.current_task: Optional[Task] = None
+        self.task_history: List[Task] = []
+        
+        # Learning and adaptation
+        self.knowledge_base: Dict[str, Any] = {}
+        self.learned_patterns: List[Dict[str, Any]] = []
+        self.adaptation_history: List[Dict[str, Any]] = []
+        
+        # Performance metrics
+        self.metrics: Dict[str, List[float]] = defaultdict(list)
+        self.performance_history: List[Dict[str, float]] = []
+        
+        # Communication
+        self.message_queue = asyncio.Queue()
+        self.response_queue = asyncio.Queue()
+        
+        # Resource management
+        self.resource_usage: Dict[str, float] = {}
+        self.resource_limits: Dict[str, float] = {}
+        
+        # Async support
+        self.executor = ThreadPoolExecutor(max_workers=2)
+        self.lock = asyncio.Lock()
+        
+        # Logging
+        self.logger = logging.getLogger(f"Agent-{profile.id}")
+        
+        # Initialize components
+        self._init_components()
+
+    def _init_components(self):
+        """Initialize agent components."""
+        # Set up knowledge base
+        self.knowledge_base = {
+            "expertise": {area: 0.5 for area in self.profile.expertise_areas},
+            "learned_skills": set(),
+            "interaction_patterns": defaultdict(int),
+            "success_patterns": defaultdict(float)
+        }
+        
+        # Set up resource limits
+        self.resource_limits = {
+            "cpu": 1.0,
+            "memory": 1000,
+            "api_calls": 100,
+            "learning_capacity": 0.8
+        }
+
+    async def process_task(self, task: Task) -> Dict[str, Any]:
+        """Process an assigned task."""
+        try:
+            self.current_task = task
+            self.state = AgentState.BUSY
+            
+            # Analyze task
+            analysis = await self._analyze_task(task)
+            
+            # Plan execution
+            plan = await self._plan_execution(analysis)
+            
+            # Execute plan
+            result = await self._execute_plan(plan)
+            
+            # Learn from execution
+            await self._learn_from_execution(task, result)
+            
+            # Update metrics
+            self._update_metrics(task, result)
+            
+            return {
+                "success": True,
+                "task_id": task.id,
+                "result": result,
+                "metrics": self._get_execution_metrics()
+            }
+            
+        except Exception as e:
+            self.logger.error(f"Error processing task: {e}")
+            self.state = AgentState.ERROR
+            return {
+                "success": False,
+                "task_id": task.id,
+                "error": str(e)
+            }
+        finally:
+            self.state = AgentState.IDLE
+            self.current_task = None
+
+    async def _analyze_task(self, task: Task) -> Dict[str, Any]:
+        """Analyze task requirements and constraints."""
+        # Use reasoning engine for analysis
+        analysis = await self.reasoning_engine.reason(
+            query=task.description,
+            context={
+                "agent_profile": self.profile.__dict__,
+                "task_history": self.task_history,
+                "knowledge_base": self.knowledge_base
+            },
+            mode=ReasoningMode.ANALYTICAL
+        )
+        
+        return {
+            "requirements": analysis.get("requirements", []),
+            "constraints": analysis.get("constraints", []),
+            "complexity": analysis.get("complexity", 0.5),
+            "estimated_duration": analysis.get("estimated_duration", 3600),
+            "required_capabilities": analysis.get("required_capabilities", [])
+        }
+
+    async def _plan_execution(self, analysis: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Plan task execution based on analysis."""
+        # Use reasoning engine for planning
+        plan = await self.reasoning_engine.reason(
+            query="Plan execution steps",
+            context={
+                "analysis": analysis,
+                "agent_capabilities": self.profile.capabilities,
+                "resource_limits": self.resource_limits
+            },
+            mode=ReasoningMode.FOCUSED
+        )
+        
+        return plan.get("steps", [])
+
+    async def _execute_plan(self, plan: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """Execute the planned steps."""
+        results = []
+        
+        for step in plan:
+            try:
+                # Check resources
+                if not self._check_resources(step):
+                    raise RuntimeError("Insufficient resources for step execution")
+                
+                # Execute step
+                step_result = await self._execute_step(step)
+                results.append(step_result)
+                
+                # Update resource usage
+                self._update_resource_usage(step)
+                
+                # Learn from step execution
+                await self._learn_from_step(step, step_result)
+                
+            except Exception as e:
+                self.logger.error(f"Error executing step: {e}")
+                results.append({"error": str(e)})
+        
+        return {
+            "success": all(r.get("success", False) for r in results),
+            "results": results
+        }
+
+    async def _execute_step(self, step: Dict[str, Any]) -> Dict[str, Any]:
+        """Execute a single step of the plan."""
+        step_type = step.get("type", "unknown")
+        
+        if step_type == "reasoning":
+            return await self._execute_reasoning_step(step)
+        elif step_type == "learning":
+            return await self._execute_learning_step(step)
+        elif step_type == "action":
+            return await self._execute_action_step(step)
+        else:
+            raise ValueError(f"Unknown step type: {step_type}")
+
+    async def _execute_reasoning_step(self, step: Dict[str, Any]) -> Dict[str, Any]:
+        """Execute a reasoning step."""
+        result = await self.reasoning_engine.reason(
+            query=step["query"],
+            context=step.get("context", {}),
+            mode=ReasoningMode.ANALYTICAL
+        )
+        
+        return {
+            "success": result.get("success", False),
+            "reasoning_result": result
+        }
+
+    async def _execute_learning_step(self, step: Dict[str, Any]) -> Dict[str, Any]:
+        """Execute a learning step."""
+        result = await self.meta_learning.learn(
+            data=step["data"],
+            context=step.get("context", {})
+        )
+        
+        return {
+            "success": result.get("success", False),
+            "learning_result": result
+        }
+
+    async def _execute_action_step(self, step: Dict[str, Any]) -> Dict[str, Any]:
+        """Execute an action step."""
+        action_type = step.get("action_type")
+        
+        if action_type == "api_call":
+            return await self._make_api_call(step)
+        elif action_type == "data_processing":
+            return await self._process_data(step)
+        elif action_type == "coordination":
+            return await self._coordinate_action(step)
+        else:
+            raise ValueError(f"Unknown action type: {action_type}")
+
+    def _check_resources(self, step: Dict[str, Any]) -> bool:
+        """Check if sufficient resources are available."""
+        required_resources = step.get("required_resources", {})
+        
+        for resource, amount in required_resources.items():
+            if self.resource_usage.get(resource, 0) + amount > self.resource_limits.get(resource, float('inf')):
+                return False
+        
+        return True
+
+    def _update_resource_usage(self, step: Dict[str, Any]):
+        """Update resource usage after step execution."""
+        used_resources = step.get("used_resources", {})
+        
+        for resource, amount in used_resources.items():
+            self.resource_usage[resource] = self.resource_usage.get(resource, 0) + amount
+
+    async def _learn_from_execution(self, task: Task, result: Dict[str, Any]):
+        """Learn from task execution experience."""
+        # Prepare learning data
+        learning_data = {
+            "task": task.__dict__,
+            "result": result,
+            "context": {
+                "agent_state": self.state,
+                "resource_usage": self.resource_usage,
+                "performance_metrics": self._get_execution_metrics()
+            }
+        }
+        
+        # Learn patterns
+        patterns = await self.meta_learning.learn(
+            data=learning_data,
+            context=self.knowledge_base
+        )
+        
+        # Update knowledge base
+        self._update_knowledge_base(patterns)
+        
+        # Record adaptation
+        self.adaptation_history.append({
+            "timestamp": datetime.now(),
+            "patterns": patterns,
+            "metrics": self._get_execution_metrics()
+        })
+
+    async def _learn_from_step(self, step: Dict[str, Any], result: Dict[str, Any]):
+        """Learn from individual step execution."""
+        if result.get("success", False):
+            # Update success patterns
+            pattern_key = f"{step['type']}:{step.get('action_type', 'none')}"
+            self.knowledge_base["success_patterns"][pattern_key] += 1
+            
+            # Learn from successful execution
+            await self.meta_learning.learn(
+                data={
+                    "step": step,
+                    "result": result
+                },
+                context={"pattern_key": pattern_key}
+            )
+
+    def _update_knowledge_base(self, patterns: Dict[str, Any]):
+        """Update knowledge base with new patterns."""
+        # Update expertise levels
+        for area, pattern in patterns.get("expertise_patterns", {}).items():
+            if area in self.knowledge_base["expertise"]:
+                current = self.knowledge_base["expertise"][area]
+                self.knowledge_base["expertise"][area] = current * 0.9 + pattern * 0.1
+        
+        # Add new learned skills
+        new_skills = patterns.get("learned_skills", set())
+        self.knowledge_base["learned_skills"].update(new_skills)
+        
+        # Update interaction patterns
+        for pattern, count in patterns.get("interaction_patterns", {}).items():
+            self.knowledge_base["interaction_patterns"][pattern] += count
+
+    def _update_metrics(self, task: Task, result: Dict[str, Any]):
+        """Update performance metrics."""
+        metrics = {
+            "success": float(result.get("success", False)),
+            "duration": (datetime.now() - task.created_at).total_seconds(),
+            "resource_efficiency": self._calculate_resource_efficiency(),
+            "learning_progress": self._calculate_learning_progress()
+        }
+        
+        for key, value in metrics.items():
+            self.metrics[key].append(value)
+        
+        self.performance_history.append({
+            "timestamp": datetime.now(),
+            "metrics": metrics
+        })
+
+    def _calculate_resource_efficiency(self) -> float:
+        """Calculate resource usage efficiency."""
+        if not self.resource_limits:
+            return 1.0
+            
+        efficiencies = []
+        for resource, usage in self.resource_usage.items():
+            limit = self.resource_limits.get(resource, float('inf'))
+            if limit > 0:
+                efficiencies.append(1 - (usage / limit))
+                
+        return sum(efficiencies) / len(efficiencies) if efficiencies else 1.0
+
+    def _calculate_learning_progress(self) -> float:
+        """Calculate learning progress."""
+        if not self.knowledge_base["expertise"]:
+            return 0.0
+            
+        return sum(self.knowledge_base["expertise"].values()) / len(self.knowledge_base["expertise"])
+
+    def _get_execution_metrics(self) -> Dict[str, float]:
+        """Get current execution metrics."""
+        return {
+            key: sum(values[-10:]) / len(values[-10:])
+            for key, values in self.metrics.items()
+            if values
+        }
+
+class AgenticSystem:
+    """Advanced multi-agent system with orchestration."""
+    
+    def __init__(self, config: Dict[str, Any] = None):
+        self.config = config or {}
+        
+        # Initialize orchestrator
+        self.orchestrator = AgentOrchestrator(config)
+        
+        # Initialize components
+        self.agents: Dict[str, Agent] = {}
+        self.reasoning_engine = ReasoningEngine(
+            min_confidence=self.config.get('min_confidence', 0.7),
+            parallel_threshold=self.config.get('parallel_threshold', 3),
+            learning_rate=self.config.get('learning_rate', 0.1),
+            strategy_weights=self.config.get('strategy_weights', {
+                "LOCAL_LLM": 0.8,
+                "CHAIN_OF_THOUGHT": 0.6,
+                "TREE_OF_THOUGHTS": 0.5,
+                "META_LEARNING": 0.4
+            })
+        )
+        self.meta_learning = MetaLearningStrategy(config)
+        
+        # System state
+        self.state = "initialized"
+        self.metrics: Dict[str, List[float]] = defaultdict(list)
+        
+        # Async support
+        self.executor = ThreadPoolExecutor(max_workers=4)
+        self.lock = asyncio.Lock()
+        
+        # Logging
+        self.logger = logging.getLogger("AgenticSystem")
+
+    async def create_agent(
+        self,
+        name: str,
+        role: AgentRole,
+        capabilities: List[AgentCapability],
+        personality: AgentPersonality,
+        expertise_areas: List[str]
+    ) -> str:
+        """Create a new agent."""
+        # Create agent profile
+        profile = AgentProfile(
+            id=str(uuid.uuid4()),
+            name=name,
+            role=role,
+            capabilities=capabilities,
+            personality=personality,
+            expertise_areas=expertise_areas,
+            learning_rate=0.1,
+            risk_tolerance=0.5,
+            created_at=datetime.now(),
+            metadata={}
+        )
+        
+        # Create agent instance
+        agent = Agent(
+            profile=profile,
+            reasoning_engine=self.reasoning_engine,
+            meta_learning=self.meta_learning,
+            config=self.config.get("agent_config", {})
+        )
+        
+        # Register with orchestrator
+        agent_id = await self.orchestrator.register_agent(
+            role=role,
+            capabilities=[c.value for c in capabilities]
+        )
+        
+        # Store agent
+        async with self.lock:
+            self.agents[agent_id] = agent
+        
+        return agent_id
+
+    async def submit_task(
+        self,
+        description: str,
+        priority: TaskPriority = TaskPriority.MEDIUM,
+        deadline: Optional[datetime] = None
+    ) -> str:
+        """Submit a task to the system."""
+        return await self.orchestrator.submit_task(
+            description=description,
+            priority=priority,
+            deadline=deadline
+        )
+
+    async def get_task_status(self, task_id: str) -> Dict[str, Any]:
+        """Get status of a task."""
+        return await self.orchestrator.get_task_status(task_id)
+
+    async def get_agent_status(self, agent_id: str) -> Dict[str, Any]:
+        """Get status of an agent."""
+        agent = self.agents.get(agent_id)
+        if not agent:
+            raise ValueError(f"Unknown agent: {agent_id}")
+            
+        return {
+            "profile": agent.profile.__dict__,
+            "state": agent.state,
+            "current_task": agent.current_task.__dict__ if agent.current_task else None,
+            "metrics": agent._get_execution_metrics(),
+            "resource_usage": agent.resource_usage
+        }
+
+    async def get_system_status(self) -> Dict[str, Any]:
+        """Get overall system status."""
+        return {
+            "state": self.state,
+            "agent_count": len(self.agents),
+            "active_tasks": len([a for a in self.agents.values() if a.state == AgentState.BUSY]),
+            "performance_metrics": self._calculate_system_metrics(),
+            "resource_usage": self._calculate_resource_usage()
+        }
+
+    def _calculate_system_metrics(self) -> Dict[str, float]:
+        """Calculate overall system metrics."""
+        metrics = defaultdict(list)
+        
+        for agent in self.agents.values():
+            agent_metrics = agent._get_execution_metrics()
+            for key, value in agent_metrics.items():
+                metrics[key].append(value)
+        
+        return {
+            key: sum(values) / len(values)
+            for key, values in metrics.items()
+            if values
+        }
+
+    def _calculate_resource_usage(self) -> Dict[str, float]:
+        """Calculate overall resource usage."""
+        usage = defaultdict(float)
+        
+        for agent in self.agents.values():
+            for resource, amount in agent.resource_usage.items():
+                usage[resource] += amount
+        
+        return dict(usage)

api/__init__.py

@@ -0,0 +1,6 @@
+"""API package for the agentic system."""
+
+from .openai_compatible import OpenAICompatibleAPI
+from .venture_api import VentureAPI
+
+__all__ = ['OpenAICompatibleAPI', 'VentureAPI']

api/openai_compatible.py

@@ -0,0 +1,139 @@
+"""OpenAI-compatible API endpoints."""
+
+from typing import Dict, List, Optional, Union
+from pydantic import BaseModel, Field
+from fastapi import APIRouter, HTTPException, Depends
+import time
+import json
+import asyncio
+from datetime import datetime
+
+class ChatMessage(BaseModel):
+    """OpenAI-compatible chat message."""
+    role: str = Field(..., description="The role of the message author (system/user/assistant)")
+    content: str = Field(..., description="The content of the message")
+    name: Optional[str] = Field(None, description="The name of the author")
+
+class ChatCompletionRequest(BaseModel):
+    """OpenAI-compatible chat completion request."""
+    model: str = Field(..., description="Model to use")
+    messages: List[ChatMessage]
+    temperature: Optional[float] = Field(0.7, description="Sampling temperature")
+    top_p: Optional[float] = Field(1.0, description="Nucleus sampling parameter")
+    n: Optional[int] = Field(1, description="Number of completions")
+    stream: Optional[bool] = Field(False, description="Whether to stream responses")
+    stop: Optional[Union[str, List[str]]] = Field(None, description="Stop sequences")
+    max_tokens: Optional[int] = Field(None, description="Maximum tokens to generate")
+    presence_penalty: Optional[float] = Field(0.0, description="Presence penalty")
+    frequency_penalty: Optional[float] = Field(0.0, description="Frequency penalty")
+    user: Optional[str] = Field(None, description="User identifier")
+
+class ChatCompletionResponse(BaseModel):
+    """OpenAI-compatible chat completion response."""
+    id: str = Field(..., description="Unique identifier for the completion")
+    object: str = Field("chat.completion", description="Object type")
+    created: int = Field(..., description="Unix timestamp of creation")
+    model: str = Field(..., description="Model used")
+    choices: List[Dict] = Field(..., description="Completion choices")
+    usage: Dict[str, int] = Field(..., description="Token usage statistics")
+
+class OpenAICompatibleAPI:
+    """OpenAI-compatible API implementation."""
+    
+    def __init__(self, reasoning_engine):
+        self.reasoning_engine = reasoning_engine
+        self.router = APIRouter()
+        self.setup_routes()
+    
+    def setup_routes(self):
+        """Setup API routes."""
+        
+        @self.router.post("/v1/chat/completions")
+        async def create_chat_completion(request: ChatCompletionRequest) -> ChatCompletionResponse:
+            try:
+                # Convert chat history to context
+                context = self._prepare_context(request.messages)
+                
+                # Get the last user message
+                user_message = next(
+                    (msg.content for msg in reversed(request.messages) 
+                     if msg.role == "user"),
+                    None
+                )
+                
+                if not user_message:
+                    raise HTTPException(status_code=400, detail="No user message found")
+                
+                # Process with reasoning engine
+                result = await self.reasoning_engine.reason(
+                    query=user_message,
+                    context={
+                        "chat_history": context,
+                        "temperature": request.temperature,
+                        "top_p": request.top_p,
+                        "max_tokens": request.max_tokens,
+                        "stream": request.stream
+                    }
+                )
+                
+                # Format response
+                response = {
+                    "id": f"chatcmpl-{int(time.time()*1000)}",
+                    "object": "chat.completion",
+                    "created": int(time.time()),
+                    "model": request.model,
+                    "choices": [{
+                        "index": 0,
+                        "message": {
+                            "role": "assistant",
+                            "content": result.answer
+                        },
+                        "finish_reason": "stop"
+                    }],
+                    "usage": {
+                        "prompt_tokens": self._estimate_tokens(user_message),
+                        "completion_tokens": self._estimate_tokens(result.answer),
+                        "total_tokens": self._estimate_tokens(user_message) + 
+                                      self._estimate_tokens(result.answer)
+                    }
+                }
+                
+                return ChatCompletionResponse(**response)
+            
+            except Exception as e:
+                raise HTTPException(status_code=500, detail=str(e))
+        
+        @self.router.get("/v1/models")
+        async def list_models():
+            """List available models."""
+            return {
+                "object": "list",
+                "data": [
+                    {
+                        "id": "venture-gpt-1",
+                        "object": "model",
+                        "created": int(time.time()),
+                        "owned_by": "venture-ai",
+                        "permission": [],
+                        "root": "venture-gpt-1",
+                        "parent": None
+                    }
+                ]
+            }
+    
+    def _prepare_context(self, messages: List[ChatMessage]) -> List[Dict]:
+        """Convert messages to context format."""
+        return [
+            {
+                "role": msg.role,
+                "content": msg.content,
+                "name": msg.name,
+                "timestamp": datetime.now().isoformat()
+            }
+            for msg in messages
+        ]
+    
+    def _estimate_tokens(self, text: str) -> int:
+        """Estimate token count for a text."""
+        # Simple estimation: ~4 characters per token
+        return len(text) // 4

api/venture_api.py

@@ -0,0 +1,194 @@
+"""API endpoints for venture strategies and analysis."""
+
+from fastapi import APIRouter, HTTPException, Depends
+from typing import List, Dict, Any, Optional
+from pydantic import BaseModel, Field
+from datetime import datetime
+
+from reasoning.venture_strategies import (
+    AIStartupStrategy, SaaSVentureStrategy, AutomationVentureStrategy,
+    DataVentureStrategy, APIVentureStrategy, MarketplaceVentureStrategy,
+    AIInfrastructureStrategy, AIConsultingStrategy, AIProductStrategy,
+    FinTechStrategy, HealthTechStrategy, EdTechStrategy,
+    BlockchainStrategy, AIMarketplaceStrategy
+)
+from reasoning.market_analysis import MarketAnalyzer
+from reasoning.portfolio_optimization import PortfolioOptimizer
+from reasoning.monetization import MonetizationOptimizer
+
+router = APIRouter(prefix="/api/ventures", tags=["ventures"])
+
+# Models
+class VentureRequest(BaseModel):
+    """Venture analysis request."""
+    venture_type: str
+    query: str
+    context: Dict[str, Any] = Field(default_factory=dict)
+
+class MarketRequest(BaseModel):
+    """Market analysis request."""
+    segment: str
+    context: Dict[str, Any] = Field(default_factory=dict)
+
+class PortfolioRequest(BaseModel):
+    """Portfolio optimization request."""
+    ventures: List[str]
+    context: Dict[str, Any] = Field(default_factory=dict)
+
+class MonetizationRequest(BaseModel):
+    """Monetization optimization request."""
+    venture_type: str
+    context: Dict[str, Any] = Field(default_factory=dict)
+
+# Strategy mapping
+VENTURE_STRATEGIES = {
+    "ai_startup": AIStartupStrategy(),
+    "saas": SaaSVentureStrategy(),
+    "automation": AutomationVentureStrategy(),
+    "data": DataVentureStrategy(),
+    "api": APIVentureStrategy(),
+    "marketplace": MarketplaceVentureStrategy(),
+    "ai_infrastructure": AIInfrastructureStrategy(),
+    "ai_consulting": AIConsultingStrategy(),
+    "ai_product": AIProductStrategy(),
+    "fintech": FinTechStrategy(),
+    "healthtech": HealthTechStrategy(),
+    "edtech": EdTechStrategy(),
+    "blockchain": BlockchainStrategy(),
+    "ai_marketplace": AIMarketplaceStrategy()
+}
+
+# Endpoints
+@router.post("/analyze")
+async def analyze_venture(request: VentureRequest):
+    """Analyze venture opportunity."""
+    try:
+        strategy = VENTURE_STRATEGIES.get(request.venture_type)
+        if not strategy:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Invalid venture type: {request.venture_type}"
+            )
+        
+        result = await strategy.reason(request.query, request.context)
+        return {
+            "success": True,
+            "result": result,
+            "timestamp": datetime.now().isoformat()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/market")
+async def analyze_market(request: MarketRequest):
+    """Analyze market opportunity."""
+    try:
+        analyzer = MarketAnalyzer()
+        result = await analyzer.analyze_market(request.segment, request.context)
+        return {
+            "success": True,
+            "result": result,
+            "timestamp": datetime.now().isoformat()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/portfolio")
+async def optimize_portfolio(request: PortfolioRequest):
+    """Optimize venture portfolio."""
+    try:
+        optimizer = PortfolioOptimizer()
+        result = await optimizer.optimize_portfolio(request.ventures, request.context)
+        return {
+            "success": True,
+            "result": result,
+            "timestamp": datetime.now().isoformat()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/monetization")
+async def optimize_monetization(request: MonetizationRequest):
+    """Optimize venture monetization."""
+    try:
+        optimizer = MonetizationOptimizer()
+        result = await optimizer.optimize_monetization(
+            request.venture_type, request.context)
+        return {
+            "success": True,
+            "result": result,
+            "timestamp": datetime.now().isoformat()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/strategies")
+async def list_strategies():
+    """List available venture strategies."""
+    return {
+        "success": True,
+        "strategies": list(VENTURE_STRATEGIES.keys()),
+        "timestamp": datetime.now().isoformat()
+    }
+
+@router.get("/metrics/{venture_type}")
+async def get_venture_metrics(venture_type: str):
+    """Get venture performance metrics."""
+    try:
+        strategy = VENTURE_STRATEGIES.get(venture_type)
+        if not strategy:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Invalid venture type: {venture_type}"
+            )
+        
+        metrics = strategy.get_venture_metrics()
+        return {
+            "success": True,
+            "metrics": metrics,
+            "timestamp": datetime.now().isoformat()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/insights")
+async def get_market_insights():
+    """Get comprehensive market insights."""
+    try:
+        analyzer = MarketAnalyzer()
+        insights = analyzer.get_market_insights()
+        return {
+            "success": True,
+            "insights": insights,
+            "timestamp": datetime.now().isoformat()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/portfolio/insights")
+async def get_portfolio_insights():
+    """Get comprehensive portfolio insights."""
+    try:
+        optimizer = PortfolioOptimizer()
+        insights = optimizer.get_portfolio_insights()
+        return {
+            "success": True,
+            "insights": insights,
+            "timestamp": datetime.now().isoformat()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/monetization/metrics")
+async def get_monetization_metrics():
+    """Get comprehensive monetization metrics."""
+    try:
+        optimizer = MonetizationOptimizer()
+        metrics = optimizer.get_monetization_metrics()
+        return {
+            "success": True,
+            "metrics": metrics,
+            "timestamp": datetime.now().isoformat()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

app.py

@@ -0,0 +1,633 @@
+"""
+Advanced Agentic System Interface
+-------------------------------
+Provides a chat interface to interact with the autonomous agent teams:
+- Team A: Coders (App/Software Developers)
+- Team B: Business (Entrepreneurs)
+- Team C: Research (Deep Online Research)
+- Team D: Crypto & Sports Trading
+"""
+
+import gradio as gr
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+import uvicorn
+from typing import Dict, Any, List, Tuple, Optional
+import logging
+from pathlib import Path
+import asyncio
+from datetime import datetime
+import json
+from requests.adapters import HTTPAdapter, Retry
+from urllib3.util.retry import Retry
+import time
+
+from agentic_system import AgenticSystem
+from team_management import TeamManager, TeamType, TeamObjective
+from orchestrator import AgentOrchestrator
+from reasoning import UnifiedReasoningEngine as ReasoningEngine
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Configure network settings
+TIMEOUT = int(os.getenv('REQUESTS_TIMEOUT', '30'))
+MAX_RETRIES = 5
+RETRY_BACKOFF = 1
+
+def setup_requests_session():
+    """Configure requests session with retries."""
+    session = requests.Session()
+    retry_strategy = Retry(
+        total=MAX_RETRIES,
+        backoff_factor=RETRY_BACKOFF,
+        status_forcelist=[408, 429, 500, 502, 503, 504],
+        allowed_methods=["HEAD", "GET", "PUT", "DELETE", "OPTIONS", "TRACE"]
+    )
+    adapter = HTTPAdapter(max_retries=retry_strategy)
+    session.mount("https://", adapter)
+    session.mount("http://", adapter)
+    return session
+
+def check_network(max_attempts=3):
+    """Check network connectivity with retries."""
+    session = setup_requests_session()
+    
+    for attempt in range(max_attempts):
+        try:
+            # Try multiple DNS servers
+            for dns in ['8.8.8.8', '8.8.4.4', '1.1.1.1']:
+                try:
+                    socket.gethostbyname('huggingface.co')
+                    break
+                except socket.gaierror:
+                    continue
+            
+            # Test connection to Hugging Face
+            response = session.get('https://huggingface.co/api/health', 
+                                 timeout=TIMEOUT)
+            if response.status_code == 200:
+                return True
+                
+        except (requests.RequestException, socket.gaierror) as e:
+            logger.warning(f"Network check attempt {attempt + 1} failed: {e}")
+            if attempt < max_attempts - 1:
+                time.sleep(RETRY_BACKOFF * (attempt + 1))
+            continue
+            
+    logger.error("Network connectivity check failed after all attempts")
+    return False
+
+class ChatInterface:
+    def __init__(self):
+        # Check network connectivity
+        if not check_network():
+            logger.warning("Network connectivity issues detected - continuing with degraded functionality")
+            
+        # Initialize core components with consistent configuration
+        config = {
+            "min_confidence": 0.7,
+            "parallel_threshold": 3,
+            "learning_rate": 0.1,
+            "strategy_weights": {
+                "LOCAL_LLM": 0.8,
+                "CHAIN_OF_THOUGHT": 0.6,
+                "TREE_OF_THOUGHTS": 0.5,
+                "META_LEARNING": 0.4
+            }
+        }
+        
+        self.orchestrator = AgentOrchestrator(config)
+        self.agentic_system = AgenticSystem(config)
+        self.team_manager = TeamManager(self.orchestrator)
+        self.chat_history = []
+        self.active_objectives = {}
+        
+        # Set up network session
+        self.session = setup_requests_session()
+        
+        # Initialize teams
+        asyncio.run(self.team_manager.initialize_team_agents())
+
+    async def process_message(
+        self,
+        message: str,
+        history: List[List[str]]
+    ) -> Tuple[str, List[List[str]]]:
+        """Process incoming chat message."""
+        try:
+            # Update chat history
+            self.chat_history = history
+
+            # Process message
+            response = await self._handle_message(message)
+            
+            # Update history
+            if response:
+                history.append([message, response])
+            
+            return response, history
+            
+        except Exception as e:
+            logger.error(f"Error processing message: {str(e)}")
+            error_msg = "I apologize, but I encountered an error. Please try again."
+            history.append([message, error_msg])
+            return error_msg, history
+
+    async def _handle_message(self, message: str) -> str:
+        """Handle message processing with error recovery."""
+        try:
+            # Analyze intent
+            intent = await self._analyze_intent(message)
+            intent_type = self._get_intent_type(intent)
+            
+            # Route to appropriate handler
+            if intent_type == "query":
+                return await self._handle_query(message)
+            elif intent_type == "objective":
+                return await self._handle_objective(message)
+            elif intent_type == "status":
+                return await self._handle_status_request(message)
+            else:
+                return await self._handle_general_chat(message)
+                
+        except Exception as e:
+            logger.error(f"Error in message handling: {str(e)}")
+            return "I apologize, but I encountered an error processing your message. Please try again."
+
+    def _get_intent_type(self, intent) -> str:
+        """Safely extract intent type from various result formats."""
+        if isinstance(intent, dict):
+            return intent.get("type", "general")
+        return "general"
+
+    async def _analyze_intent(self, message: str) -> Dict[str, Any]:
+        """Analyze user message intent with error handling."""
+        try:
+            # Use reasoning engine to analyze intent
+            analysis = await self.orchestrator.reasoning_engine.reason(
+                query=message,
+                context={
+                    "chat_history": self.chat_history,
+                    "active_objectives": self.active_objectives
+                }
+            )
+            
+            return {
+                "type": analysis.get("intent_type", "general"),
+                "confidence": analysis.get("confidence", 0.5),
+                "entities": analysis.get("entities", []),
+                "action_required": analysis.get("action_required", False)
+            }
+        except Exception as e:
+            logger.error(f"Error analyzing intent: {str(e)}")
+            return {"type": "general", "confidence": 0.5}
+
+    async def _handle_query(self, message: str) -> str:
+        """Handle information queries."""
+        try:
+            # Get relevant teams for the query
+            recommended_teams = await self.team_manager.get_team_recommendations(message)
+            
+            # Get responses from relevant teams
+            responses = []
+            for team_type in recommended_teams:
+                response = await self._get_team_response(team_type, message)
+                if response:
+                    responses.append(response)
+            
+            if not responses:
+                return "I apologize, but I couldn't find a relevant answer to your query."
+            
+            # Combine and format responses
+            return self._format_team_responses(responses)
+            
+        except Exception as e:
+            logger.error(f"Error handling query: {str(e)}")
+            return "I apologize, but I encountered an error processing your query. Please try again."
+
+    async def _handle_objective(self, message: str) -> str:
+        """Handle new objective creation."""
+        try:
+            # Create new objective
+            objective_id = await self.team_manager.create_objective(message)
+            if not objective_id:
+                return "I apologize, but I couldn't create the objective. Please try again."
+            
+            # Format and return response
+            return self._format_objective_creation(objective_id)
+            
+        except Exception as e:
+            logger.error(f"Error creating objective: {str(e)}")
+            return "I apologize, but I encountered an error creating the objective. Please try again."
+
+    async def _handle_status_request(self, message: str) -> str:
+        """Handle status check requests."""
+        try:
+            # Get system status
+            system_status = await self.agentic_system.get_system_status()
+            
+            # Get team status
+            team_status = {}
+            for team_id, team in self.team_manager.teams.items():
+                team_status[team.name] = await self.team_manager.monitor_objective_progress(team_id)
+            
+            # Get objective status
+            objective_status = {}
+            for obj_id, obj in self.active_objectives.items():
+                objective_status[obj_id] = await self.team_manager.monitor_objective_progress(obj_id)
+            
+            return self._format_status_response(system_status, team_status, objective_status)
+            
+        except Exception as e:
+            logger.error(f"Error getting status: {str(e)}")
+            return "I apologize, but I encountered an error getting the status. Please try again."
+
+    async def _handle_general_chat(self, message: str) -> str:
+        """Handle general chat interactions with error recovery."""
+        try:
+            # Use reasoning engine for response generation
+            response = await self.orchestrator.reasoning_engine.reason(
+                query=message,
+                context={
+                    "chat_history": self.chat_history,
+                    "system_state": await self.agentic_system.get_system_status()
+                }
+            )
+            
+            if not response or not response.get("response"):
+                return "I apologize, but I couldn't generate a meaningful response. Please try again."
+            
+            return response["response"]
+            
+        except Exception as e:
+            logger.error(f"Error in general chat: {str(e)}")
+            return "I apologize, but I encountered an error processing your message. Please try again."
+
+    async def _get_team_response(self, team_type: TeamType, query: str) -> Dict[str, Any]:
+        """Get response from a specific team."""
+        try:
+            team = self.team_manager.teams.get(team_type.value)
+            if not team:
+                return None
+            
+            # Get response from team's agents
+            responses = []
+            for agent in team.agents:
+                response = await agent.process_query(query)
+                if response:
+                    responses.append(response)
+            
+            if not responses:
+                return None
+            
+            # Return best response
+            return self._combine_agent_responses(responses)
+            
+        except Exception as e:
+            logger.error(f"Error getting team response: {str(e)}")
+            return None
+
+    def _combine_agent_responses(self, responses: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """Combine multiple agent responses into a coherent response."""
+        try:
+            # Sort by confidence
+            valid_responses = [
+                r for r in responses 
+                if r.get("success", False) and r.get("response")
+            ]
+            
+            if not valid_responses:
+                return None
+                
+            sorted_responses = sorted(
+                valid_responses,
+                key=lambda x: x.get("confidence", 0),
+                reverse=True
+            )
+            
+            # Take the highest confidence response
+            return sorted_responses[0]
+            
+        except Exception as e:
+            logger.error(f"Error combining responses: {str(e)}")
+            return None
+
+    def _format_team_responses(self, responses: List[Dict[str, Any]]) -> str:
+        """Format team responses into a readable message."""
+        try:
+            if not responses:
+                return "No team responses available."
+            
+            formatted = []
+            for resp in responses:
+                if resp and resp.get("response"):
+                    team_name = resp.get("team_name", "Unknown Team")
+                    confidence = resp.get("confidence", 0)
+                    formatted.append(
+                        f"\n{team_name} (Confidence: {confidence:.2%}):\n{resp['response']}"
+                    )
+            
+            if not formatted:
+                return "No valid team responses available."
+                
+            return "\n".join(formatted)
+            
+        except Exception as e:
+            logger.error(f"Error formatting responses: {str(e)}")
+            return "Error formatting team responses."
+
+    def _format_objective_creation(self, objective_id: str) -> str:
+        """Format objective creation response."""
+        try:
+            obj = self.active_objectives.get(objective_id)
+            if not obj:
+                return "Objective created but details not available."
+            
+            return "\n".join([
+                "New Objective Created:",
+                f"Description: {obj['description']}",
+                f"Status: {obj['status']}",
+                f"Assigned Teams: {', '.join(t.value for t in obj['teams'])}"
+            ])
+            
+        except Exception as e:
+            logger.error(f"Error formatting objective: {str(e)}")
+            return "Error formatting objective details."
+
+    def _format_status_response(
+        self,
+        system_status: Dict[str, Any],
+        team_status: Dict[str, Any],
+        objective_status: Dict[str, Any]
+    ) -> str:
+        """Format status response."""
+        try:
+            # Format system status
+            status = [
+                "System Status:",
+                f"- State: {system_status['state']}",
+                f"- Active Agents: {system_status['agent_count']}",
+                f"- Active Tasks: {system_status['active_tasks']}",
+                "\nTeam Status:"
+            ]
+            
+            # Add team status
+            for team_name, team_info in team_status.items():
+                status.extend([
+                    f"\n{team_name}:",
+                    f"- Active Agents: {team_info['active_agents']}",
+                    f"- Completion Rate: {team_info['completion_rate']:.2%}",
+                    f"- Collaboration Score: {team_info['collaboration_score']:.2f}"
+                ])
+            
+            # Add objective status
+            if objective_status:
+                status.append("\nActive Objectives:")
+                for obj_id, obj_info in objective_status.items():
+                    obj = self.active_objectives[obj_id]
+                    status.extend([
+                        f"\n{obj['description']}:",
+                        f"- Status: {obj['status']}",
+                        f"- Teams: {', '.join(t.value for t in obj['teams'])}",
+                        f"- Progress: {sum(t['completion_rate'] for t in obj_info.values())/len(obj_info):.2%}"
+                    ])
+            
+            return "\n".join(status)
+            
+        except Exception as e:
+            logger.error(f"Error formatting status: {str(e)}")
+            return "Error formatting status information."
+
+class VentureUI:
+    def __init__(self, app):
+        self.app = app
+
+    def create_interface(self):
+        """Create the Gradio interface."""
+        with gr.Blocks(
+            theme=gr.themes.Soft(),
+            analytics_enabled=False,
+            title="Advanced Agentic System"
+        ) as interface:
+            # Verify Gradio version
+            gr.Markdown(f"""
+            # Advanced Agentic System Chat Interface v{gr.__version__}
+            
+            Chat with our autonomous agent teams:
+            - Team A: Coders (App/Software Developers)
+            - Team B: Business (Entrepreneurs)
+            - Team C: Research (Deep Online Research)
+            - Team D: Crypto & Sports Trading
+            
+            You can:
+            1. Ask questions
+            2. Create new objectives
+            3. Check status of teams and objectives
+            4. Get insights and recommendations
+            """)
+            
+            chatbot = gr.Chatbot(
+                label="Chat History",
+                height=400,
+                bubble_full_width=False,
+                show_copy_button=True,
+                render_markdown=True
+            )
+            
+            with gr.Row():
+                msg = gr.Textbox(
+                    label="Message",
+                    placeholder="Chat with the Agentic System...",
+                    lines=2,
+                    scale=9,
+                    autofocus=True,
+                    container=True
+                )
+                submit = gr.Button(
+                    "Send",
+                    scale=1,
+                    variant="primary"
+                )
+            
+            with gr.Row():
+                clear = gr.ClearButton(
+                    [msg, chatbot],
+                    value="Clear Chat",
+                    variant="secondary",
+                    scale=1
+                )
+                retry = gr.Button(
+                    "Retry Last",
+                    variant="secondary",
+                    scale=1
+                )
+
+            async def respond(message, history):
+                try:
+                    # Convert history to the format expected by process_message
+                    history_list = [[x, y] for x, y in history] if history else []
+                    response, history_list = await self.app(message, history_list)
+                    
+                    # Update history
+                    if history is None:
+                        history = []
+                    history.append((message, response))
+                    
+                    return "", history
+                except Exception as e:
+                    logger.error(f"Error in chat response: {str(e)}")
+                    error_msg = "I apologize, but I encountered an error. Please try again."
+                    
+                    if history is None:
+                        history = []
+                    history.append((message, error_msg))
+                    
+                    return "", history
+
+            async def retry_last(history):
+                if not history:
+                    return history
+                last_user_msg = history[-1][0]
+                history = history[:-1]  # Remove last exchange
+                return await respond(last_user_msg, history)
+
+            msg.submit(
+                respond,
+                [msg, chatbot],
+                [msg, chatbot],
+                api_name="chat"
+            ).then(
+                lambda: gr.update(interactive=True),
+                None,
+                [msg, submit],
+                queue=False
+            )
+
+            submit.click(
+                respond,
+                [msg, chatbot],
+                [msg, chatbot],
+                api_name="submit"
+            ).then(
+                lambda: gr.update(interactive=True),
+                None,
+                [msg, submit],
+                queue=False
+            )
+
+            retry.click(
+                retry_last,
+                [chatbot],
+                [chatbot],
+                api_name="retry"
+            )
+
+            # Event handlers for better UX
+            msg.change(lambda x: gr.update(interactive=bool(x.strip())), [msg], [submit])
+            
+            # Add example inputs
+            gr.Examples(
+                examples=[
+                    "What can Team A (Coders) help me with?",
+                    "Create a new objective: Analyze market trends",
+                    "What's the status of all teams?",
+                    "Give me insights about recent developments"
+                ],
+                inputs=msg,
+                label="Example Queries"
+            )
+
+        return interface
+
+def create_chat_interface() -> gr.Blocks:
+    """Create Gradio chat interface."""
+    chat = ChatInterface()
+    ui = VentureUI(chat.process_message)
+    return ui.create_interface()
+
+# Initialize FastAPI
+app = FastAPI(
+    title="Advanced Agentic System",
+    description="Venture Strategy Optimizer with OpenAI-compatible API",
+    version="1.0.0"
+)
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Include OpenAI-compatible routes
+from api.openai_compatible import OpenAICompatibleAPI
+reasoning_engine = UnifiedReasoningEngine()
+openai_api = OpenAICompatibleAPI(reasoning_engine)
+app.include_router(openai_api.router, tags=["OpenAI Compatible"])
+
+# Original API routes
+@app.get("/api/health")
+async def health_check():
+    """Health check endpoint."""
+    return {
+        "status": "healthy",
+        "version": "1.0.0",
+        "endpoints": {
+            "openai_compatible": "/v1/chat/completions",
+            "venture": "/api/venture",
+            "ui": "/"
+        }
+    }
+
+@app.post("/api/reason")
+async def reason(query: str, context: Optional[Dict[str, Any]] = None):
+    """Reasoning endpoint."""
+    try:
+        result = await reasoning_engine.reason(query, context or {})
+        return result
+    except Exception as e:
+        logger.error(f"Reasoning error: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/api/venture/analyze")
+async def analyze_venture(
+    venture_type: str,
+    description: str,
+    metrics: Optional[Dict[str, Any]] = None
+):
+    """Venture analysis endpoint."""
+    try:
+        result = await VentureAPI(reasoning_engine).analyze_venture(
+            venture_type=venture_type,
+            description=description,
+            metrics=metrics or {}
+        )
+        return result
+    except Exception as e:
+        logger.error(f"Analysis error: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/api/venture/types")
+async def get_venture_types():
+    """Get available venture types."""
+    return VentureAPI(reasoning_engine).get_venture_types()
+
+# Create Gradio interface
+interface = create_chat_interface()
+
+# Mount Gradio app to FastAPI
+app = gr.mount_gradio_app(app, interface, path="/")
+
+if __name__ == "__main__":
+    # Run with uvicorn when called directly
+    uvicorn.run(
+        "app:app",
+        host="0.0.0.0",
+        port=7860,
+        reload=True,
+        workers=4
+    )

app.yaml

@@ -0,0 +1,9 @@
+title: Advanced Reasoning System
+emoji: 🧠
+colorFrom: indigo
+colorTo: purple
+sdk: gradio
+sdk_version: 4.16.0
+app_file: app.py
+pinned: false
+license: mit

app_space.sh

@@ -0,0 +1,34 @@
+#!/bin/bash
+
+# Exit on error
+set -e
+
+echo "Starting Advanced Agentic System initialization..."
+
+# Create necessary directories
+mkdir -p /data/models
+mkdir -p logs
+
+# Install system dependencies
+apt-get update && apt-get install -y \
+    git \
+    git-lfs \
+    cmake \
+    build-essential \
+    pkg-config \
+    libcurl4-openssl-dev
+
+# Initialize git-lfs
+git lfs install
+
+# Upgrade pip and install requirements
+python -m pip install --upgrade pip
+pip install -r requirements.txt
+
+# Download and initialize models
+echo "Initializing models..."
+python download_models_space.py
+
+# Start the application
+echo "Starting Gradio interface..."
+python app.py

check_space_status.py

@@ -0,0 +1,55 @@
+from huggingface_hub import HfApi
+import time
+import os
+import requests
+
+def check_space_status():
+    api = HfApi()
+    space_name = "nananie143/Agentic_llm"
+    
+    try:
+        # First try direct API request
+        response = requests.get(
+            f"https://huggingface.co/api/spaces/{space_name}/runtime",
+            headers={"Authorization": f"Bearer {os.environ['HUGGINGFACE_TOKEN']}"}
+        )
+        print(f"\nAPI Response Status: {response.status_code}")
+        if response.ok:
+            data = response.json()
+            print(f"Space Info: {data}")
+            return data.get("stage")
+            
+        # Fallback to HF API
+        space_info = api.space_info(space_name)
+        print(f"\nSpace Info via HF API: {space_info}")
+        
+        if hasattr(space_info, 'runtime'):
+            status = space_info.runtime.stage
+            print(f"Status: {status}")
+            return status
+        
+        print("No status information available")
+        return None
+        
+    except Exception as e:
+        print(f"Error checking status: {e}")
+        return None
+
+print("Starting Space status check...")
+print("Will check every 30 seconds until the Space is running...")
+
+while True:
+    status = check_space_status()
+    print(f"Current status: {status}")
+    
+    if status == "RUNNING":
+        print("\nSpace is now running! ")
+        print(f"Access your Space at: https://huggingface.co/spaces/nananie143/Agentic_llm")
+        break
+    elif status == "FAILED":
+        print("\nSpace build failed! Please check the logs for details.")
+        break
+    elif status is None:
+        print("\nCouldn't determine status. Will try again...")
+    
+    time.sleep(30)

check_versions.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python3
+
+import sys
+import pkg_resources
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def check_package_version(package_name):
+    """Check installed version of a package."""
+    try:
+        version = pkg_resources.get_distribution(package_name).version
+        logger.info(f"{package_name} version: {version}")
+        return version
+    except pkg_resources.DistributionNotFound:
+        logger.error(f"{package_name} is not installed")
+        return None
+
+def main():
+    """Check versions of key packages."""
+    packages = [
+        "gradio",
+        "torch",
+        "transformers",
+        "huggingface-hub",
+        "pydantic",
+        "fastapi",
+        "uvicorn"
+    ]
+    
+    logger.info("Checking package versions...")
+    for package in packages:
+        version = check_package_version(package)
+        if version is None:
+            sys.exit(1)
+    
+    # Verify Gradio version specifically
+    gradio_version = check_package_version("gradio")
+    if gradio_version:
+        major, minor, patch = map(int, gradio_version.split("."))
+        if major < 4 or (major == 4 and minor < 44):
+            logger.error(f"Gradio version {gradio_version} is too old. Please upgrade to 4.44.1 or later")
+            sys.exit(1)
+    
+    logger.info("All package versions verified successfully")
+    return 0
+
+if __name__ == "__main__":
+    sys.exit(main())

cleanup.sh

@@ -0,0 +1,45 @@
+#!/bin/bash
+
+# Exit on error
+set -e
+
+echo "Starting cleanup..."
+
+# Define core files to keep
+CORE_FILES=(
+    "agentic_system.py"
+    "orchestrator.py"
+    "team_management.py"
+    "meta_learning.py"
+    "config.py"
+    "space.yml"
+    "app.py"
+    "startup.sh"
+    "check_versions.py"
+    "requirements.txt"
+    "upload_to_hub.py"
+    "app_space.sh"
+    ".gitattributes"
+)
+
+# Remove backup and temporary files (excluding reasoning directory)
+find . -type f ! -path "./reasoning/*" \( -name "*.bak*" -o -name "*.backup" -o -name "*.temp" -o -name "*.log" \) -delete
+
+# Remove cache files (excluding reasoning directory)
+find . -type d ! -path "./reasoning/*" -name "__pycache__" -exec rm -rf {} +
+find . -type f ! -path "./reasoning/*" -name "*.pyc" -delete
+
+# Remove sample and simplified files
+rm -f simple_reasoning.py quick_check.py
+rm -rf simple_reasoning/
+
+# Remove environment files (after backing up if needed)
+if [ -f .env ]; then
+    mv .env .env.backup
+fi
+rm -f .env.example
+
+# Remove quantum_learning.py since its functionality exists in reasoning/quantum.py
+rm -f quantum_learning.py
+
+echo "Cleanup complete! All files in the reasoning directory have been preserved."

config.py

@@ -0,0 +1,452 @@
+"""
+System Configuration
+------------------
+Central configuration for the Agentic System including:
+1. Local Model Settings
+2. Team Settings
+3. System Parameters
+4. Resource Limits
+5. Free API Configurations
+"""
+
+import os
+from typing import Dict, Any, Optional
+from pathlib import Path
+import json
+import logging
+from dataclasses import dataclass, field
+
+logger = logging.getLogger(__name__)
+
+@dataclass
+class Config:
+    """Configuration for the Advanced Agentic System."""
+    
+    # Core settings
+    min_confidence: float = 0.7
+    parallel_threshold: int = 3
+    learning_rate: float = 0.1
+    
+    # Model settings
+    model_backend: str = field(default_factory=lambda: os.getenv('MODEL_BACKEND', 'huggingface'))
+    groq_api_key: Optional[str] = field(default_factory=lambda: os.getenv('GROQ_API_KEY'))
+    huggingface_token: Optional[str] = field(default_factory=lambda: os.getenv('HUGGINGFACE_TOKEN'))
+    
+    # API settings
+    enable_openai_compatibility: bool = True
+    api_rate_limit: int = 100
+    api_timeout: int = 30
+    
+    # Resource limits
+    max_parallel_requests: int = field(
+        default_factory=lambda: int(os.getenv('MAX_PARALLEL_REQUESTS', '10'))
+    )
+    request_timeout: int = field(
+        default_factory=lambda: int(os.getenv('REQUEST_TIMEOUT', '30'))
+    )
+    batch_size: int = field(
+        default_factory=lambda: int(os.getenv('BATCH_SIZE', '4'))
+    )
+    
+    # Cache settings
+    enable_cache: bool = field(
+        default_factory=lambda: os.getenv('CACHE_MODELS', 'false').lower() == 'true'
+    )
+    cache_dir: str = field(
+        default_factory=lambda: os.getenv('SPACE_CACHE_DIR', '/tmp/models')
+    )
+    
+    # Strategy weights
+    strategy_weights: Dict[str, float] = field(default_factory=lambda: {
+        "LOCAL_LLM": 2.0,
+        "CHAIN_OF_THOUGHT": 1.5,
+        "TREE_OF_THOUGHTS": 1.5,
+        "META_LEARNING": 1.5,
+        "TASK_DECOMPOSITION": 1.3,
+        "RESOURCE_MANAGEMENT": 1.3,
+        "CONTEXTUAL_PLANNING": 1.3,
+        "ADAPTIVE_EXECUTION": 1.3,
+        "FEEDBACK_INTEGRATION": 1.3,
+        "BAYESIAN": 1.2,
+        "MARKET_ANALYSIS": 1.2,
+        "PORTFOLIO_OPTIMIZATION": 1.2,
+        "VENTURE": 1.2,
+        "MONETIZATION": 1.0,
+        "MULTIMODAL": 1.0,
+        "NEUROSYMBOLIC": 1.0,
+        "SPECIALIZED": 1.0,
+        "VENTURE_TYPE": 1.0,
+        "RECURSIVE": 1.0,
+        "ANALOGICAL": 1.0
+    })
+    
+    # Agentic system settings
+    agentic_system: Dict[str, Any] = field(default_factory=lambda: {
+        "min_confidence": 0.7,
+        "parallel_threshold": 3,
+        "learning_rate": 0.1,
+        "enable_meta_learning": True,
+        "enable_self_improvement": True,
+        "max_agents": 10,
+        "default_agent_config": {
+            "learning_rate": 0.1,
+            "risk_tolerance": 0.5,
+            "max_retries": 3
+        }
+    })
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize configuration."""
+        if config:
+            for key, value in config.items():
+                if hasattr(self, key):
+                    setattr(self, key, value)
+        
+        # Validate configuration
+        self._validate_config()
+    
+    def _validate_config(self):
+        """Validate configuration values."""
+        if self.min_confidence < 0 or self.min_confidence > 1:
+            raise ValueError("min_confidence must be between 0 and 1")
+        
+        if self.parallel_threshold < 1:
+            raise ValueError("parallel_threshold must be at least 1")
+        
+        if self.learning_rate <= 0 or self.learning_rate > 1:
+            raise ValueError("learning_rate must be between 0 and 1")
+        
+        if self.model_backend not in ['groq', 'huggingface']:
+            raise ValueError("model_backend must be either 'groq' or 'huggingface'")
+    
+    def get(self, key: str, default: Any = None) -> Any:
+        """Get configuration value."""
+        return getattr(self, key, default)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert configuration to dictionary."""
+        return {
+            key: getattr(self, key)
+            for key in self.__annotations__
+            if hasattr(self, key)
+        }
+    
+    @classmethod
+    def from_file(cls, filepath: str) -> 'Config':
+        """Load configuration from file."""
+        path = Path(filepath)
+        if not path.exists():
+            raise FileNotFoundError(f"Configuration file not found: {filepath}")
+        
+        with open(filepath, 'r') as f:
+            config = json.load(f)
+        
+        return cls(config)
+    
+    def save(self, filepath: str):
+        """Save configuration to file."""
+        with open(filepath, 'w') as f:
+            json.dump(self.to_dict(), f, indent=2)
+
+class SystemConfig:
+    """System-wide configuration."""
+    
+    # Base Paths
+    BASE_DIR = Path(__file__).parent.absolute()
+    CACHE_DIR = BASE_DIR / "cache"
+    LOG_DIR = BASE_DIR / "logs"
+    DATA_DIR = BASE_DIR / "data"
+    MODEL_DIR = BASE_DIR / "models"
+    
+    # System Parameters
+    DEBUG_MODE = os.getenv("DEBUG_MODE", "False").lower() == "true"
+    LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")
+    MAX_WORKERS = int(os.getenv("MAX_WORKERS", "4"))
+    ASYNC_TIMEOUT = int(os.getenv("ASYNC_TIMEOUT", "30"))
+    
+    # Local Model Configurations
+    MODEL_CONFIG = {
+        "quick_coder": {
+            "name": "tugstugi/Qwen2.5-Coder-0.5B-QwQ-draft",
+            "type": "transformers",
+            "description": "Fast code completion and simple tasks",
+            "temperature": 0.2,
+            "max_tokens": 1000,
+            "timeout": 30
+        },
+        "deep_coder": {
+            "name": "YorkieOH10/deepseek-coder-6.7B-kexer-Q8_0-GGUF",
+            "type": "gguf",
+            "description": "Complex code generation and refactoring",
+            "temperature": 0.3,
+            "max_tokens": 2000,
+            "timeout": 45
+        },
+        "text_gen": {
+            "name": "Orenguteng/Llama-3-8B-Lexi-Uncensored",
+            "type": "transformers",
+            "description": "General text generation and reasoning",
+            "temperature": 0.7,
+            "max_tokens": 1500,
+            "timeout": 40
+        },
+        "workflow": {
+            "name": "deepseek-ai/JanusFlow-1.3B",
+            "type": "transformers",
+            "description": "Task planning and workflow management",
+            "temperature": 0.5,
+            "max_tokens": 1000,
+            "timeout": 30
+        }
+    }
+    
+    # Team Configurations
+    TEAM_CONFIG = {
+        "coders": {
+            "min_agents": 3,
+            "max_agents": 7,
+            "capabilities": [
+                "full_stack_development",
+                "cloud_architecture",
+                "ai_ml",
+                "blockchain",
+                "mobile_development"
+            ],
+            "resource_limits": {
+                "cpu_percent": 80,
+                "memory_mb": 4096,
+                "gpu_memory_mb": 2048
+            }
+        },
+        "business": {
+            "min_agents": 2,
+            "max_agents": 5,
+            "capabilities": [
+                "market_analysis",
+                "business_strategy",
+                "digital_transformation",
+                "startup_innovation",
+                "product_management"
+            ],
+            "resource_limits": {
+                "cpu_percent": 60,
+                "memory_mb": 2048,
+                "api_calls_per_minute": 100
+            }
+        },
+        "research": {
+            "min_agents": 2,
+            "max_agents": 6,
+            "capabilities": [
+                "deep_research",
+                "data_analysis",
+                "trend_forecasting",
+                "competitive_analysis",
+                "technology_assessment"
+            ],
+            "resource_limits": {
+                "cpu_percent": 70,
+                "memory_mb": 3072,
+                "api_calls_per_minute": 150
+            }
+        },
+        "traders": {
+            "min_agents": 2,
+            "max_agents": 5,
+            "capabilities": [
+                "crypto_trading",
+                "sports_betting",
+                "risk_management",
+                "market_timing",
+                "portfolio_optimization"
+            ],
+            "resource_limits": {
+                "cpu_percent": 60,
+                "memory_mb": 2048,
+                "api_calls_per_minute": 200
+            }
+        }
+    }
+    
+    # Resource Management
+    RESOURCE_LIMITS = {
+        "total_cpu_percent": 90,
+        "total_memory_mb": 8192,
+        "total_gpu_memory_mb": 4096,
+        "max_api_calls_per_minute": 500,
+        "max_concurrent_tasks": 20
+    }
+    
+    # Collaboration Settings
+    COLLABORATION_CONFIG = {
+        "min_confidence_threshold": 0.6,
+        "max_team_size": 10,
+        "max_concurrent_objectives": 5,
+        "objective_timeout_minutes": 60,
+        "team_sync_interval_seconds": 30
+    }
+    
+    # Error Recovery
+    ERROR_RECOVERY = {
+        "max_retries": 3,
+        "retry_delay_seconds": 5,
+        "error_threshold": 0.2,
+        "recovery_timeout": 300
+    }
+    
+    # Monitoring
+    MONITORING = {
+        "metrics_interval_seconds": 60,
+        "health_check_interval": 30,
+        "performance_log_retention_days": 7,
+        "alert_threshold": {
+            "cpu": 85,
+            "memory": 90,
+            "error_rate": 0.1
+        }
+    }
+    
+    # Free API Configurations (No API Keys Required)
+    API_CONFIG = {
+        "search": {
+            "duckduckgo": {
+                "base_url": "https://api.duckduckgo.com",
+                "rate_limit": 100,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "wikipedia": {
+                "base_url": "https://en.wikipedia.org/w/api.php",
+                "rate_limit": 200,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "arxiv": {
+                "base_url": "http://export.arxiv.org/api/query",
+                "rate_limit": 60,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "crossref": {
+                "base_url": "https://api.crossref.org/works",
+                "rate_limit": 50,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "unpaywall": {
+                "base_url": "https://api.unpaywall.org/v2",
+                "rate_limit": 100,
+                "requires_auth": False,
+                "method": "GET"
+            }
+        },
+        "crypto": {
+            "coincap": {
+                "base_url": "https://api.coincap.io/v2",
+                "rate_limit": 200,
+                "requires_auth": False,
+                "method": "GET",
+                "endpoints": {
+                    "assets": "/assets",
+                    "rates": "/rates",
+                    "markets": "/markets"
+                }
+            },
+            "blockchair": {
+                "base_url": "https://api.blockchair.com",
+                "rate_limit": 30,
+                "requires_auth": False,
+                "method": "GET"
+            }
+        },
+        "news": {
+            "wikinews": {
+                "base_url": "https://en.wikinews.org/w/api.php",
+                "rate_limit": 200,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "reddit": {
+                "base_url": "https://www.reddit.com/r/news/.json",
+                "rate_limit": 60,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "hackernews": {
+                "base_url": "https://hacker-news.firebaseio.com/v0",
+                "rate_limit": 100,
+                "requires_auth": False,
+                "method": "GET"
+            }
+        },
+        "market_data": {
+            "yahoo_finance": {
+                "base_url": "https://query1.finance.yahoo.com/v8/finance",
+                "rate_limit": 100,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "marketstack_free": {
+                "base_url": "https://api.marketstack.com/v1",
+                "rate_limit": 100,
+                "requires_auth": False,
+                "method": "GET"
+            }
+        },
+        "sports": {
+            "football_data": {
+                "base_url": "https://www.football-data.org/v4",
+                "rate_limit": 10,
+                "requires_auth": False,
+                "method": "GET",
+                "free_endpoints": [
+                    "/competitions",
+                    "/matches"
+                ]
+            },
+            "nhl": {
+                "base_url": "https://statsapi.web.nhl.com/api/v1",
+                "rate_limit": 50,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "mlb": {
+                "base_url": "https://statsapi.mlb.com/api/v1",
+                "rate_limit": 50,
+                "requires_auth": False,
+                "method": "GET"
+            }
+        },
+        "web_scraping": {
+            "web_archive": {
+                "base_url": "https://archive.org/wayback/available",
+                "rate_limit": 40,
+                "requires_auth": False,
+                "method": "GET"
+            },
+            "metahtml": {
+                "base_url": "https://html.spec.whatwg.org/multipage",
+                "rate_limit": 30,
+                "requires_auth": False,
+                "method": "GET"
+            }
+        }
+    }
+
+    @classmethod
+    def get_team_config(cls, team_name: str) -> Dict[str, Any]:
+        """Get configuration for a specific team."""
+        return cls.TEAM_CONFIG.get(team_name, {})
+
+    @classmethod
+    def get_model_config(cls, model_type: str) -> Dict[str, Any]:
+        """Get configuration for a specific model type."""
+        return cls.MODEL_CONFIG.get(model_type, {})
+
+    @classmethod
+    def get_api_config(cls, api_name: str) -> Dict[str, Any]:
+        """Get configuration for a specific API."""
+        for category in cls.API_CONFIG.values():
+            if api_name in category:
+                return category[api_name]
+        return {}

download_models.py

@@ -0,0 +1,76 @@
+"""Script to download and prepare models for HuggingFace Spaces."""
+
+import os
+import asyncio
+import logging
+from pathlib import Path
+from huggingface_hub import HfApi, upload_file
+from reasoning.model_manager import ModelManager
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+async def download_and_prepare_models():
+    """Download all models and prepare for Spaces."""
+    try:
+        # Initialize model manager
+        model_dir = os.path.join(os.getcwd(), "models")
+        manager = ModelManager(model_dir)
+        
+        # Create models directory
+        os.makedirs(model_dir, exist_ok=True)
+        
+        # Download all models
+        logger.info("Starting model downloads...")
+        await manager.initialize_all_models()
+        logger.info("All models downloaded successfully!")
+        
+        return True
+        
+    except Exception as e:
+        logger.error(f"Error downloading models: {e}")
+        return False
+
+def upload_to_spaces(space_name: str = "agentic-system-models"):
+    """Upload models to HuggingFace Spaces."""
+    try:
+        api = HfApi()
+        model_dir = os.path.join(os.getcwd(), "models")
+        
+        # Create .gitattributes for LFS
+        gitattributes_path = os.path.join(model_dir, ".gitattributes")
+        with open(gitattributes_path, "w") as f:
+            f.write("*.gguf filter=lfs diff=lfs merge=lfs -text")
+        
+        # Upload .gitattributes first
+        api.upload_file(
+            path_or_fileobj=gitattributes_path,
+            path_in_repo=".gitattributes",
+            repo_id=f"spaces/{space_name}",
+            repo_type="space"
+        )
+        
+        # Upload each model file
+        for model_file in Path(model_dir).glob("*.gguf"):
+            logger.info(f"Uploading {model_file.name}...")
+            api.upload_file(
+                path_or_fileobj=str(model_file),
+                path_in_repo=f"models/{model_file.name}",
+                repo_id=f"spaces/{space_name}",
+                repo_type="space"
+            )
+            
+        logger.info("All models uploaded to Spaces successfully!")
+        return True
+        
+    except Exception as e:
+        logger.error(f"Error uploading to Spaces: {e}")
+        return False
+
+if __name__ == "__main__":
+    # Download models
+    asyncio.run(download_and_prepare_models())
+    
+    # Upload to Spaces
+    upload_to_spaces()

download_models_space.py

@@ -0,0 +1,32 @@
+"""Initialize and download models in Hugging Face Spaces environment."""
+
+import os
+import asyncio
+import logging
+from huggingface_hub import HfApi
+from reasoning.model_manager import ModelManager
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+async def initialize_space_models():
+    """Download and initialize models in Spaces environment."""
+    try:
+        # Initialize model manager
+        manager = ModelManager()
+        
+        # Download all models
+        logger.info("Starting model downloads in Spaces environment...")
+        await manager.initialize_all_models()
+        logger.info("All models downloaded and initialized successfully!")
+        
+        return True
+        
+    except Exception as e:
+        logger.error(f"Error initializing models in Spaces: {e}")
+        return False
+
+if __name__ == "__main__":
+    # Initialize models in Spaces
+    asyncio.run(initialize_space_models())

fix_indentation.patch

@@ -0,0 +1,42 @@
+--- reasoning.py
++++ reasoning.py
+@@ -2796,297 +2796,297 @@
+     async def _cross_modal_alignment(self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
+         """Align information across different modalities."""
+         try:
+             # Extract modality types
+             modal_types = list(modalities.keys())
+             
+             # Initialize alignment results
+             alignments = []
+             
+             # Process each modality pair
+             for i in range(len(modal_types)):
+                 for j in range(i + 1, len(modal_types)):
+                     type1, type2 = modal_types[i], modal_types[j]
+                     
+                     # Get items from each modality
+                     items1 = modalities[type1]
+                     items2 = modalities[type2]
+                     
+                     # Find alignments between items
+                     for item1 in items1:
+                         for item2 in items2:
+                             similarity = self._calculate_similarity(item1, item2)
+                             if similarity > 0.5:  # Threshold for alignment
+                                 alignments.append({
+                                     "type1": type1,
+                                     "type2": type2,
+                                     "item1": item1,
+                                     "item2": item2,
+                                     "similarity": similarity
+                                 })
+             
+             # Sort alignments by similarity
+             alignments.sort(key=lambda x: x["similarity"], reverse=True)
+             
+             return alignments
+             
+         except Exception as e:
+             logging.error(f"Error in cross-modal alignment: {str(e)}")
+             return []

init_space.sh

@@ -0,0 +1,28 @@
+#!/bin/bash
+
+# Create necessary directories
+mkdir -p models
+mkdir -p logs
+
+# Install system dependencies
+apt-get update && apt-get install -y \
+    git \
+    git-lfs \
+    python3-pip \
+    python3-dev \
+    build-essential \
+    cmake \
+    pkg-config \
+    libcurl4-openssl-dev
+
+# Initialize git-lfs
+git lfs install
+
+# Install Python dependencies
+pip install -r requirements.txt
+
+# Download models
+python download_models.py
+
+# Start the application
+python app.py

main.py

@@ -0,0 +1,133 @@
+"""Main entry point for the Advanced Agentic System."""
+
+import asyncio
+import logging
+from typing import Dict, Any, Optional
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+import gradio as gr
+import uvicorn
+
+from agentic_system import AgenticSystem
+from reasoning.unified_engine import UnifiedReasoningEngine
+from api.openai_compatible import OpenAICompatibleAPI
+from api.venture_api import VentureAPI
+from ui.venture_ui import VentureUI
+from config import Config
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class AgenticSystemApp:
+    """Main application class integrating all components."""
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        self.config = Config(config)
+        
+        # Initialize core components
+        self.reasoning_engine = UnifiedReasoningEngine(
+            min_confidence=self.config.get('min_confidence', 0.7),
+            parallel_threshold=self.config.get('parallel_threshold', 3),
+            learning_rate=self.config.get('learning_rate', 0.1)
+        )
+        
+        self.agentic_system = AgenticSystem(
+            config=self.config.get('agentic_system', {})
+        )
+        
+        # Initialize APIs
+        self.venture_api = VentureAPI(self.reasoning_engine)
+        self.openai_api = OpenAICompatibleAPI(self.reasoning_engine)
+        
+        # Initialize FastAPI
+        self.app = FastAPI(
+            title="Advanced Agentic System",
+            description="Venture Strategy Optimizer with OpenAI-compatible API",
+            version="1.0.0"
+        )
+        
+        # Setup middleware
+        self._setup_middleware()
+        
+        # Setup routes
+        self._setup_routes()
+        
+        # Initialize UI
+        self.ui = VentureUI(self.venture_api)
+        self.interface = self.ui.create_interface()
+        
+        # Mount Gradio app
+        self.app = gr.mount_gradio_app(self.app, self.interface, path="/")
+    
+    def _setup_middleware(self):
+        """Setup FastAPI middleware."""
+        self.app.add_middleware(
+            CORSMiddleware,
+            allow_origins=["*"],
+            allow_credentials=True,
+            allow_methods=["*"],
+            allow_headers=["*"],
+        )
+    
+    def _setup_routes(self):
+        """Setup API routes."""
+        # Include OpenAI-compatible routes
+        self.app.include_router(
+            self.openai_api.router,
+            tags=["OpenAI Compatible"]
+        )
+        
+        # Health check
+        @self.app.get("/api/health")
+        async def health_check():
+            return {
+                "status": "healthy",
+                "version": "1.0.0",
+                "components": {
+                    "reasoning_engine": "active",
+                    "agentic_system": "active",
+                    "openai_api": "active",
+                    "venture_api": "active"
+                }
+            }
+        
+        # System status
+        @self.app.get("/api/system/status")
+        async def system_status():
+            return await self.agentic_system.get_system_status()
+        
+        # Reasoning endpoint
+        @self.app.post("/api/reason")
+        async def reason(query: str, context: Optional[Dict[str, Any]] = None):
+            return await self.reasoning_engine.reason(query, context or {})
+        
+        # Venture analysis
+        @self.app.post("/api/venture/analyze")
+        async def analyze_venture(
+            venture_type: str,
+            description: str,
+            metrics: Optional[Dict[str, Any]] = None
+        ):
+            return await self.venture_api.analyze_venture(
+                venture_type=venture_type,
+                description=description,
+                metrics=metrics or {}
+            )
+    
+    def run(self, host: str = "0.0.0.0", port: int = 7860):
+        """Run the application."""
+        uvicorn.run(
+            self.app,
+            host=host,
+            port=port,
+            workers=4
+        )
+
+def main():
+    """Main entry point."""
+    app = AgenticSystemApp()
+    app.run()
+
+if __name__ == "__main__":
+    main()

meta_learning.py

@@ -0,0 +1,436 @@
+"""
+Meta-Learning System
+------------------
+Implements meta-learning capabilities for improved learning and adaptation.
+"""
+
+from typing import Dict, Any, List, Optional, Tuple
+import numpy as np
+from dataclasses import dataclass, field
+import logging
+from datetime import datetime
+from enum import Enum
+import json
+from quantum_learning import QuantumLearningSystem, Pattern, PatternType
+
+class LearningStrategy(Enum):
+    GRADIENT_BASED = "gradient_based"
+    MEMORY_BASED = "memory_based"
+    EVOLUTIONARY = "evolutionary"
+    REINFORCEMENT = "reinforcement"
+    QUANTUM = "quantum"
+
+@dataclass
+class MetaParameters:
+    """Meta-parameters for learning strategies"""
+    learning_rate: float = 0.01
+    memory_size: int = 1000
+    evolution_rate: float = 0.1
+    exploration_rate: float = 0.2
+    quantum_interference: float = 0.5
+    adaptation_threshold: float = 0.7
+
+@dataclass
+class LearningMetrics:
+    """Metrics for learning performance"""
+    accuracy: float
+    convergence_rate: float
+    adaptation_speed: float
+    resource_usage: float
+    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())
+
+class MetaLearningSystem:
+    """Meta-learning system for optimizing learning strategies"""
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        self.logger = logging.getLogger(__name__)
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+        
+        # Initialize quantum system with shared config
+        quantum_config = {
+            'min_confidence': self.min_confidence,
+            'parallel_threshold': self.parallel_threshold,
+            'learning_rate': self.learning_rate,
+            'strategy_weights': self.strategy_weights,
+            'num_qubits': self.config.get('num_qubits', 8),
+            'entanglement_strength': self.config.get('entanglement_strength', 0.5),
+            'interference_threshold': self.config.get('interference_threshold', 0.3),
+            'tunneling_rate': self.config.get('tunneling_rate', 0.1),
+            'annealing_schedule': self.config.get('annealing_schedule', {
+                'initial_temp': 1.0,
+                'final_temp': 0.01,
+                'steps': 100,
+                'cooling_rate': 0.95
+            })
+        }
+        self.quantum_system = QuantumLearningSystem(quantum_config)
+        self.strategies = {}
+        self.performance_history = []
+        self.meta_parameters = MetaParameters()
+        
+    async def optimize_learning(
+        self,
+        observation: Dict[str, Any],
+        current_strategy: LearningStrategy
+    ) -> Tuple[Dict[str, Any], LearningMetrics]:
+        """Optimize learning strategy based on observation"""
+        try:
+            # Process with quantum system
+            quantum_result = await self.quantum_system.process_observation(observation)
+            
+            # Evaluate current strategy
+            current_metrics = self._evaluate_strategy(
+                current_strategy,
+                observation,
+                quantum_result
+            )
+            
+            # Update performance history
+            self._update_performance_history(current_metrics)
+            
+            # Adapt meta-parameters
+            self._adapt_meta_parameters(current_metrics)
+            
+            # Select optimal strategy
+            optimal_strategy = self._select_optimal_strategy(
+                observation,
+                current_metrics
+            )
+            
+            # Apply selected strategy
+            result = await self._apply_strategy(
+                optimal_strategy,
+                observation,
+                quantum_result
+            )
+            
+            return result, current_metrics
+            
+        except Exception as e:
+            self.logger.error(f"Failed to optimize learning: {str(e)}")
+            raise
+            
+    def _evaluate_strategy(
+        self,
+        strategy: LearningStrategy,
+        observation: Dict[str, Any],
+        quantum_result: Dict[str, Any]
+    ) -> LearningMetrics:
+        """Evaluate performance of current learning strategy"""
+        # Calculate accuracy
+        accuracy = self._calculate_accuracy(
+            strategy,
+            observation,
+            quantum_result
+        )
+        
+        # Calculate convergence rate
+        convergence_rate = self._calculate_convergence_rate(
+            strategy,
+            self.performance_history
+        )
+        
+        # Calculate adaptation speed
+        adaptation_speed = self._calculate_adaptation_speed(
+            strategy,
+            observation
+        )
+        
+        # Calculate resource usage
+        resource_usage = self._calculate_resource_usage(strategy)
+        
+        return LearningMetrics(
+            accuracy=accuracy,
+            convergence_rate=convergence_rate,
+            adaptation_speed=adaptation_speed,
+            resource_usage=resource_usage
+        )
+        
+    def _update_performance_history(
+        self,
+        metrics: LearningMetrics
+    ) -> None:
+        """Update performance history with new metrics"""
+        self.performance_history.append(metrics)
+        
+        # Maintain history size
+        if len(self.performance_history) > self.meta_parameters.memory_size:
+            self.performance_history.pop(0)
+            
+    def _adapt_meta_parameters(
+        self,
+        metrics: LearningMetrics
+    ) -> None:
+        """Adapt meta-parameters based on performance metrics"""
+        # Adjust learning rate
+        if metrics.convergence_rate < self.meta_parameters.adaptation_threshold:
+            self.meta_parameters.learning_rate *= 0.9
+        else:
+            self.meta_parameters.learning_rate *= 1.1
+            
+        # Adjust memory size
+        if metrics.resource_usage > 0.8:
+            self.meta_parameters.memory_size = int(
+                self.meta_parameters.memory_size * 0.9
+            )
+        elif metrics.resource_usage < 0.2:
+            self.meta_parameters.memory_size = int(
+                self.meta_parameters.memory_size * 1.1
+            )
+            
+        # Adjust evolution rate
+        if metrics.adaptation_speed < self.meta_parameters.adaptation_threshold:
+            self.meta_parameters.evolution_rate *= 1.1
+        else:
+            self.meta_parameters.evolution_rate *= 0.9
+            
+        # Adjust exploration rate
+        if metrics.accuracy < self.meta_parameters.adaptation_threshold:
+            self.meta_parameters.exploration_rate *= 1.1
+        else:
+            self.meta_parameters.exploration_rate *= 0.9
+            
+        # Adjust quantum interference
+        if metrics.accuracy > 0.8:
+            self.meta_parameters.quantum_interference *= 1.1
+        else:
+            self.meta_parameters.quantum_interference *= 0.9
+            
+        # Ensure parameters stay within reasonable bounds
+        self._normalize_parameters()
+        
+    def _normalize_parameters(self) -> None:
+        """Normalize meta-parameters to stay within bounds"""
+        self.meta_parameters.learning_rate = np.clip(
+            self.meta_parameters.learning_rate,
+            0.001,
+            0.1
+        )
+        self.meta_parameters.memory_size = np.clip(
+            self.meta_parameters.memory_size,
+            100,
+            10000
+        )
+        self.meta_parameters.evolution_rate = np.clip(
+            self.meta_parameters.evolution_rate,
+            0.01,
+            0.5
+        )
+        self.meta_parameters.exploration_rate = np.clip(
+            self.meta_parameters.exploration_rate,
+            0.1,
+            0.9
+        )
+        self.meta_parameters.quantum_interference = np.clip(
+            self.meta_parameters.quantum_interference,
+            0.1,
+            0.9
+        )
+        
+    def _select_optimal_strategy(
+        self,
+        observation: Dict[str, Any],
+        metrics: LearningMetrics
+    ) -> LearningStrategy:
+        """Select optimal learning strategy"""
+        strategies = list(LearningStrategy)
+        scores = []
+        
+        for strategy in strategies:
+            # Calculate strategy score
+            score = self._calculate_strategy_score(
+                strategy,
+                observation,
+                metrics
+            )
+            scores.append((strategy, score))
+            
+        # Select strategy with highest score
+        optimal_strategy = max(scores, key=lambda x: x[1])[0]
+        
+        return optimal_strategy
+        
+    async def _apply_strategy(
+        self,
+        strategy: LearningStrategy,
+        observation: Dict[str, Any],
+        quantum_result: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Apply selected learning strategy"""
+        if strategy == LearningStrategy.GRADIENT_BASED:
+            return await self._apply_gradient_strategy(
+                observation,
+                quantum_result
+            )
+        elif strategy == LearningStrategy.MEMORY_BASED:
+            return await self._apply_memory_strategy(
+                observation,
+                quantum_result
+            )
+        elif strategy == LearningStrategy.EVOLUTIONARY:
+            return await self._apply_evolutionary_strategy(
+                observation,
+                quantum_result
+            )
+        elif strategy == LearningStrategy.REINFORCEMENT:
+            return await self._apply_reinforcement_strategy(
+                observation,
+                quantum_result
+            )
+        else:  # QUANTUM
+            return quantum_result
+            
+    def _calculate_accuracy(
+        self,
+        strategy: LearningStrategy,
+        observation: Dict[str, Any],
+        quantum_result: Dict[str, Any]
+    ) -> float:
+        """Calculate accuracy of learning strategy"""
+        if "patterns" not in quantum_result:
+            return 0.0
+            
+        patterns = quantum_result["patterns"]
+        if not patterns:
+            return 0.0
+            
+        # Calculate pattern confidence
+        confidence_sum = sum(pattern.confidence for pattern in patterns)
+        return confidence_sum / len(patterns)
+        
+    def _calculate_convergence_rate(
+        self,
+        strategy: LearningStrategy,
+        history: List[LearningMetrics]
+    ) -> float:
+        """Calculate convergence rate of learning strategy"""
+        if not history:
+            return 0.0
+            
+        # Calculate rate of improvement
+        accuracies = [metrics.accuracy for metrics in history[-10:]]
+        if len(accuracies) < 2:
+            return 0.0
+            
+        differences = np.diff(accuracies)
+        return float(np.mean(differences > 0))
+        
+    def _calculate_adaptation_speed(
+        self,
+        strategy: LearningStrategy,
+        observation: Dict[str, Any]
+    ) -> float:
+        """Calculate adaptation speed of learning strategy"""
+        if not self.performance_history:
+            return 0.0
+            
+        # Calculate time to reach adaptation threshold
+        threshold = self.meta_parameters.adaptation_threshold
+        for i, metrics in enumerate(self.performance_history):
+            if metrics.accuracy >= threshold:
+                return 1.0 / (i + 1)
+                
+        return 0.0
+        
+    def _calculate_resource_usage(
+        self,
+        strategy: LearningStrategy
+    ) -> float:
+        """Calculate resource usage of learning strategy"""
+        # Simulate resource usage based on strategy
+        base_usage = {
+            LearningStrategy.GRADIENT_BASED: 0.4,
+            LearningStrategy.MEMORY_BASED: 0.6,
+            LearningStrategy.EVOLUTIONARY: 0.7,
+            LearningStrategy.REINFORCEMENT: 0.5,
+            LearningStrategy.QUANTUM: 0.8
+        }
+        
+        return base_usage[strategy]
+        
+    def _calculate_strategy_score(
+        self,
+        strategy: LearningStrategy,
+        observation: Dict[str, Any],
+        metrics: LearningMetrics
+    ) -> float:
+        """Calculate score for learning strategy"""
+        # Weight different factors
+        weights = {
+            "accuracy": 0.4,
+            "convergence": 0.2,
+            "adaptation": 0.2,
+            "resources": 0.2
+        }
+        
+        score = (
+            weights["accuracy"] * metrics.accuracy +
+            weights["convergence"] * metrics.convergence_rate +
+            weights["adaptation"] * metrics.adaptation_speed +
+            weights["resources"] * (1 - metrics.resource_usage)
+        )
+        
+        # Add exploration bonus
+        if np.random.random() < self.meta_parameters.exploration_rate:
+            score += 0.1
+            
+        return score
+        
+    async def _apply_gradient_strategy(
+        self,
+        observation: Dict[str, Any],
+        quantum_result: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Apply gradient-based learning strategy"""
+        return {
+            "result": "gradient_optimization",
+            "quantum_enhanced": quantum_result,
+            "meta_parameters": self.meta_parameters.__dict__
+        }
+        
+    async def _apply_memory_strategy(
+        self,
+        observation: Dict[str, Any],
+        quantum_result: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Apply memory-based learning strategy"""
+        return {
+            "result": "memory_optimization",
+            "quantum_enhanced": quantum_result,
+            "meta_parameters": self.meta_parameters.__dict__
+        }
+        
+    async def _apply_evolutionary_strategy(
+        self,
+        observation: Dict[str, Any],
+        quantum_result: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Apply evolutionary learning strategy"""
+        return {
+            "result": "evolutionary_optimization",
+            "quantum_enhanced": quantum_result,
+            "meta_parameters": self.meta_parameters.__dict__
+        }
+        
+    async def _apply_reinforcement_strategy(
+        self,
+        observation: Dict[str, Any],
+        quantum_result: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Apply reinforcement learning strategy"""
+        return {
+            "result": "reinforcement_optimization",
+            "quantum_enhanced": quantum_result,
+            "meta_parameters": self.meta_parameters.__dict__
+        }

multimodal_reasoning.py

@@ -0,0 +1,301 @@
+"""
+Multi-Modal Reasoning Implementation
+----------------------------------
+Implements reasoning across different types of information.
+"""
+
+import logging
+from typing import Dict, Any, List, Optional
+from datetime import datetime
+import json
+import numpy as np
+from .reasoning import ReasoningStrategy
+
+class MultiModalReasoning(ReasoningStrategy):
+    """Implements multi-modal reasoning across different types of information."""
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize multi-modal reasoning."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+        
+        # Multi-modal specific parameters
+        self.modality_weights = self.config.get('modality_weights', {
+            'text': 0.8,
+            'image': 0.7,
+            'audio': 0.6,
+            'video': 0.5,
+            'structured': 0.7
+        })
+        self.cross_modal_threshold = self.config.get('cross_modal_threshold', 0.6)
+        self.integration_steps = self.config.get('integration_steps', 3)
+        self.alignment_method = self.config.get('alignment_method', 'attention')
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        try:
+            # Process different modalities
+            modalities = await self._process_modalities(query, context)
+            
+            # Align across modalities
+            alignment = await self._cross_modal_alignment(modalities, context)
+            
+            # Integrated analysis
+            integration = await self._integrated_analysis(alignment, context)
+            
+            # Generate final response
+            response = await self._generate_response(integration, context)
+            
+            return {
+                "success": True,
+                "answer": response["conclusion"],
+                "modalities": modalities,
+                "alignment": alignment,
+                "integration": integration,
+                "confidence": response["confidence"]
+            }
+        except Exception as e:
+            logging.error(f"Error in multi-modal reasoning: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _process_modalities(self, query: str, context: Dict[str, Any]) -> Dict[str, List[Dict[str, Any]]]:
+        """Process query across different modalities."""
+        prompt = f"""
+        Process query across modalities:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        For each modality extract:
+        1. [Type]: Modality type
+        2. [Content]: Relevant content
+        3. [Features]: Key features
+        4. [Quality]: Content quality
+        
+        Format as:
+        [M1]
+        Type: ...
+        Content: ...
+        Features: ...
+        Quality: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_modalities(response["answer"])
+
+    async def _cross_modal_alignment(self, modalities: Dict[str, List[Dict[str, Any]]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Align information across different modalities."""
+        try:
+            # Extract modality types
+            modal_types = list(modalities.keys())
+            
+            # Initialize alignment results
+            alignments = []
+            
+            # Process each modality pair
+            for i in range(len(modal_types)):
+                for j in range(i + 1, len(modal_types)):
+                    type1, type2 = modal_types[i], modal_types[j]
+                    
+                    # Get items from each modality
+                    items1 = modalities[type1]
+                    items2 = modalities[type2]
+                    
+                    # Find alignments between items
+                    for item1 in items1:
+                        for item2 in items2:
+                            similarity = self._calculate_similarity(item1, item2)
+                            if similarity > self.cross_modal_threshold:  # Threshold for alignment
+                                alignments.append({
+                                    "type1": type1,
+                                    "type2": type2,
+                                    "item1": item1,
+                                    "item2": item2,
+                                    "similarity": similarity
+                                })
+            
+            # Sort alignments by similarity
+            alignments.sort(key=lambda x: x["similarity"], reverse=True)
+            
+            return alignments
+            
+        except Exception as e:
+            logging.error(f"Error in cross-modal alignment: {str(e)}")
+            return []
+
+    def _calculate_similarity(self, item1: Dict[str, Any], item2: Dict[str, Any]) -> float:
+        """Calculate similarity between two items from different modalities."""
+        try:
+            # Extract content from items
+            content1 = str(item1.get("content", ""))
+            content2 = str(item2.get("content", ""))
+            
+            # Calculate basic similarity (can be enhanced with more sophisticated methods)
+            common_words = set(content1.lower().split()) & set(content2.lower().split())
+            total_words = set(content1.lower().split()) | set(content2.lower().split())
+            
+            if not total_words:
+                return 0.0
+                
+            return len(common_words) / len(total_words)
+            
+        except Exception as e:
+            logging.error(f"Error calculating similarity: {str(e)}")
+            return 0.0
+
+    async def _integrated_analysis(self, alignment: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        prompt = f"""
+        Perform integrated multi-modal analysis:
+        Alignment: {json.dumps(alignment)}
+        Context: {json.dumps(context)}
+        
+        For each insight:
+        1. [Insight]: Key finding
+        2. [Sources]: Contributing modalities
+        3. [Support]: Supporting evidence
+        4. [Confidence]: Confidence level
+        
+        Format as:
+        [I1]
+        Insight: ...
+        Sources: ...
+        Support: ...
+        Confidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_integration(response["answer"])
+
+    async def _generate_response(self, integration: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
+        prompt = f"""
+        Generate unified multi-modal response:
+        Integration: {json.dumps(integration)}
+        Context: {json.dumps(context)}
+        
+        Provide:
+        1. Main conclusion
+        2. Modal contributions
+        3. Integration benefits
+        4. Confidence level (0-1)
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_response(response["answer"])
+
+    def _parse_modalities(self, response: str) -> Dict[str, List[Dict[str, Any]]]:
+        """Parse modalities from response."""
+        modalities = {}
+        current_modality = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[M'):
+                if current_modality:
+                    if current_modality["type"] not in modalities:
+                        modalities[current_modality["type"]] = []
+                    modalities[current_modality["type"]].append(current_modality)
+                current_modality = {
+                    "type": "",
+                    "content": "",
+                    "features": "",
+                    "quality": ""
+                }
+            elif current_modality:
+                if line.startswith('Type:'):
+                    current_modality["type"] = line[5:].strip()
+                elif line.startswith('Content:'):
+                    current_modality["content"] = line[8:].strip()
+                elif line.startswith('Features:'):
+                    current_modality["features"] = line[9:].strip()
+                elif line.startswith('Quality:'):
+                    current_modality["quality"] = line[8:].strip()
+        
+        if current_modality:
+            if current_modality["type"] not in modalities:
+                modalities[current_modality["type"]] = []
+            modalities[current_modality["type"]].append(current_modality)
+        
+        return modalities
+
+    def _parse_integration(self, response: str) -> List[Dict[str, Any]]:
+        """Parse integration from response."""
+        integration = []
+        current_insight = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[I'):
+                if current_insight:
+                    integration.append(current_insight)
+                current_insight = {
+                    "insight": "",
+                    "sources": "",
+                    "support": "",
+                    "confidence": 0.0
+                }
+            elif current_insight:
+                if line.startswith('Insight:'):
+                    current_insight["insight"] = line[8:].strip()
+                elif line.startswith('Sources:'):
+                    current_insight["sources"] = line[8:].strip()
+                elif line.startswith('Support:'):
+                    current_insight["support"] = line[8:].strip()
+                elif line.startswith('Confidence:'):
+                    try:
+                        current_insight["confidence"] = float(line[11:].strip())
+                    except:
+                        pass
+        
+        if current_insight:
+            integration.append(current_insight)
+        
+        return integration
+
+    def _parse_response(self, response: str) -> Dict[str, Any]:
+        """Parse response from response."""
+        response_dict = {
+            "conclusion": "",
+            "modal_contributions": [],
+            "integration_benefits": [],
+            "confidence": 0.0
+        }
+        
+        mode = None
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('Conclusion:'):
+                response_dict["conclusion"] = line[11:].strip()
+            elif line.startswith('Modal Contributions:'):
+                mode = "modal"
+            elif line.startswith('Integration Benefits:'):
+                mode = "integration"
+            elif line.startswith('Confidence:'):
+                try:
+                    response_dict["confidence"] = float(line[11:].strip())
+                except:
+                    response_dict["confidence"] = 0.5
+                mode = None
+            elif mode == "modal" and line.startswith('- '):
+                response_dict["modal_contributions"].append(line[2:].strip())
+            elif mode == "integration" and line.startswith('- '):
+                response_dict["integration_benefits"].append(line[2:].strip())
+        
+        return response_dict

orchestrator.py

@@ -0,0 +1,628 @@
+"""
+Agentic Orchestrator for Advanced AI System
+-----------------------------------------
+Manages and coordinates multiple agentic components:
+1. Task Planning & Decomposition
+2. Resource Management
+3. Agent Communication
+4. State Management
+5. Error Recovery
+6. Performance Monitoring
+"""
+
+import logging
+from typing import Dict, Any, List, Optional, Union, TypeVar, Generic
+from dataclasses import dataclass, field
+from enum import Enum
+import json
+import asyncio
+from datetime import datetime
+import uuid
+from concurrent.futures import ThreadPoolExecutor
+import networkx as nx
+from collections import defaultdict
+import numpy as np
+
+from reasoning import UnifiedReasoningEngine as ReasoningEngine, StrategyType as ReasoningMode
+from reasoning.meta_learning import MetaLearningStrategy
+
+T = TypeVar('T')
+
+class AgentRole(Enum):
+    """Different roles an agent can take."""
+    PLANNER = "planner"
+    EXECUTOR = "executor"
+    MONITOR = "monitor"
+    COORDINATOR = "coordinator"
+    LEARNER = "learner"
+
+class AgentState(Enum):
+    """Possible states of an agent."""
+    IDLE = "idle"
+    BUSY = "busy"
+    ERROR = "error"
+    LEARNING = "learning"
+    TERMINATED = "terminated"
+
+class TaskPriority(Enum):
+    """Task priority levels."""
+    LOW = 0
+    MEDIUM = 1
+    HIGH = 2
+    CRITICAL = 3
+
+@dataclass
+class AgentMetadata:
+    """Metadata about an agent."""
+    id: str
+    role: AgentRole
+    capabilities: List[str]
+    state: AgentState
+    load: float
+    last_active: datetime
+    metrics: Dict[str, float]
+
+@dataclass
+class Task:
+    """Represents a task in the system."""
+    id: str
+    description: str
+    priority: TaskPriority
+    dependencies: List[str]
+    assigned_to: Optional[str]
+    state: str
+    created_at: datetime
+    deadline: Optional[datetime]
+    metadata: Dict[str, Any]
+
+class AgentOrchestrator:
+    """Advanced orchestrator for managing agentic system."""
+    
+    def __init__(self, config: Dict[str, Any] = None):
+        self.config = config or {}
+        
+        # Core components
+        self.agents: Dict[str, AgentMetadata] = {}
+        self.tasks: Dict[str, Task] = {}
+        self.task_graph = nx.DiGraph()
+        
+        # State management
+        self.state_history: List[Dict[str, Any]] = []
+        self.global_state: Dict[str, Any] = {}
+        
+        # Resource management
+        self.resource_pool: Dict[str, Any] = {}
+        self.resource_locks: Dict[str, asyncio.Lock] = {}
+        
+        # Communication
+        self.message_queue = asyncio.Queue()
+        self.event_bus = asyncio.Queue()
+        
+        # Performance monitoring
+        self.metrics = defaultdict(list)
+        self.performance_log = []
+        
+        # Error handling
+        self.error_handlers: Dict[str, callable] = {}
+        self.recovery_strategies: Dict[str, callable] = {}
+        
+        # Async support
+        self.executor = ThreadPoolExecutor(max_workers=4)
+        self.lock = asyncio.Lock()
+        
+        # Logging
+        self.logger = logging.getLogger(__name__)
+        
+        # Initialize components
+        self._init_components()
+
+    def _init_components(self):
+        """Initialize orchestrator components."""
+        # Initialize reasoning engine
+        self.reasoning_engine = ReasoningEngine(
+            min_confidence=0.7,
+            parallel_threshold=5,
+            learning_rate=0.1,
+            strategy_weights={
+                "LOCAL_LLM": 2.0,
+                "CHAIN_OF_THOUGHT": 1.0,
+                "TREE_OF_THOUGHTS": 1.0,
+                "META_LEARNING": 1.5
+            }
+        )
+        
+        # Initialize meta-learning
+        self.meta_learning = MetaLearningStrategy()
+        
+        # Register basic error handlers
+        self._register_error_handlers()
+
+    async def register_agent(
+        self,
+        role: AgentRole,
+        capabilities: List[str]
+    ) -> str:
+        """Register a new agent with the orchestrator."""
+        agent_id = str(uuid.uuid4())
+        
+        agent = AgentMetadata(
+            id=agent_id,
+            role=role,
+            capabilities=capabilities,
+            state=AgentState.IDLE,
+            load=0.0,
+            last_active=datetime.now(),
+            metrics={}
+        )
+        
+        async with self.lock:
+            self.agents[agent_id] = agent
+            self.logger.info(f"Registered new agent: {agent_id} with role {role}")
+            
+        return agent_id
+
+    async def submit_task(
+        self,
+        description: str,
+        priority: TaskPriority = TaskPriority.MEDIUM,
+        dependencies: List[str] = None,
+        deadline: Optional[datetime] = None,
+        metadata: Dict[str, Any] = None
+    ) -> str:
+        """Submit a new task to the orchestrator."""
+        task_id = str(uuid.uuid4())
+        
+        task = Task(
+            id=task_id,
+            description=description,
+            priority=priority,
+            dependencies=dependencies or [],
+            assigned_to=None,
+            state="pending",
+            created_at=datetime.now(),
+            deadline=deadline,
+            metadata=metadata or {}
+        )
+        
+        async with self.lock:
+            self.tasks[task_id] = task
+            self._update_task_graph(task)
+            
+        # Trigger task planning
+        await self._plan_task_execution(task_id)
+        
+        return task_id
+
+    async def _plan_task_execution(self, task_id: str) -> None:
+        """Plan the execution of a task."""
+        task = self.tasks[task_id]
+        
+        # Check dependencies
+        if not await self._check_dependencies(task):
+            self.logger.info(f"Task {task_id} waiting for dependencies")
+            return
+        
+        # Find suitable agent
+        agent_id = await self._find_suitable_agent(task)
+        if not agent_id:
+            self.logger.warning(f"No suitable agent found for task {task_id}")
+            return
+        
+        # Assign task
+        await self._assign_task(task_id, agent_id)
+
+    async def _check_dependencies(self, task: Task) -> bool:
+        """Check if all task dependencies are satisfied."""
+        for dep_id in task.dependencies:
+            if dep_id not in self.tasks:
+                return False
+            if self.tasks[dep_id].state != "completed":
+                return False
+        return True
+
+    async def _find_suitable_agent(self, task: Task) -> Optional[str]:
+        """Find the most suitable agent for a task."""
+        best_agent = None
+        best_score = float('-inf')
+        
+        for agent_id, agent in self.agents.items():
+            if agent.state != AgentState.IDLE:
+                continue
+                
+            score = await self._calculate_agent_suitability(agent, task)
+            if score > best_score:
+                best_score = score
+                best_agent = agent_id
+                
+        return best_agent
+
+    async def _calculate_agent_suitability(
+        self,
+        agent: AgentMetadata,
+        task: Task
+    ) -> float:
+        """Calculate how suitable an agent is for a task."""
+        # Base score on capabilities match
+        capability_score = sum(
+            1 for cap in task.metadata.get("required_capabilities", [])
+            if cap in agent.capabilities
+        )
+        
+        # Consider agent load
+        load_score = 1 - agent.load
+        
+        # Consider agent's recent performance
+        performance_score = sum(agent.metrics.values()) / len(agent.metrics) if agent.metrics else 0.5
+        
+        # Weighted combination
+        weights = self.config.get("agent_selection_weights", {
+            "capabilities": 0.5,
+            "load": 0.3,
+            "performance": 0.2
+        })
+        
+        return (
+            weights["capabilities"] * capability_score +
+            weights["load"] * load_score +
+            weights["performance"] * performance_score
+        )
+
+    async def _assign_task(self, task_id: str, agent_id: str) -> None:
+        """Assign a task to an agent."""
+        async with self.lock:
+            task = self.tasks[task_id]
+            agent = self.agents[agent_id]
+            
+            task.assigned_to = agent_id
+            task.state = "assigned"
+            agent.state = AgentState.BUSY
+            agent.load += 1
+            agent.last_active = datetime.now()
+            
+            self.logger.info(f"Assigned task {task_id} to agent {agent_id}")
+            
+            # Notify agent
+            await self.message_queue.put({
+                "type": "task_assignment",
+                "task_id": task_id,
+                "agent_id": agent_id,
+                "timestamp": datetime.now()
+            })
+
+    def _update_task_graph(self, task: Task) -> None:
+        """Update the task dependency graph."""
+        self.task_graph.add_node(task.id, task=task)
+        for dep_id in task.dependencies:
+            self.task_graph.add_edge(dep_id, task.id)
+
+    async def _monitor_system_state(self):
+        """Monitor overall system state."""
+        while True:
+            try:
+                # Collect agent states
+                agent_states = {
+                    agent_id: {
+                        "state": agent.state,
+                        "load": agent.load,
+                        "metrics": agent.metrics
+                    }
+                    for agent_id, agent in self.agents.items()
+                }
+                
+                # Collect task states
+                task_states = {
+                    task_id: {
+                        "state": task.state,
+                        "assigned_to": task.assigned_to,
+                        "deadline": task.deadline
+                    }
+                    for task_id, task in self.tasks.items()
+                }
+                
+                # Update global state
+                self.global_state = {
+                    "timestamp": datetime.now(),
+                    "agents": agent_states,
+                    "tasks": task_states,
+                    "resource_usage": self._get_resource_usage(),
+                    "performance_metrics": self._calculate_performance_metrics()
+                }
+                
+                # Archive state
+                self.state_history.append(self.global_state.copy())
+                
+                # Trim history if too long
+                if len(self.state_history) > 1000:
+                    self.state_history = self.state_history[-1000:]
+                
+                # Check for anomalies
+                await self._check_anomalies()
+                
+                await asyncio.sleep(1)  # Monitor frequency
+                
+            except Exception as e:
+                self.logger.error(f"Error in system monitoring: {e}")
+                await self._handle_error("monitoring_error", e)
+
+    def _get_resource_usage(self) -> Dict[str, float]:
+        """Get current resource usage statistics."""
+        return {
+            "cpu_usage": sum(agent.load for agent in self.agents.values()) / len(self.agents),
+            "memory_usage": len(self.state_history) * 1000,  # Rough estimate
+            "queue_size": self.message_queue.qsize()
+        }
+
+    def _calculate_performance_metrics(self) -> Dict[str, float]:
+        """Calculate current performance metrics."""
+        metrics = {}
+        
+        # Task completion rate
+        completed_tasks = sum(1 for task in self.tasks.values() if task.state == "completed")
+        total_tasks = len(self.tasks)
+        metrics["task_completion_rate"] = completed_tasks / max(1, total_tasks)
+        
+        # Average task duration
+        durations = []
+        for task in self.tasks.values():
+            if task.state == "completed" and "completion_time" in task.metadata:
+                duration = (task.metadata["completion_time"] - task.created_at).total_seconds()
+                durations.append(duration)
+        metrics["avg_task_duration"] = sum(durations) / len(durations) if durations else 0
+        
+        # Agent utilization
+        metrics["agent_utilization"] = sum(agent.load for agent in self.agents.values()) / len(self.agents)
+        
+        return metrics
+
+    async def _check_anomalies(self):
+        """Check for system anomalies."""
+        # Check for overloaded agents
+        for agent_id, agent in self.agents.items():
+            if agent.load > 0.9:  # 90% load threshold
+                await self._handle_overload(agent_id)
+        
+        # Check for stalled tasks
+        now = datetime.now()
+        for task_id, task in self.tasks.items():
+            if task.state == "assigned":
+                duration = (now - task.created_at).total_seconds()
+                if duration > 3600:  # 1 hour threshold
+                    await self._handle_stalled_task(task_id)
+        
+        # Check for missed deadlines
+        for task_id, task in self.tasks.items():
+            if task.deadline and now > task.deadline and task.state != "completed":
+                await self._handle_missed_deadline(task_id)
+
+    async def _handle_overload(self, agent_id: str):
+        """Handle an overloaded agent."""
+        agent = self.agents[agent_id]
+        
+        # Try to redistribute tasks
+        assigned_tasks = [
+            task_id for task_id, task in self.tasks.items()
+            if task.assigned_to == agent_id and task.state == "assigned"
+        ]
+        
+        for task_id in assigned_tasks:
+            # Find another suitable agent
+            new_agent_id = await self._find_suitable_agent(self.tasks[task_id])
+            if new_agent_id:
+                await self._reassign_task(task_id, new_agent_id)
+
+    async def _handle_stalled_task(self, task_id: str):
+        """Handle a stalled task."""
+        task = self.tasks[task_id]
+        
+        # First, try to ping the assigned agent
+        if task.assigned_to:
+            agent = self.agents[task.assigned_to]
+            if agent.state == AgentState.ERROR:
+                # Agent is in error state, reassign task
+                await self._reassign_task(task_id, None)
+            else:
+                # Request status update from agent
+                await self.message_queue.put({
+                    "type": "status_request",
+                    "task_id": task_id,
+                    "agent_id": task.assigned_to,
+                    "timestamp": datetime.now()
+                })
+
+    async def _handle_missed_deadline(self, task_id: str):
+        """Handle a missed deadline."""
+        task = self.tasks[task_id]
+        
+        # Log the incident
+        self.logger.warning(f"Task {task_id} missed deadline: {task.deadline}")
+        
+        # Update task priority to CRITICAL
+        task.priority = TaskPriority.CRITICAL
+        
+        # If task is assigned, try to speed it up
+        if task.assigned_to:
+            await self.message_queue.put({
+                "type": "expedite_request",
+                "task_id": task_id,
+                "agent_id": task.assigned_to,
+                "timestamp": datetime.now()
+            })
+        else:
+            # If not assigned, try to assign to fastest available agent
+            await self._plan_task_execution(task_id)
+
+    async def _reassign_task(self, task_id: str, new_agent_id: Optional[str] = None):
+        """Reassign a task to a new agent."""
+        task = self.tasks[task_id]
+        old_agent_id = task.assigned_to
+        
+        if old_agent_id:
+            # Update old agent
+            old_agent = self.agents[old_agent_id]
+            old_agent.load -= 1
+            if old_agent.load <= 0:
+                old_agent.state = AgentState.IDLE
+        
+        if new_agent_id is None:
+            # Find new suitable agent
+            new_agent_id = await self._find_suitable_agent(task)
+        
+        if new_agent_id:
+            # Assign to new agent
+            await self._assign_task(task_id, new_agent_id)
+        else:
+            # No suitable agent found, mark task as pending
+            task.state = "pending"
+            task.assigned_to = None
+
+    def _register_error_handlers(self):
+        """Register basic error handlers."""
+        self.error_handlers.update({
+            "monitoring_error": self._handle_monitoring_error,
+            "agent_error": self._handle_agent_error,
+            "task_error": self._handle_task_error,
+            "resource_error": self._handle_resource_error
+        })
+        
+        self.recovery_strategies.update({
+            "agent_recovery": self._recover_agent,
+            "task_recovery": self._recover_task,
+            "resource_recovery": self._recover_resource
+        })
+
+    async def _handle_error(self, error_type: str, error: Exception):
+        """Handle an error using registered handlers."""
+        handler = self.error_handlers.get(error_type)
+        if handler:
+            try:
+                await handler(error)
+            except Exception as e:
+                self.logger.error(f"Error in error handler: {e}")
+        else:
+            self.logger.error(f"No handler for error type: {error_type}")
+            self.logger.error(f"Error: {error}")
+
+    async def _handle_monitoring_error(self, error: Exception):
+        """Handle monitoring system errors."""
+        self.logger.error(f"Monitoring error: {error}")
+        # Implement recovery logic
+        pass
+
+    async def _handle_agent_error(self, error: Exception):
+        """Handle agent-related errors."""
+        self.logger.error(f"Agent error: {error}")
+        # Implement recovery logic
+        pass
+
+    async def _handle_task_error(self, error: Exception):
+        """Handle task-related errors."""
+        self.logger.error(f"Task error: {error}")
+        # Implement recovery logic
+        pass
+
+    async def _handle_resource_error(self, error: Exception):
+        """Handle resource-related errors."""
+        self.logger.error(f"Resource error: {error}")
+        # Implement recovery logic
+        pass
+
+    async def _recover_agent(self, agent_id: str):
+        """Recover a failed agent."""
+        try:
+            agent = self.agents[agent_id]
+            
+            # Log recovery attempt
+            self.logger.info(f"Attempting to recover agent {agent_id}")
+            
+            # Reset agent state
+            agent.state = AgentState.IDLE
+            agent.load = 0
+            agent.last_active = datetime.now()
+            
+            # Reassign any tasks that were assigned to this agent
+            for task_id, task in self.tasks.items():
+                if task.assigned_to == agent_id:
+                    await self._reassign_task(task_id)
+            
+            # Update metrics
+            agent.metrics["recovery_attempts"] = agent.metrics.get("recovery_attempts", 0) + 1
+            
+            self.logger.info(f"Successfully recovered agent {agent_id}")
+            return True
+            
+        except Exception as e:
+            self.logger.error(f"Failed to recover agent {agent_id}: {e}")
+            return False
+
+    async def _recover_task(self, task_id: str):
+        """Recover a failed task."""
+        try:
+            task = self.tasks[task_id]
+            
+            # Log recovery attempt
+            self.logger.info(f"Attempting to recover task {task_id}")
+            
+            # Reset task state
+            task.state = "pending"
+            task.assigned_to = None
+            
+            # Try to reassign the task
+            await self._reassign_task(task_id)
+            
+            self.logger.info(f"Successfully recovered task {task_id}")
+            return True
+            
+        except Exception as e:
+            self.logger.error(f"Failed to recover task {task_id}: {e}")
+            return False
+
+    async def _recover_resource(self, resource_id: str):
+        """Recover a failed resource."""
+        try:
+            # Log recovery attempt
+            self.logger.info(f"Attempting to recover resource {resource_id}")
+            
+            # Release any locks on the resource
+            if resource_id in self.resource_locks:
+                lock = self.resource_locks[resource_id]
+                if lock.locked():
+                    lock.release()
+            
+            # Reset resource state
+            if resource_id in self.resource_pool:
+                self.resource_pool[resource_id] = {
+                    "state": "available",
+                    "last_error": None,
+                    "last_recovery": datetime.now()
+                }
+            
+            self.logger.info(f"Successfully recovered resource {resource_id}")
+            return True
+            
+        except Exception as e:
+            self.logger.error(f"Failed to recover resource {resource_id}: {e}")
+            return False
+
+    async def create_agent(self, role: AgentRole, capabilities: List[str]) -> str:
+        """Create a new agent with specified role and capabilities."""
+        agent_id = str(uuid.uuid4())
+        
+        agent_metadata = AgentMetadata(
+            id=agent_id,
+            role=role,
+            capabilities=capabilities,
+            state=AgentState.IDLE,
+            load=0.0,
+            last_active=datetime.now(),
+            metrics={
+                "tasks_completed": 0,
+                "success_rate": 1.0,
+                "avg_response_time": 0.0,
+                "resource_usage": 0.0
+            }
+        )
+        
+        self.agents[agent_id] = agent_metadata
+        self.logger.info(f"Created new agent {agent_id} with role {role}")
+        
+        return agent_id

reasoning/__init__.py

@@ -0,0 +1,70 @@
+"""
+Advanced Reasoning Engine for Multi-Model System
+---------------------------------------------
+A highly sophisticated reasoning system combining multiple reasoning strategies.
+
+Core Reasoning:
+1. Chain of Thought (CoT)
+2. Tree of Thoughts (ToT)
+3. Recursive Reasoning
+4. Analogical Reasoning
+5. Meta-Learning
+6. Local LLM
+
+Advanced Reasoning:
+7. Neurosymbolic Reasoning
+8. Bayesian Reasoning
+9. Quantum Reasoning
+10. Emergent Reasoning
+11. Multimodal Reasoning
+12. Specialized Reasoning
+
+Learning & Adaptation:
+13. Market Analysis
+14. Portfolio Optimization
+15. Venture Strategies
+16. Monetization Strategies
+"""
+
+from .base import ReasoningStrategy
+from .multimodal import MultiModalReasoning
+from .bayesian import BayesianReasoning
+from .quantum import QuantumReasoning
+from .neurosymbolic import NeurosymbolicReasoning
+from .emergent import EmergentReasoning
+from .meta_learning import MetaLearningStrategy
+from .chain_of_thought import ChainOfThoughtStrategy
+from .tree_of_thoughts import TreeOfThoughtsStrategy
+from .recursive import RecursiveReasoning
+from .analogical import AnalogicalReasoning
+from .specialized import SpecializedReasoning
+from .local_llm import LocalLLMStrategy
+from .market_analysis import MarketAnalysisStrategy
+from .portfolio_optimization import PortfolioOptimizationStrategy
+from .venture_strategies import VentureStrategy
+from .monetization import MonetizationStrategy
+from .unified_engine import UnifiedReasoningEngine, StrategyType, StrategyResult, UnifiedResult
+
+__all__ = [
+    'ReasoningStrategy',
+    'MultiModalReasoning',
+    'BayesianReasoning',
+    'QuantumReasoning',
+    'NeurosymbolicReasoning',
+    'EmergentReasoning',
+    'MetaLearningStrategy',
+    'ChainOfThoughtStrategy',
+    'TreeOfThoughtsStrategy',
+    'RecursiveReasoning',
+    'AnalogicalReasoning',
+    'SpecializedReasoning',
+    'LocalLLMStrategy',
+    'MarketAnalysisStrategy',
+    'PortfolioOptimizationStrategy',
+    'VentureStrategy',
+    'MonetizationStrategy',
+    'UnifiedReasoningEngine',
+    'StrategyType',
+    'StrategyResult',
+    'UnifiedResult'
+]

reasoning/agentic.py

@@ -0,0 +1,345 @@
+"""Specialized reasoning strategies for Agentic Workflow."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import asyncio
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+class TaskType(Enum):
+    """Types of tasks in agentic workflow."""
+    CODE_GENERATION = "code_generation"
+    CODE_MODIFICATION = "code_modification"
+    CODE_REVIEW = "code_review"
+    DEBUGGING = "debugging"
+    ARCHITECTURE = "architecture"
+    OPTIMIZATION = "optimization"
+    DOCUMENTATION = "documentation"
+    TESTING = "testing"
+
+class ResourceType(Enum):
+    """Types of resources in agentic workflow."""
+    CODE_CONTEXT = "code_context"
+    SYSTEM_CONTEXT = "system_context"
+    USER_CONTEXT = "user_context"
+    TOOLS = "tools"
+    APIS = "apis"
+    DOCUMENTATION = "documentation"
+    DEPENDENCIES = "dependencies"
+    HISTORY = "history"
+
+@dataclass
+class TaskComponent:
+    """Component of a decomposed task."""
+    id: str
+    type: TaskType
+    description: str
+    dependencies: List[str]
+    resources: Dict[ResourceType, Any]
+    constraints: List[str]
+    priority: float
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class ResourceAllocation:
+    """Resource allocation for a task."""
+    resource_type: ResourceType
+    quantity: Union[int, float]
+    priority: float
+    constraints: List[str]
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class ExecutionStep:
+    """Step in task execution."""
+    id: str
+    task_id: str
+    action: str
+    resources: Dict[ResourceType, Any]
+    status: str
+    result: Optional[Dict[str, Any]]
+    feedback: List[str]
+    timestamp: datetime = field(default_factory=datetime.now)
+
+class TaskDecompositionStrategy(ReasoningStrategy):
+    """
+    Advanced task decomposition strategy that:
+    1. Analyzes task complexity and dependencies
+    2. Breaks down tasks into manageable components
+    3. Identifies resource requirements
+    4. Establishes execution order
+    5. Manages constraints and priorities
+    """
+    
+    def __init__(self, max_components: int = 10):
+        self.max_components = max_components
+        self.components: Dict[str, TaskComponent] = {}
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Decompose task into components."""
+        try:
+            # Analyze task
+            task_analysis = await self._analyze_task(query, context)
+            
+            # Generate components
+            components = await self._generate_components(task_analysis, context)
+            
+            # Establish dependencies
+            dependency_graph = await self._establish_dependencies(components, context)
+            
+            # Determine execution order
+            execution_order = await self._determine_execution_order(
+                components, dependency_graph, context)
+            
+            return {
+                "success": True,
+                "components": [self._component_to_dict(c) for c in components],
+                "dependency_graph": dependency_graph,
+                "execution_order": execution_order,
+                "metadata": {
+                    "total_components": len(components),
+                    "complexity_score": task_analysis.get("complexity_score", 0.0),
+                    "resource_requirements": task_analysis.get("resource_requirements", {})
+                }
+            }
+        except Exception as e:
+            logging.error(f"Error in task decomposition: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class ResourceManagementStrategy(ReasoningStrategy):
+    """
+    Advanced resource management strategy that:
+    1. Tracks available resources
+    2. Allocates resources to tasks
+    3. Handles resource constraints
+    4. Optimizes resource utilization
+    5. Manages resource dependencies
+    """
+    
+    def __init__(self):
+        self.allocations: Dict[str, ResourceAllocation] = {}
+        self.utilization_history: List[Dict[str, Any]] = []
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Manage resource allocation."""
+        try:
+            # Analyze resource requirements
+            requirements = await self._analyze_requirements(query, context)
+            
+            # Check resource availability
+            availability = await self._check_availability(requirements, context)
+            
+            # Generate allocation plan
+            allocation_plan = await self._generate_allocation_plan(
+                requirements, availability, context)
+            
+            # Optimize allocations
+            optimized_plan = await self._optimize_allocations(allocation_plan, context)
+            
+            return {
+                "success": True,
+                "allocation_plan": optimized_plan,
+                "resource_metrics": {
+                    "utilization": self._calculate_utilization(),
+                    "efficiency": self._calculate_efficiency(),
+                    "constraints_satisfied": self._check_constraints(optimized_plan)
+                }
+            }
+        except Exception as e:
+            logging.error(f"Error in resource management: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class ContextualPlanningStrategy(ReasoningStrategy):
+    """
+    Advanced contextual planning strategy that:
+    1. Analyzes multiple context types
+    2. Generates context-aware plans
+    3. Handles context changes
+    4. Maintains context consistency
+    5. Optimizes for context constraints
+    """
+    
+    def __init__(self):
+        self.context_history: List[Dict[str, Any]] = []
+        self.plan_adaptations: List[Dict[str, Any]] = []
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate context-aware plan."""
+        try:
+            # Analyze contexts
+            context_analysis = await self._analyze_contexts(query, context)
+            
+            # Generate base plan
+            base_plan = await self._generate_base_plan(context_analysis, context)
+            
+            # Adapt to contexts
+            adapted_plan = await self._adapt_to_contexts(base_plan, context_analysis)
+            
+            # Validate plan
+            validation = await self._validate_plan(adapted_plan, context)
+            
+            return {
+                "success": True,
+                "plan": adapted_plan,
+                "context_impact": context_analysis.get("impact_assessment", {}),
+                "adaptations": self.plan_adaptations,
+                "validation_results": validation
+            }
+        except Exception as e:
+            logging.error(f"Error in contextual planning: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class AdaptiveExecutionStrategy(ReasoningStrategy):
+    """
+    Advanced adaptive execution strategy that:
+    1. Monitors execution progress
+    2. Adapts to changes and feedback
+    3. Handles errors and exceptions
+    4. Optimizes execution flow
+    5. Maintains execution state
+    """
+    
+    def __init__(self):
+        self.execution_steps: List[ExecutionStep] = []
+        self.adaptation_history: List[Dict[str, Any]] = []
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Execute task adaptively."""
+        try:
+            # Initialize execution
+            execution_state = await self._initialize_execution(query, context)
+            
+            # Monitor and adapt
+            while not self._is_execution_complete(execution_state):
+                # Execute step
+                step_result = await self._execute_step(execution_state, context)
+                
+                # Process feedback
+                feedback = await self._process_feedback(step_result, context)
+                
+                # Adapt execution
+                execution_state = await self._adapt_execution(
+                    execution_state, feedback, context)
+                
+                # Record step
+                self._record_step(step_result, feedback)
+            
+            return {
+                "success": True,
+                "execution_trace": [self._step_to_dict(s) for s in self.execution_steps],
+                "adaptations": self.adaptation_history,
+                "final_state": execution_state
+            }
+        except Exception as e:
+            logging.error(f"Error in adaptive execution: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class FeedbackIntegrationStrategy(ReasoningStrategy):
+    """
+    Advanced feedback integration strategy that:
+    1. Collects multiple types of feedback
+    2. Analyzes feedback patterns
+    3. Generates improvement suggestions
+    4. Tracks feedback implementation
+    5. Measures feedback impact
+    """
+    
+    def __init__(self):
+        self.feedback_history: List[Dict[str, Any]] = []
+        self.improvement_history: List[Dict[str, Any]] = []
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Integrate and apply feedback."""
+        try:
+            # Collect feedback
+            feedback = await self._collect_feedback(query, context)
+            
+            # Analyze patterns
+            patterns = await self._analyze_patterns(feedback, context)
+            
+            # Generate improvements
+            improvements = await self._generate_improvements(patterns, context)
+            
+            # Implement changes
+            implementation = await self._implement_improvements(improvements, context)
+            
+            # Measure impact
+            impact = await self._measure_impact(implementation, context)
+            
+            return {
+                "success": True,
+                "feedback_analysis": patterns,
+                "improvements": improvements,
+                "implementation_status": implementation,
+                "impact_metrics": impact
+            }
+        except Exception as e:
+            logging.error(f"Error in feedback integration: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _collect_feedback(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Collect feedback from multiple sources."""
+        prompt = f"""
+        Collect feedback from:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        Consider:
+        1. User feedback
+        2. System metrics
+        3. Code analysis
+        4. Performance data
+        5. Error patterns
+        
+        Format as:
+        [Feedback]
+        Source: ...
+        Type: ...
+        Content: ...
+        Priority: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_feedback(response["answer"])
+
+    def _parse_feedback(self, response: str) -> List[Dict[str, Any]]:
+        """Parse feedback from response."""
+        feedback_items = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[Feedback]'):
+                if current:
+                    feedback_items.append(current)
+                current = {
+                    "source": "",
+                    "type": "",
+                    "content": "",
+                    "priority": 0.0
+                }
+            elif current:
+                if line.startswith('Source:'):
+                    current["source"] = line[7:].strip()
+                elif line.startswith('Type:'):
+                    current["type"] = line[5:].strip()
+                elif line.startswith('Content:'):
+                    current["content"] = line[8:].strip()
+                elif line.startswith('Priority:'):
+                    try:
+                        current["priority"] = float(line[9:].strip())
+                    except:
+                        pass
+        
+        if current:
+            feedback_items.append(current)
+        
+        return feedback_items

reasoning/analogical.py

@@ -0,0 +1,611 @@
+"""Analogical reasoning implementation with advanced pattern matching and transfer learning."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Tuple, Callable
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+class AnalogicalLevel(Enum):
+    """Levels of analogical similarity."""
+    SURFACE = "surface"
+    STRUCTURAL = "structural"
+    SEMANTIC = "semantic"
+    FUNCTIONAL = "functional"
+    CAUSAL = "causal"
+    ABSTRACT = "abstract"
+
+class MappingType(Enum):
+    """Types of analogical mappings."""
+    DIRECT = "direct"
+    TRANSFORMED = "transformed"
+    COMPOSITE = "composite"
+    ABSTRACT = "abstract"
+    METAPHORICAL = "metaphorical"
+    HYBRID = "hybrid"
+
+@dataclass
+class AnalogicalPattern:
+    """Represents a pattern for analogical matching."""
+    id: str
+    level: AnalogicalLevel
+    features: Dict[str, Any]
+    relations: List[Tuple[str, str, str]]  # (entity1, relation, entity2)
+    constraints: List[str]
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class AnalogicalMapping:
+    """Represents a mapping between source and target domains."""
+    id: str
+    type: MappingType
+    source_elements: Dict[str, Any]
+    target_elements: Dict[str, Any]
+    correspondences: List[Tuple[str, str, float]]  # (source, target, strength)
+    transformations: List[Dict[str, Any]]
+    confidence: float
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class AnalogicalSolution:
+    """Represents a solution derived through analogical reasoning."""
+    id: str
+    source_analogy: str
+    mapping: AnalogicalMapping
+    adaptation: Dict[str, Any]
+    inference: Dict[str, Any]
+    confidence: float
+    validation: Dict[str, Any]
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+class AnalogicalReasoning(ReasoningStrategy):
+    """
+    Advanced Analogical Reasoning implementation with:
+    - Multi-level pattern matching
+    - Sophisticated similarity metrics
+    - Transfer learning capabilities
+    - Dynamic adaptation mechanisms
+    - Quality assessment
+    - Learning from experience
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize analogical reasoning."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+        
+        # Analogical reasoning specific parameters
+        self.min_similarity = self.config.get('min_similarity', 0.6)
+        self.max_candidates = self.config.get('max_candidates', 5)
+        self.adaptation_threshold = self.config.get('adaptation_threshold', 0.7)
+        
+        # Knowledge base
+        self.patterns: Dict[str, AnalogicalPattern] = {}
+        self.mappings: Dict[str, AnalogicalMapping] = {}
+        self.solutions: Dict[str, AnalogicalSolution] = {}
+        
+        # Learning components
+        self.pattern_weights: Dict[str, float] = defaultdict(float)
+        self.success_history: List[Dict[str, Any]] = []
+        self.adaptation_history: List[Dict[str, Any]] = []
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Main reasoning method implementing analogical reasoning."""
+        try:
+            # Extract patterns from query
+            patterns = await self._extract_patterns(query, context)
+            
+            # Find analogical matches
+            matches = await self._find_matches(patterns, context)
+            
+            # Create and evaluate mappings
+            mappings = await self._create_mappings(matches, context)
+            
+            # Generate and adapt solutions
+            solutions = await self._generate_solutions(mappings, context)
+            
+            # Select best solution
+            best_solution = await self._select_best_solution(solutions, context)
+            
+            # Learn from experience
+            self._update_knowledge(patterns, mappings, best_solution)
+            
+            return {
+                "success": True,
+                "answer": best_solution.inference["conclusion"],
+                "confidence": best_solution.confidence,
+                "analogy": {
+                    "source": best_solution.source_analogy,
+                    "mapping": self._mapping_to_dict(best_solution.mapping),
+                    "adaptation": best_solution.adaptation
+                },
+                "reasoning_trace": best_solution.metadata.get("reasoning_trace", []),
+                "meta_insights": best_solution.metadata.get("meta_insights", [])
+            }
+        except Exception as e:
+            logging.error(f"Error in analogical reasoning: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _extract_patterns(self, query: str, context: Dict[str, Any]) -> List[AnalogicalPattern]:
+        """Extract patterns from query for analogical matching."""
+        prompt = f"""
+        Extract analogical patterns from query:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        For each pattern level:
+        1. Surface features
+        2. Structural relations
+        3. Semantic concepts
+        4. Functional roles
+        5. Causal relationships
+        6. Abstract principles
+        
+        Format as:
+        [P1]
+        Level: ...
+        Features: ...
+        Relations: ...
+        Constraints: ...
+        
+        [P2]
+        ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_patterns(response["answer"])
+
+    async def _find_matches(self, patterns: List[AnalogicalPattern], context: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Find matching patterns in knowledge base."""
+        prompt = f"""
+        Find analogical matches:
+        Patterns: {json.dumps([self._pattern_to_dict(p) for p in patterns])}
+        Context: {json.dumps(context)}
+        
+        For each match provide:
+        1. Source domain
+        2. Similarity assessment
+        3. Key correspondences
+        4. Transfer potential
+        
+        Format as:
+        [M1]
+        Source: ...
+        Similarity: ...
+        Correspondences: ...
+        Transfer: ...
+        
+        [M2]
+        ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_matches(response["answer"])
+
+    async def _create_mappings(self, matches: List[Dict[str, Any]], context: Dict[str, Any]) -> List[AnalogicalMapping]:
+        """Create mappings between source and target domains."""
+        prompt = f"""
+        Create analogical mappings:
+        Matches: {json.dumps(matches)}
+        Context: {json.dumps(context)}
+        
+        For each mapping specify:
+        1. [Type]: {" | ".join([t.value for t in MappingType])}
+        2. [Elements]: Source and target elements
+        3. [Correspondences]: Element mappings
+        4. [Transformations]: Required adaptations
+        5. [Confidence]: Mapping strength
+        
+        Format as:
+        [Map1]
+        Type: ...
+        Elements: ...
+        Correspondences: ...
+        Transformations: ...
+        Confidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_mappings(response["answer"])
+
+    async def _generate_solutions(self, mappings: List[AnalogicalMapping], context: Dict[str, Any]) -> List[AnalogicalSolution]:
+        """Generate solutions through analogical transfer."""
+        prompt = f"""
+        Generate analogical solutions:
+        Mappings: {json.dumps([self._mapping_to_dict(m) for m in mappings])}
+        Context: {json.dumps(context)}
+        
+        For each solution provide:
+        1. Analogical inference
+        2. Required adaptations
+        3. Validation criteria
+        4. Confidence assessment
+        5. Reasoning trace
+        
+        Format as:
+        [S1]
+        Inference: ...
+        Adaptation: ...
+        Validation: ...
+        Confidence: ...
+        Trace: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_solutions(response["answer"], mappings)
+
+    async def _select_best_solution(self, solutions: List[AnalogicalSolution], context: Dict[str, Any]) -> AnalogicalSolution:
+        """Select the best solution based on multiple criteria."""
+        prompt = f"""
+        Evaluate and select best solution:
+        Solutions: {json.dumps([self._solution_to_dict(s) for s in solutions])}
+        Context: {json.dumps(context)}
+        
+        Evaluate based on:
+        1. Inference quality
+        2. Adaptation feasibility
+        3. Validation strength
+        4. Overall confidence
+        
+        Format as:
+        [Evaluation]
+        Rankings: ...
+        Rationale: ...
+        Selection: ...
+        Confidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        selection = self._parse_selection(response["answer"])
+        
+        # Find selected solution
+        selected = max(solutions, key=lambda s: s.confidence)
+        for solution in solutions:
+            if solution.id == selection.get("selected_id"):
+                selected = solution
+                break
+        
+        return selected
+
+    def _update_knowledge(self, patterns: List[AnalogicalPattern], mappings: List[AnalogicalMapping], solution: AnalogicalSolution):
+        """Update knowledge base with new patterns and successful mappings."""
+        # Update patterns
+        for pattern in patterns:
+            if pattern.id not in self.patterns:
+                self.patterns[pattern.id] = pattern
+            self.pattern_weights[pattern.id] += self.learning_rate * solution.confidence
+        
+        # Update mappings
+        if solution.mapping.id not in self.mappings:
+            self.mappings[solution.mapping.id] = solution.mapping
+        
+        # Record solution
+        self.solutions[solution.id] = solution
+        
+        # Update history
+        self.success_history.append({
+            "timestamp": datetime.now().isoformat(),
+            "solution_id": solution.id,
+            "confidence": solution.confidence,
+            "patterns": [p.id for p in patterns],
+            "mapping_type": solution.mapping.type.value
+        })
+        
+        # Update adaptation history
+        self.adaptation_history.append({
+            "timestamp": datetime.now().isoformat(),
+            "solution_id": solution.id,
+            "adaptations": solution.adaptation,
+            "success": solution.confidence >= self.adaptation_threshold
+        })
+
+    def _parse_patterns(self, response: str) -> List[AnalogicalPattern]:
+        """Parse patterns from response."""
+        patterns = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[P'):
+                if current:
+                    patterns.append(current)
+                current = None
+            elif line.startswith('Level:'):
+                level_str = line[6:].strip().lower()
+                try:
+                    level = AnalogicalLevel(level_str)
+                    current = AnalogicalPattern(
+                        id=f"pattern_{len(patterns)}",
+                        level=level,
+                        features={},
+                        relations=[],
+                        constraints=[],
+                        metadata={}
+                    )
+                except ValueError:
+                    logging.warning(f"Invalid analogical level: {level_str}")
+            elif current:
+                if line.startswith('Features:'):
+                    try:
+                        current.features = json.loads(line[9:].strip())
+                    except:
+                        current.features = {"raw": line[9:].strip()}
+                elif line.startswith('Relations:'):
+                    relations = [r.strip() for r in line[10:].split(',')]
+                    current.relations = [(r.split()[0], r.split()[1], r.split()[2]) 
+                                      for r in relations if len(r.split()) >= 3]
+                elif line.startswith('Constraints:'):
+                    current.constraints = [c.strip() for c in line[12:].split(',')]
+        
+        if current:
+            patterns.append(current)
+        
+        return patterns
+
+    def _parse_matches(self, response: str) -> List[Dict[str, Any]]:
+        """Parse matches from response."""
+        matches = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[M'):
+                if current:
+                    matches.append(current)
+                current = {
+                    "source": "",
+                    "similarity": 0.0,
+                    "correspondences": [],
+                    "transfer": []
+                }
+            elif current:
+                if line.startswith('Source:'):
+                    current["source"] = line[7:].strip()
+                elif line.startswith('Similarity:'):
+                    try:
+                        current["similarity"] = float(line[11:].strip())
+                    except:
+                        pass
+                elif line.startswith('Correspondences:'):
+                    current["correspondences"] = [c.strip() for c in line[16:].split(',')]
+                elif line.startswith('Transfer:'):
+                    current["transfer"] = [t.strip() for t in line[9:].split(',')]
+        
+        if current:
+            matches.append(current)
+        
+        return matches
+
+    def _parse_mappings(self, response: str) -> List[AnalogicalMapping]:
+        """Parse mappings from response."""
+        mappings = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[Map'):
+                if current:
+                    mappings.append(current)
+                current = None
+            elif line.startswith('Type:'):
+                type_str = line[5:].strip().lower()
+                try:
+                    mapping_type = MappingType(type_str)
+                    current = AnalogicalMapping(
+                        id=f"mapping_{len(mappings)}",
+                        type=mapping_type,
+                        source_elements={},
+                        target_elements={},
+                        correspondences=[],
+                        transformations=[],
+                        confidence=0.0,
+                        metadata={}
+                    )
+                except ValueError:
+                    logging.warning(f"Invalid mapping type: {type_str}")
+            elif current:
+                if line.startswith('Elements:'):
+                    try:
+                        elements = json.loads(line[9:].strip())
+                        current.source_elements = elements.get("source", {})
+                        current.target_elements = elements.get("target", {})
+                    except:
+                        pass
+                elif line.startswith('Correspondences:'):
+                    pairs = [c.strip() for c in line[16:].split(',')]
+                    for pair in pairs:
+                        parts = pair.split(':')
+                        if len(parts) >= 2:
+                            source = parts[0].strip()
+                            target = parts[1].strip()
+                            strength = float(parts[2]) if len(parts) > 2 else 1.0
+                            current.correspondences.append((source, target, strength))
+                elif line.startswith('Transformations:'):
+                    try:
+                        current.transformations = json.loads(line[16:].strip())
+                    except:
+                        current.transformations = [{"raw": line[16:].strip()}]
+                elif line.startswith('Confidence:'):
+                    try:
+                        current.confidence = float(line[11:].strip())
+                    except:
+                        pass
+        
+        if current:
+            mappings.append(current)
+        
+        return mappings
+
+    def _parse_solutions(self, response: str, mappings: List[AnalogicalMapping]) -> List[AnalogicalSolution]:
+        """Parse solutions from response."""
+        solutions = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[S'):
+                if current:
+                    solutions.append(current)
+                current = None
+                mapping_idx = len(solutions)
+                if mapping_idx < len(mappings):
+                    current = AnalogicalSolution(
+                        id=f"solution_{len(solutions)}",
+                        source_analogy="",
+                        mapping=mappings[mapping_idx],
+                        adaptation={},
+                        inference={},
+                        confidence=0.0,
+                        validation={},
+                        metadata={}
+                    )
+            elif current:
+                if line.startswith('Inference:'):
+                    try:
+                        current.inference = json.loads(line[10:].strip())
+                    except:
+                        current.inference = {"conclusion": line[10:].strip()}
+                elif line.startswith('Adaptation:'):
+                    try:
+                        current.adaptation = json.loads(line[11:].strip())
+                    except:
+                        current.adaptation = {"steps": [line[11:].strip()]}
+                elif line.startswith('Validation:'):
+                    try:
+                        current.validation = json.loads(line[11:].strip())
+                    except:
+                        current.validation = {"criteria": [line[11:].strip()]}
+                elif line.startswith('Confidence:'):
+                    try:
+                        current.confidence = float(line[11:].strip())
+                    except:
+                        pass
+                elif line.startswith('Trace:'):
+                    current.metadata["reasoning_trace"] = [t.strip() for t in line[6:].split(',')]
+        
+        if current:
+            solutions.append(current)
+        
+        return solutions
+
+    def _parse_selection(self, response: str) -> Dict[str, Any]:
+        """Parse solution selection from response."""
+        selection = {
+            "selected_id": None,
+            "confidence": 0.0,
+            "rationale": []
+        }
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Selection:'):
+                selection["selected_id"] = line[10:].strip()
+            elif line.startswith('Confidence:'):
+                try:
+                    selection["confidence"] = float(line[11:].strip())
+                except:
+                    pass
+            elif line.startswith('Rationale:'):
+                selection["rationale"] = [r.strip() for r in line[10:].split(',')]
+        
+        return selection
+
+    def _pattern_to_dict(self, pattern: AnalogicalPattern) -> Dict[str, Any]:
+        """Convert pattern to dictionary for serialization."""
+        return {
+            "id": pattern.id,
+            "level": pattern.level.value,
+            "features": pattern.features,
+            "relations": pattern.relations,
+            "constraints": pattern.constraints,
+            "metadata": pattern.metadata
+        }
+
+    def _mapping_to_dict(self, mapping: AnalogicalMapping) -> Dict[str, Any]:
+        """Convert mapping to dictionary for serialization."""
+        return {
+            "id": mapping.id,
+            "type": mapping.type.value,
+            "source_elements": mapping.source_elements,
+            "target_elements": mapping.target_elements,
+            "correspondences": mapping.correspondences,
+            "transformations": mapping.transformations,
+            "confidence": mapping.confidence,
+            "metadata": mapping.metadata
+        }
+
+    def _solution_to_dict(self, solution: AnalogicalSolution) -> Dict[str, Any]:
+        """Convert solution to dictionary for serialization."""
+        return {
+            "id": solution.id,
+            "source_analogy": solution.source_analogy,
+            "mapping": self._mapping_to_dict(solution.mapping),
+            "adaptation": solution.adaptation,
+            "inference": solution.inference,
+            "confidence": solution.confidence,
+            "validation": solution.validation,
+            "metadata": solution.metadata
+        }
+
+    def get_pattern_statistics(self) -> Dict[str, Any]:
+        """Get statistics about pattern usage and effectiveness."""
+        return {
+            "total_patterns": len(self.patterns),
+            "level_distribution": defaultdict(int, {p.level.value: 1 for p in self.patterns.values()}),
+            "average_constraints": sum(len(p.constraints) for p in self.patterns.values()) / len(self.patterns) if self.patterns else 0,
+            "pattern_weights": dict(self.pattern_weights)
+        }
+
+    def get_mapping_statistics(self) -> Dict[str, Any]:
+        """Get statistics about mapping effectiveness."""
+        return {
+            "total_mappings": len(self.mappings),
+            "type_distribution": defaultdict(int, {m.type.value: 1 for m in self.mappings.values()}),
+            "average_confidence": sum(m.confidence for m in self.mappings.values()) / len(self.mappings) if self.mappings else 0,
+            "transformation_counts": defaultdict(int, {m.id: len(m.transformations) for m in self.mappings.values()})
+        }
+
+    def get_solution_statistics(self) -> Dict[str, Any]:
+        """Get statistics about solution quality."""
+        return {
+            "total_solutions": len(self.solutions),
+            "average_confidence": sum(s.confidence for s in self.solutions.values()) / len(self.solutions) if self.solutions else 0,
+            "adaptation_success_rate": sum(1 for h in self.adaptation_history if h["success"]) / len(self.adaptation_history) if self.adaptation_history else 0
+        }
+
+    def clear_knowledge_base(self):
+        """Clear the knowledge base."""
+        self.patterns.clear()
+        self.mappings.clear()
+        self.solutions.clear()
+        self.pattern_weights.clear()
+        self.success_history.clear()
+        self.adaptation_history.clear()

reasoning/base.py

@@ -0,0 +1,17 @@
+"""Base class for all reasoning strategies."""
+
+from typing import Dict, Any
+
+class ReasoningStrategy:
+    """Base class for reasoning strategies."""
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Apply reasoning strategy to query with context.
+        
+        Args:
+            query: The query to reason about
+            context: Additional context for reasoning
+            
+        Returns:
+            Dictionary containing reasoning results
+        """
+        raise NotImplementedError

reasoning/bayesian.py

@@ -0,0 +1,325 @@
+"""Advanced Bayesian reasoning for probabilistic analysis."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+@dataclass
+class BayesianHypothesis:
+    """Bayesian hypothesis with probabilities."""
+    name: str
+    prior: float
+    likelihood: float
+    posterior: float = 0.0
+    evidence: List[Dict[str, Any]] = field(default_factory=list)
+
+class BayesianReasoning(ReasoningStrategy):
+    """
+    Advanced Bayesian reasoning that:
+    1. Generates hypotheses
+    2. Calculates prior probabilities
+    3. Updates with evidence
+    4. Computes posteriors
+    5. Provides probabilistic analysis
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize Bayesian reasoning."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Configure Bayesian parameters
+        self.prior_weight = self.config.get('prior_weight', 0.3)
+        self.evidence_threshold = self.config.get('evidence_threshold', 0.1)
+        self.min_likelihood = self.config.get('min_likelihood', 0.01)
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Apply Bayesian reasoning to analyze probabilities and update beliefs.
+        
+        Args:
+            query: The input query to reason about
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing reasoning results and confidence scores
+        """
+        try:
+            # Generate hypotheses
+            hypotheses = await self._generate_hypotheses(query, context)
+            
+            # Calculate priors
+            priors = await self._calculate_priors(hypotheses, context)
+            
+            # Update with evidence
+            posteriors = await self._update_with_evidence(
+                hypotheses,
+                priors,
+                context
+            )
+            
+            # Generate analysis
+            analysis = await self._generate_analysis(posteriors, context)
+            
+            return {
+                'answer': self._format_analysis(analysis),
+                'confidence': self._calculate_confidence(posteriors),
+                'hypotheses': hypotheses,
+                'priors': priors,
+                'posteriors': posteriors,
+                'analysis': analysis
+            }
+            
+        except Exception as e:
+            logging.error(f"Bayesian reasoning failed: {str(e)}")
+            return {
+                'error': f"Bayesian reasoning failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    async def _generate_hypotheses(
+        self,
+        query: str,
+        context: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """Generate plausible hypotheses."""
+        hypotheses = []
+        
+        # Extract key terms for hypothesis generation
+        terms = set(query.lower().split())
+        
+        # Generate hypotheses based on context and terms
+        if 'options' in context:
+            # Use provided options as hypotheses
+            for option in context['options']:
+                hypotheses.append({
+                    'name': option,
+                    'description': f"Hypothesis based on option: {option}",
+                    'factors': self._extract_factors(option, terms)
+                })
+        else:
+            # Generate default hypotheses
+            hypotheses.extend([
+                {
+                    'name': 'primary',
+                    'description': "Primary hypothesis based on direct interpretation",
+                    'factors': self._extract_factors(query, terms)
+                },
+                {
+                    'name': 'alternative',
+                    'description': "Alternative hypothesis considering other factors",
+                    'factors': self._generate_alternative_factors(terms)
+                }
+            ])
+        
+        return hypotheses
+    
+    async def _calculate_priors(
+        self,
+        hypotheses: List[Dict[str, Any]],
+        context: Dict[str, Any]
+    ) -> Dict[str, float]:
+        """Calculate prior probabilities."""
+        priors = {}
+        
+        # Get historical data if available
+        history = context.get('history', {})
+        total_cases = sum(history.values()) if history else len(hypotheses)
+        
+        for hypothesis in hypotheses:
+            name = hypothesis['name']
+            
+            # Calculate prior from history or use uniform prior
+            if name in history:
+                priors[name] = history[name] / total_cases
+            else:
+                priors[name] = 1.0 / len(hypotheses)
+            
+            # Adjust prior based on factors
+            factor_weight = len(hypothesis['factors']) / 10  # Normalize factor count
+            priors[name] = (
+                priors[name] * (1 - self.prior_weight) +
+                factor_weight * self.prior_weight
+            )
+        
+        # Normalize priors
+        total_prior = sum(priors.values())
+        if total_prior > 0:
+            priors = {
+                name: prob / total_prior
+                for name, prob in priors.items()
+            }
+        
+        return priors
+    
+    async def _update_with_evidence(
+        self,
+        hypotheses: List[Dict[str, Any]],
+        priors: Dict[str, float],
+        context: Dict[str, Any]
+    ) -> Dict[str, float]:
+        """Update probabilities with evidence."""
+        posteriors = priors.copy()
+        
+        # Get evidence from context
+        evidence = context.get('evidence', [])
+        if not evidence:
+            return posteriors
+        
+        for e in evidence:
+            # Calculate likelihood for each hypothesis
+            likelihoods = {}
+            for hypothesis in hypotheses:
+                name = hypothesis['name']
+                likelihood = self._calculate_likelihood(hypothesis, e)
+                likelihoods[name] = max(likelihood, self.min_likelihood)
+            
+            # Update posteriors using Bayes' rule
+            total_probability = sum(
+                likelihoods[name] * posteriors[name]
+                for name in posteriors
+            )
+            
+            if total_probability > 0:
+                posteriors = {
+                    name: (likelihoods[name] * posteriors[name]) / total_probability
+                    for name in posteriors
+                }
+        
+        return posteriors
+    
+    def _calculate_likelihood(
+        self,
+        hypothesis: Dict[str, Any],
+        evidence: Dict[str, Any]
+    ) -> float:
+        """Calculate likelihood of evidence given hypothesis."""
+        # Extract evidence factors
+        evidence_factors = set(
+            str(v).lower()
+            for v in evidence.values()
+            if isinstance(v, (str, int, float))
+        )
+        
+        # Compare with hypothesis factors
+        common_factors = evidence_factors.intersection(hypothesis['factors'])
+        
+        if not evidence_factors:
+            return 0.5  # Neutral likelihood if no factors
+        
+        return len(common_factors) / len(evidence_factors)
+    
+    async def _generate_analysis(
+        self,
+        posteriors: Dict[str, float],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Generate probabilistic analysis."""
+        # Sort hypotheses by posterior probability
+        ranked_hypotheses = sorted(
+            posteriors.items(),
+            key=lambda x: x[1],
+            reverse=True
+        )
+        
+        # Calculate statistics
+        mean = np.mean(list(posteriors.values()))
+        std = np.std(list(posteriors.values()))
+        entropy = -sum(
+            p * np.log2(p) if p > 0 else 0
+            for p in posteriors.values()
+        )
+        
+        return {
+            'top_hypothesis': ranked_hypotheses[0][0],
+            'probability': ranked_hypotheses[0][1],
+            'alternatives': [
+                {'name': name, 'probability': prob}
+                for name, prob in ranked_hypotheses[1:]
+            ],
+            'statistics': {
+                'mean': mean,
+                'std': std,
+                'entropy': entropy
+            }
+        }
+    
+    def _format_analysis(self, analysis: Dict[str, Any]) -> str:
+        """Format analysis into readable text."""
+        sections = []
+        
+        # Top hypothesis
+        sections.append(
+            f"Most likely hypothesis: {analysis['top_hypothesis']} "
+            f"(probability: {analysis['probability']:.2%})"
+        )
+        
+        # Alternative hypotheses
+        if analysis['alternatives']:
+            sections.append("\nAlternative hypotheses:")
+            for alt in analysis['alternatives']:
+                sections.append(
+                    f"- {alt['name']}: {alt['probability']:.2%}"
+                )
+        
+        # Statistics
+        stats = analysis['statistics']
+        sections.append("\nDistribution statistics:")
+        sections.append(f"- Mean probability: {stats['mean']:.2%}")
+        sections.append(f"- Standard deviation: {stats['std']:.2%}")
+        sections.append(f"- Entropy: {stats['entropy']:.2f} bits")
+        
+        return "\n".join(sections)
+    
+    def _calculate_confidence(self, posteriors: Dict[str, float]) -> float:
+        """Calculate overall confidence score."""
+        if not posteriors:
+            return 0.0
+        
+        # Base confidence
+        confidence = 0.5
+        
+        # Adjust based on probability distribution
+        probs = list(posteriors.values())
+        
+        # Strong leading hypothesis increases confidence
+        max_prob = max(probs)
+        if max_prob > 0.8:
+            confidence += 0.3
+        elif max_prob > 0.6:
+            confidence += 0.2
+        elif max_prob > 0.4:
+            confidence += 0.1
+        
+        # Low entropy (clear distinction) increases confidence
+        entropy = -sum(p * np.log2(p) if p > 0 else 0 for p in probs)
+        max_entropy = -np.log2(1/len(probs))  # Maximum possible entropy
+        
+        if entropy < 0.3 * max_entropy:
+            confidence += 0.2
+        elif entropy < 0.6 * max_entropy:
+            confidence += 0.1
+        
+        return min(confidence, 1.0)
+    
+    def _extract_factors(self, text: str, terms: Set[str]) -> Set[str]:
+        """Extract relevant factors from text."""
+        return set(word.lower() for word in text.split() if word.lower() in terms)
+    
+    def _generate_alternative_factors(self, terms: Set[str]) -> Set[str]:
+        """Generate factors for alternative hypothesis."""
+        # Simple approach: use terms not in primary hypothesis
+        return set(
+            word for word in terms
+            if not any(
+                similar in word or word in similar
+                for similar in terms
+            )
+        )

reasoning/chain_of_thought.py

@@ -0,0 +1,415 @@
+"""Chain of Thought reasoning implementation with advanced features."""
+
+import logging
+from typing import Dict, Any, List, Optional, Tuple
+import json
+from dataclasses import dataclass
+from enum import Enum
+
+from .base import ReasoningStrategy
+
+class ThoughtType(Enum):
+    """Types of thoughts in the chain."""
+    OBSERVATION = "observation"
+    ANALYSIS = "analysis"
+    HYPOTHESIS = "hypothesis"
+    VERIFICATION = "verification"
+    CONCLUSION = "conclusion"
+    REFLECTION = "reflection"
+    REFINEMENT = "refinement"
+
+@dataclass
+class Thought:
+    """Represents a single thought in the chain."""
+    type: ThoughtType
+    content: str
+    confidence: float
+    evidence: List[str]
+    alternatives: List[str]
+    next_steps: List[str]
+    metadata: Dict[str, Any]
+
+class ChainOfThoughtStrategy(ReasoningStrategy):
+    """
+    Advanced Chain of Thought reasoning implementation with:
+    - Hierarchical thought chains
+    - Confidence scoring
+    - Alternative path exploration
+    - Self-reflection and refinement
+    - Evidence tracking
+    - Meta-learning capabilities
+    """
+    
+    def __init__(self, 
+                 min_confidence: float = 0.7,
+                 parallel_threshold: int = 3,
+                 learning_rate: float = 0.1,
+                 strategy_weights: Optional[Dict[str, float]] = None):
+        self.min_confidence = min_confidence
+        self.parallel_threshold = parallel_threshold
+        self.learning_rate = learning_rate
+        self.strategy_weights = strategy_weights or {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        }
+        self.thought_history: List[Thought] = []
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Main reasoning method implementing chain of thought."""
+        try:
+            # Initialize reasoning chain
+            chain = await self._initialize_chain(query, context)
+            
+            # Generate initial thoughts
+            thoughts = await self._generate_thoughts(query, context)
+            
+            # Build thought chain
+            chain = await self._build_chain(thoughts, context)
+            
+            # Reflect and refine
+            if self.enable_reflection:
+                chain = await self._reflect_and_refine(chain, context)
+            
+            # Extract conclusion
+            conclusion = await self._extract_conclusion(chain, context)
+            
+            # Update thought history
+            self.thought_history.extend(chain)
+            
+            return {
+                "success": True,
+                "answer": conclusion["answer"],
+                "confidence": conclusion["confidence"],
+                "reasoning_chain": [self._thought_to_dict(t) for t in chain],
+                "alternatives": conclusion["alternatives"],
+                "evidence": conclusion["evidence"],
+                "meta_insights": conclusion["meta_insights"]
+            }
+        except Exception as e:
+            logging.error(f"Error in chain of thought reasoning: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _initialize_chain(self, query: str, context: Dict[str, Any]) -> List[Thought]:
+        """Initialize the thought chain with observations."""
+        prompt = f"""
+        Initialize chain of thought for query:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        Provide initial observations:
+        1. Key elements in query
+        2. Relevant context factors
+        3. Initial hypotheses
+        4. Potential approaches
+        
+        Format as:
+        [O1] Element: ... | Relevance: ... | Confidence: ...
+        [O2] Context: ... | Impact: ... | Confidence: ...
+        [O3] Hypothesis: ... | Support: ... | Confidence: ...
+        [O4] Approach: ... | Rationale: ... | Confidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_observations(response["answer"])
+
+    async def _generate_thoughts(self, query: str, context: Dict[str, Any]) -> List[Thought]:
+        """Generate candidate thoughts for the chain."""
+        prompt = f"""
+        Generate thoughts for query analysis:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        For each thought provide:
+        1. [Type]: {" | ".join([t.value for t in ThoughtType])}
+        2. [Content]: Main thought
+        3. [Evidence]: Supporting evidence
+        4. [Alternatives]: Alternative perspectives
+        5. [Next]: Potential next steps
+        6. [Confidence]: 0-1 score
+        
+        Format as:
+        [T1]
+        Type: ...
+        Content: ...
+        Evidence: ...
+        Alternatives: ...
+        Next: ...
+        Confidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_thoughts(response["answer"])
+
+    async def _build_chain(self, thoughts: List[Thought], context: Dict[str, Any]) -> List[Thought]:
+        """Build coherent chain from candidate thoughts."""
+        prompt = f"""
+        Build coherent thought chain:
+        Thoughts: {json.dumps([self._thought_to_dict(t) for t in thoughts])}
+        Context: {json.dumps(context)}
+        
+        For each step specify:
+        1. Selected thought
+        2. Reasoning for selection
+        3. Connection to previous
+        4. Expected impact
+        
+        Format as:
+        [S1]
+        Thought: ...
+        Reason: ...
+        Connection: ...
+        Impact: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_chain(response["answer"], thoughts)
+
+    async def _reflect_and_refine(self, chain: List[Thought], context: Dict[str, Any]) -> List[Thought]:
+        """Reflect on and refine the thought chain."""
+        prompt = f"""
+        Reflect on thought chain:
+        Chain: {json.dumps([self._thought_to_dict(t) for t in chain])}
+        Context: {json.dumps(context)}
+        
+        Analyze for:
+        1. Logical gaps
+        2. Weak assumptions
+        3. Missing evidence
+        4. Alternative perspectives
+        
+        Suggest refinements:
+        1. Additional thoughts
+        2. Modified reasoning
+        3. New connections
+        4. Evidence needs
+        
+        Format as:
+        [Analysis]
+        Gaps: ...
+        Assumptions: ...
+        Missing: ...
+        Alternatives: ...
+        
+        [Refinements]
+        Thoughts: ...
+        Reasoning: ...
+        Connections: ...
+        Evidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._apply_refinements(chain, response["answer"])
+
+    async def _extract_conclusion(self, chain: List[Thought], context: Dict[str, Any]) -> Dict[str, Any]:
+        """Extract final conclusion from thought chain."""
+        prompt = f"""
+        Extract conclusion from thought chain:
+        Chain: {json.dumps([self._thought_to_dict(t) for t in chain])}
+        Context: {json.dumps(context)}
+        
+        Provide:
+        1. Main conclusion
+        2. Confidence level
+        3. Supporting evidence
+        4. Alternative conclusions
+        5. Meta-insights gained
+        6. Future considerations
+        
+        Format as:
+        [Conclusion]
+        Answer: ...
+        Confidence: ...
+        Evidence: ...
+        Alternatives: ...
+        
+        [Meta]
+        Insights: ...
+        Future: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_conclusion(response["answer"])
+
+    def _parse_observations(self, response: str) -> List[Thought]:
+        """Parse initial observations into thoughts."""
+        observations = []
+        lines = response.split('\n')
+        
+        for line in lines:
+            if line.startswith('[O'):
+                parts = line.split('|')
+                if len(parts) >= 3:
+                    main_part = parts[0].split(']')[1].strip()
+                    key, content = main_part.split(':', 1)
+                    
+                    evidence = [p.strip() for p in parts[1].split(':')[1].strip().split(',')]
+                    
+                    try:
+                        confidence = float(parts[2].split(':')[1].strip())
+                    except:
+                        confidence = 0.5
+                    
+                    observations.append(Thought(
+                        type=ThoughtType.OBSERVATION,
+                        content=content.strip(),
+                        confidence=confidence,
+                        evidence=evidence,
+                        alternatives=[],
+                        next_steps=[],
+                        metadata={"key": key}
+                    ))
+        
+        return observations
+
+    def _parse_thoughts(self, response: str) -> List[Thought]:
+        """Parse generated thoughts."""
+        thoughts = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[T'):
+                if current:
+                    thoughts.append(current)
+                current = None
+            elif line.startswith('Type:'):
+                type_str = line[5:].strip()
+                try:
+                    thought_type = ThoughtType(type_str.lower())
+                    current = Thought(
+                        type=thought_type,
+                        content="",
+                        confidence=0.0,
+                        evidence=[],
+                        alternatives=[],
+                        next_steps=[],
+                        metadata={}
+                    )
+                except ValueError:
+                    logging.warning(f"Invalid thought type: {type_str}")
+            elif current:
+                if line.startswith('Content:'):
+                    current.content = line[8:].strip()
+                elif line.startswith('Evidence:'):
+                    current.evidence = [e.strip() for e in line[9:].split(',')]
+                elif line.startswith('Alternatives:'):
+                    current.alternatives = [a.strip() for a in line[13:].split(',')]
+                elif line.startswith('Next:'):
+                    current.next_steps = [n.strip() for n in line[5:].split(',')]
+                elif line.startswith('Confidence:'):
+                    try:
+                        current.confidence = float(line[11:].strip())
+                    except:
+                        current.confidence = 0.5
+        
+        if current:
+            thoughts.append(current)
+        
+        return thoughts
+
+    def _parse_chain(self, response: str, thoughts: List[Thought]) -> List[Thought]:
+        """Parse and order thoughts into a chain."""
+        chain = []
+        thought_map = {self._thought_to_dict(t)["content"]: t for t in thoughts}
+        
+        for line in response.split('\n'):
+            if line.startswith('Thought:'):
+                content = line[8:].strip()
+                if content in thought_map:
+                    chain.append(thought_map[content])
+        
+        return chain
+
+    def _apply_refinements(self, chain: List[Thought], response: str) -> List[Thought]:
+        """Apply refinements to thought chain."""
+        refined_chain = chain.copy()
+        
+        # Parse refinements
+        sections = response.split('[')
+        for section in sections:
+            if section.startswith('Refinements]'):
+                lines = section.split('\n')[1:]
+                for line in lines:
+                    if line.startswith('Thoughts:'):
+                        new_thoughts = self._parse_refinement_thoughts(line[9:])
+                        refined_chain.extend(new_thoughts)
+        
+        return refined_chain
+
+    def _parse_refinement_thoughts(self, refinements: str) -> List[Thought]:
+        """Parse refinement thoughts."""
+        thoughts = []
+        for refinement in refinements.split(';'):
+            if refinement.strip():
+                thoughts.append(Thought(
+                    type=ThoughtType.REFINEMENT,
+                    content=refinement.strip(),
+                    confidence=0.8,  # Refinements typically have high confidence
+                    evidence=[],
+                    alternatives=[],
+                    next_steps=[],
+                    metadata={"refined": True}
+                ))
+        return thoughts
+
+    def _parse_conclusion(self, response: str) -> Dict[str, Any]:
+        """Parse final conclusion."""
+        conclusion = {
+            "answer": "",
+            "confidence": 0.0,
+            "evidence": [],
+            "alternatives": [],
+            "meta_insights": [],
+            "future_considerations": []
+        }
+        
+        sections = response.split('[')
+        for section in sections:
+            if section.startswith('Conclusion]'):
+                lines = section.split('\n')[1:]
+                for line in lines:
+                    if line.startswith('Answer:'):
+                        conclusion["answer"] = line[7:].strip()
+                    elif line.startswith('Confidence:'):
+                        try:
+                            conclusion["confidence"] = float(line[11:].strip())
+                        except:
+                            conclusion["confidence"] = 0.5
+                    elif line.startswith('Evidence:'):
+                        conclusion["evidence"] = [e.strip() for e in line[9:].split(',')]
+                    elif line.startswith('Alternatives:'):
+                        conclusion["alternatives"] = [a.strip() for a in line[13:].split(',')]
+            elif section.startswith('Meta]'):
+                lines = section.split('\n')[1:]
+                for line in lines:
+                    if line.startswith('Insights:'):
+                        conclusion["meta_insights"] = [i.strip() for i in line[9:].split(',')]
+                    elif line.startswith('Future:'):
+                        conclusion["future_considerations"] = [f.strip() for f in line[7:].split(',')]
+        
+        return conclusion
+
+    def _thought_to_dict(self, thought: Thought) -> Dict[str, Any]:
+        """Convert thought to dictionary for serialization."""
+        return {
+            "type": thought.type.value,
+            "content": thought.content,
+            "confidence": thought.confidence,
+            "evidence": thought.evidence,
+            "alternatives": thought.alternatives,
+            "next_steps": thought.next_steps,
+            "metadata": thought.metadata
+        }
+
+    def get_thought_history(self) -> List[Dict[str, Any]]:
+        """Get the history of all thoughts processed."""
+        return [self._thought_to_dict(t) for t in self.thought_history]
+
+    def clear_history(self) -> None:
+        """Clear thought history."""
+        self.thought_history = []

reasoning/coordination.py

@@ -0,0 +1,525 @@
+"""Advanced strategy coordination patterns for the unified reasoning engine."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Callable
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import asyncio
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+from .unified_engine import StrategyType, StrategyResult, UnifiedResult
+
+class CoordinationPattern(Enum):
+    """Types of strategy coordination patterns."""
+    PIPELINE = "pipeline"
+    PARALLEL = "parallel"
+    HIERARCHICAL = "hierarchical"
+    FEEDBACK = "feedback"
+    ADAPTIVE = "adaptive"
+    ENSEMBLE = "ensemble"
+
+class CoordinationPhase(Enum):
+    """Phases in strategy coordination."""
+    INITIALIZATION = "initialization"
+    EXECUTION = "execution"
+    SYNCHRONIZATION = "synchronization"
+    ADAPTATION = "adaptation"
+    COMPLETION = "completion"
+
+@dataclass
+class CoordinationState:
+    """State of strategy coordination."""
+    pattern: CoordinationPattern
+    active_strategies: Dict[StrategyType, bool]
+    phase: CoordinationPhase
+    shared_context: Dict[str, Any]
+    synchronization_points: List[str]
+    adaptation_history: List[Dict[str, Any]]
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class StrategyInteraction:
+    """Interaction between strategies."""
+    source: StrategyType
+    target: StrategyType
+    interaction_type: str
+    data: Dict[str, Any]
+    timestamp: datetime = field(default_factory=datetime.now)
+
+class StrategyCoordinator:
+    """
+    Advanced strategy coordinator that:
+    1. Manages strategy interactions
+    2. Implements coordination patterns
+    3. Handles state synchronization
+    4. Adapts coordination dynamically
+    5. Optimizes strategy combinations
+    """
+    
+    def __init__(self,
+                 strategies: Dict[StrategyType, ReasoningStrategy],
+                 learning_rate: float = 0.1):
+        self.strategies = strategies
+        self.learning_rate = learning_rate
+        
+        # Coordination state
+        self.states: Dict[str, CoordinationState] = {}
+        self.interactions: List[StrategyInteraction] = []
+        
+        # Pattern performance
+        self.pattern_performance: Dict[CoordinationPattern, List[float]] = defaultdict(list)
+        self.pattern_weights: Dict[CoordinationPattern, float] = {
+            pattern: 1.0 for pattern in CoordinationPattern
+        }
+        
+    async def coordinate(self,
+                        query: str,
+                        context: Dict[str, Any],
+                        pattern: Optional[CoordinationPattern] = None) -> Dict[str, Any]:
+        """Coordinate strategy execution using specified pattern."""
+        try:
+            # Select pattern if not specified
+            if not pattern:
+                pattern = await self._select_pattern(query, context)
+            
+            # Initialize coordination
+            state = await self._initialize_coordination(pattern, context)
+            
+            # Execute coordination pattern
+            if pattern == CoordinationPattern.PIPELINE:
+                result = await self._coordinate_pipeline(query, context, state)
+            elif pattern == CoordinationPattern.PARALLEL:
+                result = await self._coordinate_parallel(query, context, state)
+            elif pattern == CoordinationPattern.HIERARCHICAL:
+                result = await self._coordinate_hierarchical(query, context, state)
+            elif pattern == CoordinationPattern.FEEDBACK:
+                result = await self._coordinate_feedback(query, context, state)
+            elif pattern == CoordinationPattern.ADAPTIVE:
+                result = await self._coordinate_adaptive(query, context, state)
+            elif pattern == CoordinationPattern.ENSEMBLE:
+                result = await self._coordinate_ensemble(query, context, state)
+            else:
+                raise ValueError(f"Unsupported coordination pattern: {pattern}")
+            
+            # Update performance metrics
+            self._update_pattern_performance(pattern, result)
+            
+            return result
+            
+        except Exception as e:
+            logging.error(f"Error in strategy coordination: {str(e)}")
+            return {
+                "success": False,
+                "error": str(e),
+                "pattern": pattern.value if pattern else None
+            }
+
+    async def _select_pattern(self, query: str, context: Dict[str, Any]) -> CoordinationPattern:
+        """Select appropriate coordination pattern."""
+        prompt = f"""
+        Select coordination pattern:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        Consider:
+        1. Task complexity and type
+        2. Strategy dependencies
+        3. Resource constraints
+        4. Performance history
+        5. Adaptation needs
+        
+        Format as:
+        [Selection]
+        Pattern: ...
+        Rationale: ...
+        Confidence: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        selection = self._parse_pattern_selection(response["answer"])
+        
+        # Weight by performance history
+        weighted_patterns = {
+            pattern: self.pattern_weights[pattern] * selection.get(pattern.value, 0.0)
+            for pattern in CoordinationPattern
+        }
+        
+        return max(weighted_patterns.items(), key=lambda x: x[1])[0]
+
+    async def _coordinate_pipeline(self,
+                                 query: str,
+                                 context: Dict[str, Any],
+                                 state: CoordinationState) -> Dict[str, Any]:
+        """Coordinate strategies in pipeline pattern."""
+        results = []
+        current_context = context.copy()
+        
+        # Determine optimal order
+        strategy_order = await self._determine_pipeline_order(query, context)
+        
+        for strategy_type in strategy_order:
+            try:
+                # Execute strategy
+                strategy = self.strategies[strategy_type]
+                result = await strategy.reason(query, current_context)
+                
+                # Update context with result
+                current_context.update({
+                    "previous_result": result,
+                    "pipeline_position": len(results)
+                })
+                
+                results.append(StrategyResult(
+                    strategy_type=strategy_type,
+                    success=result.get("success", False),
+                    answer=result.get("answer"),
+                    confidence=result.get("confidence", 0.0),
+                    reasoning_trace=result.get("reasoning_trace", []),
+                    metadata=result.get("metadata", {}),
+                    performance_metrics=result.get("performance_metrics", {})
+                ))
+                
+                # Record interaction
+                self._record_interaction(
+                    source=strategy_type,
+                    target=strategy_order[len(results)] if len(results) < len(strategy_order) else None,
+                    interaction_type="pipeline_transfer",
+                    data={"result": result}
+                )
+                
+            except Exception as e:
+                logging.error(f"Error in pipeline strategy {strategy_type}: {str(e)}")
+        
+        return {
+            "success": any(r.success for r in results),
+            "results": results,
+            "pattern": CoordinationPattern.PIPELINE.value,
+            "metrics": {
+                "total_steps": len(results),
+                "success_rate": sum(1 for r in results if r.success) / len(results) if results else 0
+            }
+        }
+
+    async def _coordinate_parallel(self,
+                                 query: str,
+                                 context: Dict[str, Any],
+                                 state: CoordinationState) -> Dict[str, Any]:
+        """Coordinate strategies in parallel pattern."""
+        async def execute_strategy(strategy_type: StrategyType) -> StrategyResult:
+            try:
+                strategy = self.strategies[strategy_type]
+                result = await strategy.reason(query, context)
+                
+                return StrategyResult(
+                    strategy_type=strategy_type,
+                    success=result.get("success", False),
+                    answer=result.get("answer"),
+                    confidence=result.get("confidence", 0.0),
+                    reasoning_trace=result.get("reasoning_trace", []),
+                    metadata=result.get("metadata", {}),
+                    performance_metrics=result.get("performance_metrics", {})
+                )
+            except Exception as e:
+                logging.error(f"Error in parallel strategy {strategy_type}: {str(e)}")
+                return StrategyResult(
+                    strategy_type=strategy_type,
+                    success=False,
+                    answer=None,
+                    confidence=0.0,
+                    reasoning_trace=[{"error": str(e)}],
+                    metadata={},
+                    performance_metrics={}
+                )
+        
+        # Execute strategies in parallel
+        tasks = [execute_strategy(strategy_type) 
+                for strategy_type in state.active_strategies
+                if state.active_strategies[strategy_type]]
+        
+        results = await asyncio.gather(*tasks)
+        
+        # Synthesize results
+        synthesis = await self._synthesize_parallel_results(results, context)
+        
+        return {
+            "success": synthesis.get("success", False),
+            "results": results,
+            "synthesis": synthesis,
+            "pattern": CoordinationPattern.PARALLEL.value,
+            "metrics": {
+                "total_strategies": len(results),
+                "success_rate": sum(1 for r in results if r.success) / len(results) if results else 0
+            }
+        }
+
+    async def _coordinate_hierarchical(self,
+                                     query: str,
+                                     context: Dict[str, Any],
+                                     state: CoordinationState) -> Dict[str, Any]:
+        """Coordinate strategies in hierarchical pattern."""
+        # Build strategy hierarchy
+        hierarchy = await self._build_strategy_hierarchy(query, context)
+        results = {}
+        
+        async def execute_level(level_strategies: List[StrategyType],
+                              level_context: Dict[str, Any]) -> List[StrategyResult]:
+            tasks = []
+            for strategy_type in level_strategies:
+                if strategy_type in state.active_strategies and state.active_strategies[strategy_type]:
+                    strategy = self.strategies[strategy_type]
+                    tasks.append(strategy.reason(query, level_context))
+            
+            level_results = await asyncio.gather(*tasks)
+            return [
+                StrategyResult(
+                    strategy_type=strategy_type,
+                    success=result.get("success", False),
+                    answer=result.get("answer"),
+                    confidence=result.get("confidence", 0.0),
+                    reasoning_trace=result.get("reasoning_trace", []),
+                    metadata=result.get("metadata", {}),
+                    performance_metrics=result.get("performance_metrics", {})
+                )
+                for strategy_type, result in zip(level_strategies, level_results)
+            ]
+        
+        # Execute hierarchy levels
+        current_context = context.copy()
+        for level, level_strategies in enumerate(hierarchy):
+            results[level] = await execute_level(level_strategies, current_context)
+            
+            # Update context for next level
+            current_context.update({
+                "previous_level_results": results[level],
+                "hierarchy_level": level
+            })
+        
+        return {
+            "success": any(any(r.success for r in level_results) 
+                         for level_results in results.values()),
+            "results": results,
+            "hierarchy": hierarchy,
+            "pattern": CoordinationPattern.HIERARCHICAL.value,
+            "metrics": {
+                "total_levels": len(hierarchy),
+                "level_success_rates": {
+                    level: sum(1 for r in results[level] if r.success) / len(results[level])
+                    for level in results if results[level]
+                }
+            }
+        }
+
+    async def _coordinate_feedback(self,
+                                 query: str,
+                                 context: Dict[str, Any],
+                                 state: CoordinationState) -> Dict[str, Any]:
+        """Coordinate strategies with feedback loops."""
+        results = []
+        feedback_history = []
+        current_context = context.copy()
+        
+        max_iterations = 5  # Prevent infinite loops
+        iteration = 0
+        
+        while iteration < max_iterations:
+            iteration += 1
+            
+            # Execute strategies
+            iteration_results = []
+            for strategy_type in state.active_strategies:
+                if state.active_strategies[strategy_type]:
+                    try:
+                        strategy = self.strategies[strategy_type]
+                        result = await strategy.reason(query, current_context)
+                        
+                        strategy_result = StrategyResult(
+                            strategy_type=strategy_type,
+                            success=result.get("success", False),
+                            answer=result.get("answer"),
+                            confidence=result.get("confidence", 0.0),
+                            reasoning_trace=result.get("reasoning_trace", []),
+                            metadata=result.get("metadata", {}),
+                            performance_metrics=result.get("performance_metrics", {})
+                        )
+                        
+                        iteration_results.append(strategy_result)
+                        
+                    except Exception as e:
+                        logging.error(f"Error in feedback strategy {strategy_type}: {str(e)}")
+            
+            results.append(iteration_results)
+            
+            # Generate feedback
+            feedback = await self._generate_feedback(iteration_results, current_context)
+            feedback_history.append(feedback)
+            
+            # Check termination condition
+            if self._should_terminate_feedback(feedback, iteration_results):
+                break
+            
+            # Update context with feedback
+            current_context.update({
+                "previous_results": iteration_results,
+                "feedback": feedback,
+                "iteration": iteration
+            })
+        
+        return {
+            "success": any(any(r.success for r in iteration_results) 
+                         for iteration_results in results),
+            "results": results,
+            "feedback_history": feedback_history,
+            "pattern": CoordinationPattern.FEEDBACK.value,
+            "metrics": {
+                "total_iterations": iteration,
+                "feedback_impact": self._calculate_feedback_impact(results, feedback_history)
+            }
+        }
+
+    async def _coordinate_adaptive(self,
+                                 query: str,
+                                 context: Dict[str, Any],
+                                 state: CoordinationState) -> Dict[str, Any]:
+        """Coordinate strategies with adaptive selection."""
+        results = []
+        adaptations = []
+        current_context = context.copy()
+        
+        while len(results) < len(state.active_strategies):
+            # Select next strategy
+            next_strategy = await self._select_next_strategy(
+                results, state.active_strategies, current_context)
+            
+            if not next_strategy:
+                break
+            
+            try:
+                # Execute strategy
+                strategy = self.strategies[next_strategy]
+                result = await strategy.reason(query, current_context)
+                
+                strategy_result = StrategyResult(
+                    strategy_type=next_strategy,
+                    success=result.get("success", False),
+                    answer=result.get("answer"),
+                    confidence=result.get("confidence", 0.0),
+                    reasoning_trace=result.get("reasoning_trace", []),
+                    metadata=result.get("metadata", {}),
+                    performance_metrics=result.get("performance_metrics", {})
+                )
+                
+                results.append(strategy_result)
+                
+                # Adapt strategy selection
+                adaptation = await self._adapt_strategy_selection(
+                    strategy_result, current_context)
+                adaptations.append(adaptation)
+                
+                # Update context
+                current_context.update({
+                    "previous_results": results,
+                    "adaptations": adaptations,
+                    "current_strategy": next_strategy
+                })
+                
+            except Exception as e:
+                logging.error(f"Error in adaptive strategy {next_strategy}: {str(e)}")
+        
+        return {
+            "success": any(r.success for r in results),
+            "results": results,
+            "adaptations": adaptations,
+            "pattern": CoordinationPattern.ADAPTIVE.value,
+            "metrics": {
+                "total_strategies": len(results),
+                "adaptation_impact": self._calculate_adaptation_impact(results, adaptations)
+            }
+        }
+
+    async def _coordinate_ensemble(self,
+                                 query: str,
+                                 context: Dict[str, Any],
+                                 state: CoordinationState) -> Dict[str, Any]:
+        """Coordinate strategies as an ensemble."""
+        # Execute all strategies
+        results = []
+        for strategy_type in state.active_strategies:
+            if state.active_strategies[strategy_type]:
+                try:
+                    strategy = self.strategies[strategy_type]
+                    result = await strategy.reason(query, context)
+                    
+                    strategy_result = StrategyResult(
+                        strategy_type=strategy_type,
+                        success=result.get("success", False),
+                        answer=result.get("answer"),
+                        confidence=result.get("confidence", 0.0),
+                        reasoning_trace=result.get("reasoning_trace", []),
+                        metadata=result.get("metadata", {}),
+                        performance_metrics=result.get("performance_metrics", {})
+                    )
+                    
+                    results.append(strategy_result)
+                    
+                except Exception as e:
+                    logging.error(f"Error in ensemble strategy {strategy_type}: {str(e)}")
+        
+        # Combine results using ensemble methods
+        ensemble_result = await self._combine_ensemble_results(results, context)
+        
+        return {
+            "success": ensemble_result.get("success", False),
+            "results": results,
+            "ensemble_result": ensemble_result,
+            "pattern": CoordinationPattern.ENSEMBLE.value,
+            "metrics": {
+                "total_members": len(results),
+                "ensemble_confidence": ensemble_result.get("confidence", 0.0)
+            }
+        }
+
+    def _record_interaction(self,
+                          source: StrategyType,
+                          target: Optional[StrategyType],
+                          interaction_type: str,
+                          data: Dict[str, Any]):
+        """Record strategy interaction."""
+        self.interactions.append(StrategyInteraction(
+            source=source,
+            target=target,
+            interaction_type=interaction_type,
+            data=data
+        ))
+
+    def _update_pattern_performance(self, pattern: CoordinationPattern, result: Dict[str, Any]):
+        """Update pattern performance metrics."""
+        success_rate = result["metrics"].get("success_rate", 0.0)
+        self.pattern_performance[pattern].append(success_rate)
+        
+        # Update weights using exponential moving average
+        current_weight = self.pattern_weights[pattern]
+        self.pattern_weights[pattern] = (
+            (1 - self.learning_rate) * current_weight +
+            self.learning_rate * success_rate
+        )
+
+    def get_performance_metrics(self) -> Dict[str, Any]:
+        """Get comprehensive performance metrics."""
+        return {
+            "pattern_weights": dict(self.pattern_weights),
+            "average_performance": {
+                pattern.value: sum(scores) / len(scores) if scores else 0
+                for pattern, scores in self.pattern_performance.items()
+            },
+            "interaction_counts": defaultdict(int, {
+                interaction.interaction_type: 1
+                for interaction in self.interactions
+            }),
+            "active_patterns": [
+                pattern.value for pattern, weight in self.pattern_weights.items()
+                if weight > 0.5
+            ]
+        }

reasoning/emergent.py

@@ -0,0 +1,133 @@
+"""
+Emergent Reasoning Module
+------------------------
+Implements emergent reasoning capabilities that arise from the interaction
+of multiple reasoning strategies and patterns.
+"""
+
+from typing import Dict, Any, List, Optional
+from .base import ReasoningStrategy
+from .meta_learning import MetaLearningStrategy
+from .chain_of_thought import ChainOfThoughtStrategy
+from .tree_of_thoughts import TreeOfThoughtsStrategy
+
+class EmergentReasoning(ReasoningStrategy):
+    """
+    A reasoning strategy that combines multiple approaches to discover
+    emergent patterns and solutions.
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize emergent reasoning with component strategies."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+        
+        # Initialize component strategies with shared config
+        strategy_config = {
+            'min_confidence': self.min_confidence,
+            'parallel_threshold': self.parallel_threshold,
+            'learning_rate': self.learning_rate,
+            'strategy_weights': self.strategy_weights
+        }
+        
+        self.meta_learner = MetaLearningStrategy(strategy_config)
+        self.chain_of_thought = ChainOfThoughtStrategy(strategy_config)
+        self.tree_of_thoughts = TreeOfThoughtsStrategy(strategy_config)
+        
+        # Configure weights for strategy combination
+        self.weights = self.config.get('combination_weights', {
+            'meta': 0.4,
+            'chain': 0.3,
+            'tree': 0.3
+        })
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Apply emergent reasoning by combining multiple strategies and
+        identifying patterns that emerge from their interaction.
+        
+        Args:
+            query: The input query to reason about
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing reasoning results and confidence scores
+        """
+        try:
+            # Get results from each strategy
+            meta_result = await self.meta_learner.reason(query, context)
+            chain_result = await self.chain_of_thought.reason(query, context)
+            tree_result = await self.tree_of_thoughts.reason(query, context)
+            
+            # Combine results with weighted averaging
+            combined_answer = self._combine_results([
+                (meta_result.get('answer', ''), self.weights['meta']),
+                (chain_result.get('answer', ''), self.weights['chain']), 
+                (tree_result.get('answer', ''), self.weights['tree'])
+            ])
+            
+            # Calculate overall confidence
+            confidence = (
+                meta_result.get('confidence', 0) * self.weights['meta'] +
+                chain_result.get('confidence', 0) * self.weights['chain'] +
+                tree_result.get('confidence', 0) * self.weights['tree']
+            )
+            
+            return {
+                'answer': combined_answer,
+                'confidence': confidence,
+                'reasoning_path': {
+                    'meta': meta_result.get('reasoning_path'),
+                    'chain': chain_result.get('reasoning_path'),
+                    'tree': tree_result.get('reasoning_path')
+                },
+                'emergent_patterns': self._identify_patterns([
+                    meta_result, chain_result, tree_result
+                ])
+            }
+            
+        except Exception as e:
+            return {
+                'error': f"Emergent reasoning failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    def _combine_results(self, weighted_results: List[tuple[str, float]]) -> str:
+        """Combine multiple reasoning results with weights."""
+        if not weighted_results:
+            return ""
+            
+        # For now, use the highest weighted result
+        return max(weighted_results, key=lambda x: x[1])[0]
+    
+    def _identify_patterns(self, results: List[Dict[str, Any]]) -> List[str]:
+        """Identify common patterns across different reasoning strategies."""
+        patterns = []
+        
+        # Extract common themes or conclusions
+        answers = [r.get('answer', '') for r in results if r.get('answer')]
+        if len(set(answers)) == 1:
+            patterns.append("All strategies reached the same conclusion")
+        elif len(set(answers)) < len(answers):
+            patterns.append("Some strategies converged on similar conclusions")
+            
+        # Look for common confidence patterns
+        confidences = [r.get('confidence', 0) for r in results]
+        avg_confidence = sum(confidences) / len(confidences) if confidences else 0
+        if avg_confidence > 0.8:
+            patterns.append("High confidence across all strategies")
+        elif avg_confidence < 0.3:
+            patterns.append("Low confidence across strategies")
+            
+        return patterns

reasoning/groq_strategy.py

@@ -0,0 +1,332 @@
+"""Groq API integration with streaming and optimizations."""
+
+import os
+import logging
+import asyncio
+from typing import Dict, Any, Optional, List, AsyncGenerator, Union
+import groq
+from datetime import datetime
+import json
+from dataclasses import dataclass
+from concurrent.futures import ThreadPoolExecutor
+
+from .base import ReasoningStrategy, StrategyResult
+
+logger = logging.getLogger(__name__)
+
+@dataclass
+class GroqConfig:
+    """Configuration for Groq models."""
+    model_name: str
+    max_tokens: int
+    temperature: float
+    top_p: float
+    top_k: Optional[int] = None
+    presence_penalty: float = 0.0
+    frequency_penalty: float = 0.0
+    stop_sequences: Optional[List[str]] = None
+    chunk_size: int = 1024
+    retry_attempts: int = 3
+    retry_delay: float = 1.0
+
+class GroqStrategy(ReasoningStrategy):
+    """Enhanced reasoning strategy using Groq's API with streaming and optimizations."""
+    
+    def __init__(self, api_key: Optional[str] = None):
+        """Initialize Groq strategy."""
+        super().__init__()
+        self.api_key = api_key or os.getenv("GROQ_API_KEY")
+        if not self.api_key:
+            raise ValueError("GROQ_API_KEY must be set")
+        
+        # Initialize Groq client with optimized settings
+        self.client = groq.Groq(
+            api_key=self.api_key,
+            timeout=30,
+            max_retries=3
+        )
+        
+        # Optimized model configurations
+        self.model_configs = {
+            "mixtral": GroqConfig(
+                model_name="mixtral-8x7b-32768",
+                max_tokens=32768,
+                temperature=0.7,
+                top_p=0.9,
+                top_k=40,
+                presence_penalty=0.1,
+                frequency_penalty=0.1,
+                chunk_size=4096
+            ),
+            "llama": GroqConfig(
+                model_name="llama2-70b-4096",
+                max_tokens=4096,
+                temperature=0.8,
+                top_p=0.9,
+                top_k=50,
+                presence_penalty=0.2,
+                frequency_penalty=0.2,
+                chunk_size=1024
+            )
+        }
+        
+        # Initialize thread pool for parallel processing
+        self.executor = ThreadPoolExecutor(max_workers=4)
+        
+        # Response cache
+        self.cache: Dict[str, Any] = {}
+        self.cache_ttl = 3600  # 1 hour
+    
+    async def reason_stream(
+        self,
+        query: str,
+        context: Dict[str, Any],
+        model: str = "mixtral",
+        chunk_handler: Optional[callable] = None
+    ) -> AsyncGenerator[str, None]:
+        """
+        Stream reasoning results from Groq's API.
+        
+        Args:
+            query: The query to reason about
+            context: Additional context
+            model: Model to use ('mixtral' or 'llama')
+            chunk_handler: Optional callback for handling chunks
+        """
+        config = self.model_configs[model]
+        messages = self._prepare_messages(query, context)
+        
+        try:
+            stream = await self.client.chat.completions.create(
+                model=config.model_name,
+                messages=messages,
+                temperature=config.temperature,
+                top_p=config.top_p,
+                top_k=config.top_k,
+                presence_penalty=config.presence_penalty,
+                frequency_penalty=config.frequency_penalty,
+                max_tokens=config.max_tokens,
+                stream=True
+            )
+            
+            collected_content = []
+            async for chunk in stream:
+                if chunk.choices[0].delta.content:
+                    content = chunk.choices[0].delta.content
+                    collected_content.append(content)
+                    
+                    if chunk_handler:
+                        await chunk_handler(content)
+                    
+                    yield content
+            
+            # Cache the complete response
+            cache_key = self._generate_cache_key(query, context, model)
+            self.cache[cache_key] = {
+                "content": "".join(collected_content),
+                "timestamp": datetime.now()
+            }
+            
+        except Exception as e:
+            logger.error(f"Groq streaming error: {str(e)}")
+            yield f"Error: {str(e)}"
+    
+    async def reason(
+        self,
+        query: str,
+        context: Dict[str, Any],
+        model: str = "mixtral"
+    ) -> StrategyResult:
+        """
+        Enhanced reasoning with Groq's API including optimizations.
+        
+        Args:
+            query: The query to reason about
+            context: Additional context
+            model: Model to use ('mixtral' or 'llama')
+        """
+        # Check cache first
+        cache_key = self._generate_cache_key(query, context, model)
+        cached_response = self._get_from_cache(cache_key)
+        if cached_response:
+            return self._create_result(cached_response, model, from_cache=True)
+        
+        config = self.model_configs[model]
+        messages = self._prepare_messages(query, context)
+        
+        # Implement retry logic with exponential backoff
+        for attempt in range(config.retry_attempts):
+            try:
+                start_time = datetime.now()
+                
+                # Make API call with optimized parameters
+                response = await self.client.chat.completions.create(
+                    model=config.model_name,
+                    messages=messages,
+                    temperature=config.temperature,
+                    top_p=config.top_p,
+                    top_k=config.top_k,
+                    presence_penalty=config.presence_penalty,
+                    frequency_penalty=config.frequency_penalty,
+                    max_tokens=config.max_tokens,
+                    stream=False
+                )
+                
+                end_time = datetime.now()
+                
+                # Cache successful response
+                self.cache[cache_key] = {
+                    "content": response.choices[0].message.content,
+                    "timestamp": datetime.now()
+                }
+                
+                return self._create_result(response, model)
+                
+            except Exception as e:
+                delay = config.retry_delay * (2 ** attempt)
+                logger.warning(f"Groq API attempt {attempt + 1} failed: {str(e)}")
+                if attempt < config.retry_attempts - 1:
+                    await asyncio.sleep(delay)
+                else:
+                    logger.error(f"All Groq API attempts failed: {str(e)}")
+                    return self._create_error_result(str(e))
+    
+    def _create_result(
+        self,
+        response: Union[Dict, Any],
+        model: str,
+        from_cache: bool = False
+    ) -> StrategyResult:
+        """Create a strategy result from response."""
+        if from_cache:
+            answer = response["content"]
+            confidence = 0.9  # Higher confidence for cached responses
+            performance_metrics = {
+                "from_cache": True,
+                "cache_age": (datetime.now() - response["timestamp"]).total_seconds()
+            }
+        else:
+            answer = response.choices[0].message.content
+            confidence = self._calculate_confidence(response)
+            performance_metrics = {
+                "latency": response.usage.total_tokens / 1000,  # tokens per second
+                "tokens_used": response.usage.total_tokens,
+                "prompt_tokens": response.usage.prompt_tokens,
+                "completion_tokens": response.usage.completion_tokens,
+                "model": self.model_configs[model].model_name
+            }
+        
+        return StrategyResult(
+            strategy_type="groq",
+            success=True,
+            answer=answer,
+            confidence=confidence,
+            reasoning_trace=[{
+                "step": "groq_api_call",
+                "model": self.model_configs[model].model_name,
+                "timestamp": datetime.now().isoformat(),
+                "metrics": performance_metrics
+            }],
+            metadata={
+                "model": self.model_configs[model].model_name,
+                "from_cache": from_cache
+            },
+            performance_metrics=performance_metrics
+        )
+    
+    def _create_error_result(self, error: str) -> StrategyResult:
+        """Create an error result."""
+        return StrategyResult(
+            strategy_type="groq",
+            success=False,
+            answer=None,
+            confidence=0.0,
+            reasoning_trace=[{
+                "step": "groq_api_error",
+                "error": error,
+                "timestamp": datetime.now().isoformat()
+            }],
+            metadata={"error": error},
+            performance_metrics={}
+        )
+    
+    def _generate_cache_key(
+        self,
+        query: str,
+        context: Dict[str, Any],
+        model: str
+    ) -> str:
+        """Generate a cache key."""
+        key_data = {
+            "query": query,
+            "context": context,
+            "model": model
+        }
+        return json.dumps(key_data, sort_keys=True)
+    
+    def _get_from_cache(self, cache_key: str) -> Optional[Dict]:
+        """Get response from cache if valid."""
+        if cache_key in self.cache:
+            cached = self.cache[cache_key]
+            age = (datetime.now() - cached["timestamp"]).total_seconds()
+            if age < self.cache_ttl:
+                return cached
+            else:
+                del self.cache[cache_key]
+        return None
+    
+    def _calculate_confidence(self, response: Any) -> float:
+        """Calculate confidence score from response."""
+        confidence = 0.8  # Base confidence
+        
+        # Adjust based on token usage and model behavior
+        if hasattr(response, 'usage'):
+            completion_tokens = response.usage.completion_tokens
+            total_tokens = response.usage.total_tokens
+            
+            # Length-based adjustment
+            if completion_tokens < 10:
+                confidence *= 0.8  # Reduce confidence for very short responses
+            elif completion_tokens > 100:
+                confidence *= 1.1  # Increase confidence for detailed responses
+            
+            # Token efficiency adjustment
+            token_efficiency = completion_tokens / total_tokens
+            if token_efficiency > 0.5:
+                confidence *= 1.1  # Good token efficiency
+            
+            # Response completeness check
+            if hasattr(response.choices[0], 'finish_reason'):
+                if response.choices[0].finish_reason == "stop":
+                    confidence *= 1.1  # Natural completion
+                elif response.choices[0].finish_reason == "length":
+                    confidence *= 0.9  # Truncated response
+        
+        return min(1.0, max(0.0, confidence))  # Ensure between 0 and 1
+    
+    def _prepare_messages(
+        self,
+        query: str,
+        context: Dict[str, Any]
+    ) -> List[Dict[str, str]]:
+        """Prepare messages for the Groq API."""
+        messages = []
+        
+        # Add system message if provided
+        if "system_message" in context:
+            messages.append({
+                "role": "system",
+                "content": context["system_message"]
+            })
+        
+        # Add chat history if provided
+        if "chat_history" in context:
+            messages.extend(context["chat_history"])
+        
+        # Add the current query
+        messages.append({
+            "role": "user",
+            "content": query
+        })
+        
+        return messages

reasoning/learning.py

@@ -0,0 +1,394 @@
+"""Enhanced learning mechanisms for reasoning strategies."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+@dataclass
+class LearningEvent:
+    """Event for strategy learning."""
+    strategy_type: str
+    event_type: str
+    data: Dict[str, Any]
+    outcome: Optional[float]
+    timestamp: datetime = field(default_factory=datetime.now)
+
+class LearningMode(Enum):
+    """Types of learning modes."""
+    SUPERVISED = "supervised"
+    REINFORCEMENT = "reinforcement"
+    ACTIVE = "active"
+    TRANSFER = "transfer"
+    META = "meta"
+    ENSEMBLE = "ensemble"
+
+@dataclass
+class LearningState:
+    """State for learning process."""
+    mode: LearningMode
+    parameters: Dict[str, Any]
+    history: List[LearningEvent]
+    metrics: Dict[str, float]
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+class EnhancedLearningManager:
+    """
+    Advanced learning manager that:
+    1. Implements multiple learning modes
+    2. Tracks learning progress
+    3. Adapts learning parameters
+    4. Optimizes strategy performance
+    5. Transfers knowledge between strategies
+    """
+    
+    def __init__(self,
+                 learning_rate: float = 0.1,
+                 exploration_rate: float = 0.2,
+                 memory_size: int = 1000):
+        self.learning_rate = learning_rate
+        self.exploration_rate = exploration_rate
+        self.memory_size = memory_size
+        
+        # Learning states
+        self.states: Dict[str, LearningState] = {}
+        
+        # Performance tracking
+        self.performance_history: List[Dict[str, Any]] = []
+        self.strategy_metrics: Dict[str, List[float]] = defaultdict(list)
+        
+        # Knowledge transfer
+        self.knowledge_base: Dict[str, Any] = {}
+        self.transfer_history: List[Dict[str, Any]] = []
+        
+    async def learn(self,
+                   strategy_type: str,
+                   event: LearningEvent,
+                   context: Dict[str, Any]) -> Dict[str, Any]:
+        """Learn from strategy execution event."""
+        try:
+            # Initialize or get learning state
+            state = self._get_learning_state(strategy_type)
+            
+            # Select learning mode
+            mode = await self._select_learning_mode(event, state, context)
+            
+            # Execute learning
+            if mode == LearningMode.SUPERVISED:
+                result = await self._supervised_learning(event, state, context)
+            elif mode == LearningMode.REINFORCEMENT:
+                result = await self._reinforcement_learning(event, state, context)
+            elif mode == LearningMode.ACTIVE:
+                result = await self._active_learning(event, state, context)
+            elif mode == LearningMode.TRANSFER:
+                result = await self._transfer_learning(event, state, context)
+            elif mode == LearningMode.META:
+                result = await self._meta_learning(event, state, context)
+            elif mode == LearningMode.ENSEMBLE:
+                result = await self._ensemble_learning(event, state, context)
+            else:
+                raise ValueError(f"Unsupported learning mode: {mode}")
+            
+            # Update state
+            self._update_learning_state(state, result)
+            
+            # Record performance
+            self._record_performance(strategy_type, result)
+            
+            return result
+            
+        except Exception as e:
+            logging.error(f"Error in learning: {str(e)}")
+            return {
+                "success": False,
+                "error": str(e),
+                "mode": mode.value if 'mode' in locals() else None
+            }
+
+    async def _supervised_learning(self,
+                                 event: LearningEvent,
+                                 state: LearningState,
+                                 context: Dict[str, Any]) -> Dict[str, Any]:
+        """Implement supervised learning."""
+        # Extract features and labels
+        features = await self._extract_features(event.data, context)
+        labels = event.outcome if event.outcome is not None else 0.0
+        
+        # Train model
+        model_update = await self._update_model(features, labels, state, context)
+        
+        # Validate performance
+        validation = await self._validate_model(model_update, state, context)
+        
+        return {
+            "success": True,
+            "mode": LearningMode.SUPERVISED.value,
+            "model_update": model_update,
+            "validation": validation,
+            "metrics": {
+                "accuracy": validation.get("accuracy", 0.0),
+                "loss": validation.get("loss", 0.0)
+            }
+        }
+
+    async def _reinforcement_learning(self,
+                                    event: LearningEvent,
+                                    state: LearningState,
+                                    context: Dict[str, Any]) -> Dict[str, Any]:
+        """Implement reinforcement learning."""
+        # Extract state and action
+        current_state = await self._extract_state(event.data, context)
+        action = event.data.get("action")
+        reward = event.outcome if event.outcome is not None else 0.0
+        
+        # Update policy
+        policy_update = await self._update_policy(
+            current_state, action, reward, state, context)
+        
+        # Optimize value function
+        value_update = await self._update_value_function(
+            current_state, reward, state, context)
+        
+        return {
+            "success": True,
+            "mode": LearningMode.REINFORCEMENT.value,
+            "policy_update": policy_update,
+            "value_update": value_update,
+            "metrics": {
+                "reward": reward,
+                "value_error": value_update.get("error", 0.0)
+            }
+        }
+
+    async def _active_learning(self,
+                             event: LearningEvent,
+                             state: LearningState,
+                             context: Dict[str, Any]) -> Dict[str, Any]:
+        """Implement active learning."""
+        # Query selection
+        query = await self._select_query(event.data, state, context)
+        
+        # Get feedback
+        feedback = await self._get_feedback(query, context)
+        
+        # Update model
+        model_update = await self._update_model_active(
+            query, feedback, state, context)
+        
+        return {
+            "success": True,
+            "mode": LearningMode.ACTIVE.value,
+            "query": query,
+            "feedback": feedback,
+            "model_update": model_update,
+            "metrics": {
+                "uncertainty": query.get("uncertainty", 0.0),
+                "feedback_quality": feedback.get("quality", 0.0)
+            }
+        }
+
+    async def _transfer_learning(self,
+                               event: LearningEvent,
+                               state: LearningState,
+                               context: Dict[str, Any]) -> Dict[str, Any]:
+        """Implement transfer learning."""
+        # Source task selection
+        source_task = await self._select_source_task(event.data, state, context)
+        
+        # Knowledge extraction
+        knowledge = await self._extract_knowledge(source_task, context)
+        
+        # Transfer adaptation
+        adaptation = await self._adapt_knowledge(
+            knowledge, event.data, state, context)
+        
+        # Apply transfer
+        transfer = await self._apply_transfer(adaptation, state, context)
+        
+        return {
+            "success": True,
+            "mode": LearningMode.TRANSFER.value,
+            "source_task": source_task,
+            "knowledge": knowledge,
+            "adaptation": adaptation,
+            "transfer": transfer,
+            "metrics": {
+                "transfer_efficiency": transfer.get("efficiency", 0.0),
+                "adaptation_quality": adaptation.get("quality", 0.0)
+            }
+        }
+
+    async def _meta_learning(self,
+                           event: LearningEvent,
+                           state: LearningState,
+                           context: Dict[str, Any]) -> Dict[str, Any]:
+        """Implement meta-learning."""
+        # Task characterization
+        task_char = await self._characterize_task(event.data, context)
+        
+        # Strategy selection
+        strategy = await self._select_strategy(task_char, state, context)
+        
+        # Parameter optimization
+        optimization = await self._optimize_parameters(
+            strategy, task_char, state, context)
+        
+        # Apply meta-learning
+        meta_update = await self._apply_meta_learning(
+            optimization, state, context)
+        
+        return {
+            "success": True,
+            "mode": LearningMode.META.value,
+            "task_characterization": task_char,
+            "strategy": strategy,
+            "optimization": optimization,
+            "meta_update": meta_update,
+            "metrics": {
+                "strategy_fit": strategy.get("fit_score", 0.0),
+                "optimization_improvement": optimization.get("improvement", 0.0)
+            }
+        }
+
+    async def _ensemble_learning(self,
+                               event: LearningEvent,
+                               state: LearningState,
+                               context: Dict[str, Any]) -> Dict[str, Any]:
+        """Implement ensemble learning."""
+        # Member selection
+        members = await self._select_members(event.data, state, context)
+        
+        # Weight optimization
+        weights = await self._optimize_weights(members, state, context)
+        
+        # Combine predictions
+        combination = await self._combine_predictions(
+            members, weights, event.data, context)
+        
+        return {
+            "success": True,
+            "mode": LearningMode.ENSEMBLE.value,
+            "members": members,
+            "weights": weights,
+            "combination": combination,
+            "metrics": {
+                "ensemble_diversity": weights.get("diversity", 0.0),
+                "combination_strength": combination.get("strength", 0.0)
+            }
+        }
+
+    def _get_learning_state(self, strategy_type: str) -> LearningState:
+        """Get or initialize learning state for strategy."""
+        if strategy_type not in self.states:
+            self.states[strategy_type] = LearningState(
+                mode=LearningMode.SUPERVISED,
+                parameters={
+                    "learning_rate": self.learning_rate,
+                    "exploration_rate": self.exploration_rate
+                },
+                history=[],
+                metrics={}
+            )
+        return self.states[strategy_type]
+
+    def _update_learning_state(self, state: LearningState, result: Dict[str, Any]):
+        """Update learning state with result."""
+        # Update history
+        state.history.append(LearningEvent(
+            strategy_type=result.get("strategy_type", "unknown"),
+            event_type="learning_update",
+            data=result,
+            outcome=result.get("metrics", {}).get("accuracy", 0.0),
+            timestamp=datetime.now()
+        ))
+        
+        # Update metrics
+        for metric, value in result.get("metrics", {}).items():
+            if metric in state.metrics:
+                state.metrics[metric] = (
+                    0.9 * state.metrics[metric] + 0.1 * value  # Exponential moving average
+                )
+            else:
+                state.metrics[metric] = value
+        
+        # Adapt parameters
+        self._adapt_parameters(state, result)
+
+    def _record_performance(self, strategy_type: str, result: Dict[str, Any]):
+        """Record learning performance."""
+        self.performance_history.append({
+            "timestamp": datetime.now().isoformat(),
+            "strategy_type": strategy_type,
+            "mode": result.get("mode"),
+            "metrics": result.get("metrics", {}),
+            "success": result.get("success", False)
+        })
+        
+        # Update strategy metrics
+        for metric, value in result.get("metrics", {}).items():
+            self.strategy_metrics[f"{strategy_type}_{metric}"].append(value)
+        
+        # Maintain memory size
+        if len(self.performance_history) > self.memory_size:
+            self.performance_history = self.performance_history[-self.memory_size:]
+
+    def _adapt_parameters(self, state: LearningState, result: Dict[str, Any]):
+        """Adapt learning parameters based on performance."""
+        # Adapt learning rate
+        if "accuracy" in result.get("metrics", {}):
+            accuracy = result["metrics"]["accuracy"]
+            if accuracy > 0.8:
+                state.parameters["learning_rate"] *= 0.95  # Decrease if performing well
+            elif accuracy < 0.6:
+                state.parameters["learning_rate"] *= 1.05  # Increase if performing poorly
+        
+        # Adapt exploration rate
+        if "reward" in result.get("metrics", {}):
+            reward = result["metrics"]["reward"]
+            if reward > 0:
+                state.parameters["exploration_rate"] *= 0.95  # Decrease if getting rewards
+            else:
+                state.parameters["exploration_rate"] *= 1.05  # Increase if not getting rewards
+        
+        # Clip parameters to reasonable ranges
+        state.parameters["learning_rate"] = np.clip(
+            state.parameters["learning_rate"], 0.001, 0.5)
+        state.parameters["exploration_rate"] = np.clip(
+            state.parameters["exploration_rate"], 0.01, 0.5)
+
+    def get_performance_metrics(self) -> Dict[str, Any]:
+        """Get comprehensive performance metrics."""
+        return {
+            "learning_states": {
+                strategy_type: {
+                    "mode": state.mode.value,
+                    "parameters": state.parameters,
+                    "metrics": state.metrics
+                }
+                for strategy_type, state in self.states.items()
+            },
+            "strategy_performance": {
+                metric: {
+                    "mean": np.mean(values) if values else 0.0,
+                    "std": np.std(values) if values else 0.0,
+                    "min": min(values) if values else 0.0,
+                    "max": max(values) if values else 0.0
+                }
+                for metric, values in self.strategy_metrics.items()
+            },
+            "transfer_metrics": {
+                "total_transfers": len(self.transfer_history),
+                "success_rate": sum(1 for t in self.transfer_history if t.get("success", False)) / len(self.transfer_history) if self.transfer_history else 0
+            }
+        }
+
+    def clear_history(self):
+        """Clear learning history and reset states."""
+        self.states.clear()
+        self.performance_history.clear()
+        self.strategy_metrics.clear()
+        self.transfer_history.clear()

reasoning/local_llm.py

@@ -0,0 +1,117 @@
+"""Local LLM integration for the reasoning system."""
+
+import os
+from typing import Dict, Any, Optional
+from datetime import datetime
+import logging
+from llama_cpp import Llama
+import huggingface_hub
+from .base import ReasoningStrategy
+from .model_manager import ModelManager, ModelType
+
+class LocalLLMStrategy(ReasoningStrategy):
+    """Implements reasoning using local LLM."""
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize the local LLM strategy."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Initialize model manager
+        self.model_manager = ModelManager(self.config.get('model_dir', "models"))
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+        
+        self.logger = logging.getLogger(__name__)
+        
+    async def initialize(self):
+        """Initialize all models."""
+        await self.model_manager.initialize_all_models()
+            
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate reasoning response using appropriate local LLM."""
+        try:
+            # Determine best model for the task
+            task_type = context.get('task_type', 'general')
+            model_key = self.model_manager.get_best_model_for_task(task_type)
+            
+            # Get or initialize the model
+            model = await self.model_manager.get_model(model_key)
+            if not model:
+                raise Exception(f"Failed to initialize {model_key} model")
+            
+            # Format prompt with context
+            prompt = self._format_prompt(query, context)
+            
+            # Generate response
+            response = model(
+                prompt,
+                max_tokens=1024 if model.n_ctx >= 4096 else 512,
+                temperature=0.7,
+                top_p=0.95,
+                repeat_penalty=1.1,
+                echo=False
+            )
+            
+            # Extract and structure the response
+            result = self._parse_response(response['choices'][0]['text'])
+            
+            return {
+                'success': True,
+                'answer': result['answer'],
+                'reasoning': result['reasoning'],
+                'confidence': result['confidence'],
+                'timestamp': datetime.now(),
+                'metadata': {
+                    'model': model_key,
+                    'strategy': 'local_llm',
+                    'context_length': len(prompt),
+                    'response_length': len(response['choices'][0]['text'])
+                }
+            }
+                
+        except Exception as e:
+            self.logger.error(f"Error in reasoning: {e}")
+            return {
+                'success': False,
+                'error': str(e),
+                'timestamp': datetime.now()
+            }
+            
+    def _format_prompt(self, query: str, context: Dict[str, Any]) -> str:
+        """Format the prompt with query and context."""
+        # Include relevant context
+        context_str = "\n".join([
+            f"{k}: {v}" for k, v in context.items()
+            if k in ['objective', 'constraints', 'background']
+        ])
+        
+        return f"""Let's solve this problem step by step.
+
+Context:
+{context_str}
+
+Question: {query}
+
+Let me break this down:
+1."""
+        
+    def _parse_response(self, text: str) -> Dict[str, Any]:
+        """Parse the response into structured output."""
+        # Simple parsing for now
+        lines = text.strip().split('\n')
+        
+        return {
+            'answer': lines[-1] if lines else '',
+            'reasoning': '\n'.join(lines[:-1]) if len(lines) > 1 else '',
+            'confidence': 0.8  # Default confidence
+        }

reasoning/market_analysis.py

@@ -0,0 +1,450 @@
+"""Advanced market analysis tools for venture strategies."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+@dataclass
+class MarketSegment:
+    """Market segment analysis."""
+    size: float
+    growth_rate: float
+    cagr: float
+    competition: List[Dict[str, Any]]
+    barriers: List[str]
+    opportunities: List[str]
+    risks: List[str]
+
+@dataclass
+class CompetitorAnalysis:
+    """Competitor analysis."""
+    name: str
+    market_share: float
+    strengths: List[str]
+    weaknesses: List[str]
+    strategy: str
+    revenue: Optional[float]
+    valuation: Optional[float]
+
+@dataclass
+class MarketTrend:
+    """Market trend analysis."""
+    name: str
+    impact: float
+    timeline: str
+    adoption_rate: float
+    market_potential: float
+    risk_level: float
+
+class MarketAnalyzer:
+    """
+    Advanced market analysis toolkit that:
+    1. Analyzes market segments
+    2. Tracks competitors
+    3. Identifies trends
+    4. Predicts opportunities
+    5. Assesses risks
+    """
+    
+    def __init__(self):
+        self.segments: Dict[str, MarketSegment] = {}
+        self.competitors: Dict[str, CompetitorAnalysis] = {}
+        self.trends: List[MarketTrend] = []
+        
+    async def analyze_market(self, 
+                           segment: str,
+                           context: Dict[str, Any]) -> Dict[str, Any]:
+        """Perform comprehensive market analysis."""
+        try:
+            # Segment analysis
+            segment_analysis = await self._analyze_segment(segment, context)
+            
+            # Competitor analysis
+            competitor_analysis = await self._analyze_competitors(segment, context)
+            
+            # Trend analysis
+            trend_analysis = await self._analyze_trends(segment, context)
+            
+            # Opportunity analysis
+            opportunity_analysis = await self._analyze_opportunities(
+                segment_analysis, competitor_analysis, trend_analysis, context)
+            
+            # Risk analysis
+            risk_analysis = await self._analyze_risks(
+                segment_analysis, competitor_analysis, trend_analysis, context)
+            
+            return {
+                "success": True,
+                "segment_analysis": segment_analysis,
+                "competitor_analysis": competitor_analysis,
+                "trend_analysis": trend_analysis,
+                "opportunity_analysis": opportunity_analysis,
+                "risk_analysis": risk_analysis,
+                "metrics": {
+                    "market_score": self._calculate_market_score(segment_analysis),
+                    "opportunity_score": self._calculate_opportunity_score(opportunity_analysis),
+                    "risk_score": self._calculate_risk_score(risk_analysis)
+                }
+            }
+        except Exception as e:
+            logging.error(f"Error in market analysis: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _analyze_segment(self,
+                             segment: str,
+                             context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze market segment."""
+        prompt = f"""
+        Analyze market segment:
+        Segment: {segment}
+        Context: {json.dumps(context)}
+        
+        Analyze:
+        1. Market size and growth
+        2. Customer segments
+        3. Value chain
+        4. Entry barriers
+        5. Competitive dynamics
+        
+        Format as:
+        [Analysis]
+        Size: ...
+        Growth: ...
+        Segments: ...
+        Value_Chain: ...
+        Barriers: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_segment_analysis(response["answer"])
+
+    async def _analyze_competitors(self,
+                                 segment: str,
+                                 context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze competitors in segment."""
+        prompt = f"""
+        Analyze competitors:
+        Segment: {segment}
+        Context: {json.dumps(context)}
+        
+        For each competitor analyze:
+        1. Market share
+        2. Business model
+        3. Strengths/weaknesses
+        4. Strategy
+        5. Performance metrics
+        
+        Format as:
+        [Competitor1]
+        Share: ...
+        Model: ...
+        Strengths: ...
+        Weaknesses: ...
+        Strategy: ...
+        Metrics: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_competitor_analysis(response["answer"])
+
+    async def _analyze_trends(self,
+                            segment: str,
+                            context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze market trends."""
+        prompt = f"""
+        Analyze market trends:
+        Segment: {segment}
+        Context: {json.dumps(context)}
+        
+        Analyze trends in:
+        1. Technology
+        2. Customer behavior
+        3. Business models
+        4. Regulation
+        5. Market dynamics
+        
+        Format as:
+        [Trend1]
+        Type: ...
+        Impact: ...
+        Timeline: ...
+        Adoption: ...
+        Potential: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_trend_analysis(response["answer"])
+
+    async def _analyze_opportunities(self,
+                                   segment_analysis: Dict[str, Any],
+                                   competitor_analysis: Dict[str, Any],
+                                   trend_analysis: Dict[str, Any],
+                                   context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze market opportunities."""
+        prompt = f"""
+        Analyze market opportunities:
+        Segment: {json.dumps(segment_analysis)}
+        Competitors: {json.dumps(competitor_analysis)}
+        Trends: {json.dumps(trend_analysis)}
+        Context: {json.dumps(context)}
+        
+        Identify opportunities in:
+        1. Unmet needs
+        2. Market gaps
+        3. Innovation potential
+        4. Scaling potential
+        5. Value creation
+        
+        Format as:
+        [Opportunity1]
+        Type: ...
+        Description: ...
+        Potential: ...
+        Requirements: ...
+        Timeline: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_opportunity_analysis(response["answer"])
+
+    async def _analyze_risks(self,
+                           segment_analysis: Dict[str, Any],
+                           competitor_analysis: Dict[str, Any],
+                           trend_analysis: Dict[str, Any],
+                           context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze market risks."""
+        prompt = f"""
+        Analyze market risks:
+        Segment: {json.dumps(segment_analysis)}
+        Competitors: {json.dumps(competitor_analysis)}
+        Trends: {json.dumps(trend_analysis)}
+        Context: {json.dumps(context)}
+        
+        Analyze risks in:
+        1. Market dynamics
+        2. Competition
+        3. Technology
+        4. Regulation
+        5. Execution
+        
+        Format as:
+        [Risk1]
+        Type: ...
+        Description: ...
+        Impact: ...
+        Probability: ...
+        Mitigation: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_risk_analysis(response["answer"])
+
+    def _calculate_market_score(self, analysis: Dict[str, Any]) -> float:
+        """Calculate market attractiveness score."""
+        weights = {
+            "size": 0.3,
+            "growth": 0.3,
+            "competition": 0.2,
+            "barriers": 0.1,
+            "dynamics": 0.1
+        }
+        
+        scores = {
+            "size": min(analysis.get("size", 0) / 1e9, 1.0),  # Normalize to 1B
+            "growth": min(analysis.get("growth", 0) / 30, 1.0),  # Normalize to 30%
+            "competition": 1.0 - min(len(analysis.get("competitors", [])) / 10, 1.0),
+            "barriers": 1.0 - min(len(analysis.get("barriers", [])) / 5, 1.0),
+            "dynamics": analysis.get("dynamics_score", 0.5)
+        }
+        
+        return sum(weights[k] * scores[k] for k in weights)
+
+    def _calculate_opportunity_score(self, analysis: Dict[str, Any]) -> float:
+        """Calculate opportunity attractiveness score."""
+        weights = {
+            "market_potential": 0.3,
+            "innovation_potential": 0.2,
+            "execution_feasibility": 0.2,
+            "competitive_advantage": 0.2,
+            "timing": 0.1
+        }
+        
+        scores = {
+            "market_potential": analysis.get("market_potential", 0.5),
+            "innovation_potential": analysis.get("innovation_potential", 0.5),
+            "execution_feasibility": analysis.get("execution_feasibility", 0.5),
+            "competitive_advantage": analysis.get("competitive_advantage", 0.5),
+            "timing": analysis.get("timing_score", 0.5)
+        }
+        
+        return sum(weights[k] * scores[k] for k in weights)
+
+    def _calculate_risk_score(self, analysis: Dict[str, Any]) -> float:
+        """Calculate risk level score."""
+        weights = {
+            "market_risk": 0.2,
+            "competition_risk": 0.2,
+            "technology_risk": 0.2,
+            "regulatory_risk": 0.2,
+            "execution_risk": 0.2
+        }
+        
+        scores = {
+            "market_risk": analysis.get("market_risk", 0.5),
+            "competition_risk": analysis.get("competition_risk", 0.5),
+            "technology_risk": analysis.get("technology_risk", 0.5),
+            "regulatory_risk": analysis.get("regulatory_risk", 0.5),
+            "execution_risk": analysis.get("execution_risk", 0.5)
+        }
+        
+        return sum(weights[k] * scores[k] for k in weights)
+
+    def get_market_insights(self) -> Dict[str, Any]:
+        """Get comprehensive market insights."""
+        return {
+            "segment_insights": {
+                segment: {
+                    "size": s.size,
+                    "growth_rate": s.growth_rate,
+                    "cagr": s.cagr,
+                    "opportunity_score": self._calculate_market_score({
+                        "size": s.size,
+                        "growth": s.growth_rate,
+                        "competitors": s.competition,
+                        "barriers": s.barriers
+                    })
+                }
+                for segment, s in self.segments.items()
+            },
+            "competitor_insights": {
+                competitor: {
+                    "market_share": c.market_share,
+                    "strength_score": len(c.strengths) / (len(c.strengths) + len(c.weaknesses)),
+                    "revenue": c.revenue,
+                    "valuation": c.valuation
+                }
+                for competitor, c in self.competitors.items()
+            },
+            "trend_insights": [
+                {
+                    "name": t.name,
+                    "impact": t.impact,
+                    "potential": t.market_potential,
+                    "risk": t.risk_level
+                }
+                for t in self.trends
+            ]
+        }
+
+class MarketAnalysisStrategy(ReasoningStrategy):
+    """
+    Advanced market analysis strategy that combines multiple analytical tools
+    to provide comprehensive market insights.
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize market analysis strategy."""
+        super().__init__()
+        self.config = config or {}
+        self.analyzer = MarketAnalyzer()
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Perform market analysis based on query and context.
+        
+        Args:
+            query: The market analysis query
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing market analysis results and confidence scores
+        """
+        try:
+            # Extract market segment from query/context
+            segment = self._extract_segment(query, context)
+            
+            # Perform market analysis
+            analysis = await self._analyze_market(segment, context)
+            
+            # Get insights
+            insights = self.analyzer.get_market_insights()
+            
+            # Calculate confidence based on data quality and completeness
+            confidence = self._calculate_confidence(analysis, insights)
+            
+            return {
+                'answer': self._format_insights(insights),
+                'confidence': confidence,
+                'analysis': analysis,
+                'insights': insights,
+                'segment': segment
+            }
+            
+        except Exception as e:
+            logging.error(f"Market analysis failed: {str(e)}")
+            return {
+                'error': f"Market analysis failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    def _extract_segment(self, query: str, context: Dict[str, Any]) -> str:
+        """Extract market segment from query and context."""
+        # Use context if available
+        if 'segment' in context:
+            return context['segment']
+            
+        # Default to general market
+        return 'general'
+    
+    async def _analyze_market(self, segment: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Perform comprehensive market analysis."""
+        return await self.analyzer.analyze_market(segment, context)
+    
+    def _calculate_confidence(self, analysis: Dict[str, Any], insights: Dict[str, Any]) -> float:
+        """Calculate confidence score based on analysis quality."""
+        # Base confidence
+        confidence = 0.5
+        
+        # Adjust based on data completeness
+        if analysis.get('segment_analysis'):
+            confidence += 0.1
+        if analysis.get('competitor_analysis'):
+            confidence += 0.1
+        if analysis.get('trend_analysis'):
+            confidence += 0.1
+            
+        # Adjust based on insight quality
+        if insights.get('opportunities'):
+            confidence += 0.1
+        if insights.get('risks'):
+            confidence += 0.1
+            
+        return min(confidence, 1.0)
+    
+    def _format_insights(self, insights: Dict[str, Any]) -> str:
+        """Format market insights into readable text."""
+        sections = []
+        
+        if 'market_overview' in insights:
+            sections.append(f"Market Overview: {insights['market_overview']}")
+            
+        if 'opportunities' in insights:
+            opps = insights['opportunities']
+            sections.append("Key Opportunities:\n- " + "\n- ".join(opps))
+            
+        if 'risks' in insights:
+            risks = insights['risks']
+            sections.append("Key Risks:\n- " + "\n- ".join(risks))
+            
+        if 'recommendations' in insights:
+            recs = insights['recommendations']
+            sections.append("Recommendations:\n- " + "\n- ".join(recs))
+            
+        return "\n\n".join(sections)

reasoning/meta_learning.py

@@ -0,0 +1,339 @@
+"""Advanced meta-learning strategy for adaptive reasoning."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+@dataclass
+class MetaTask:
+    """Meta-learning task with parameters and performance metrics."""
+    name: str
+    parameters: Dict[str, Any]
+    metrics: Dict[str, float]
+    history: List[Dict[str, Any]] = field(default_factory=list)
+
+class MetaLearningStrategy(ReasoningStrategy):
+    """
+    Advanced meta-learning strategy that:
+    1. Adapts to new tasks
+    2. Learns from experience
+    3. Optimizes parameters
+    4. Transfers knowledge
+    5. Improves over time
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize meta-learning strategy."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Configure parameters
+        self.learning_rate = self.config.get('learning_rate', 0.01)
+        self.memory_size = self.config.get('memory_size', 100)
+        self.adaptation_threshold = self.config.get('adaptation_threshold', 0.7)
+        
+        # Initialize task memory
+        self.task_memory: List[MetaTask] = []
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Apply meta-learning to adapt and optimize reasoning.
+        
+        Args:
+            query: The input query to reason about
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing reasoning results and confidence scores
+        """
+        try:
+            # Identify similar tasks
+            similar_tasks = await self._find_similar_tasks(query, context)
+            
+            # Adapt parameters
+            adapted_params = await self._adapt_parameters(similar_tasks, context)
+            
+            # Apply meta-learning
+            results = await self._apply_meta_learning(
+                query,
+                adapted_params,
+                context
+            )
+            
+            # Update memory
+            await self._update_memory(query, results, context)
+            
+            # Generate analysis
+            analysis = await self._generate_analysis(results, context)
+            
+            return {
+                'answer': self._format_analysis(analysis),
+                'confidence': self._calculate_confidence(results),
+                'similar_tasks': similar_tasks,
+                'adapted_params': adapted_params,
+                'results': results,
+                'analysis': analysis
+            }
+            
+        except Exception as e:
+            logging.error(f"Meta-learning failed: {str(e)}")
+            return {
+                'error': f"Meta-learning failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    async def _find_similar_tasks(
+        self,
+        query: str,
+        context: Dict[str, Any]
+    ) -> List[MetaTask]:
+        """Find similar tasks in memory."""
+        similar_tasks = []
+        
+        # Extract query features
+        query_features = self._extract_features(query)
+        
+        for task in self.task_memory:
+            # Calculate similarity
+            similarity = self._calculate_similarity(
+                query_features,
+                self._extract_features(task.name)
+            )
+            
+            if similarity > self.adaptation_threshold:
+                similar_tasks.append(task)
+        
+        # Sort by similarity
+        similar_tasks.sort(
+            key=lambda x: np.mean(list(x.metrics.values())),
+            reverse=True
+        )
+        
+        return similar_tasks
+    
+    def _extract_features(self, text: str) -> np.ndarray:
+        """Extract features from text."""
+        # Simple bag of words for now
+        words = set(text.lower().split())
+        return np.array([hash(word) % 100 for word in words])
+    
+    def _calculate_similarity(
+        self,
+        features1: np.ndarray,
+        features2: np.ndarray
+    ) -> float:
+        """Calculate similarity between feature sets."""
+        # Simple Jaccard similarity
+        intersection = np.intersect1d(features1, features2)
+        union = np.union1d(features1, features2)
+        
+        return len(intersection) / len(union) if len(union) > 0 else 0
+    
+    async def _adapt_parameters(
+        self,
+        similar_tasks: List[MetaTask],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Adapt parameters based on similar tasks."""
+        if not similar_tasks:
+            return self.config.copy()
+        
+        adapted_params = {}
+        
+        # Weight tasks by performance
+        total_performance = sum(
+            np.mean(list(task.metrics.values()))
+            for task in similar_tasks
+        )
+        
+        if total_performance > 0:
+            # Weighted average of parameters
+            for param_name in self.config:
+                adapted_params[param_name] = sum(
+                    task.parameters.get(param_name, self.config[param_name]) *
+                    (np.mean(list(task.metrics.values())) / total_performance)
+                    for task in similar_tasks
+                )
+        else:
+            adapted_params = self.config.copy()
+        
+        return adapted_params
+    
+    async def _apply_meta_learning(
+        self,
+        query: str,
+        parameters: Dict[str, Any],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Apply meta-learning with adapted parameters."""
+        results = {
+            'query': query,
+            'parameters': parameters,
+            'metrics': {}
+        }
+        
+        # Apply learning rate
+        for param_name, value in parameters.items():
+            if isinstance(value, (int, float)):
+                results['parameters'][param_name] = (
+                    value * (1 - self.learning_rate) +
+                    self.config[param_name] * self.learning_rate
+                )
+        
+        # Calculate performance metrics
+        results['metrics'] = {
+            'adaptation_score': np.mean([
+                p / self.config[name]
+                for name, p in results['parameters'].items()
+                if isinstance(p, (int, float)) and self.config[name] != 0
+            ]),
+            'novelty_score': 1 - max(
+                self._calculate_similarity(
+                    self._extract_features(query),
+                    self._extract_features(task.name)
+                )
+                for task in self.task_memory
+            ) if self.task_memory else 1.0
+        }
+        
+        return results
+    
+    async def _update_memory(
+        self,
+        query: str,
+        results: Dict[str, Any],
+        context: Dict[str, Any]
+    ) -> None:
+        """Update task memory."""
+        # Create new task
+        task = MetaTask(
+            name=query,
+            parameters=results['parameters'],
+            metrics=results['metrics'],
+            history=[{
+                'timestamp': datetime.now().isoformat(),
+                'context': context,
+                'results': results
+            }]
+        )
+        
+        # Add to memory
+        self.task_memory.append(task)
+        
+        # Maintain memory size
+        if len(self.task_memory) > self.memory_size:
+            # Remove worst performing task
+            self.task_memory.sort(
+                key=lambda x: np.mean(list(x.metrics.values()))
+            )
+            self.task_memory.pop(0)
+    
+    async def _generate_analysis(
+        self,
+        results: Dict[str, Any],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Generate meta-learning analysis."""
+        # Calculate statistics
+        param_stats = {
+            name: {
+                'value': value,
+                'adaptation': value / self.config[name]
+                if isinstance(value, (int, float)) and self.config[name] != 0
+                else 1.0
+            }
+            for name, value in results['parameters'].items()
+        }
+        
+        # Calculate overall metrics
+        metrics = {
+            'adaptation_score': results['metrics']['adaptation_score'],
+            'novelty_score': results['metrics']['novelty_score'],
+            'memory_usage': len(self.task_memory) / self.memory_size
+        }
+        
+        return {
+            'parameters': param_stats,
+            'metrics': metrics,
+            'memory_size': len(self.task_memory),
+            'total_tasks_seen': len(self.task_memory)
+        }
+    
+    def _format_analysis(self, analysis: Dict[str, Any]) -> str:
+        """Format analysis into readable text."""
+        sections = []
+        
+        # Parameter adaptations
+        sections.append("Parameter adaptations:")
+        for name, stats in analysis['parameters'].items():
+            sections.append(
+                f"- {name}: {stats['value']:.2f} "
+                f"({stats['adaptation']:.1%} of original)"
+            )
+        
+        # Performance metrics
+        sections.append("\nPerformance metrics:")
+        metrics = analysis['metrics']
+        sections.append(f"- Adaptation score: {metrics['adaptation_score']:.1%}")
+        sections.append(f"- Novelty score: {metrics['novelty_score']:.1%}")
+        sections.append(f"- Memory usage: {metrics['memory_usage']:.1%}")
+        
+        # Memory statistics
+        sections.append("\nMemory statistics:")
+        sections.append(f"- Current tasks in memory: {analysis['memory_size']}")
+        sections.append(f"- Total tasks seen: {analysis['total_tasks_seen']}")
+        
+        return "\n".join(sections)
+    
+    def _calculate_confidence(self, results: Dict[str, Any]) -> float:
+        """Calculate overall confidence score."""
+        if not results.get('metrics'):
+            return 0.0
+        
+        # Base confidence
+        confidence = 0.5
+        
+        # Adjust based on adaptation score
+        adaptation_score = results['metrics']['adaptation_score']
+        if adaptation_score > 0.8:
+            confidence += 0.3
+        elif adaptation_score > 0.6:
+            confidence += 0.2
+        elif adaptation_score > 0.4:
+            confidence += 0.1
+        
+        # Adjust based on novelty
+        novelty_score = results['metrics']['novelty_score']
+        if novelty_score < 0.2:  # Very similar to known tasks
+            confidence += 0.2
+        elif novelty_score < 0.4:
+            confidence += 0.1
+        
+        return min(confidence, 1.0)
+
+    def get_performance_metrics(self) -> Dict[str, Any]:
+        """Get current performance metrics."""
+        return {
+            "success_rate": 0.0,
+            "adaptation_rate": 0.0,
+            "exploration_count": 0,
+            "episode_count": len(self.task_memory),
+            "pattern_count": 0,
+            "learning_rate": self.learning_rate,
+            "exploration_rate": 0.0
+        }
+
+    def get_top_patterns(self, n: int = 10) -> List[Tuple[str, float]]:
+        """Get top performing patterns."""
+        return []
+
+    def clear_memory(self):
+        """Clear learning memory."""
+        self.task_memory.clear()

reasoning/model_manager.py

@@ -0,0 +1,145 @@
+"""Model manager for handling multiple LLMs in Hugging Face Spaces."""
+
+import os
+from typing import Dict, Any, Optional, List
+import logging
+from dataclasses import dataclass
+from enum import Enum
+import huggingface_hub
+from llama_cpp import Llama
+
+class ModelType(Enum):
+    """Types of models and their specific tasks."""
+    REASONING = "reasoning"
+    CODE = "code"
+    CHAT = "chat"
+    PLANNING = "planning"
+    ANALYSIS = "analysis"
+
+@dataclass
+class ModelConfig:
+    """Configuration for a specific model."""
+    repo_id: str
+    filename: str
+    model_type: ModelType
+    context_size: int = 4096
+    gpu_layers: int = 35
+    batch_size: int = 512
+    threads: int = 8
+
+class ModelManager:
+    """Manages multiple LLM models for different tasks in Spaces."""
+    
+    def __init__(self):
+        # In Spaces, models are stored in the cache directory
+        self.model_dir = os.getenv('SPACE_CACHE_DIR', '/tmp/models')
+        self.models: Dict[str, Llama] = {}
+        self.logger = logging.getLogger(__name__)
+        
+        # Define model configurations
+        self.model_configs = {
+            "reasoning": ModelConfig(
+                repo_id="rrbale/pruned-qwen-moe",
+                filename="model-Q6_K.gguf",
+                model_type=ModelType.REASONING
+            ),
+            "code": ModelConfig(
+                repo_id="YorkieOH10/deepseek-coder-6.7B-kexer-Q8_0-GGUF",
+                filename="model.gguf",
+                model_type=ModelType.CODE
+            ),
+            "chat": ModelConfig(
+                repo_id="Nidum-Llama-3.2-3B-Uncensored-GGUF",
+                filename="model-Q6_K.gguf",
+                model_type=ModelType.CHAT
+            ),
+            "planning": ModelConfig(
+                repo_id="deepseek-ai/JanusFlow-1.3B",
+                filename="model.gguf",
+                model_type=ModelType.PLANNING
+            ),
+            "analysis": ModelConfig(
+                repo_id="prithivMLmods/QwQ-4B-Instruct",
+                filename="model.gguf",
+                model_type=ModelType.ANALYSIS,
+                context_size=8192,
+                gpu_layers=40
+            ),
+            "general": ModelConfig(
+                repo_id="gpt-omni/mini-omni2",
+                filename="mini-omni2.gguf",
+                model_type=ModelType.CHAT
+            )
+        }
+        
+        os.makedirs(self.model_dir, exist_ok=True)
+        
+    async def initialize_model(self, model_key: str) -> Optional[Llama]:
+        """Initialize a specific model in Spaces."""
+        try:
+            config = self.model_configs[model_key]
+            cache_dir = os.path.join(self.model_dir, model_key)
+            os.makedirs(cache_dir, exist_ok=True)
+            
+            # Download model using HF Hub
+            self.logger.info(f"Downloading {model_key} model...")
+            model_path = huggingface_hub.hf_hub_download(
+                repo_id=config.repo_id,
+                filename=config.filename,
+                repo_type="model",
+                cache_dir=cache_dir,
+                local_dir_use_symlinks=False
+            )
+            
+            # Configure for Spaces GPU environment
+            try:
+                model = Llama(
+                    model_path=model_path,
+                    n_ctx=config.context_size,
+                    n_batch=config.batch_size,
+                    n_threads=config.threads,
+                    n_gpu_layers=config.gpu_layers,
+                    main_gpu=0,
+                    tensor_split=None  # Let it use all available GPU memory
+                )
+                self.logger.info(f"{model_key} model loaded with GPU acceleration!")
+            except Exception as e:
+                self.logger.warning(f"GPU loading failed for {model_key}: {e}, falling back to CPU...")
+                model = Llama(
+                    model_path=model_path,
+                    n_ctx=2048,
+                    n_batch=256,
+                    n_threads=4,
+                    n_gpu_layers=0
+                )
+                self.logger.info(f"{model_key} model loaded in CPU-only mode")
+            
+            self.models[model_key] = model
+            return model
+            
+        except Exception as e:
+            self.logger.error(f"Error initializing {model_key} model: {e}")
+            return None
+    
+    async def get_model(self, model_key: str) -> Optional[Llama]:
+        """Get a model, initializing it if necessary."""
+        if model_key not in self.models:
+            return await self.initialize_model(model_key)
+        return self.models[model_key]
+    
+    async def initialize_all_models(self):
+        """Initialize all configured models."""
+        for model_key in self.model_configs.keys():
+            await self.initialize_model(model_key)
+    
+    def get_best_model_for_task(self, task_type: str) -> str:
+        """Get the best model key for a specific task type."""
+        task_model_mapping = {
+            "reasoning": "reasoning",
+            "code": "code",
+            "chat": "chat",
+            "planning": "planning",
+            "analysis": "analysis",
+            "general": "general"
+        }
+        return task_model_mapping.get(task_type, "general")

reasoning/monetization.py

@@ -0,0 +1,447 @@
+"""Advanced monetization strategies for venture optimization."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+@dataclass
+class MonetizationModel:
+    """Monetization model configuration."""
+    name: str
+    type: str
+    pricing_tiers: List[Dict[str, Any]]
+    features: List[str]
+    constraints: List[str]
+    metrics: Dict[str, float]
+
+@dataclass
+class RevenueStream:
+    """Revenue stream configuration."""
+    name: str
+    type: str
+    volume: float
+    unit_economics: Dict[str, float]
+    growth_rate: float
+    churn_rate: float
+
+class MonetizationOptimizer:
+    """
+    Advanced monetization optimization that:
+    1. Designs pricing models
+    2. Optimizes revenue streams
+    3. Maximizes customer value
+    4. Reduces churn
+    5. Increases lifetime value
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize monetization optimizer."""
+        self.config = config or {}
+        
+        # Configure optimization parameters
+        self.min_revenue = self.config.get('min_revenue', 1_000_000)
+        self.min_margin = self.config.get('min_margin', 0.3)
+        self.max_churn = self.config.get('max_churn', 0.1)
+        self.target_ltv = self.config.get('target_ltv', 1000)
+        
+        self.models: Dict[str, MonetizationModel] = {}
+        self.streams: Dict[str, RevenueStream] = {}
+        
+    async def optimize_monetization(self,
+                                  venture_type: str,
+                                  context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize monetization strategy."""
+        try:
+            # Design models
+            models = await self._design_models(venture_type, context)
+            
+            # Optimize pricing
+            pricing = await self._optimize_pricing(models, context)
+            
+            # Revenue optimization
+            revenue = await self._optimize_revenue(pricing, context)
+            
+            # Value optimization
+            value = await self._optimize_value(revenue, context)
+            
+            # Performance projections
+            projections = await self._project_performance(value, context)
+            
+            return {
+                "success": projections["annual_revenue"] >= 1_000_000,
+                "models": models,
+                "pricing": pricing,
+                "revenue": revenue,
+                "value": value,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in monetization optimization: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _design_models(self,
+                           venture_type: str,
+                           context: Dict[str, Any]) -> Dict[str, Any]:
+        """Design monetization models."""
+        prompt = f"""
+        Design monetization models:
+        Venture: {venture_type}
+        Context: {json.dumps(context)}
+        
+        Design models for:
+        1. Subscription tiers
+        2. Usage-based pricing
+        3. Hybrid models
+        4. Enterprise pricing
+        5. Marketplace fees
+        
+        Format as:
+        [Model1]
+        Name: ...
+        Type: ...
+        Tiers: ...
+        Features: ...
+        Constraints: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_model_design(response["answer"])
+
+    async def _optimize_pricing(self,
+                              models: Dict[str, Any],
+                              context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize pricing strategy."""
+        prompt = f"""
+        Optimize pricing strategy:
+        Models: {json.dumps(models)}
+        Context: {json.dumps(context)}
+        
+        Optimize for:
+        1. Market positioning
+        2. Value perception
+        3. Competitive dynamics
+        4. Customer segments
+        5. Growth potential
+        
+        Format as:
+        [Strategy1]
+        Model: ...
+        Positioning: ...
+        Value_Props: ...
+        Segments: ...
+        Growth: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_pricing_strategy(response["answer"])
+
+    async def _optimize_revenue(self,
+                              pricing: Dict[str, Any],
+                              context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize revenue streams."""
+        prompt = f"""
+        Optimize revenue streams:
+        Pricing: {json.dumps(pricing)}
+        Context: {json.dumps(context)}
+        
+        Optimize for:
+        1. Revenue mix
+        2. Growth drivers
+        3. Retention factors
+        4. Expansion potential
+        5. Risk mitigation
+        
+        Format as:
+        [Stream1]
+        Type: ...
+        Drivers: ...
+        Retention: ...
+        Expansion: ...
+        Risks: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_revenue_optimization(response["answer"])
+
+    async def _optimize_value(self,
+                            revenue: Dict[str, Any],
+                            context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize customer value."""
+        prompt = f"""
+        Optimize customer value:
+        Revenue: {json.dumps(revenue)}
+        Context: {json.dumps(context)}
+        
+        Optimize for:
+        1. Acquisition cost
+        2. Lifetime value
+        3. Churn reduction
+        4. Upsell potential
+        5. Network effects
+        
+        Format as:
+        [Value1]
+        Metric: ...
+        Strategy: ...
+        Potential: ...
+        Actions: ...
+        Timeline: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_value_optimization(response["answer"])
+
+    async def _project_performance(self,
+                                 value: Dict[str, Any],
+                                 context: Dict[str, Any]) -> Dict[str, Any]:
+        """Project monetization performance."""
+        prompt = f"""
+        Project performance:
+        Value: {json.dumps(value)}
+        Context: {json.dumps(context)}
+        
+        Project:
+        1. Revenue growth
+        2. Customer metrics
+        3. Unit economics
+        4. Profitability
+        5. Scale effects
+        
+        Format as:
+        [Projections]
+        Revenue: ...
+        Metrics: ...
+        Economics: ...
+        Profit: ...
+        Scale: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_performance_projections(response["answer"])
+
+    def _calculate_revenue_potential(self, model: MonetizationModel) -> float:
+        """Calculate revenue potential for model."""
+        base_potential = sum(
+            tier.get("price", 0) * tier.get("volume", 0)
+            for tier in model.pricing_tiers
+        )
+        
+        growth_factor = 1.0 + (model.metrics.get("growth_rate", 0) / 100)
+        retention_factor = 1.0 - (model.metrics.get("churn_rate", 0) / 100)
+        
+        return base_potential * growth_factor * retention_factor
+
+    def _calculate_customer_ltv(self, stream: RevenueStream) -> float:
+        """Calculate customer lifetime value."""
+        monthly_revenue = stream.volume * stream.unit_economics.get("arpu", 0)
+        churn_rate = stream.churn_rate / 100
+        discount_rate = 0.1  # 10% annual discount rate
+        
+        if churn_rate > 0:
+            ltv = monthly_revenue / churn_rate
+        else:
+            ltv = monthly_revenue * 12  # Assume 1 year if no churn
+            
+        return ltv / (1 + discount_rate)
+
+    def get_monetization_metrics(self) -> Dict[str, Any]:
+        """Get comprehensive monetization metrics."""
+        return {
+            "model_metrics": {
+                model.name: {
+                    "revenue_potential": self._calculate_revenue_potential(model),
+                    "tier_count": len(model.pricing_tiers),
+                    "feature_count": len(model.features),
+                    "constraint_count": len(model.constraints)
+                }
+                for model in self.models.values()
+            },
+            "stream_metrics": {
+                stream.name: {
+                    "monthly_revenue": stream.volume * stream.unit_economics.get("arpu", 0),
+                    "ltv": self._calculate_customer_ltv(stream),
+                    "growth_rate": stream.growth_rate,
+                    "churn_rate": stream.churn_rate
+                }
+                for stream in self.streams.values()
+            },
+            "aggregate_metrics": {
+                "total_revenue_potential": sum(
+                    self._calculate_revenue_potential(model)
+                    for model in self.models.values()
+                ),
+                "average_ltv": np.mean([
+                    self._calculate_customer_ltv(stream)
+                    for stream in self.streams.values()
+                ]) if self.streams else 0,
+                "weighted_growth_rate": np.average(
+                    [stream.growth_rate for stream in self.streams.values()],
+                    weights=[stream.volume for stream in self.streams.values()]
+                ) if self.streams else 0
+            }
+        }
+
+class MonetizationStrategy(ReasoningStrategy):
+    """
+    Advanced monetization strategy that:
+    1. Designs optimal pricing models
+    2. Optimizes revenue streams
+    3. Maximizes customer lifetime value
+    4. Reduces churn
+    5. Increases profitability
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize monetization strategy."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+        
+        # Initialize optimizer with shared config
+        optimizer_config = {
+            'min_revenue': self.config.get('min_revenue', 1_000_000),
+            'min_margin': self.config.get('min_margin', 0.3),
+            'max_churn': self.config.get('max_churn', 0.1),
+            'target_ltv': self.config.get('target_ltv', 1000)
+        }
+        self.optimizer = MonetizationOptimizer(optimizer_config)
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Generate monetization strategy based on query and context.
+        
+        Args:
+            query: The monetization query
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing monetization strategy and confidence scores
+        """
+        try:
+            # Extract venture type
+            venture_type = self._extract_venture_type(query, context)
+            
+            # Optimize monetization
+            optimization_result = await self.optimizer.optimize_monetization(
+                venture_type=venture_type,
+                context=context
+            )
+            
+            # Format results
+            formatted_result = self._format_strategy(optimization_result)
+            
+            return {
+                'answer': formatted_result,
+                'confidence': self._calculate_confidence(optimization_result),
+                'optimization': optimization_result
+            }
+            
+        except Exception as e:
+            logging.error(f"Monetization strategy generation failed: {str(e)}")
+            return {
+                'error': f"Monetization strategy generation failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    def _extract_venture_type(self, query: str, context: Dict[str, Any]) -> str:
+        """Extract venture type from query and context."""
+        # Use context if available
+        if 'venture_type' in context:
+            return context['venture_type']
+        
+        # Simple keyword matching
+        query_lower = query.lower()
+        if any(term in query_lower for term in ['ai', 'ml', 'model']):
+            return 'ai_startup'
+        elif any(term in query_lower for term in ['saas', 'software']):
+            return 'saas'
+        elif any(term in query_lower for term in ['api', 'service']):
+            return 'api_service'
+        elif any(term in query_lower for term in ['data', 'analytics']):
+            return 'data_analytics'
+        
+        # Default to SaaS if unclear
+        return 'saas'
+    
+    def _calculate_confidence(self, result: Dict[str, Any]) -> float:
+        """Calculate confidence score based on optimization quality."""
+        # Base confidence
+        confidence = 0.5
+        
+        # Adjust based on optimization completeness
+        if result.get('models'):
+            confidence += 0.1
+        if result.get('pricing'):
+            confidence += 0.1
+        if result.get('revenue'):
+            confidence += 0.1
+        if result.get('value'):
+            confidence += 0.1
+            
+        # Adjust based on projected performance
+        performance = result.get('performance', {})
+        if performance.get('roi', 0) > 2.0:
+            confidence += 0.1
+        if performance.get('ltv', 0) > 1000:
+            confidence += 0.1
+            
+        return min(confidence, 1.0)
+    
+    def _format_strategy(self, result: Dict[str, Any]) -> str:
+        """Format monetization strategy into readable text."""
+        sections = []
+        
+        # Monetization models
+        if 'models' in result:
+            models = result['models']
+            sections.append("Monetization Models:")
+            for model in models:
+                sections.append(f"- {model['name']}: {model['type']}")
+                if 'pricing_tiers' in model:
+                    sections.append("  Pricing Tiers:")
+                    for tier in model['pricing_tiers']:
+                        sections.append(f"  * {tier['name']}: ${tier['price']}/mo")
+        
+        # Revenue optimization
+        if 'revenue' in result:
+            revenue = result['revenue']
+            sections.append("\nRevenue Optimization:")
+            for stream, details in revenue.items():
+                sections.append(f"- {stream.replace('_', ' ').title()}:")
+                sections.append(f"  * Projected Revenue: ${details['projected_revenue']:,.2f}")
+                sections.append(f"  * Growth Rate: {details['growth_rate']*100:.1f}%")
+        
+        # Customer value optimization
+        if 'value' in result:
+            value = result['value']
+            sections.append("\nCustomer Value Optimization:")
+            sections.append(f"- Customer Acquisition Cost: ${value['cac']:,.2f}")
+            sections.append(f"- Lifetime Value: ${value['ltv']:,.2f}")
+            sections.append(f"- Churn Rate: {value['churn_rate']*100:.1f}%")
+        
+        # Performance projections
+        if 'performance' in result:
+            perf = result['performance']
+            sections.append("\nPerformance Projections:")
+            sections.append(f"- ROI: {perf['roi']*100:.1f}%")
+            sections.append(f"- Payback Period: {perf['payback_months']:.1f} months")
+            sections.append(f"- Break-even Point: ${perf['breakeven']:,.2f}")
+        
+        return "\n".join(sections)

reasoning/multimodal.py

@@ -0,0 +1,305 @@
+"""Advanced multimodal reasoning combining different types of information."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+@dataclass
+class ModalityFeatures:
+    """Features extracted from different modalities."""
+    text: List[Dict[str, Any]]
+    image: Optional[List[Dict[str, Any]]] = None
+    audio: Optional[List[Dict[str, Any]]] = None
+    video: Optional[List[Dict[str, Any]]] = None
+    structured: Optional[List[Dict[str, Any]]] = None
+
+class MultiModalReasoning(ReasoningStrategy):
+    """
+    Advanced multimodal reasoning that:
+    1. Processes different types of information
+    2. Aligns cross-modal features
+    3. Integrates multimodal context
+    4. Generates coherent responses
+    5. Handles uncertainty
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize multimodal reasoning."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+        
+        # Configure model repositories
+        self.models = self.config.get('models', {
+            'img2img': {
+                'repo_id': 'enhanceaiteam/Flux-Uncensored-V2',
+                'filename': 'Flux-Uncensored-V2.safetensors'
+            },
+            'img2vid': {
+                'repo_id': 'stabilityai/stable-video-diffusion-img2vid-xt',
+                'filename': 'svd_xt.safetensors'
+            },
+            'any2any': {
+                'repo_id': 'deepseek-ai/JanusFlow-1.3B',
+                'filename': 'janusflow-1.3b.safetensors'
+            }
+        })
+        
+        # Configure modality weights
+        self.weights = self.config.get('modality_weights', {
+            'text': 0.4,
+            'image': 0.3,
+            'audio': 0.1,
+            'video': 0.1,
+            'structured': 0.1
+        })
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Apply multimodal reasoning to process and integrate different types of information.
+        
+        Args:
+            query: The input query to reason about
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing reasoning results and confidence scores
+        """
+        try:
+            # Process across modalities
+            modalities = await self._process_modalities(query, context)
+            
+            # Align cross-modal information
+            alignment = await self._cross_modal_alignment(modalities, context)
+            
+            # Integrate aligned information
+            integration = await self._integrated_analysis(alignment, context)
+            
+            # Generate final response
+            response = await self._generate_response(integration, context)
+            
+            return {
+                'answer': response.get('text', ''),
+                'confidence': self._calculate_confidence(integration),
+                'modalities': modalities,
+                'alignment': alignment,
+                'integration': integration
+            }
+            
+        except Exception as e:
+            logging.error(f"Multimodal reasoning failed: {str(e)}")
+            return {
+                'error': f"Multimodal reasoning failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    async def _process_modalities(
+        self,
+        query: str,
+        context: Dict[str, Any]
+    ) -> Dict[str, List[Dict[str, Any]]]:
+        """Process query across different modalities."""
+        modalities = {}
+        
+        # Process text
+        if 'text' in context:
+            modalities['text'] = self._process_text(context['text'])
+            
+        # Process images
+        if 'images' in context:
+            modalities['image'] = self._process_images(context['images'])
+            
+        # Process audio
+        if 'audio' in context:
+            modalities['audio'] = self._process_audio(context['audio'])
+            
+        # Process video
+        if 'video' in context:
+            modalities['video'] = self._process_video(context['video'])
+            
+        # Process structured data
+        if 'structured' in context:
+            modalities['structured'] = self._process_structured(context['structured'])
+            
+        return modalities
+    
+    async def _cross_modal_alignment(
+        self,
+        modalities: Dict[str, List[Dict[str, Any]]],
+        context: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """Align information across different modalities."""
+        alignments = []
+        
+        # Get all modality pairs
+        modality_pairs = [
+            (m1, m2) for i, m1 in enumerate(modalities.keys())
+            for m2 in list(modalities.keys())[i+1:]
+        ]
+        
+        # Align each pair
+        for mod1, mod2 in modality_pairs:
+            items1 = modalities[mod1]
+            items2 = modalities[mod2]
+            
+            # Calculate cross-modal similarities
+            for item1 in items1:
+                for item2 in items2:
+                    similarity = self._calculate_similarity(item1, item2)
+                    if similarity > 0.7:  # Alignment threshold
+                        alignments.append({
+                            'modality1': mod1,
+                            'modality2': mod2,
+                            'item1': item1,
+                            'item2': item2,
+                            'similarity': similarity
+                        })
+        
+        return alignments
+    
+    def _calculate_similarity(
+        self,
+        item1: Dict[str, Any],
+        item2: Dict[str, Any]
+    ) -> float:
+        """Calculate similarity between two items from different modalities."""
+        # Simple feature overlap for now
+        features1 = set(str(v) for v in item1.values())
+        features2 = set(str(v) for v in item2.values())
+        
+        if not features1 or not features2:
+            return 0.0
+            
+        overlap = len(features1.intersection(features2))
+        total = len(features1.union(features2))
+        
+        return overlap / total if total > 0 else 0.0
+    
+    async def _integrated_analysis(
+        self,
+        alignment: List[Dict[str, Any]],
+        context: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
+        """Perform integrated analysis of aligned information."""
+        integrated = []
+        
+        # Group alignments by similarity
+        similarity_groups = defaultdict(list)
+        for align in alignment:
+            similarity_groups[align['similarity']].append(align)
+        
+        # Process groups in order of similarity
+        for similarity, group in sorted(
+            similarity_groups.items(),
+            key=lambda x: x[0],
+            reverse=True
+        ):
+            # Combine aligned features
+            for align in group:
+                integrated.append({
+                    'features': {
+                        **align['item1'],
+                        **align['item2']
+                    },
+                    'modalities': [align['modality1'], align['modality2']],
+                    'confidence': align['similarity']
+                })
+        
+        return integrated
+    
+    async def _generate_response(
+        self,
+        integration: List[Dict[str, Any]],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Generate coherent response from integrated analysis."""
+        if not integration:
+            return {'text': '', 'confidence': 0.0}
+        
+        # Combine all integrated features
+        all_features = {}
+        for item in integration:
+            all_features.update(item['features'])
+        
+        # Generate response text
+        response_text = []
+        
+        # Add main findings
+        response_text.append("Main findings across modalities:")
+        for feature, value in all_features.items():
+            response_text.append(f"- {feature}: {value}")
+        
+        # Add confidence
+        confidence = sum(item['confidence'] for item in integration) / len(integration)
+        response_text.append(f"\nOverall confidence: {confidence:.2f}")
+        
+        return {
+            'text': "\n".join(response_text),
+            'confidence': confidence
+        }
+    
+    def _calculate_confidence(self, integration: List[Dict[str, Any]]) -> float:
+        """Calculate overall confidence score."""
+        if not integration:
+            return 0.0
+        
+        # Base confidence
+        confidence = 0.5
+        
+        # Adjust based on number of modalities
+        unique_modalities = set()
+        for item in integration:
+            unique_modalities.update(item['modalities'])
+        
+        modality_bonus = len(unique_modalities) * 0.1
+        confidence += min(modality_bonus, 0.3)
+        
+        # Adjust based on integration quality
+        avg_similarity = sum(
+            item['confidence'] for item in integration
+        ) / len(integration)
+        confidence += avg_similarity * 0.2
+        
+        return min(confidence, 1.0)
+
+    def _process_text(self, text: str) -> List[Dict[str, Any]]:
+        """Process text modality."""
+        # Simple text processing for now
+        return [{'text': text}]
+
+    def _process_images(self, images: List[str]) -> List[Dict[str, Any]]:
+        """Process image modality."""
+        # Simple image processing for now
+        return [{'image': image} for image in images]
+
+    def _process_audio(self, audio: List[str]) -> List[Dict[str, Any]]:
+        """Process audio modality."""
+        # Simple audio processing for now
+        return [{'audio': audio_file} for audio_file in audio]
+
+    def _process_video(self, video: List[str]) -> List[Dict[str, Any]]:
+        """Process video modality."""
+        # Simple video processing for now
+        return [{'video': video_file} for video_file in video]
+
+    def _process_structured(self, structured: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Process structured data modality."""
+        # Simple structured data processing for now
+        return [{'structured': structured}]

reasoning/neurosymbolic.py

@@ -0,0 +1,316 @@
+"""Advanced neurosymbolic reasoning combining neural and symbolic approaches."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+@dataclass
+class NeuralFeature:
+    """Neural features extracted from data."""
+    name: str
+    values: np.ndarray
+    importance: float
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class SymbolicRule:
+    """Symbolic rule with conditions and confidence."""
+    name: str
+    conditions: List[str]
+    conclusion: str
+    confidence: float
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+class NeurosymbolicReasoning(ReasoningStrategy):
+    """
+    Advanced neurosymbolic reasoning that:
+    1. Extracts neural features
+    2. Generates symbolic rules
+    3. Combines approaches
+    4. Handles uncertainty
+    5. Provides interpretable results
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize neurosymbolic reasoning."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+        
+        # Neurosymbolic specific parameters
+        self.feature_threshold = self.config.get('feature_threshold', 0.1)
+        self.rule_confidence_threshold = self.config.get('rule_confidence', 0.7)
+        self.max_rules = self.config.get('max_rules', 10)
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Apply neurosymbolic reasoning to combine neural and symbolic approaches.
+        
+        Args:
+            query: The input query to reason about
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing reasoning results and confidence scores
+        """
+        try:
+            # Extract neural features
+            features = await self._extract_features(query, context)
+            
+            # Generate symbolic rules
+            rules = await self._generate_rules(features, context)
+            
+            # Combine approaches
+            combined = await self._combine_approaches(features, rules, context)
+            
+            # Generate analysis
+            analysis = await self._generate_analysis(combined, context)
+            
+            return {
+                'answer': self._format_analysis(analysis),
+                'confidence': self._calculate_confidence(combined),
+                'features': features,
+                'rules': rules,
+                'combined': combined,
+                'analysis': analysis
+            }
+            
+        except Exception as e:
+            logging.error(f"Neurosymbolic reasoning failed: {str(e)}")
+            return {
+                'error': f"Neurosymbolic reasoning failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    async def _extract_features(
+        self,
+        query: str,
+        context: Dict[str, Any]
+    ) -> List[NeuralFeature]:
+        """Extract neural features from input."""
+        features = []
+        
+        # Extract key terms
+        terms = query.lower().split()
+        
+        # Process each term
+        for term in terms:
+            # Simple feature extraction for now
+            values = np.random.randn(10)  # Placeholder for real feature extraction
+            importance = np.abs(values).mean()
+            
+            if importance > self.feature_threshold:
+                features.append(NeuralFeature(
+                    name=term,
+                    values=values,
+                    importance=importance,
+                    metadata={'source': 'term_extraction'}
+                ))
+        
+        # Sort by importance
+        features.sort(key=lambda x: x.importance, reverse=True)
+        
+        return features
+    
+    async def _generate_rules(
+        self,
+        features: List[NeuralFeature],
+        context: Dict[str, Any]
+    ) -> List[SymbolicRule]:
+        """Generate symbolic rules from features."""
+        rules = []
+        
+        # Process feature combinations
+        for i, feature1 in enumerate(features):
+            for j, feature2 in enumerate(features[i+1:], i+1):
+                # Calculate correlation
+                correlation = np.corrcoef(feature1.values, feature2.values)[0, 1]
+                
+                if abs(correlation) > self.rule_confidence_threshold:
+                    # Create rule based on correlation
+                    if correlation > 0:
+                        condition = f"{feature1.name} AND {feature2.name}"
+                        conclusion = "positively_correlated"
+                    else:
+                        condition = f"{feature1.name} XOR {feature2.name}"
+                        conclusion = "negatively_correlated"
+                    
+                    rules.append(SymbolicRule(
+                        name=f"rule_{len(rules)}",
+                        conditions=[condition],
+                        conclusion=conclusion,
+                        confidence=abs(correlation),
+                        metadata={
+                            'features': [feature1.name, feature2.name],
+                            'correlation': correlation
+                        }
+                    ))
+                
+                if len(rules) >= self.max_rules:
+                    break
+            
+            if len(rules) >= self.max_rules:
+                break
+        
+        return rules
+    
+    async def _combine_approaches(
+        self,
+        features: List[NeuralFeature],
+        rules: List[SymbolicRule],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Combine neural and symbolic approaches."""
+        combined = {
+            'neural_weights': {},
+            'symbolic_weights': {},
+            'combined_scores': {}
+        }
+        
+        # Calculate neural weights
+        total_importance = sum(f.importance for f in features)
+        if total_importance > 0:
+            combined['neural_weights'] = {
+                f.name: f.importance / total_importance
+                for f in features
+            }
+        
+        # Calculate symbolic weights
+        total_confidence = sum(r.confidence for r in rules)
+        if total_confidence > 0:
+            combined['symbolic_weights'] = {
+                r.name: r.confidence / total_confidence
+                for r in rules
+            }
+        
+        # Combine scores
+        all_elements = set(
+            list(combined['neural_weights'].keys()) +
+            list(combined['symbolic_weights'].keys())
+        )
+        
+        for element in all_elements:
+            neural_score = combined['neural_weights'].get(element, 0)
+            symbolic_score = combined['symbolic_weights'].get(element, 0)
+            
+            # Simple weighted average
+            combined['combined_scores'][element] = (
+                neural_score * 0.6 +  # Favor neural slightly
+                symbolic_score * 0.4
+            )
+        
+        return combined
+    
+    async def _generate_analysis(
+        self,
+        combined: Dict[str, Any],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Generate neurosymbolic analysis."""
+        # Sort elements by combined score
+        ranked_elements = sorted(
+            combined['combined_scores'].items(),
+            key=lambda x: x[1],
+            reverse=True
+        )
+        
+        # Calculate statistics
+        scores = list(combined['combined_scores'].values())
+        mean = np.mean(scores) if scores else 0
+        std = np.std(scores) if scores else 0
+        
+        # Calculate entropy
+        entropy = -sum(
+            s * np.log2(s) if s > 0 else 0
+            for s in combined['combined_scores'].values()
+        )
+        
+        return {
+            'top_element': ranked_elements[0][0] if ranked_elements else '',
+            'score': ranked_elements[0][1] if ranked_elements else 0,
+            'alternatives': [
+                {'name': name, 'score': score}
+                for name, score in ranked_elements[1:]
+            ],
+            'statistics': {
+                'mean': mean,
+                'std': std,
+                'entropy': entropy
+            }
+        }
+    
+    def _format_analysis(self, analysis: Dict[str, Any]) -> str:
+        """Format analysis into readable text."""
+        sections = []
+        
+        # Top element
+        if analysis['top_element']:
+            sections.append(
+                f"Most significant element: {analysis['top_element']} "
+                f"(score: {analysis['score']:.2%})"
+            )
+        
+        # Alternative elements
+        if analysis['alternatives']:
+            sections.append("\nAlternative elements:")
+            for alt in analysis['alternatives']:
+                sections.append(
+                    f"- {alt['name']}: {alt['score']:.2%}"
+                )
+        
+        # Statistics
+        stats = analysis['statistics']
+        sections.append("\nAnalysis statistics:")
+        sections.append(f"- Mean score: {stats['mean']:.2%}")
+        sections.append(f"- Standard deviation: {stats['std']:.2%}")
+        sections.append(f"- Information entropy: {stats['entropy']:.2f} bits")
+        
+        return "\n".join(sections)
+    
+    def _calculate_confidence(self, combined: Dict[str, Any]) -> float:
+        """Calculate overall confidence score."""
+        if not combined['combined_scores']:
+            return 0.0
+        
+        # Base confidence
+        confidence = 0.5
+        
+        # Get scores
+        scores = list(combined['combined_scores'].values())
+        
+        # Strong leading score increases confidence
+        max_score = max(scores)
+        if max_score > 0.8:
+            confidence += 0.3
+        elif max_score > 0.6:
+            confidence += 0.2
+        elif max_score > 0.4:
+            confidence += 0.1
+        
+        # Low entropy (clear distinction) increases confidence
+        entropy = -sum(s * np.log2(s) if s > 0 else 0 for s in scores)
+        max_entropy = -np.log2(1/len(scores))  # Maximum possible entropy
+        
+        if entropy < 0.3 * max_entropy:
+            confidence += 0.2
+        elif entropy < 0.6 * max_entropy:
+            confidence += 0.1
+        
+        return min(confidence, 1.0)

reasoning/portfolio_optimization.py

@@ -0,0 +1,549 @@
+"""Advanced portfolio optimization for venture strategies."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+@dataclass
+class VentureMetrics:
+    """Venture performance metrics."""
+    revenue: float
+    profit: float
+    growth_rate: float
+    risk_score: float
+    resource_usage: Dict[str, float]
+    synergy_score: float
+
+@dataclass
+class ResourceAllocation:
+    """Resource allocation configuration."""
+    venture_id: str
+    resources: Dict[str, float]
+    constraints: List[str]
+    dependencies: List[str]
+    priority: float
+
+class PortfolioOptimizer:
+    """
+    Advanced portfolio optimization that:
+    1. Optimizes venture mix
+    2. Allocates resources
+    3. Manages risks
+    4. Maximizes synergies
+    5. Balances growth
+    """
+    
+    def __init__(self):
+        self.ventures: Dict[str, VentureMetrics] = {}
+        self.allocations: Dict[str, ResourceAllocation] = {}
+        
+    async def optimize_portfolio(self,
+                               ventures: List[str],
+                               context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize venture portfolio."""
+        try:
+            # Analyze ventures
+            analysis = await self._analyze_ventures(ventures, context)
+            
+            # Optimize allocation
+            allocation = await self._optimize_allocation(analysis, context)
+            
+            # Risk optimization
+            risk = await self._optimize_risk(allocation, context)
+            
+            # Synergy optimization
+            synergy = await self._optimize_synergies(risk, context)
+            
+            # Performance projections
+            projections = await self._project_performance(synergy, context)
+            
+            return {
+                "success": projections["annual_profit"] >= 1_000_000,
+                "analysis": analysis,
+                "allocation": allocation,
+                "risk": risk,
+                "synergy": synergy,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in portfolio optimization: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _analyze_ventures(self,
+                              ventures: List[str],
+                              context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze venture characteristics."""
+        prompt = f"""
+        Analyze ventures:
+        Ventures: {json.dumps(ventures)}
+        Context: {json.dumps(context)}
+        
+        Analyze:
+        1. Performance metrics
+        2. Resource requirements
+        3. Risk factors
+        4. Growth potential
+        5. Synergy opportunities
+        
+        Format as:
+        [Venture1]
+        Metrics: ...
+        Resources: ...
+        Risks: ...
+        Growth: ...
+        Synergies: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_venture_analysis(response["answer"])
+
+    async def _optimize_allocation(self,
+                                 analysis: Dict[str, Any],
+                                 context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize resource allocation."""
+        prompt = f"""
+        Optimize resource allocation:
+        Analysis: {json.dumps(analysis)}
+        Context: {json.dumps(context)}
+        
+        Optimize for:
+        1. Resource efficiency
+        2. Growth potential
+        3. Risk balance
+        4. Synergy capture
+        5. Constraint satisfaction
+        
+        Format as:
+        [Allocation1]
+        Venture: ...
+        Resources: ...
+        Constraints: ...
+        Dependencies: ...
+        Priority: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_allocation_optimization(response["answer"])
+
+    async def _optimize_risk(self,
+                           allocation: Dict[str, Any],
+                           context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize risk management."""
+        prompt = f"""
+        Optimize risk management:
+        Allocation: {json.dumps(allocation)}
+        Context: {json.dumps(context)}
+        
+        Optimize for:
+        1. Risk diversification
+        2. Exposure limits
+        3. Correlation management
+        4. Hedging strategies
+        5. Contingency planning
+        
+        Format as:
+        [Risk1]
+        Type: ...
+        Exposure: ...
+        Mitigation: ...
+        Contingency: ...
+        Impact: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_risk_optimization(response["answer"])
+
+    async def _optimize_synergies(self,
+                                risk: Dict[str, Any],
+                                context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize portfolio synergies."""
+        prompt = f"""
+        Optimize synergies:
+        Risk: {json.dumps(risk)}
+        Context: {json.dumps(context)}
+        
+        Optimize for:
+        1. Resource sharing
+        2. Knowledge transfer
+        3. Market leverage
+        4. Technology reuse
+        5. Customer cross-sell
+        
+        Format as:
+        [Synergy1]
+        Type: ...
+        Ventures: ...
+        Potential: ...
+        Requirements: ...
+        Timeline: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_synergy_optimization(response["answer"])
+
+    async def _project_performance(self,
+                                 synergy: Dict[str, Any],
+                                 context: Dict[str, Any]) -> Dict[str, Any]:
+        """Project portfolio performance."""
+        prompt = f"""
+        Project performance:
+        Synergy: {json.dumps(synergy)}
+        Context: {json.dumps(context)}
+        
+        Project:
+        1. Revenue growth
+        2. Profit margins
+        3. Resource utilization
+        4. Risk metrics
+        5. Synergy capture
+        
+        Format as:
+        [Projections]
+        Revenue: ...
+        Profit: ...
+        Resources: ...
+        Risk: ...
+        Synergies: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_performance_projections(response["answer"])
+
+    def _calculate_portfolio_metrics(self) -> Dict[str, float]:
+        """Calculate comprehensive portfolio metrics."""
+        if not self.ventures:
+            return {
+                "total_revenue": 0.0,
+                "total_profit": 0.0,
+                "avg_growth": 0.0,
+                "avg_risk": 0.0,
+                "resource_efficiency": 0.0,
+                "synergy_capture": 0.0
+            }
+        
+        metrics = {
+            "total_revenue": sum(v.revenue for v in self.ventures.values()),
+            "total_profit": sum(v.profit for v in self.ventures.values()),
+            "avg_growth": np.mean([v.growth_rate for v in self.ventures.values()]),
+            "avg_risk": np.mean([v.risk_score for v in self.ventures.values()]),
+            "resource_efficiency": self._calculate_resource_efficiency(),
+            "synergy_capture": np.mean([v.synergy_score for v in self.ventures.values()])
+        }
+        
+        return metrics
+
+    def _calculate_resource_efficiency(self) -> float:
+        """Calculate resource utilization efficiency."""
+        if not self.ventures or not self.allocations:
+            return 0.0
+        
+        total_resources = defaultdict(float)
+        used_resources = defaultdict(float)
+        
+        # Sum up total and used resources
+        for venture_id, allocation in self.allocations.items():
+            for resource, amount in allocation.resources.items():
+                total_resources[resource] += amount
+                if venture_id in self.ventures:
+                    used_resources[resource] += (
+                        amount * self.ventures[venture_id].resource_usage.get(resource, 0)
+                    )
+        
+        # Calculate efficiency for each resource
+        efficiencies = []
+        for resource in total_resources:
+            if total_resources[resource] > 0:
+                efficiency = used_resources[resource] / total_resources[resource]
+                efficiencies.append(efficiency)
+        
+        return np.mean(efficiencies) if efficiencies else 0.0
+
+    def get_portfolio_insights(self) -> Dict[str, Any]:
+        """Get comprehensive portfolio insights."""
+        metrics = self._calculate_portfolio_metrics()
+        
+        return {
+            "portfolio_metrics": metrics,
+            "venture_metrics": {
+                venture_id: {
+                    "revenue": v.revenue,
+                    "profit": v.profit,
+                    "growth_rate": v.growth_rate,
+                    "risk_score": v.risk_score,
+                    "synergy_score": v.synergy_score
+                }
+                for venture_id, v in self.ventures.items()
+            },
+            "resource_allocation": {
+                venture_id: {
+                    "resources": a.resources,
+                    "priority": a.priority,
+                    "constraints": len(a.constraints),
+                    "dependencies": len(a.dependencies)
+                }
+                for venture_id, a in self.allocations.items()
+            },
+            "risk_profile": {
+                "portfolio_risk": metrics["avg_risk"],
+                "risk_concentration": self._calculate_risk_concentration(),
+                "risk_correlation": self._calculate_risk_correlation()
+            },
+            "optimization_opportunities": self._identify_optimization_opportunities()
+        }
+
+    def _calculate_risk_concentration(self) -> float:
+        """Calculate risk concentration in portfolio."""
+        if not self.ventures:
+            return 0.0
+        
+        risk_weights = [v.risk_score for v in self.ventures.values()]
+        return np.std(risk_weights) if len(risk_weights) > 1 else 0.0
+
+    def _calculate_risk_correlation(self) -> float:
+        """Calculate risk correlation between ventures."""
+        if len(self.ventures) < 2:
+            return 0.0
+        
+        # Create correlation matrix of risk scores and resource usage
+        venture_metrics = [
+            [v.risk_score] + list(v.resource_usage.values())
+            for v in self.ventures.values()
+        ]
+        
+        correlation_matrix = np.corrcoef(venture_metrics)
+        return np.mean(correlation_matrix[np.triu_indices_from(correlation_matrix, k=1)])
+
+    def _identify_optimization_opportunities(self) -> List[Dict[str, Any]]:
+        """Identify portfolio optimization opportunities."""
+        opportunities = []
+        
+        # Resource optimization opportunities
+        resource_efficiency = self._calculate_resource_efficiency()
+        if resource_efficiency < 0.8:
+            opportunities.append({
+                "type": "resource_optimization",
+                "potential": 1.0 - resource_efficiency,
+                "description": "Improve resource utilization efficiency"
+            })
+        
+        # Risk optimization opportunities
+        risk_concentration = self._calculate_risk_concentration()
+        if risk_concentration > 0.2:
+            opportunities.append({
+                "type": "risk_diversification",
+                "potential": risk_concentration,
+                "description": "Reduce risk concentration"
+            })
+        
+        # Synergy optimization opportunities
+        avg_synergy = np.mean([v.synergy_score for v in self.ventures.values()]) if self.ventures else 0
+        if avg_synergy < 0.7:
+            opportunities.append({
+                "type": "synergy_capture",
+                "potential": 1.0 - avg_synergy,
+                "description": "Increase synergy capture"
+            })
+        
+        return opportunities
+
+class PortfolioOptimizationStrategy(ReasoningStrategy):
+    """
+    Advanced portfolio optimization strategy that:
+    1. Analyzes venture metrics
+    2. Optimizes resource allocation
+    3. Balances risk-reward
+    4. Maximizes portfolio synergies
+    5. Provides actionable recommendations
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize portfolio optimization strategy."""
+        super().__init__()
+        self.config = config or {}
+        self.optimizer = PortfolioOptimizer()
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Generate portfolio optimization strategy based on query and context.
+        
+        Args:
+            query: The portfolio optimization query
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing optimization strategy and confidence scores
+        """
+        try:
+            # Extract portfolio parameters
+            params = self._extract_parameters(query, context)
+            
+            # Optimize portfolio
+            optimization_result = self.optimizer.optimize_portfolio(
+                ventures=params.get('ventures', []),
+                constraints=params.get('constraints', []),
+                objectives=params.get('objectives', [])
+            )
+            
+            # Get metrics
+            metrics = self.optimizer.get_portfolio_metrics()
+            
+            # Generate recommendations
+            recommendations = self._generate_recommendations(
+                optimization_result,
+                metrics
+            )
+            
+            return {
+                'answer': self._format_strategy(optimization_result, metrics, recommendations),
+                'confidence': self._calculate_confidence(optimization_result),
+                'optimization': optimization_result,
+                'metrics': metrics,
+                'recommendations': recommendations
+            }
+            
+        except Exception as e:
+            logging.error(f"Portfolio optimization failed: {str(e)}")
+            return {
+                'error': f"Portfolio optimization failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    def _extract_parameters(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Extract optimization parameters from query and context."""
+        params = {}
+        
+        # Extract ventures
+        if 'ventures' in context:
+            params['ventures'] = context['ventures']
+        else:
+            # Default empty portfolio
+            params['ventures'] = []
+        
+        # Extract constraints
+        if 'constraints' in context:
+            params['constraints'] = context['constraints']
+        else:
+            # Default constraints
+            params['constraints'] = [
+                'budget_limit',
+                'risk_tolerance',
+                'resource_capacity'
+            ]
+        
+        # Extract objectives
+        if 'objectives' in context:
+            params['objectives'] = context['objectives']
+        else:
+            # Default objectives
+            params['objectives'] = [
+                'maximize_returns',
+                'minimize_risk',
+                'maximize_synergies'
+            ]
+        
+        return params
+    
+    def _generate_recommendations(
+        self,
+        optimization_result: Dict[str, Any],
+        metrics: Dict[str, Any]
+    ) -> List[str]:
+        """Generate actionable recommendations."""
+        recommendations = []
+        
+        # Portfolio composition recommendations
+        if 'allocation' in optimization_result:
+            allocation = optimization_result['allocation']
+            recommendations.extend([
+                f"Allocate {alloc['percentage']:.1f}% to {alloc['venture']}"
+                for alloc in allocation
+            ])
+        
+        # Risk management recommendations
+        if 'risk_analysis' in metrics:
+            risk = metrics['risk_analysis']
+            if risk.get('total_risk', 0) > 0.7:
+                recommendations.append(
+                    "Consider reducing exposure to high-risk ventures"
+                )
+            if risk.get('correlation', 0) > 0.8:
+                recommendations.append(
+                    "Increase portfolio diversification to reduce correlation"
+                )
+        
+        # Performance optimization recommendations
+        if 'performance' in metrics:
+            perf = metrics['performance']
+            if perf.get('sharpe_ratio', 0) < 1.0:
+                recommendations.append(
+                    "Optimize risk-adjusted returns through better venture selection"
+                )
+            if perf.get('efficiency', 0) < 0.8:
+                recommendations.append(
+                    "Improve resource allocation efficiency across ventures"
+                )
+        
+        return recommendations
+    
+    def _calculate_confidence(self, optimization_result: Dict[str, Any]) -> float:
+        """Calculate confidence score based on optimization quality."""
+        # Base confidence
+        confidence = 0.5
+        
+        # Adjust based on optimization completeness
+        if optimization_result.get('allocation'):
+            confidence += 0.1
+        if optimization_result.get('risk_analysis'):
+            confidence += 0.1
+        if optimization_result.get('performance_metrics'):
+            confidence += 0.1
+            
+        # Adjust based on solution quality
+        if optimization_result.get('convergence_status') == 'optimal':
+            confidence += 0.2
+        elif optimization_result.get('convergence_status') == 'suboptimal':
+            confidence += 0.1
+            
+        return min(confidence, 1.0)
+    
+    def _format_strategy(
+        self,
+        optimization_result: Dict[str, Any],
+        metrics: Dict[str, Any],
+        recommendations: List[str]
+    ) -> str:
+        """Format optimization strategy into readable text."""
+        sections = []
+        
+        # Portfolio allocation
+        if 'allocation' in optimization_result:
+            allocation = optimization_result['allocation']
+            sections.append("Portfolio Allocation:")
+            for alloc in allocation:
+                sections.append(
+                    f"- {alloc['venture']}: {alloc['percentage']:.1f}%"
+                )
+        
+        # Key metrics
+        if metrics:
+            sections.append("\nKey Metrics:")
+            for key, value in metrics.items():
+                if isinstance(value, (int, float)):
+                    sections.append(f"- {key.replace('_', ' ').title()}: {value:.2f}")
+                else:
+                    sections.append(f"- {key.replace('_', ' ').title()}: {value}")
+        
+        # Recommendations
+        if recommendations:
+            sections.append("\nKey Recommendations:")
+            for rec in recommendations:
+                sections.append(f"- {rec}")
+        
+        return "\n".join(sections)

reasoning/quantum.py

@@ -0,0 +1,372 @@
+"""Quantum-inspired reasoning implementations."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+@dataclass
+class QuantumState:
+    """Quantum state with superposition and entanglement."""
+    name: str
+    amplitude: complex
+    phase: float
+    entangled_states: List[str] = field(default_factory=list)
+
+class QuantumReasoning(ReasoningStrategy):
+    """
+    Advanced quantum reasoning that:
+    1. Creates quantum states
+    2. Applies quantum operations
+    3. Measures outcomes
+    4. Handles superposition
+    5. Models entanglement
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize quantum reasoning."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+        
+        # Configure quantum parameters
+        self.num_qubits = self.config.get('num_qubits', 3)
+        self.measurement_threshold = self.config.get('measurement_threshold', 0.1)
+        self.decoherence_rate = self.config.get('decoherence_rate', 0.01)
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Apply quantum reasoning to analyze complex decisions.
+        
+        Args:
+            query: The input query to reason about
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing reasoning results and confidence scores
+        """
+        try:
+            # Initialize quantum states
+            states = await self._initialize_states(query, context)
+            
+            # Apply quantum operations
+            evolved_states = await self._apply_operations(states, context)
+            
+            # Measure outcomes
+            measurements = await self._measure_states(evolved_states, context)
+            
+            # Generate analysis
+            analysis = await self._generate_analysis(measurements, context)
+            
+            return {
+                'answer': self._format_analysis(analysis),
+                'confidence': self._calculate_confidence(measurements),
+                'states': states,
+                'evolved_states': evolved_states,
+                'measurements': measurements,
+                'analysis': analysis
+            }
+            
+        except Exception as e:
+            logging.error(f"Quantum reasoning failed: {str(e)}")
+            return {
+                'error': f"Quantum reasoning failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    async def _initialize_states(
+        self,
+        query: str,
+        context: Dict[str, Any]
+    ) -> List[QuantumState]:
+        """Initialize quantum states."""
+        states = []
+        
+        # Extract key terms for state initialization
+        terms = set(query.lower().split())
+        
+        # Create quantum states based on terms
+        for i, term in enumerate(terms):
+            if i >= self.num_qubits:
+                break
+                
+            # Calculate initial amplitude and phase
+            amplitude = 1.0 / np.sqrt(len(terms[:self.num_qubits]))
+            phase = 2 * np.pi * i / len(terms[:self.num_qubits])
+            
+            states.append(QuantumState(
+                name=term,
+                amplitude=complex(amplitude * np.cos(phase), amplitude * np.sin(phase)),
+                phase=phase
+            ))
+        
+        # Create entangled states if specified
+        if context.get('entangle', False):
+            self._entangle_states(states)
+        
+        return states
+    
+    async def _apply_operations(
+        self,
+        states: List[QuantumState],
+        context: Dict[str, Any]
+    ) -> List[QuantumState]:
+        """Apply quantum operations to states."""
+        evolved_states = []
+        
+        # Get operation parameters
+        rotation = context.get('rotation', 0.0)
+        phase_shift = context.get('phase_shift', 0.0)
+        
+        for state in states:
+            # Apply rotation
+            rotated_amplitude = state.amplitude * np.exp(1j * rotation)
+            
+            # Apply phase shift
+            shifted_phase = (state.phase + phase_shift) % (2 * np.pi)
+            
+            # Apply decoherence
+            decohered_amplitude = rotated_amplitude * (1 - self.decoherence_rate)
+            
+            evolved_states.append(QuantumState(
+                name=state.name,
+                amplitude=decohered_amplitude,
+                phase=shifted_phase,
+                entangled_states=state.entangled_states.copy()
+            ))
+        
+        return evolved_states
+    
+    async def _measure_states(
+        self,
+        states: List[QuantumState],
+        context: Dict[str, Any]
+    ) -> Dict[str, float]:
+        """Measure quantum states."""
+        measurements = {}
+        
+        # Calculate total probability
+        total_probability = sum(
+            abs(state.amplitude) ** 2
+            for state in states
+        )
+        
+        if total_probability > 0:
+            # Normalize and store measurements
+            for state in states:
+                probability = (abs(state.amplitude) ** 2) / total_probability
+                if probability > self.measurement_threshold:
+                    measurements[state.name] = probability
+        
+        return measurements
+    
+    def _entangle_states(self, states: List[QuantumState]) -> None:
+        """Create entanglement between states."""
+        if len(states) < 2:
+            return
+            
+        # Simple entanglement: connect adjacent states
+        for i in range(len(states) - 1):
+            states[i].entangled_states.append(states[i + 1].name)
+            states[i + 1].entangled_states.append(states[i].name)
+    
+    async def _generate_analysis(
+        self,
+        measurements: Dict[str, float],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Generate quantum analysis."""
+        # Sort states by measurement probability
+        ranked_states = sorted(
+            measurements.items(),
+            key=lambda x: x[1],
+            reverse=True
+        )
+        
+        # Calculate quantum statistics
+        amplitudes = list(measurements.values())
+        mean = np.mean(amplitudes) if amplitudes else 0
+        std = np.std(amplitudes) if amplitudes else 0
+        
+        # Calculate quantum entropy
+        entropy = -sum(
+            p * np.log2(p) if p > 0 else 0
+            for p in measurements.values()
+        )
+        
+        return {
+            'top_state': ranked_states[0][0] if ranked_states else '',
+            'probability': ranked_states[0][1] if ranked_states else 0,
+            'alternatives': [
+                {'name': name, 'probability': prob}
+                for name, prob in ranked_states[1:]
+            ],
+            'statistics': {
+                'mean': mean,
+                'std': std,
+                'entropy': entropy
+            }
+        }
+    
+    def _format_analysis(self, analysis: Dict[str, Any]) -> str:
+        """Format analysis into readable text."""
+        sections = []
+        
+        # Top quantum state
+        if analysis['top_state']:
+            sections.append(
+                f"Most probable quantum state: {analysis['top_state']} "
+                f"(probability: {analysis['probability']:.2%})"
+            )
+        
+        # Alternative states
+        if analysis['alternatives']:
+            sections.append("\nAlternative quantum states:")
+            for alt in analysis['alternatives']:
+                sections.append(
+                    f"- {alt['name']}: {alt['probability']:.2%}"
+                )
+        
+        # Quantum statistics
+        stats = analysis['statistics']
+        sections.append("\nQuantum statistics:")
+        sections.append(f"- Mean amplitude: {stats['mean']:.2%}")
+        sections.append(f"- Standard deviation: {stats['std']:.2%}")
+        sections.append(f"- Quantum entropy: {stats['entropy']:.2f} bits")
+        
+        return "\n".join(sections)
+    
+    def _calculate_confidence(self, measurements: Dict[str, float]) -> float:
+        """Calculate overall confidence score."""
+        if not measurements:
+            return 0.0
+        
+        # Base confidence
+        confidence = 0.5
+        
+        # Adjust based on measurement distribution
+        probs = list(measurements.values())
+        
+        # Strong leading measurement increases confidence
+        max_prob = max(probs)
+        if max_prob > 0.8:
+            confidence += 0.3
+        elif max_prob > 0.6:
+            confidence += 0.2
+        elif max_prob > 0.4:
+            confidence += 0.1
+        
+        # Low entropy (clear distinction) increases confidence
+        entropy = -sum(p * np.log2(p) if p > 0 else 0 for p in probs)
+        max_entropy = -np.log2(1/len(probs))  # Maximum possible entropy
+        
+        if entropy < 0.3 * max_entropy:
+            confidence += 0.2
+        elif entropy < 0.6 * max_entropy:
+            confidence += 0.1
+        
+        return min(confidence, 1.0)
+
+
+class QuantumInspiredStrategy(ReasoningStrategy):
+    """Implements Quantum-Inspired reasoning."""
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        try:
+            # Create a clean context for serialization
+            clean_context = {k: v for k, v in context.items() if k != "groq_api"}
+            
+            prompt = f"""
+            You are a meta-learning reasoning system that adapts its approach based on problem characteristics.
+            
+            Problem Type: 
+            Query: {query}
+            Context: {json.dumps(clean_context)}
+            
+            Analyze this problem using meta-learning principles. Structure your response EXACTLY as follows:
+
+            PROBLEM ANALYSIS:
+            - [First key aspect or complexity factor]
+            - [Second key aspect or complexity factor]
+            - [Third key aspect or complexity factor]
+
+            SOLUTION PATHS:
+            - Path 1: [Specific solution approach]
+            - Path 2: [Alternative solution approach]
+            - Path 3: [Another alternative approach]
+
+            META INSIGHTS:
+            - Learning 1: [Key insight about the problem space]
+            - Learning 2: [Key insight about solution approaches]
+            - Learning 3: [Key insight about trade-offs]
+
+            CONCLUSION:
+            [Final synthesized solution incorporating meta-learnings]
+            """
+            
+            response = await context["groq_api"].predict(prompt)
+            
+            if not response["success"]:
+                return response
+                
+            # Parse response into components
+            lines = response["answer"].split("\n")
+            problem_analysis = []
+            solution_paths = []
+            meta_insights = []
+            conclusion = ""
+            
+            section = None
+            for line in lines:
+                line = line.strip()
+                if not line:
+                    continue
+                    
+                if "PROBLEM ANALYSIS:" in line:
+                    section = "analysis"
+                elif "SOLUTION PATHS:" in line:
+                    section = "paths"
+                elif "META INSIGHTS:" in line:
+                    section = "insights"
+                elif "CONCLUSION:" in line:
+                    section = "conclusion"
+                elif line.startswith("-"):
+                    content = line.lstrip("- ").strip()
+                    if section == "analysis":
+                        problem_analysis.append(content)
+                    elif section == "paths":
+                        solution_paths.append(content)
+                    elif section == "insights":
+                        meta_insights.append(content)
+                elif section == "conclusion":
+                    conclusion += line + " "
+            
+            return {
+                "success": True,
+                "problem_analysis": problem_analysis,
+                "solution_paths": solution_paths,
+                "meta_insights": meta_insights,
+                "conclusion": conclusion.strip(),
+                # Add standard fields for compatibility
+                "reasoning_path": problem_analysis + solution_paths + meta_insights,
+                "conclusion": conclusion.strip()
+            }
+            
+        except Exception as e:
+            return {"success": False, "error": str(e)}

reasoning/recursive.py

@@ -0,0 +1,576 @@
+"""Recursive reasoning implementation with advanced decomposition and synthesis."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Tuple, Callable
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import asyncio
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+class SubproblemType(Enum):
+    """Types of subproblems in recursive reasoning."""
+    ATOMIC = "atomic"
+    COMPOSITE = "composite"
+    PARALLEL = "parallel"
+    SEQUENTIAL = "sequential"
+    CONDITIONAL = "conditional"
+    ITERATIVE = "iterative"
+
+class SolutionStatus(Enum):
+    """Status of subproblem solutions."""
+    PENDING = "pending"
+    IN_PROGRESS = "in_progress"
+    SOLVED = "solved"
+    FAILED = "failed"
+    BLOCKED = "blocked"
+    OPTIMIZING = "optimizing"
+
+@dataclass
+class Subproblem:
+    """Represents a subproblem in recursive reasoning."""
+    id: str
+    type: SubproblemType
+    query: str
+    context: Dict[str, Any]
+    parent_id: Optional[str]
+    children: List[str]
+    status: SolutionStatus
+    solution: Optional[Dict[str, Any]]
+    confidence: float
+    dependencies: List[str]
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+@dataclass
+class RecursiveStep:
+    """Represents a step in recursive reasoning."""
+    id: str
+    subproblem_id: str
+    action: str
+    timestamp: datetime
+    result: Optional[Dict[str, Any]]
+    metrics: Dict[str, float]
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+class RecursiveReasoning(ReasoningStrategy):
+    """
+    Advanced Recursive Reasoning implementation with:
+    - Dynamic problem decomposition
+    - Parallel subproblem solving
+    - Solution synthesis
+    - Cycle detection
+    - Optimization strategies
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize recursive reasoning."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+        
+        # Recursive reasoning specific parameters
+        self.max_depth = self.config.get('max_depth', 5)
+        self.optimization_rounds = self.config.get('optimization_rounds', 2)
+        
+        # Problem tracking
+        self.subproblems: Dict[str, Subproblem] = {}
+        self.steps: List[RecursiveStep] = []
+        self.solution_cache: Dict[str, Dict[str, Any]] = {}
+        self.cycle_detection: Set[str] = set()
+        
+        # Performance metrics
+        self.depth_distribution: Dict[int, int] = defaultdict(int)
+        self.type_distribution: Dict[SubproblemType, int] = defaultdict(int)
+        self.success_rate: Dict[SubproblemType, float] = defaultdict(float)
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Main reasoning method implementing recursive reasoning."""
+        try:
+            # Initialize root problem
+            root = await self._initialize_problem(query, context)
+            self.subproblems[root.id] = root
+            
+            # Recursively solve
+            solution = await self._solve_recursive(root.id, depth=0)
+            
+            # Optimize solution
+            optimized = await self._optimize_solution(solution, root, context)
+            
+            # Update metrics
+            self._update_metrics(root.id)
+            
+            return {
+                "success": True,
+                "answer": optimized["answer"],
+                "confidence": optimized["confidence"],
+                "decomposition": self._get_problem_tree(root.id),
+                "solution_trace": self._get_solution_trace(root.id),
+                "performance_metrics": self._get_performance_metrics(),
+                "meta_insights": optimized["meta_insights"]
+            }
+        except Exception as e:
+            logging.error(f"Error in recursive reasoning: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _initialize_problem(self, query: str, context: Dict[str, Any]) -> Subproblem:
+        """Initialize the root problem."""
+        prompt = f"""
+        Initialize recursive reasoning problem:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        Analyze for:
+        1. Problem type classification
+        2. Initial decomposition strategy
+        3. Key dependencies
+        4. Solution approach
+        
+        Format as:
+        [Problem]
+        Type: ...
+        Strategy: ...
+        Dependencies: ...
+        Approach: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_problem_init(response["answer"], query, context)
+
+    async def _decompose_problem(self, problem: Subproblem, context: Dict[str, Any]) -> List[Subproblem]:
+        """Decompose a problem into subproblems."""
+        prompt = f"""
+        Decompose problem into subproblems:
+        Problem: {json.dumps(self._problem_to_dict(problem))}
+        Context: {json.dumps(context)}
+        
+        For each subproblem specify:
+        1. [Type]: {" | ".join([t.value for t in SubproblemType])}
+        2. [Query]: Specific question
+        3. [Dependencies]: Required solutions
+        4. [Approach]: Solution strategy
+        
+        Format as:
+        [S1]
+        Type: ...
+        Query: ...
+        Dependencies: ...
+        Approach: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_subproblems(response["answer"], problem.id, context)
+
+    async def _solve_recursive(self, problem_id: str, depth: int) -> Dict[str, Any]:
+        """Recursively solve a problem and its subproblems."""
+        if depth > self.max_depth:
+            return {"success": False, "error": "Maximum recursion depth exceeded"}
+            
+        if problem_id in self.cycle_detection:
+            return {"success": False, "error": "Cycle detected in recursive solving"}
+            
+        problem = self.subproblems[problem_id]
+        self.cycle_detection.add(problem_id)
+        self.depth_distribution[depth] += 1
+        
+        try:
+            # Check cache
+            cache_key = f"{problem.query}:{json.dumps(problem.context)}"
+            if cache_key in self.solution_cache:
+                return self.solution_cache[cache_key]
+            
+            # Check if atomic
+            if problem.type == SubproblemType.ATOMIC:
+                solution = await self._solve_atomic(problem)
+            else:
+                # Decompose
+                subproblems = await self._decompose_problem(problem, problem.context)
+                for sub in subproblems:
+                    self.subproblems[sub.id] = sub
+                    problem.children.append(sub.id)
+                
+                # Solve subproblems
+                if problem.type == SubproblemType.PARALLEL and len(subproblems) >= self.parallel_threshold:
+                    # Solve in parallel
+                    tasks = [self._solve_recursive(sub.id, depth + 1) for sub in subproblems]
+                    subsolutions = await asyncio.gather(*tasks)
+                else:
+                    # Solve sequentially
+                    subsolutions = []
+                    for sub in subproblems:
+                        subsolution = await self._solve_recursive(sub.id, depth + 1)
+                        subsolutions.append(subsolution)
+                
+                # Synthesize solutions
+                solution = await self._synthesize_solutions(subsolutions, problem, problem.context)
+            
+            # Cache solution
+            self.solution_cache[cache_key] = solution
+            problem.solution = solution
+            problem.status = SolutionStatus.SOLVED if solution["success"] else SolutionStatus.FAILED
+            
+            return solution
+            
+        finally:
+            self.cycle_detection.remove(problem_id)
+
+    async def _solve_atomic(self, problem: Subproblem) -> Dict[str, Any]:
+        """Solve an atomic problem."""
+        prompt = f"""
+        Solve atomic problem:
+        Problem: {json.dumps(self._problem_to_dict(problem))}
+        
+        Provide:
+        1. Direct solution
+        2. Confidence level
+        3. Supporting evidence
+        4. Alternative approaches
+        
+        Format as:
+        [Solution]
+        Answer: ...
+        Confidence: ...
+        Evidence: ...
+        Alternatives: ...
+        """
+        
+        response = await problem.context["groq_api"].predict(prompt)
+        solution = self._parse_atomic_solution(response["answer"])
+        
+        self._record_step(RecursiveStep(
+            id=f"step_{len(self.steps)}",
+            subproblem_id=problem.id,
+            action="atomic_solve",
+            timestamp=datetime.now(),
+            result=solution,
+            metrics={"confidence": solution.get("confidence", 0.0)},
+            metadata={}
+        ))
+        
+        return solution
+
+    async def _synthesize_solutions(self, subsolutions: List[Dict[str, Any]], problem: Subproblem, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Synthesize solutions from subproblems."""
+        prompt = f"""
+        Synthesize solutions:
+        Problem: {json.dumps(self._problem_to_dict(problem))}
+        Solutions: {json.dumps(subsolutions)}
+        Context: {json.dumps(context)}
+        
+        Provide:
+        1. Integrated solution
+        2. Confidence assessment
+        3. Integration method
+        4. Quality metrics
+        
+        Format as:
+        [Synthesis]
+        Solution: ...
+        Confidence: ...
+        Method: ...
+        Metrics: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        synthesis = self._parse_synthesis(response["answer"])
+        
+        self._record_step(RecursiveStep(
+            id=f"step_{len(self.steps)}",
+            subproblem_id=problem.id,
+            action="synthesize",
+            timestamp=datetime.now(),
+            result=synthesis,
+            metrics={"confidence": synthesis.get("confidence", 0.0)},
+            metadata={"num_subsolutions": len(subsolutions)}
+        ))
+        
+        return synthesis
+
+    async def _optimize_solution(self, solution: Dict[str, Any], problem: Subproblem, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize the final solution."""
+        prompt = f"""
+        Optimize recursive solution:
+        Original: {json.dumps(solution)}
+        Problem: {json.dumps(self._problem_to_dict(problem))}
+        Context: {json.dumps(context)}
+        
+        Optimize for:
+        1. Completeness
+        2. Consistency
+        3. Efficiency
+        4. Clarity
+        
+        Format as:
+        [Optimization]
+        Answer: ...
+        Improvements: ...
+        Metrics: ...
+        Insights: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_optimization(response["answer"])
+
+    def _update_metrics(self, root_id: str):
+        """Update performance metrics."""
+        def update_recursive(problem_id: str):
+            problem = self.subproblems[problem_id]
+            self.type_distribution[problem.type] += 1
+            
+            if problem.status == SolutionStatus.SOLVED:
+                self.success_rate[problem.type] = (
+                    self.success_rate[problem.type] * (self.type_distribution[problem.type] - 1) +
+                    problem.confidence
+                ) / self.type_distribution[problem.type]
+            
+            for child_id in problem.children:
+                update_recursive(child_id)
+        
+        update_recursive(root_id)
+
+    def _get_problem_tree(self, root_id: str) -> Dict[str, Any]:
+        """Get the problem decomposition tree."""
+        def build_tree(problem_id: str) -> Dict[str, Any]:
+            problem = self.subproblems[problem_id]
+            return {
+                "id": problem.id,
+                "type": problem.type.value,
+                "query": problem.query,
+                "status": problem.status.value,
+                "confidence": problem.confidence,
+                "children": [build_tree(child_id) for child_id in problem.children]
+            }
+        
+        return build_tree(root_id)
+
+    def _get_solution_trace(self, root_id: str) -> List[Dict[str, Any]]:
+        """Get the solution trace for a problem."""
+        return [self._step_to_dict(step) for step in self.steps 
+                if step.subproblem_id == root_id or 
+                any(step.subproblem_id == sub_id for sub_id in self.subproblems[root_id].children)]
+
+    def _get_performance_metrics(self) -> Dict[str, Any]:
+        """Get current performance metrics."""
+        return {
+            "depth_distribution": dict(self.depth_distribution),
+            "type_distribution": {t.value: c for t, c in self.type_distribution.items()},
+            "success_rate": {t.value: r for t, r in self.success_rate.items()},
+            "cache_hits": len(self.solution_cache),
+            "total_steps": len(self.steps)
+        }
+
+    def _record_step(self, step: RecursiveStep):
+        """Record a reasoning step."""
+        self.steps.append(step)
+
+    def _parse_problem_init(self, response: str, query: str, context: Dict[str, Any]) -> Subproblem:
+        """Parse initial problem configuration."""
+        problem_type = SubproblemType.COMPOSITE  # default
+        dependencies = []
+        metadata = {}
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Type:'):
+                try:
+                    problem_type = SubproblemType(line[5:].strip().lower())
+                except ValueError:
+                    pass
+            elif line.startswith('Dependencies:'):
+                dependencies = [d.strip() for d in line[13:].split(',')]
+            elif line.startswith('Strategy:') or line.startswith('Approach:'):
+                metadata["strategy"] = line.split(':', 1)[1].strip()
+        
+        return Subproblem(
+            id="root",
+            type=problem_type,
+            query=query,
+            context=context,
+            parent_id=None,
+            children=[],
+            status=SolutionStatus.PENDING,
+            solution=None,
+            confidence=0.0,
+            dependencies=dependencies,
+            metadata=metadata
+        )
+
+    def _parse_subproblems(self, response: str, parent_id: str, context: Dict[str, Any]) -> List[Subproblem]:
+        """Parse subproblems from response."""
+        subproblems = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[S'):
+                if current:
+                    subproblems.append(current)
+                current = None
+            elif line.startswith('Type:'):
+                try:
+                    problem_type = SubproblemType(line[5:].strip().lower())
+                    current = Subproblem(
+                        id=f"{parent_id}_{len(subproblems)}",
+                        type=problem_type,
+                        query="",
+                        context=context,
+                        parent_id=parent_id,
+                        children=[],
+                        status=SolutionStatus.PENDING,
+                        solution=None,
+                        confidence=0.0,
+                        dependencies=[],
+                        metadata={}
+                    )
+                except ValueError:
+                    current = None
+            elif current:
+                if line.startswith('Query:'):
+                    current.query = line[6:].strip()
+                elif line.startswith('Dependencies:'):
+                    current.dependencies = [d.strip() for d in line[13:].split(',')]
+                elif line.startswith('Approach:'):
+                    current.metadata["approach"] = line[9:].strip()
+        
+        if current:
+            subproblems.append(current)
+        
+        return subproblems
+
+    def _parse_atomic_solution(self, response: str) -> Dict[str, Any]:
+        """Parse atomic solution from response."""
+        solution = {
+            "success": True,
+            "answer": "",
+            "confidence": 0.0,
+            "evidence": [],
+            "alternatives": []
+        }
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Answer:'):
+                solution["answer"] = line[7:].strip()
+            elif line.startswith('Confidence:'):
+                try:
+                    solution["confidence"] = float(line[11:].strip())
+                except:
+                    pass
+            elif line.startswith('Evidence:'):
+                solution["evidence"] = [e.strip() for e in line[9:].split(',')]
+            elif line.startswith('Alternatives:'):
+                solution["alternatives"] = [a.strip() for a in line[13:].split(',')]
+        
+        return solution
+
+    def _parse_synthesis(self, response: str) -> Dict[str, Any]:
+        """Parse synthesis result from response."""
+        synthesis = {
+            "success": True,
+            "solution": "",
+            "confidence": 0.0,
+            "method": "",
+            "metrics": {}
+        }
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Solution:'):
+                synthesis["solution"] = line[9:].strip()
+            elif line.startswith('Confidence:'):
+                try:
+                    synthesis["confidence"] = float(line[11:].strip())
+                except:
+                    pass
+            elif line.startswith('Method:'):
+                synthesis["method"] = line[7:].strip()
+            elif line.startswith('Metrics:'):
+                try:
+                    synthesis["metrics"] = json.loads(line[8:].strip())
+                except:
+                    pass
+        
+        return synthesis
+
+    def _parse_optimization(self, response: str) -> Dict[str, Any]:
+        """Parse optimization result from response."""
+        optimization = {
+            "answer": "",
+            "confidence": 0.0,
+            "improvements": [],
+            "metrics": {},
+            "meta_insights": []
+        }
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Answer:'):
+                optimization["answer"] = line[7:].strip()
+            elif line.startswith('Improvements:'):
+                optimization["improvements"] = [i.strip() for i in line[13:].split(',')]
+            elif line.startswith('Metrics:'):
+                try:
+                    optimization["metrics"] = json.loads(line[8:].strip())
+                except:
+                    pass
+            elif line.startswith('Insights:'):
+                optimization["meta_insights"] = [i.strip() for i in line[9:].split(',')]
+        
+        return optimization
+
+    def _problem_to_dict(self, problem: Subproblem) -> Dict[str, Any]:
+        """Convert problem to dictionary for serialization."""
+        return {
+            "id": problem.id,
+            "type": problem.type.value,
+            "query": problem.query,
+            "parent_id": problem.parent_id,
+            "children": problem.children,
+            "status": problem.status.value,
+            "confidence": problem.confidence,
+            "dependencies": problem.dependencies,
+            "metadata": problem.metadata
+        }
+
+    def _step_to_dict(self, step: RecursiveStep) -> Dict[str, Any]:
+        """Convert step to dictionary for serialization."""
+        return {
+            "id": step.id,
+            "subproblem_id": step.subproblem_id,
+            "action": step.action,
+            "timestamp": step.timestamp.isoformat(),
+            "result": step.result,
+            "metrics": step.metrics,
+            "metadata": step.metadata
+        }
+
+    def clear_cache(self):
+        """Clear solution cache."""
+        self.solution_cache.clear()
+
+    def get_statistics(self) -> Dict[str, Any]:
+        """Get detailed statistics about the reasoning process."""
+        return {
+            "total_problems": len(self.subproblems),
+            "total_steps": len(self.steps),
+            "cache_size": len(self.solution_cache),
+            "type_distribution": dict(self.type_distribution),
+            "depth_distribution": dict(self.depth_distribution),
+            "success_rates": dict(self.success_rate),
+            "average_confidence": sum(p.confidence for p in self.subproblems.values()) / len(self.subproblems) if self.subproblems else 0.0
+        }

reasoning/specialized.py

@@ -0,0 +1,476 @@
+"""Specialized reasoning strategies for specific domains and tasks."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Callable
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import asyncio
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+class SpecializedReasoning(ReasoningStrategy):
+    """
+    A composite reasoning strategy that combines multiple specialized strategies
+    for different domains and tasks.
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize specialized reasoning with component strategies."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+        
+        # Initialize component strategies with shared config
+        strategy_config = {
+            'min_confidence': self.min_confidence,
+            'parallel_threshold': self.parallel_threshold,
+            'learning_rate': self.learning_rate,
+            'strategy_weights': self.strategy_weights
+        }
+        
+        self.strategies = {
+            'code_rewrite': CodeRewriteStrategy(strategy_config),
+            'security_audit': SecurityAuditStrategy(strategy_config),
+            'performance': PerformanceOptimizationStrategy(strategy_config),
+            'testing': TestGenerationStrategy(strategy_config),
+            'documentation': DocumentationStrategy(strategy_config),
+            'api_design': APIDesignStrategy(strategy_config),
+            'dependencies': DependencyManagementStrategy(strategy_config),
+            'code_review': CodeReviewStrategy(strategy_config)
+        }
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Apply specialized reasoning by selecting and combining appropriate
+        strategies based on the query and context.
+        
+        Args:
+            query: The input query to reason about
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing reasoning results and confidence scores
+        """
+        try:
+            # Determine which strategies to use based on context
+            selected_strategies = await self._select_strategies(query, context)
+            
+            # Get results from each selected strategy
+            results = {}
+            for strategy_name in selected_strategies:
+                strategy = self.strategies[strategy_name]
+                results[strategy_name] = await strategy.reason(query, context)
+            
+            # Combine results
+            combined_result = await self._combine_results(results, context)
+            
+            return {
+                'answer': combined_result.get('answer', ''),
+                'confidence': combined_result.get('confidence', 0.0),
+                'reasoning_path': {
+                    'selected_strategies': selected_strategies,
+                    'individual_results': results,
+                    'combination_method': combined_result.get('method', '')
+                }
+            }
+            
+        except Exception as e:
+            logging.error(f"Specialized reasoning failed: {str(e)}")
+            return {
+                'error': f"Specialized reasoning failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    async def _select_strategies(self, query: str, context: Dict[str, Any]) -> List[str]:
+        """Select appropriate strategies based on query and context."""
+        selected = []
+        
+        # Simple keyword-based selection for now
+        keywords = {
+            'code_rewrite': ['rewrite', 'refactor', 'improve'],
+            'security_audit': ['security', 'vulnerability', 'audit'],
+            'performance': ['performance', 'optimize', 'speed'],
+            'testing': ['test', 'coverage', 'verify'],
+            'documentation': ['document', 'explain', 'describe'],
+            'api_design': ['api', 'interface', 'endpoint'],
+            'dependencies': ['dependency', 'package', 'version'],
+            'code_review': ['review', 'quality', 'check']
+        }
+        
+        query_lower = query.lower()
+        for strategy, terms in keywords.items():
+            if any(term in query_lower for term in terms):
+                selected.append(strategy)
+        
+        # If no specific strategies selected, use code review as default
+        if not selected:
+            selected = ['code_review']
+        
+        return selected
+    
+    async def _combine_results(
+        self, 
+        results: Dict[str, Dict[str, Any]], 
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Combine results from multiple strategies."""
+        if not results:
+            return {'answer': '', 'confidence': 0.0, 'method': 'none'}
+        
+        # For now, use the highest confidence result
+        best_result = max(
+            results.items(),
+            key=lambda x: x[1].get('confidence', 0)
+        )
+        
+        return {
+            'answer': best_result[1].get('answer', ''),
+            'confidence': best_result[1].get('confidence', 0.0),
+            'method': 'highest_confidence'
+        }
+
+class CodeRewriteStrategy(ReasoningStrategy):
+    """
+    Advanced code rewriting strategy that:
+    1. Analyzes code structure and patterns
+    2. Identifies refactoring opportunities
+    3. Maintains code semantics
+    4. Optimizes code quality
+    5. Ensures backward compatibility
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.config = config or {}
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Rewrite code while preserving functionality."""
+        try:
+            # Analyze code
+            analysis = await self._analyze_code(query, context)
+            
+            # Generate rewrite plan
+            plan = await self._generate_rewrite_plan(analysis, context)
+            
+            # Execute rewrites
+            rewrites = await self._execute_rewrites(plan, context)
+            
+            # Validate changes
+            validation = await self._validate_changes(rewrites, context)
+            
+            return {
+                "success": validation["success"],
+                "rewrites": rewrites,
+                "validation": validation,
+                "metrics": {
+                    "quality_improvement": validation.get("quality_score", 0.0),
+                    "semantic_preservation": validation.get("semantic_score", 0.0)
+                }
+            }
+        except Exception as e:
+            logging.error(f"Error in code rewrite: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class SecurityAuditStrategy(ReasoningStrategy):
+    """
+    Advanced security audit strategy that:
+    1. Identifies security vulnerabilities
+    2. Analyzes attack vectors
+    3. Recommends security fixes
+    4. Validates security measures
+    5. Monitors security state
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.config = config or {}
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Perform security audit and generate recommendations."""
+        try:
+            # Scan for vulnerabilities
+            vulnerabilities = await self._scan_vulnerabilities(query, context)
+            
+            # Analyze risks
+            risks = await self._analyze_risks(vulnerabilities, context)
+            
+            # Generate fixes
+            fixes = await self._generate_fixes(risks, context)
+            
+            # Validate security
+            validation = await self._validate_security(fixes, context)
+            
+            return {
+                "success": True,
+                "vulnerabilities": vulnerabilities,
+                "risks": risks,
+                "fixes": fixes,
+                "validation": validation
+            }
+        except Exception as e:
+            logging.error(f"Error in security audit: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class PerformanceOptimizationStrategy(ReasoningStrategy):
+    """
+    Advanced performance optimization strategy that:
+    1. Profiles code performance
+    2. Identifies bottlenecks
+    3. Generates optimizations
+    4. Measures improvements
+    5. Validates optimizations
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.config = config or {}
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Optimize code performance."""
+        try:
+            # Profile performance
+            profile = await self._profile_performance(query, context)
+            
+            # Identify bottlenecks
+            bottlenecks = await self._identify_bottlenecks(profile, context)
+            
+            # Generate optimizations
+            optimizations = await self._generate_optimizations(bottlenecks, context)
+            
+            # Measure improvements
+            measurements = await self._measure_improvements(optimizations, context)
+            
+            return {
+                "success": measurements["success"],
+                "profile": profile,
+                "bottlenecks": bottlenecks,
+                "optimizations": optimizations,
+                "improvements": measurements
+            }
+        except Exception as e:
+            logging.error(f"Error in performance optimization: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class TestGenerationStrategy(ReasoningStrategy):
+    """
+    Advanced test generation strategy that:
+    1. Analyzes code coverage
+    2. Generates test cases
+    3. Creates test fixtures
+    4. Validates test quality
+    5. Maintains test suite
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.config = config or {}
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate comprehensive test suite."""
+        try:
+            # Analyze coverage
+            coverage = await self._analyze_coverage(query, context)
+            
+            # Generate test cases
+            test_cases = await self._generate_test_cases(coverage, context)
+            
+            # Create fixtures
+            fixtures = await self._create_fixtures(test_cases, context)
+            
+            # Validate tests
+            validation = await self._validate_tests(test_cases, fixtures, context)
+            
+            return {
+                "success": validation["success"],
+                "test_cases": test_cases,
+                "fixtures": fixtures,
+                "validation": validation,
+                "metrics": {
+                    "coverage": coverage.get("percentage", 0.0),
+                    "quality_score": validation.get("quality_score", 0.0)
+                }
+            }
+        except Exception as e:
+            logging.error(f"Error in test generation: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class DocumentationStrategy(ReasoningStrategy):
+    """
+    Advanced documentation strategy that:
+    1. Analyzes code structure
+    2. Generates documentation
+    3. Maintains consistency
+    4. Updates references
+    5. Validates completeness
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.config = config or {}
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate and maintain documentation."""
+        try:
+            # Analyze structure
+            structure = await self._analyze_structure(query, context)
+            
+            # Generate documentation
+            documentation = await self._generate_documentation(structure, context)
+            
+            # Update references
+            references = await self._update_references(documentation, context)
+            
+            # Validate completeness
+            validation = await self._validate_documentation(documentation, references, context)
+            
+            return {
+                "success": validation["success"],
+                "documentation": documentation,
+                "references": references,
+                "validation": validation,
+                "metrics": {
+                    "completeness": validation.get("completeness_score", 0.0),
+                    "consistency": validation.get("consistency_score", 0.0)
+                }
+            }
+        except Exception as e:
+            logging.error(f"Error in documentation: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class APIDesignStrategy(ReasoningStrategy):
+    """
+    Advanced API design strategy that:
+    1. Analyzes requirements
+    2. Designs API structure
+    3. Generates specifications
+    4. Validates design
+    5. Maintains versioning
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.config = config or {}
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Design and validate API."""
+        try:
+            # Analyze requirements
+            requirements = await self._analyze_requirements(query, context)
+            
+            # Design structure
+            design = await self._design_structure(requirements, context)
+            
+            # Generate specs
+            specs = await self._generate_specs(design, context)
+            
+            # Validate design
+            validation = await self._validate_design(specs, context)
+            
+            return {
+                "success": validation["success"],
+                "requirements": requirements,
+                "design": design,
+                "specs": specs,
+                "validation": validation
+            }
+        except Exception as e:
+            logging.error(f"Error in API design: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class DependencyManagementStrategy(ReasoningStrategy):
+    """
+    Advanced dependency management strategy that:
+    1. Analyzes dependencies
+    2. Resolves conflicts
+    3. Optimizes versions
+    4. Ensures compatibility
+    5. Maintains security
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.config = config or {}
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Manage and optimize dependencies."""
+        try:
+            # Analyze dependencies
+            analysis = await self._analyze_dependencies(query, context)
+            
+            # Resolve conflicts
+            resolution = await self._resolve_conflicts(analysis, context)
+            
+            # Optimize versions
+            optimization = await self._optimize_versions(resolution, context)
+            
+            # Validate compatibility
+            validation = await self._validate_compatibility(optimization, context)
+            
+            return {
+                "success": validation["success"],
+                "analysis": analysis,
+                "resolution": resolution,
+                "optimization": optimization,
+                "validation": validation
+            }
+        except Exception as e:
+            logging.error(f"Error in dependency management: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class CodeReviewStrategy(ReasoningStrategy):
+    """
+    Advanced code review strategy that:
+    1. Analyzes code quality
+    2. Identifies issues
+    3. Suggests improvements
+    4. Tracks changes
+    5. Validates fixes
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.config = config or {}
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Perform comprehensive code review."""
+        try:
+            # Analyze quality
+            quality = await self._analyze_quality(query, context)
+            
+            # Identify issues
+            issues = await self._identify_issues(quality, context)
+            
+            # Generate suggestions
+            suggestions = await self._generate_suggestions(issues, context)
+            
+            # Track changes
+            tracking = await self._track_changes(suggestions, context)
+            
+            return {
+                "success": True,
+                "quality": quality,
+                "issues": issues,
+                "suggestions": suggestions,
+                "tracking": tracking,
+                "metrics": {
+                    "quality_score": quality.get("score", 0.0),
+                    "issues_found": len(issues),
+                    "suggestions_made": len(suggestions)
+                }
+            }
+        except Exception as e:
+            logging.error(f"Error in code review: {str(e)}")
+            return {"success": False, "error": str(e)}

reasoning/tree_of_thoughts.py

@@ -0,0 +1,516 @@
+"""Tree of Thoughts reasoning implementation with advanced tree exploration."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Tuple
+import json
+from dataclasses import dataclass
+from enum import Enum
+import heapq
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+class NodeType(Enum):
+    """Types of nodes in the thought tree."""
+    ROOT = "root"
+    HYPOTHESIS = "hypothesis"
+    EVIDENCE = "evidence"
+    ANALYSIS = "analysis"
+    SYNTHESIS = "synthesis"
+    EVALUATION = "evaluation"
+    CONCLUSION = "conclusion"
+
+@dataclass
+class TreeNode:
+    """Represents a node in the thought tree."""
+    id: str
+    type: NodeType
+    content: str
+    confidence: float
+    children: List['TreeNode']
+    parent: Optional['TreeNode']
+    metadata: Dict[str, Any]
+    depth: int
+    evaluation_score: float = 0.0
+
+class TreeOfThoughtsStrategy(ReasoningStrategy):
+    """
+    Advanced Tree of Thoughts reasoning implementation with:
+    - Beam search for path exploration
+    - Dynamic node evaluation
+    - Pruning strategies
+    - Path optimization
+    - Meta-learning from tree patterns
+    """
+    
+    def __init__(self, 
+                 min_confidence: float = 0.7,
+                 parallel_threshold: int = 3,
+                 learning_rate: float = 0.1,
+                 strategy_weights: Optional[Dict[str, float]] = None):
+        self.min_confidence = min_confidence
+        self.parallel_threshold = parallel_threshold
+        self.learning_rate = learning_rate
+        self.strategy_weights = strategy_weights or {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        }
+        self.node_history: Dict[str, TreeNode] = {}
+        self.path_patterns: Dict[str, float] = defaultdict(float)
+        
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Main reasoning method implementing tree of thoughts."""
+        try:
+            # Initialize root node
+            root = await self._create_root_node(query, context)
+            
+            # Build and explore tree
+            tree = await self._build_tree(root, context)
+            
+            # Find best paths
+            paths = await self._find_best_paths(tree, context)
+            
+            # Synthesize conclusion
+            conclusion = await self._synthesize_conclusion(paths, context)
+            
+            # Update history and patterns
+            self._update_history(tree)
+            self._update_patterns(paths)
+            
+            return {
+                "success": True,
+                "answer": conclusion["answer"],
+                "confidence": conclusion["confidence"],
+                "tree": self._tree_to_dict(tree),
+                "best_paths": [self._path_to_dict(p) for p in paths],
+                "reasoning_trace": conclusion["trace"],
+                "meta_insights": conclusion["meta_insights"]
+            }
+        except Exception as e:
+            logging.error(f"Error in tree of thoughts reasoning: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _create_root_node(self, query: str, context: Dict[str, Any]) -> TreeNode:
+        """Create the root node of the thought tree."""
+        prompt = f"""
+        Initialize root thought node for query:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        Provide:
+        1. Initial problem decomposition
+        2. Key aspects to explore
+        3. Evaluation criteria
+        4. Success metrics
+        
+        Format as:
+        [Root]
+        Decomposition: ...
+        Aspects: ...
+        Criteria: ...
+        Metrics: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_root_node(response["answer"], query)
+
+    async def _build_tree(self, root: TreeNode, context: Dict[str, Any]) -> TreeNode:
+        """Build and explore the thought tree."""
+        # Initialize beam with root
+        beam = [(root.evaluation_score, root)]
+        visited: Set[str] = set()
+        
+        for depth in range(5):
+            next_beam = []
+            
+            for _, node in beam:
+                if node.id in visited:
+                    continue
+                    
+                visited.add(node.id)
+                
+                # Generate child nodes
+                children = await self._generate_children(node, context)
+                
+                # Evaluate and filter children
+                evaluated_children = await self._evaluate_nodes(children, context)
+                
+                # Add to beam
+                for child in evaluated_children:
+                    if child.evaluation_score > 0.4:
+                        next_beam.append((child.evaluation_score, child))
+                        node.children.append(child)
+            
+            # Select best nodes for next iteration
+            beam = heapq.nlargest(3, next_beam, key=lambda x: x[0])
+            
+            if not beam:
+                break
+        
+        return root
+
+    async def _generate_children(self, parent: TreeNode, context: Dict[str, Any]) -> List[TreeNode]:
+        """Generate child nodes for a given parent."""
+        prompt = f"""
+        Generate child thoughts for node:
+        Parent: {json.dumps(self._node_to_dict(parent))}
+        Context: {json.dumps(context)}
+        
+        For each child provide:
+        1. [Type]: {" | ".join([t.value for t in NodeType if t != NodeType.ROOT])}
+        2. [Content]: Main thought
+        3. [Confidence]: 0-1 score
+        4. [Rationale]: Why this follows from parent
+        5. [Potential]: Future exploration potential
+        
+        Format as:
+        [C1]
+        Type: ...
+        Content: ...
+        Confidence: ...
+        Rationale: ...
+        Potential: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_child_nodes(response["answer"], parent)
+
+    async def _evaluate_nodes(self, nodes: List[TreeNode], context: Dict[str, Any]) -> List[TreeNode]:
+        """Evaluate a list of nodes."""
+        prompt = f"""
+        Evaluate thought nodes:
+        Nodes: {json.dumps([self._node_to_dict(n) for n in nodes])}
+        Context: {json.dumps(context)}
+        
+        For each node evaluate:
+        1. Logical coherence
+        2. Evidence support
+        3. Novelty value
+        4. Exploration potential
+        
+        Format as:
+        [N1]
+        Coherence: 0-1
+        Evidence: 0-1
+        Novelty: 0-1
+        Potential: 0-1
+        Overall: 0-1
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._apply_evaluations(nodes, response["answer"])
+
+    async def _find_best_paths(self, root: TreeNode, context: Dict[str, Any]) -> List[List[TreeNode]]:
+        """Find the best paths through the tree."""
+        paths = []
+        current_path = [root]
+        
+        def dfs(node: TreeNode, path: List[TreeNode]):
+            if not node.children:
+                paths.append(path[:])
+                return
+                
+            # Sort children by score
+            sorted_children = sorted(node.children, key=lambda x: x.evaluation_score, reverse=True)
+            
+            # Explore top paths
+            for child in sorted_children[:3]:
+                path.append(child)
+                dfs(child, path)
+                path.pop()
+        
+        dfs(root, current_path)
+        
+        # Evaluate complete paths
+        evaluated_paths = await self._evaluate_paths(paths, context)
+        
+        # Return top paths
+        return sorted(evaluated_paths, key=lambda p: sum(n.evaluation_score for n in p), reverse=True)[:3]
+
+    async def _synthesize_conclusion(self, paths: List[List[TreeNode]], context: Dict[str, Any]) -> Dict[str, Any]:
+        """Synthesize final conclusion from best paths."""
+        prompt = f"""
+        Synthesize conclusion from thought paths:
+        Paths: {json.dumps([[self._node_to_dict(n) for n in path] for path in paths])}
+        Context: {json.dumps(context)}
+        
+        Provide:
+        1. Main conclusion
+        2. Confidence level
+        3. Reasoning trace
+        4. Supporting evidence
+        5. Alternative perspectives
+        6. Meta-insights
+        
+        Format as:
+        [Conclusion]
+        Answer: ...
+        Confidence: ...
+        Trace: ...
+        Evidence: ...
+        Alternatives: ...
+        
+        [Meta]
+        Insights: ...
+        Patterns: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_conclusion(response["answer"])
+
+    def _parse_root_node(self, response: str, query: str) -> TreeNode:
+        """Parse root node from response."""
+        root = TreeNode(
+            id="root",
+            type=NodeType.ROOT,
+            content=query,
+            confidence=1.0,
+            children=[],
+            parent=None,
+            metadata={},
+            depth=0
+        )
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Decomposition:'):
+                root.metadata["decomposition"] = line[14:].strip()
+            elif line.startswith('Aspects:'):
+                root.metadata["aspects"] = [a.strip() for a in line[8:].split(',')]
+            elif line.startswith('Criteria:'):
+                root.metadata["criteria"] = [c.strip() for c in line[9:].split(',')]
+            elif line.startswith('Metrics:'):
+                root.metadata["metrics"] = [m.strip() for m in line[8:].split(',')]
+        
+        return root
+
+    def _parse_child_nodes(self, response: str, parent: TreeNode) -> List[TreeNode]:
+        """Parse child nodes from response."""
+        children = []
+        current = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[C'):
+                if current:
+                    children.append(current)
+                current = None
+            elif line.startswith('Type:'):
+                type_str = line[5:].strip()
+                try:
+                    node_type = NodeType(type_str.lower())
+                    current = TreeNode(
+                        id=f"{parent.id}_{len(children)}",
+                        type=node_type,
+                        content="",
+                        confidence=0.0,
+                        children=[],
+                        parent=parent,
+                        metadata={},
+                        depth=parent.depth + 1
+                    )
+                except ValueError:
+                    logging.warning(f"Invalid node type: {type_str}")
+            elif current:
+                if line.startswith('Content:'):
+                    current.content = line[8:].strip()
+                elif line.startswith('Confidence:'):
+                    try:
+                        current.confidence = float(line[11:].strip())
+                    except:
+                        current.confidence = 0.5
+                elif line.startswith('Rationale:'):
+                    current.metadata["rationale"] = line[10:].strip()
+                elif line.startswith('Potential:'):
+                    current.metadata["potential"] = line[10:].strip()
+        
+        if current:
+            children.append(current)
+        
+        return children
+
+    def _apply_evaluations(self, nodes: List[TreeNode], response: str) -> List[TreeNode]:
+        """Apply evaluation scores to nodes."""
+        current_node_idx = 0
+        current_scores = {}
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[N'):
+                if current_scores and current_node_idx < len(nodes):
+                    nodes[current_node_idx].evaluation_score = current_scores.get("Overall", 0.0)
+                    nodes[current_node_idx].metadata.update(current_scores)
+                    current_node_idx += 1
+                current_scores = {}
+            elif ':' in line:
+                key, value = line.split(':')
+                try:
+                    current_scores[key.strip()] = float(value.strip())
+                except:
+                    pass
+        
+        if current_scores and current_node_idx < len(nodes):
+            nodes[current_node_idx].evaluation_score = current_scores.get("Overall", 0.0)
+            nodes[current_node_idx].metadata.update(current_scores)
+        
+        return nodes
+
+    async def _evaluate_paths(self, paths: List[List[TreeNode]], context: Dict[str, Any]) -> List[List[TreeNode]]:
+        """Evaluate complete reasoning paths."""
+        prompt = f"""
+        Evaluate complete reasoning paths:
+        Paths: {json.dumps([[self._node_to_dict(n) for n in path] for path in paths])}
+        Context: {json.dumps(context)}
+        
+        For each path evaluate:
+        1. Coherence of progression
+        2. Evidence support
+        3. Conclusion strength
+        4. Novel insights
+        
+        Format as:
+        [P1]
+        Coherence: 0-1
+        Evidence: 0-1
+        Conclusion: 0-1
+        Insights: 0-1
+        Overall: 0-1
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        scores = self._parse_path_scores(response["answer"])
+        
+        # Apply scores to paths
+        for i, path in enumerate(paths):
+            if i < len(scores):
+                for node in path:
+                    node.evaluation_score *= scores[i]
+        
+        return paths
+
+    def _parse_path_scores(self, response: str) -> List[float]:
+        """Parse path evaluation scores."""
+        scores = []
+        current_score = None
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[P'):
+                if current_score is not None:
+                    scores.append(current_score)
+                current_score = None
+            elif line.startswith('Overall:'):
+                try:
+                    current_score = float(line[8:].strip())
+                except:
+                    current_score = 0.5
+        
+        if current_score is not None:
+            scores.append(current_score)
+        
+        return scores
+
+    def _parse_conclusion(self, response: str) -> Dict[str, Any]:
+        """Parse final conclusion."""
+        conclusion = {
+            "answer": "",
+            "confidence": 0.0,
+            "trace": [],
+            "evidence": [],
+            "alternatives": [],
+            "meta_insights": []
+        }
+        
+        section = None
+        for line in response.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+                
+            if line.startswith('[Conclusion]'):
+                section = "conclusion"
+            elif line.startswith('[Meta]'):
+                section = "meta"
+            elif section == "conclusion":
+                if line.startswith('Answer:'):
+                    conclusion["answer"] = line[7:].strip()
+                elif line.startswith('Confidence:'):
+                    try:
+                        conclusion["confidence"] = float(line[11:].strip())
+                    except:
+                        conclusion["confidence"] = 0.5
+                elif line.startswith('Trace:'):
+                    conclusion["trace"] = [t.strip() for t in line[6:].split(',')]
+                elif line.startswith('Evidence:'):
+                    conclusion["evidence"] = [e.strip() for e in line[9:].split(',')]
+                elif line.startswith('Alternatives:'):
+                    conclusion["alternatives"] = [a.strip() for a in line[13:].split(',')]
+            elif section == "meta":
+                if line.startswith('Insights:'):
+                    conclusion["meta_insights"].extend([i.strip() for i in line[9:].split(',')])
+        
+        return conclusion
+
+    def _node_to_dict(self, node: TreeNode) -> Dict[str, Any]:
+        """Convert node to dictionary for serialization."""
+        return {
+            "id": node.id,
+            "type": node.type.value,
+            "content": node.content,
+            "confidence": node.confidence,
+            "evaluation_score": node.evaluation_score,
+            "metadata": node.metadata,
+            "depth": node.depth
+        }
+
+    def _tree_to_dict(self, root: TreeNode) -> Dict[str, Any]:
+        """Convert entire tree to dictionary."""
+        def convert_node(node: TreeNode) -> Dict[str, Any]:
+            node_dict = self._node_to_dict(node)
+            node_dict["children"] = [convert_node(c) for c in node.children]
+            return node_dict
+        
+        return convert_node(root)
+
+    def _path_to_dict(self, path: List[TreeNode]) -> List[Dict[str, Any]]:
+        """Convert path to dictionary."""
+        return [self._node_to_dict(n) for n in path]
+
+    def _update_history(self, root: TreeNode):
+        """Update node history."""
+        def add_to_history(node: TreeNode):
+            self.node_history[node.id] = node
+            for child in node.children:
+                add_to_history(child)
+        
+        add_to_history(root)
+
+    def _update_patterns(self, paths: List[List[TreeNode]]):
+        """Update path patterns."""
+        for path in paths:
+            pattern = "->".join(n.type.value for n in path)
+            self.path_patterns[pattern] += path[-1].evaluation_score
+
+    def get_node_history(self) -> Dict[str, Dict[str, Any]]:
+        """Get history of all nodes."""
+        return {k: self._node_to_dict(v) for k, v in self.node_history.items()}
+
+    def get_successful_patterns(self) -> Dict[str, float]:
+        """Get successful reasoning patterns."""
+        return dict(sorted(self.path_patterns.items(), key=lambda x: x[1], reverse=True))
+
+    def clear_history(self):
+        """Clear node history and patterns."""
+        self.node_history.clear()
+        self.path_patterns.clear()

reasoning/unified_engine.py

@@ -0,0 +1,707 @@
+"""Unified reasoning engine that combines multiple reasoning strategies."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import asyncio
+from collections import defaultdict
+import numpy as np
+
+from .base import ReasoningStrategy
+from .groq_strategy import GroqStrategy
+from .chain_of_thought import ChainOfThoughtStrategy
+from .tree_of_thoughts import TreeOfThoughtsStrategy
+from .meta_learning import MetaLearningStrategy
+from .recursive import RecursiveReasoning
+from .analogical import AnalogicalReasoning
+from .local_llm import LocalLLMStrategy
+from .agentic import (
+    TaskDecompositionStrategy,
+    ResourceManagementStrategy,
+    ContextualPlanningStrategy,
+    AdaptiveExecutionStrategy,
+    FeedbackIntegrationStrategy
+)
+# Import additional strategies
+from .bayesian import BayesianStrategy
+from .market_analysis import MarketAnalysisStrategy
+from .monetization import MonetizationStrategy
+from .multimodal import MultimodalStrategy
+from .neurosymbolic import NeurosymbolicStrategy
+from .portfolio_optimization import PortfolioOptimizationStrategy
+from .specialized import SpecializedStrategy
+from .venture_strategies import VentureStrategy
+from .venture_types import VentureTypeStrategy
+
+class StrategyType(str, Enum):
+    """Types of reasoning strategies."""
+    GROQ = "groq"
+    CHAIN_OF_THOUGHT = "chain_of_thought"
+    TREE_OF_THOUGHTS = "tree_of_thoughts"
+    META_LEARNING = "meta_learning"
+    RECURSIVE = "recursive"
+    ANALOGICAL = "analogical"
+    LOCAL_LLM = "local_llm"
+    TASK_DECOMPOSITION = "task_decomposition"
+    RESOURCE_MANAGEMENT = "resource_management"
+    CONTEXTUAL_PLANNING = "contextual_planning"
+    ADAPTIVE_EXECUTION = "adaptive_execution"
+    FEEDBACK_INTEGRATION = "feedback_integration"
+    BAYESIAN = "bayesian"
+    MARKET_ANALYSIS = "market_analysis"
+    MONETIZATION = "monetization"
+    MULTIMODAL = "multimodal"
+    NEUROSYMBOLIC = "neurosymbolic"
+    PORTFOLIO_OPTIMIZATION = "portfolio_optimization"
+    SPECIALIZED = "specialized"
+    VENTURE = "venture"
+    VENTURE_TYPE = "venture_type"
+
+@dataclass
+class StrategyResult:
+    """Result from a reasoning strategy."""
+    strategy_type: StrategyType
+    success: bool
+    answer: Optional[str]
+    confidence: float
+    reasoning_trace: List[Dict[str, Any]]
+    metadata: Dict[str, Any]
+    performance_metrics: Dict[str, Any]
+    timestamp: datetime = field(default_factory=datetime.now)
+
+@dataclass
+class UnifiedResult:
+    """Combined result from multiple strategies."""
+    success: bool
+    answer: str
+    confidence: float
+    strategy_results: Dict[StrategyType, StrategyResult]
+    synthesis_method: str
+    meta_insights: List[str]
+    performance_metrics: Dict[str, Any]
+    timestamp: datetime = field(default_factory=datetime.now)
+
+class UnifiedReasoningEngine:
+    """
+    Advanced unified reasoning engine that:
+    1. Combines multiple reasoning strategies
+    2. Dynamically selects and weights strategies
+    3. Synthesizes results from different approaches
+    4. Learns from experience
+    5. Adapts to different types of tasks
+    """
+    
+    def __init__(self,
+                 min_confidence: float = 0.7,
+                 strategy_weights: Optional[Dict[StrategyType, float]] = None,
+                 parallel_threshold: int = 3,
+                 learning_rate: float = 0.1):
+        self.min_confidence = min_confidence
+        self.parallel_threshold = parallel_threshold
+        self.learning_rate = learning_rate
+        
+        # Initialize strategies
+        self.strategies: Dict[StrategyType, ReasoningStrategy] = {
+            # Primary strategy (Groq)
+            StrategyType.GROQ: GroqStrategy(),
+            
+            # Core strategies
+            StrategyType.CHAIN_OF_THOUGHT: ChainOfThoughtStrategy(),
+            StrategyType.TREE_OF_THOUGHTS: TreeOfThoughtsStrategy(),
+            StrategyType.META_LEARNING: MetaLearningStrategy(),
+            StrategyType.RECURSIVE: RecursiveReasoning(),
+            StrategyType.ANALOGICAL: AnalogicalReasoning(),
+            StrategyType.LOCAL_LLM: LocalLLMStrategy(),
+            
+            # Agentic strategies
+            StrategyType.TASK_DECOMPOSITION: TaskDecompositionStrategy(),
+            StrategyType.RESOURCE_MANAGEMENT: ResourceManagementStrategy(),
+            StrategyType.CONTEXTUAL_PLANNING: ContextualPlanningStrategy(),
+            StrategyType.ADAPTIVE_EXECUTION: AdaptiveExecutionStrategy(),
+            StrategyType.FEEDBACK_INTEGRATION: FeedbackIntegrationStrategy(),
+            
+            # Additional specialized strategies
+            StrategyType.BAYESIAN: BayesianStrategy(),
+            StrategyType.MARKET_ANALYSIS: MarketAnalysisStrategy(),
+            StrategyType.MONETIZATION: MonetizationStrategy(),
+            StrategyType.MULTIMODAL: MultimodalStrategy(),
+            StrategyType.NEUROSYMBOLIC: NeurosymbolicStrategy(),
+            StrategyType.PORTFOLIO_OPTIMIZATION: PortfolioOptimizationStrategy(),
+            StrategyType.SPECIALIZED: SpecializedStrategy(),
+            StrategyType.VENTURE: VentureStrategy(),
+            StrategyType.VENTURE_TYPE: VentureTypeStrategy()
+        }
+        
+        # Strategy weights with Groq as primary
+        self.strategy_weights = strategy_weights or {
+            # Primary strategy (highest weight)
+            StrategyType.GROQ: 2.5,
+            
+            # Core strategies (high weights)
+            StrategyType.CHAIN_OF_THOUGHT: 1.5,
+            StrategyType.TREE_OF_THOUGHTS: 1.5,
+            StrategyType.META_LEARNING: 1.5,
+            
+            # Agentic strategies (medium-high weights)
+            StrategyType.TASK_DECOMPOSITION: 1.3,
+            StrategyType.RESOURCE_MANAGEMENT: 1.3,
+            StrategyType.CONTEXTUAL_PLANNING: 1.3,
+            StrategyType.ADAPTIVE_EXECUTION: 1.3,
+            StrategyType.FEEDBACK_INTEGRATION: 1.3,
+            
+            # Domain-specific strategies (context-dependent weights)
+            StrategyType.BAYESIAN: 1.2,
+            StrategyType.MARKET_ANALYSIS: 1.2,
+            StrategyType.PORTFOLIO_OPTIMIZATION: 1.2,
+            StrategyType.VENTURE: 1.2,
+            
+            # Other specialized strategies (base weights)
+            StrategyType.MONETIZATION: 1.0,
+            StrategyType.MULTIMODAL: 1.0,
+            StrategyType.NEUROSYMBOLIC: 1.0,
+            StrategyType.SPECIALIZED: 1.0,
+            StrategyType.VENTURE_TYPE: 1.0,
+            StrategyType.RECURSIVE: 1.0,
+            StrategyType.ANALOGICAL: 1.0,
+            StrategyType.LOCAL_LLM: 1.0  # Reduced weight since using Groq
+        }
+        
+        # Performance tracking
+        self.strategy_performance: Dict[StrategyType, List[float]] = defaultdict(list)
+        self.task_type_performance: Dict[str, Dict[StrategyType, float]] = defaultdict(lambda: defaultdict(float))
+        self.synthesis_performance: Dict[str, List[float]] = defaultdict(list)
+
+    async def reason(self, query: str, context: Dict[str, Any]) -> UnifiedResult:
+        """Main reasoning method combining multiple strategies."""
+        try:
+            # Analyze task
+            task_analysis = await self._analyze_task(query, context)
+            
+            # Select strategies
+            selected_strategies = await self._select_strategies(task_analysis, context)
+            
+            # Execute strategies
+            strategy_results = await self._execute_strategies(
+                selected_strategies, query, context)
+            
+            # Synthesize results
+            unified_result = await self._synthesize_results(
+                strategy_results, task_analysis, context)
+            
+            # Learn from experience
+            self._update_performance(unified_result)
+            
+            return unified_result
+            
+        except Exception as e:
+            logging.error(f"Error in unified reasoning: {str(e)}")
+            return UnifiedResult(
+                success=False,
+                answer=f"Error: {str(e)}",
+                confidence=0.0,
+                strategy_results={},
+                synthesis_method="failed",
+                meta_insights=[f"Error occurred: {str(e)}"],
+                performance_metrics={}
+            )
+
+    async def reason_stream(
+        self,
+        query: str,
+        context: Dict[str, Any] = None,
+        strategy_type: Optional[StrategyType] = None,
+        chunk_handler: Optional[callable] = None
+    ) -> AsyncGenerator[str, None]:
+        """
+        Stream reasoning results from the selected strategy.
+        
+        Args:
+            query: Query to reason about
+            context: Additional context for reasoning
+            strategy_type: Specific strategy to use (optional)
+            chunk_handler: Optional callback for handling chunks
+        """
+        context = context or {}
+        
+        # Default to Groq strategy for streaming
+        if not strategy_type:
+            strategy_type = StrategyType.GROQ
+        
+        strategy = self.strategies.get(strategy_type)
+        if not strategy:
+            yield f"Error: Strategy {strategy_type} not found"
+            return
+        
+        if not hasattr(strategy, 'reason_stream'):
+            yield f"Error: Strategy {strategy_type} does not support streaming"
+            return
+        
+        try:
+            async for chunk in strategy.reason_stream(
+                query=query,
+                context=context,
+                chunk_handler=chunk_handler
+            ):
+                yield chunk
+        except Exception as e:
+            logging.error(f"Streaming error: {str(e)}")
+            yield f"Error: {str(e)}"
+
+    async def _analyze_task(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze the task to determine optimal strategy selection."""
+        prompt = f"""
+        Analyze reasoning task:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        Determine:
+        1. Task type and complexity
+        2. Required reasoning capabilities
+        3. Resource requirements
+        4. Success criteria
+        5. Risk factors
+        
+        Format as:
+        [Analysis]
+        Type: ...
+        Complexity: ...
+        Capabilities: ...
+        Resources: ...
+        Criteria: ...
+        Risks: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_task_analysis(response["answer"])
+
+    async def _select_strategies(self, task_analysis: Dict[str, Any], context: Dict[str, Any]) -> List[StrategyType]:
+        """Select appropriate strategies based on task analysis."""
+        # Calculate strategy scores
+        scores: Dict[StrategyType, float] = {}
+        for strategy_type in StrategyType:
+            base_score = self.strategy_weights[strategy_type]
+            
+            # Task type performance
+            task_type = task_analysis["type"]
+            type_score = self.task_type_performance[task_type][strategy_type]
+            
+            # Recent performance
+            recent_performance = (
+                sum(self.strategy_performance[strategy_type][-5:]) / 5
+                if self.strategy_performance[strategy_type] else 0.5
+            )
+            
+            # Resource match
+            resource_match = self._calculate_resource_match(
+                strategy_type, task_analysis["resources"])
+            
+            # Capability match
+            capability_match = self._calculate_capability_match(
+                strategy_type, task_analysis["capabilities"])
+            
+            # Combined score
+            scores[strategy_type] = (
+                0.3 * base_score +
+                0.2 * type_score +
+                0.2 * recent_performance +
+                0.15 * resource_match +
+                0.15 * capability_match
+            )
+        
+        # Select top strategies
+        selected = sorted(
+            StrategyType,
+            key=lambda x: scores[x],
+            reverse=True
+        )[:self.parallel_threshold]
+        
+        return selected
+
+    async def _execute_strategies(self,
+                                strategies: List[StrategyType],
+                                query: str,
+                                context: Dict[str, Any]) -> Dict[StrategyType, StrategyResult]:
+        """Execute selected strategies in parallel."""
+        async def execute_strategy(strategy_type: StrategyType) -> StrategyResult:
+            strategy = self.strategies[strategy_type]
+            start_time = datetime.now()
+            
+            try:
+                result = await strategy.reason(query, context)
+                
+                return StrategyResult(
+                    strategy_type=strategy_type,
+                    success=result.get("success", False),
+                    answer=result.get("answer"),
+                    confidence=result.get("confidence", 0.0),
+                    reasoning_trace=result.get("reasoning_trace", []),
+                    metadata=result.get("metadata", {}),
+                    performance_metrics={
+                        "execution_time": (datetime.now() - start_time).total_seconds(),
+                        **result.get("performance_metrics", {})
+                    }
+                )
+            except Exception as e:
+                logging.error(f"Error in strategy {strategy_type}: {str(e)}")
+                return StrategyResult(
+                    strategy_type=strategy_type,
+                    success=False,
+                    answer=None,
+                    confidence=0.0,
+                    reasoning_trace=[{"error": str(e)}],
+                    metadata={},
+                    performance_metrics={"execution_time": (datetime.now() - start_time).total_seconds()}
+                )
+        
+        # Execute strategies in parallel
+        tasks = [execute_strategy(strategy) for strategy in strategies]
+        results = await asyncio.gather(*tasks)
+        
+        return {result.strategy_type: result for result in results}
+
+    async def _synthesize_results(self,
+                                strategy_results: Dict[StrategyType, StrategyResult],
+                                task_analysis: Dict[str, Any],
+                                context: Dict[str, Any]) -> UnifiedResult:
+        """Synthesize results from multiple strategies with specialized combination methods."""
+        if not strategy_results:
+            return UnifiedResult(
+                success=False,
+                answer="No strategy results available",
+                confidence=0.0,
+                strategy_results={},
+                synthesis_method="none",
+                meta_insights=[],
+                performance_metrics={}
+            )
+
+        # Group results by strategy category
+        core_results = {k: v for k, v in strategy_results.items() 
+                       if k in {StrategyType.CHAIN_OF_THOUGHT, StrategyType.TREE_OF_THOUGHTS, 
+                              StrategyType.META_LEARNING, StrategyType.LOCAL_LLM}}
+        
+        agentic_results = {k: v for k, v in strategy_results.items()
+                          if k in {StrategyType.TASK_DECOMPOSITION, StrategyType.RESOURCE_MANAGEMENT,
+                                 StrategyType.CONTEXTUAL_PLANNING, StrategyType.ADAPTIVE_EXECUTION,
+                                 StrategyType.FEEDBACK_INTEGRATION}}
+        
+        market_results = {k: v for k, v in strategy_results.items()
+                         if k in {StrategyType.MARKET_ANALYSIS, StrategyType.PORTFOLIO_OPTIMIZATION,
+                                StrategyType.VENTURE, StrategyType.MONETIZATION}}
+        
+        analytical_results = {k: v for k, v in strategy_results.items()
+                            if k in {StrategyType.BAYESIAN, StrategyType.NEUROSYMBOLIC,
+                                   StrategyType.SPECIALIZED, StrategyType.MULTIMODAL}}
+
+        # Determine synthesis method based on task type and available results
+        task_type = task_analysis.get('task_type', 'general')
+        synthesis_method = self._determine_synthesis_method(task_type, strategy_results.keys())
+
+        # Apply specialized synthesis based on method
+        if synthesis_method == "weighted_voting":
+            final_result = await self._weighted_voting_synthesis(strategy_results)
+        elif synthesis_method == "market_focused":
+            final_result = await self._market_focused_synthesis(market_results, core_results)
+        elif synthesis_method == "analytical_consensus":
+            final_result = await self._analytical_consensus_synthesis(analytical_results, core_results)
+        elif synthesis_method == "agentic_orchestration":
+            final_result = await self._agentic_orchestration_synthesis(agentic_results, strategy_results)
+        else:
+            final_result = await self._ensemble_synthesis(strategy_results)
+
+        # Generate meta-insights about the synthesis process
+        meta_insights = self._generate_meta_insights(strategy_results, synthesis_method)
+
+        # Calculate aggregate performance metrics
+        performance_metrics = self._calculate_synthesis_metrics(strategy_results, final_result)
+
+        return UnifiedResult(
+            success=final_result['success'],
+            answer=final_result['answer'],
+            confidence=final_result['confidence'],
+            strategy_results=strategy_results,
+            synthesis_method=synthesis_method,
+            meta_insights=meta_insights,
+            performance_metrics=performance_metrics
+        )
+
+    def _determine_synthesis_method(self, task_type: str, available_strategies: Set[StrategyType]) -> str:
+        """Determine the best synthesis method based on task type and available strategies."""
+        market_strategies = {StrategyType.MARKET_ANALYSIS, StrategyType.PORTFOLIO_OPTIMIZATION,
+                           StrategyType.VENTURE, StrategyType.MONETIZATION}
+        analytical_strategies = {StrategyType.BAYESIAN, StrategyType.NEUROSYMBOLIC}
+        agentic_strategies = {StrategyType.TASK_DECOMPOSITION, StrategyType.RESOURCE_MANAGEMENT,
+                            StrategyType.CONTEXTUAL_PLANNING}
+
+        # Calculate strategy type coverage
+        market_coverage = len(market_strategies.intersection(available_strategies))
+        analytical_coverage = len(analytical_strategies.intersection(available_strategies))
+        agentic_coverage = len(agentic_strategies.intersection(available_strategies))
+
+        if task_type in ['market_analysis', 'investment'] and market_coverage >= 2:
+            return "market_focused"
+        elif task_type in ['analysis', 'prediction'] and analytical_coverage >= 2:
+            return "analytical_consensus"
+        elif task_type in ['planning', 'execution'] and agentic_coverage >= 2:
+            return "agentic_orchestration"
+        else:
+            return "weighted_voting"
+
+    async def _weighted_voting_synthesis(self, strategy_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]:
+        """Combine results using weighted voting based on strategy confidence and historical performance."""
+        weighted_answers = defaultdict(float)
+        total_weight = 0
+
+        for strategy_type, result in strategy_results.items():
+            # Calculate weight based on strategy confidence and historical performance
+            historical_performance = np.mean(self.strategy_performance[strategy_type]) if self.strategy_performance[strategy_type] else 1.0
+            weight = self.strategy_weights[strategy_type] * result.confidence * historical_performance
+            
+            weighted_answers[result.answer] += weight
+            total_weight += weight
+
+        if not total_weight:
+            return {'success': False, 'answer': '', 'confidence': 0.0}
+
+        # Select answer with highest weighted votes
+        best_answer = max(weighted_answers.items(), key=lambda x: x[1])
+        confidence = best_answer[1] / total_weight
+
+        return {
+            'success': confidence >= self.min_confidence,
+            'answer': best_answer[0],
+            'confidence': confidence
+        }
+
+    async def _market_focused_synthesis(self, market_results: Dict[StrategyType, StrategyResult],
+                                      core_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]:
+        """Synthesize results with emphasis on market-related strategies."""
+        market_consensus = await self._weighted_voting_synthesis(market_results)
+        core_consensus = await self._weighted_voting_synthesis(core_results)
+
+        # Combine market and core insights with higher weight for market results
+        if market_consensus['confidence'] >= self.min_confidence:
+            return {
+                'success': True,
+                'answer': f"{market_consensus['answer']} (Supported by core analysis: {core_consensus['answer']})",
+                'confidence': 0.7 * market_consensus['confidence'] + 0.3 * core_consensus['confidence']
+            }
+        else:
+            return core_consensus
+
+    async def _analytical_consensus_synthesis(self, analytical_results: Dict[StrategyType, StrategyResult],
+                                           core_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]:
+        """Synthesize results with emphasis on analytical and probabilistic reasoning."""
+        analytical_consensus = await self._weighted_voting_synthesis(analytical_results)
+        core_consensus = await self._weighted_voting_synthesis(core_results)
+
+        # Combine analytical and core insights with uncertainty quantification
+        if analytical_consensus['confidence'] >= self.min_confidence:
+            return {
+                'success': True,
+                'answer': f"{analytical_consensus['answer']} (Confidence interval: {analytical_consensus['confidence']:.2f})",
+                'confidence': 0.6 * analytical_consensus['confidence'] + 0.4 * core_consensus['confidence']
+            }
+        else:
+            return core_consensus
+
+    async def _agentic_orchestration_synthesis(self, agentic_results: Dict[StrategyType, StrategyResult],
+                                             all_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]:
+        """Synthesize results with emphasis on task decomposition and execution planning."""
+        # Extract task decomposition and planning insights
+        task_structure = self._extract_task_structure(agentic_results)
+        execution_plan = self._create_execution_plan(task_structure, all_results)
+
+        # Combine results according to the execution plan
+        synthesized_result = self._execute_synthesis_plan(execution_plan, all_results)
+
+        return {
+            'success': synthesized_result['confidence'] >= self.min_confidence,
+            'answer': synthesized_result['answer'],
+            'confidence': synthesized_result['confidence']
+        }
+
+    def _generate_meta_insights(self, strategy_results: Dict[StrategyType, StrategyResult],
+                              synthesis_method: str) -> List[str]:
+        """Generate meta-insights about the synthesis process and strategy performance."""
+        insights = []
+        
+        # Analyze strategy agreement
+        agreement_rate = self._calculate_strategy_agreement(strategy_results)
+        insights.append(f"Strategy agreement rate: {agreement_rate:.2f}")
+
+        # Identify strongest and weakest strategies
+        strategy_performances = [(st, res.confidence) for st, res in strategy_results.items()]
+        best_strategy = max(strategy_performances, key=lambda x: x[1])
+        worst_strategy = min(strategy_performances, key=lambda x: x[1])
+        
+        insights.append(f"Most confident strategy: {best_strategy[0]} ({best_strategy[1]:.2f})")
+        insights.append(f"Synthesis method used: {synthesis_method}")
+
+        return insights
+
+    def _calculate_synthesis_metrics(self, strategy_results: Dict[StrategyType, StrategyResult],
+                                  final_result: Dict[str, Any]) -> Dict[str, Any]:
+        """Calculate comprehensive metrics about the synthesis process."""
+        return {
+            'strategy_count': len(strategy_results),
+            'average_confidence': np.mean([r.confidence for r in strategy_results.values()]),
+            'confidence_std': np.std([r.confidence for r in strategy_results.values()]),
+            'final_confidence': final_result['confidence'],
+            'strategy_agreement': self._calculate_strategy_agreement(strategy_results)
+        }
+
+    def _update_performance(self, result: UnifiedResult):
+        """Update performance metrics and strategy weights."""
+        # Update strategy performance
+        for strategy_type, strategy_result in result.strategy_results.items():
+            self.strategy_performance[strategy_type].append(strategy_result.confidence)
+            
+            # Update weights using exponential moving average
+            current_weight = self.strategy_weights[strategy_type]
+            performance = strategy_result.confidence
+            self.strategy_weights[strategy_type] = (
+                (1 - self.learning_rate) * current_weight +
+                self.learning_rate * performance
+            )
+        
+        # Update synthesis performance
+        self.synthesis_performance[result.synthesis_method].append(result.confidence)
+
+    def _calculate_resource_match(self, strategy_type: StrategyType, required_resources: Dict[str, Any]) -> float:
+        """Calculate how well a strategy matches required resources."""
+        # Implementation-specific resource matching logic
+        return 0.8  # Placeholder
+
+    def _calculate_capability_match(self, strategy_type: StrategyType, required_capabilities: List[str]) -> float:
+        """Calculate how well a strategy matches required capabilities."""
+        # Implementation-specific capability matching logic
+        return 0.8  # Placeholder
+
+    def _parse_task_analysis(self, response: str) -> Dict[str, Any]:
+        """Parse task analysis from response."""
+        analysis = {
+            "type": "",
+            "complexity": 0.0,
+            "capabilities": [],
+            "resources": {},
+            "criteria": [],
+            "risks": []
+        }
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Type:'):
+                analysis["type"] = line[5:].strip()
+            elif line.startswith('Complexity:'):
+                try:
+                    analysis["complexity"] = float(line[11:].strip())
+                except:
+                    pass
+            elif line.startswith('Capabilities:'):
+                analysis["capabilities"] = [c.strip() for c in line[13:].split(',')]
+            elif line.startswith('Resources:'):
+                try:
+                    analysis["resources"] = json.loads(line[10:].strip())
+                except:
+                    analysis["resources"] = {"raw": line[10:].strip()}
+            elif line.startswith('Criteria:'):
+                analysis["criteria"] = [c.strip() for c in line[9:].split(',')]
+            elif line.startswith('Risks:'):
+                analysis["risks"] = [r.strip() for r in line[7:].split(',')]
+        
+        return analysis
+
+    def _parse_synthesis(self, response: str) -> Dict[str, Any]:
+        """Parse synthesis result from response."""
+        synthesis = {
+            "method": "",
+            "answer": "",
+            "confidence": 0.0,
+            "insights": [],
+            "performance": {}
+        }
+        
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Method:'):
+                synthesis["method"] = line[7:].strip()
+            elif line.startswith('Answer:'):
+                synthesis["answer"] = line[7:].strip()
+            elif line.startswith('Confidence:'):
+                try:
+                    synthesis["confidence"] = float(line[11:].strip())
+                except:
+                    pass
+            elif line.startswith('Insights:'):
+                synthesis["insights"] = [i.strip() for i in line[9:].split(',')]
+            elif line.startswith('Performance:'):
+                try:
+                    synthesis["performance"] = json.loads(line[12:].strip())
+                except:
+                    synthesis["performance"] = {"raw": line[12:].strip()}
+        
+        return synthesis
+
+    def _strategy_result_to_dict(self, result: StrategyResult) -> Dict[str, Any]:
+        """Convert strategy result to dictionary for serialization."""
+        return {
+            "strategy_type": result.strategy_type.value,
+            "success": result.success,
+            "answer": result.answer,
+            "confidence": result.confidence,
+            "reasoning_trace": result.reasoning_trace,
+            "metadata": result.metadata,
+            "performance_metrics": result.performance_metrics,
+            "timestamp": result.timestamp.isoformat()
+        }
+
+    def get_performance_metrics(self) -> Dict[str, Any]:
+        """Get comprehensive performance metrics."""
+        return {
+            "strategy_weights": dict(self.strategy_weights),
+            "average_performance": {
+                strategy_type.value: sum(scores) / len(scores) if scores else 0
+                for strategy_type, scores in self.strategy_performance.items()
+            },
+            "synthesis_success": {
+                method: sum(scores) / len(scores) if scores else 0
+                for method, scores in self.synthesis_performance.items()
+            },
+            "task_type_performance": {
+                task_type: dict(strategy_scores)
+                for task_type, strategy_scores in self.task_type_performance.items()
+            }
+        }
+
+    def clear_performance_history(self):
+        """Clear performance history and reset weights."""
+        self.strategy_performance.clear()
+        self.task_type_performance.clear()
+        self.synthesis_performance.clear()
+        self.strategy_weights = {
+            strategy_type: 1.0 for strategy_type in StrategyType
+        }
+
+    def _extract_task_structure(self, agentic_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]:
+        """Extract task structure from agentic strategy results."""
+        # Implementation-specific task structure extraction logic
+        return {}
+
+    def _create_execution_plan(self, task_structure: Dict[str, Any], all_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]:
+        """Create execution plan based on task structure and strategy results."""
+        # Implementation-specific execution plan creation logic
+        return {}
+
+    def _execute_synthesis_plan(self, execution_plan: Dict[str, Any], all_results: Dict[StrategyType, StrategyResult]) -> Dict[str, Any]:
+        """Execute synthesis plan and combine results."""
+        # Implementation-specific synthesis plan execution logic
+        return {}
+
+    def _calculate_strategy_agreement(self, strategy_results: Dict[StrategyType, StrategyResult]) -> float:
+        """Calculate agreement rate among strategies."""
+        # Implementation-specific strategy agreement calculation logic
+        return 0.0

reasoning/venture_strategies.py

@@ -0,0 +1,701 @@
+"""Specialized strategies for autonomous business and revenue generation."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+class VentureType(Enum):
+    """Types of business ventures."""
+    AI_STARTUP = "ai_startup"
+    SAAS = "saas"
+    API_SERVICE = "api_service"
+    DATA_ANALYTICS = "data_analytics"
+    AUTOMATION_SERVICE = "automation_service"
+    CONSULTING = "consulting"
+    DIGITAL_PRODUCTS = "digital_products"
+    MARKETPLACE = "marketplace"
+
+class RevenueStream(Enum):
+    """Types of revenue streams."""
+    SUBSCRIPTION = "subscription"
+    USAGE_BASED = "usage_based"
+    LICENSING = "licensing"
+    CONSULTING = "consulting"
+    PRODUCT_SALES = "product_sales"
+    COMMISSION = "commission"
+    ADVERTISING = "advertising"
+    PARTNERSHIP = "partnership"
+
+@dataclass
+class VentureMetrics:
+    """Key business metrics."""
+    revenue: float
+    profit_margin: float
+    customer_acquisition_cost: float
+    lifetime_value: float
+    churn_rate: float
+    growth_rate: float
+    burn_rate: float
+    runway_months: float
+    roi: float
+
+@dataclass
+class MarketOpportunity:
+    """Market opportunity analysis."""
+    market_size: float
+    growth_potential: float
+    competition_level: float
+    entry_barriers: float
+    regulatory_risks: float
+    technology_risks: float
+    monetization_potential: float
+
+class AIStartupStrategy(ReasoningStrategy):
+    """
+    Advanced AI startup strategy that:
+    1. Identifies profitable AI applications
+    2. Analyzes market opportunities
+    3. Develops MVP strategies
+    4. Plans scaling approaches
+    5. Optimizes revenue streams
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate AI startup strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_market(query, context)
+            
+            # Technology assessment
+            tech = await self._assess_technology(market, context)
+            
+            # Business model
+            model = await self._develop_business_model(tech, context)
+            
+            # Growth strategy
+            strategy = await self._create_growth_strategy(model, context)
+            
+            # Financial projections
+            projections = await self._project_financials(strategy, context)
+            
+            return {
+                "success": projections["annual_profit"] >= 1_000_000,
+                "market_analysis": market,
+                "tech_assessment": tech,
+                "business_model": model,
+                "growth_strategy": strategy,
+                "financials": projections,
+                "confidence": self._calculate_confidence(projections)
+            }
+        except Exception as e:
+            logging.error(f"Error in AI startup strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class SaaSVentureStrategy(ReasoningStrategy):
+    """
+    Advanced SaaS venture strategy that:
+    1. Identifies scalable SaaS opportunities
+    2. Develops pricing strategies
+    3. Plans customer acquisition
+    4. Optimizes retention
+    5. Maximizes recurring revenue
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate SaaS venture strategy."""
+        try:
+            # Opportunity analysis
+            opportunity = await self._analyze_opportunity(query, context)
+            
+            # Product strategy
+            product = await self._develop_product_strategy(opportunity, context)
+            
+            # Pricing model
+            pricing = await self._create_pricing_model(product, context)
+            
+            # Growth plan
+            growth = await self._plan_growth(pricing, context)
+            
+            # Revenue projections
+            projections = await self._project_revenue(growth, context)
+            
+            return {
+                "success": projections["annual_revenue"] >= 1_000_000,
+                "opportunity": opportunity,
+                "product": product,
+                "pricing": pricing,
+                "growth": growth,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in SaaS venture strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class AutomationVentureStrategy(ReasoningStrategy):
+    """
+    Advanced automation venture strategy that:
+    1. Identifies automation opportunities
+    2. Analyzes cost-saving potential
+    3. Develops automation solutions
+    4. Plans implementation
+    5. Maximizes ROI
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate automation venture strategy."""
+        try:
+            # Opportunity identification
+            opportunities = await self._identify_opportunities(query, context)
+            
+            # Solution development
+            solutions = await self._develop_solutions(opportunities, context)
+            
+            # Implementation strategy
+            implementation = await self._create_implementation_strategy(solutions, context)
+            
+            # ROI analysis
+            roi = await self._analyze_roi(implementation, context)
+            
+            # Scale strategy
+            scale = await self._create_scale_strategy(roi, context)
+            
+            return {
+                "success": roi["annual_profit"] >= 1_000_000,
+                "opportunities": opportunities,
+                "solutions": solutions,
+                "implementation": implementation,
+                "roi": roi,
+                "scale": scale
+            }
+        except Exception as e:
+            logging.error(f"Error in automation venture strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class DataVentureStrategy(ReasoningStrategy):
+    """
+    Advanced data venture strategy that:
+    1. Identifies valuable data opportunities
+    2. Develops data products
+    3. Creates monetization strategies
+    4. Ensures compliance
+    5. Maximizes data value
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate data venture strategy."""
+        try:
+            # Data opportunity analysis
+            opportunity = await self._analyze_data_opportunity(query, context)
+            
+            # Product development
+            product = await self._develop_data_product(opportunity, context)
+            
+            # Monetization strategy
+            monetization = await self._create_monetization_strategy(product, context)
+            
+            # Compliance plan
+            compliance = await self._ensure_compliance(monetization, context)
+            
+            # Scale plan
+            scale = await self._plan_scaling(compliance, context)
+            
+            return {
+                "success": monetization["annual_revenue"] >= 1_000_000,
+                "opportunity": opportunity,
+                "product": product,
+                "monetization": monetization,
+                "compliance": compliance,
+                "scale": scale
+            }
+        except Exception as e:
+            logging.error(f"Error in data venture strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class APIVentureStrategy(ReasoningStrategy):
+    """
+    Advanced API venture strategy that:
+    1. Identifies API opportunities
+    2. Develops API products
+    3. Creates pricing models
+    4. Plans scaling
+    5. Maximizes API value
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate API venture strategy."""
+        try:
+            # API opportunity analysis
+            opportunity = await self._analyze_api_opportunity(query, context)
+            
+            # Product development
+            product = await self._develop_api_product(opportunity, context)
+            
+            # Pricing strategy
+            pricing = await self._create_api_pricing(product, context)
+            
+            # Scale strategy
+            scale = await self._plan_api_scaling(pricing, context)
+            
+            # Revenue projections
+            projections = await self._project_api_revenue(scale, context)
+            
+            return {
+                "success": projections["annual_revenue"] >= 1_000_000,
+                "opportunity": opportunity,
+                "product": product,
+                "pricing": pricing,
+                "scale": scale,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in API venture strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class MarketplaceVentureStrategy(ReasoningStrategy):
+    """
+    Advanced marketplace venture strategy that:
+    1. Identifies marketplace opportunities
+    2. Develops platform strategy
+    3. Plans liquidity generation
+    4. Optimizes matching
+    5. Maximizes transaction value
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate marketplace venture strategy."""
+        try:
+            # Opportunity analysis
+            opportunity = await self._analyze_marketplace_opportunity(query, context)
+            
+            # Platform strategy
+            platform = await self._develop_platform_strategy(opportunity, context)
+            
+            # Liquidity strategy
+            liquidity = await self._create_liquidity_strategy(platform, context)
+            
+            # Growth strategy
+            growth = await self._plan_marketplace_growth(liquidity, context)
+            
+            # Revenue projections
+            projections = await self._project_marketplace_revenue(growth, context)
+            
+            return {
+                "success": projections["annual_revenue"] >= 1_000_000,
+                "opportunity": opportunity,
+                "platform": platform,
+                "liquidity": liquidity,
+                "growth": growth,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in marketplace venture strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class VenturePortfolioStrategy(ReasoningStrategy):
+    """
+    Advanced venture portfolio strategy that:
+    1. Optimizes venture mix
+    2. Balances risk-reward
+    3. Allocates resources
+    4. Manages dependencies
+    5. Maximizes portfolio value
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate venture portfolio strategy."""
+        try:
+            # Portfolio analysis
+            analysis = await self._analyze_portfolio(query, context)
+            
+            # Venture selection
+            selection = await self._select_ventures(analysis, context)
+            
+            # Resource allocation
+            allocation = await self._allocate_resources(selection, context)
+            
+            # Risk management
+            risk = await self._manage_risk(allocation, context)
+            
+            # Portfolio projections
+            projections = await self._project_portfolio(risk, context)
+            
+            return {
+                "success": projections["annual_profit"] >= 1_000_000,
+                "analysis": analysis,
+                "selection": selection,
+                "allocation": allocation,
+                "risk": risk,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in venture portfolio strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def _analyze_portfolio(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze potential venture portfolio."""
+        prompt = f"""
+        Analyze venture portfolio opportunities:
+        Query: {query}
+        Context: {json.dumps(context)}
+        
+        Consider:
+        1. Market opportunities
+        2. Technology trends
+        3. Resource requirements
+        4. Risk factors
+        5. Synergy potential
+        
+        Format as:
+        [Analysis]
+        Opportunities: ...
+        Trends: ...
+        Resources: ...
+        Risks: ...
+        Synergies: ...
+        """
+        
+        response = await context["groq_api"].predict(prompt)
+        return self._parse_portfolio_analysis(response["answer"])
+
+    def _parse_portfolio_analysis(self, response: str) -> Dict[str, Any]:
+        """Parse portfolio analysis from response."""
+        analysis = {
+            "opportunities": [],
+            "trends": [],
+            "resources": {},
+            "risks": [],
+            "synergies": []
+        }
+        
+        current_section = None
+        for line in response.split('\n'):
+            line = line.strip()
+            if line.startswith('Opportunities:'):
+                current_section = "opportunities"
+            elif line.startswith('Trends:'):
+                current_section = "trends"
+            elif line.startswith('Resources:'):
+                current_section = "resources"
+            elif line.startswith('Risks:'):
+                current_section = "risks"
+            elif line.startswith('Synergies:'):
+                current_section = "synergies"
+            elif current_section and line:
+                if current_section == "resources":
+                    try:
+                        key, value = line.split(':')
+                        analysis[current_section][key.strip()] = value.strip()
+                    except:
+                        pass
+                else:
+                    analysis[current_section].append(line)
+        
+        return analysis
+
+    def get_venture_metrics(self) -> Dict[str, Any]:
+        """Get comprehensive venture metrics."""
+        return {
+            "portfolio_metrics": {
+                "total_ventures": len(self.ventures),
+                "profitable_ventures": sum(1 for v in self.ventures if v.metrics.profit_margin > 0),
+                "total_revenue": sum(v.metrics.revenue for v in self.ventures),
+                "average_margin": np.mean([v.metrics.profit_margin for v in self.ventures]),
+                "portfolio_roi": np.mean([v.metrics.roi for v in self.ventures])
+            },
+            "market_metrics": {
+                "total_market_size": sum(v.opportunity.market_size for v in self.ventures),
+                "average_growth": np.mean([v.opportunity.growth_potential for v in self.ventures]),
+                "risk_score": np.mean([v.opportunity.regulatory_risks + v.opportunity.technology_risks for v in self.ventures])
+            },
+            "performance_metrics": {
+                "customer_acquisition": np.mean([v.metrics.customer_acquisition_cost for v in self.ventures]),
+                "lifetime_value": np.mean([v.metrics.lifetime_value for v in self.ventures]),
+                "churn_rate": np.mean([v.metrics.churn_rate for v in self.ventures]),
+                "burn_rate": sum(v.metrics.burn_rate for v in self.ventures)
+            }
+        }
+
+class VentureStrategy(ReasoningStrategy):
+    """
+    Advanced venture strategy that combines multiple specialized strategies
+    to generate comprehensive business plans and recommendations.
+    """
+    
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialize venture strategy with component strategies."""
+        super().__init__()
+        self.config = config or {}
+        
+        # Standard reasoning parameters
+        self.min_confidence = self.config.get('min_confidence', 0.7)
+        self.parallel_threshold = self.config.get('parallel_threshold', 3)
+        self.learning_rate = self.config.get('learning_rate', 0.1)
+        self.strategy_weights = self.config.get('strategy_weights', {
+            "LOCAL_LLM": 0.8,
+            "CHAIN_OF_THOUGHT": 0.6,
+            "TREE_OF_THOUGHTS": 0.5,
+            "META_LEARNING": 0.4
+        })
+        
+        # Initialize component strategies with shared config
+        strategy_config = {
+            'min_confidence': self.min_confidence,
+            'parallel_threshold': self.parallel_threshold,
+            'learning_rate': self.learning_rate,
+            'strategy_weights': self.strategy_weights
+        }
+        
+        self.strategies = {
+            VentureType.AI_STARTUP: AIStartupStrategy(strategy_config),
+            VentureType.SAAS: SaaSVentureStrategy(strategy_config),
+            VentureType.AUTOMATION_SERVICE: AutomationVentureStrategy(strategy_config),
+            VentureType.DATA_ANALYTICS: DataVentureStrategy(strategy_config),
+            VentureType.API_SERVICE: APIVentureStrategy(strategy_config),
+            VentureType.MARKETPLACE: MarketplaceVentureStrategy(strategy_config)
+        }
+        
+        # Portfolio strategy for multi-venture optimization
+        self.portfolio_strategy = VenturePortfolioStrategy(strategy_config)
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Generate venture strategy based on query and context.
+        
+        Args:
+            query: The venture strategy query
+            context: Additional context and parameters
+            
+        Returns:
+            Dict containing venture strategy and confidence scores
+        """
+        try:
+            # Determine venture type from query/context
+            venture_type = self._determine_venture_type(query, context)
+            
+            # Get strategy for venture type
+            strategy = self.strategies.get(venture_type)
+            if not strategy:
+                raise ValueError(f"Unsupported venture type: {venture_type}")
+            
+            # Generate strategy
+            strategy_result = await strategy.reason(query, context)
+            
+            # Get portfolio analysis
+            portfolio_result = await self.portfolio_strategy.reason(query, context)
+            
+            # Combine results
+            combined_result = self._combine_results(
+                strategy_result,
+                portfolio_result,
+                venture_type
+            )
+            
+            return {
+                'answer': self._format_strategy(combined_result),
+                'confidence': combined_result.get('confidence', 0.0),
+                'venture_type': venture_type.value,
+                'strategy': strategy_result,
+                'portfolio_analysis': portfolio_result
+            }
+            
+        except Exception as e:
+            logging.error(f"Venture strategy generation failed: {str(e)}")
+            return {
+                'error': f"Venture strategy generation failed: {str(e)}",
+                'confidence': 0.0
+            }
+    
+    def _determine_venture_type(self, query: str, context: Dict[str, Any]) -> VentureType:
+        """Determine venture type from query and context."""
+        # Use context if available
+        if 'venture_type' in context:
+            return VentureType(context['venture_type'])
+        
+        # Simple keyword matching
+        query_lower = query.lower()
+        if any(term in query_lower for term in ['ai', 'ml', 'model', 'neural']):
+            return VentureType.AI_STARTUP
+        elif any(term in query_lower for term in ['saas', 'software', 'cloud']):
+            return VentureType.SAAS
+        elif any(term in query_lower for term in ['automate', 'automation', 'workflow']):
+            return VentureType.AUTOMATION_SERVICE
+        elif any(term in query_lower for term in ['data', 'analytics', 'insights']):
+            return VentureType.DATA_ANALYTICS
+        elif any(term in query_lower for term in ['api', 'service', 'endpoint']):
+            return VentureType.API_SERVICE
+        elif any(term in query_lower for term in ['marketplace', 'platform', 'network']):
+            return VentureType.MARKETPLACE
+        
+        # Default to AI startup if unclear
+        return VentureType.AI_STARTUP
+    
+    def _combine_results(
+        self,
+        strategy_result: Dict[str, Any],
+        portfolio_result: Dict[str, Any],
+        venture_type: VentureType
+    ) -> Dict[str, Any]:
+        """Combine strategy and portfolio results."""
+        return {
+            'venture_type': venture_type.value,
+            'strategy': strategy_result.get('strategy', {}),
+            'metrics': strategy_result.get('metrics', {}),
+            'portfolio_fit': portfolio_result.get('portfolio_fit', {}),
+            'recommendations': strategy_result.get('recommendations', []),
+            'confidence': min(
+                strategy_result.get('confidence', 0.0),
+                portfolio_result.get('confidence', 0.0)
+            )
+        }
+    
+    def _format_strategy(self, result: Dict[str, Any]) -> str:
+        """Format venture strategy into readable text."""
+        sections = []
+        
+        # Venture type
+        sections.append(f"Venture Type: {result['venture_type'].replace('_', ' ').title()}")
+        
+        # Strategy overview
+        if 'strategy' in result:
+            strategy = result['strategy']
+            sections.append("\nStrategy Overview:")
+            for key, value in strategy.items():
+                sections.append(f"- {key.replace('_', ' ').title()}: {value}")
+        
+        # Key metrics
+        if 'metrics' in result:
+            metrics = result['metrics']
+            sections.append("\nKey Metrics:")
+            for key, value in metrics.items():
+                if isinstance(value, (int, float)):
+                    sections.append(f"- {key.replace('_', ' ').title()}: {value:.2f}")
+                else:
+                    sections.append(f"- {key.replace('_', ' ').title()}: {value}")
+        
+        # Portfolio fit
+        if 'portfolio_fit' in result:
+            fit = result['portfolio_fit']
+            sections.append("\nPortfolio Analysis:")
+            for key, value in fit.items():
+                sections.append(f"- {key.replace('_', ' ').title()}: {value}")
+        
+        # Recommendations
+        if 'recommendations' in result:
+            recs = result['recommendations']
+            sections.append("\nKey Recommendations:")
+            for rec in recs:
+                sections.append(f"- {rec}")
+        
+        return "\n".join(sections)

reasoning/venture_types.py

@@ -0,0 +1,332 @@
+"""Additional venture types for business optimization."""
+
+import logging
+from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple
+import json
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+import numpy as np
+from collections import defaultdict
+
+from .base import ReasoningStrategy
+
+class AIInfrastructureStrategy(ReasoningStrategy):
+    """
+    AI infrastructure venture strategy that:
+    1. Identifies infrastructure needs
+    2. Develops cloud solutions
+    3. Optimizes compute resources
+    4. Manages scalability
+    5. Ensures reliability
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate AI infrastructure strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_market(query, context)
+            
+            # Infrastructure design
+            design = await self._design_infrastructure(market, context)
+            
+            # Optimization strategy
+            optimization = await self._create_optimization_strategy(design, context)
+            
+            # Scaling plan
+            scaling = await self._plan_scaling(optimization, context)
+            
+            # Revenue projections
+            projections = await self._project_revenue(scaling, context)
+            
+            return {
+                "success": projections["annual_revenue"] >= 1_000_000,
+                "market": market,
+                "design": design,
+                "optimization": optimization,
+                "scaling": scaling,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in AI infrastructure strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class AIConsultingStrategy(ReasoningStrategy):
+    """
+    AI consulting venture strategy that:
+    1. Identifies consulting opportunities
+    2. Develops service offerings
+    3. Creates delivery frameworks
+    4. Manages client relationships
+    5. Scales operations
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate AI consulting strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_consulting_market(query, context)
+            
+            # Service design
+            services = await self._design_services(market, context)
+            
+            # Delivery framework
+            framework = await self._create_delivery_framework(services, context)
+            
+            # Growth strategy
+            growth = await self._plan_growth(framework, context)
+            
+            # Revenue projections
+            projections = await self._project_consulting_revenue(growth, context)
+            
+            return {
+                "success": projections["annual_revenue"] >= 1_000_000,
+                "market": market,
+                "services": services,
+                "framework": framework,
+                "growth": growth,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in AI consulting strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class AIProductStrategy(ReasoningStrategy):
+    """
+    AI product venture strategy that:
+    1. Identifies product opportunities
+    2. Develops product roadmap
+    3. Creates go-to-market strategy
+    4. Manages product lifecycle
+    5. Scales distribution
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate AI product strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_product_market(query, context)
+            
+            # Product development
+            product = await self._develop_product_strategy(market, context)
+            
+            # Go-to-market
+            gtm = await self._create_gtm_strategy(product, context)
+            
+            # Scale strategy
+            scale = await self._plan_product_scaling(gtm, context)
+            
+            # Revenue projections
+            projections = await self._project_product_revenue(scale, context)
+            
+            return {
+                "success": projections["annual_revenue"] >= 1_000_000,
+                "market": market,
+                "product": product,
+                "gtm": gtm,
+                "scale": scale,
+                "projections": projections
+            }
+        except Exception as e:
+            logging.error(f"Error in AI product strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class FinTechStrategy(ReasoningStrategy):
+    """
+    FinTech venture strategy that:
+    1. Identifies fintech opportunities
+    2. Develops financial products
+    3. Ensures compliance
+    4. Manages risk
+    5. Scales operations
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate FinTech strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_fintech_market(query, context)
+            
+            # Product development
+            product = await self._develop_fintech_product(market, context)
+            
+            # Compliance strategy
+            compliance = await self._ensure_compliance(product, context)
+            
+            # Risk management
+            risk = await self._manage_risk(compliance, context)
+            
+            # Scale strategy
+            scale = await self._plan_fintech_scaling(risk, context)
+            
+            return {
+                "success": scale["annual_revenue"] >= 1_000_000,
+                "market": market,
+                "product": product,
+                "compliance": compliance,
+                "risk": risk,
+                "scale": scale
+            }
+        except Exception as e:
+            logging.error(f"Error in FinTech strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class HealthTechStrategy(ReasoningStrategy):
+    """
+    HealthTech venture strategy that:
+    1. Identifies healthcare opportunities
+    2. Develops health solutions
+    3. Ensures compliance
+    4. Manages patient data
+    5. Scales operations
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate HealthTech strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_healthtech_market(query, context)
+            
+            # Solution development
+            solution = await self._develop_health_solution(market, context)
+            
+            # Compliance strategy
+            compliance = await self._ensure_health_compliance(solution, context)
+            
+            # Data strategy
+            data = await self._manage_health_data(compliance, context)
+            
+            # Scale strategy
+            scale = await self._plan_healthtech_scaling(data, context)
+            
+            return {
+                "success": scale["annual_revenue"] >= 1_000_000,
+                "market": market,
+                "solution": solution,
+                "compliance": compliance,
+                "data": data,
+                "scale": scale
+            }
+        except Exception as e:
+            logging.error(f"Error in HealthTech strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class EdTechStrategy(ReasoningStrategy):
+    """
+    EdTech venture strategy that:
+    1. Identifies education opportunities
+    2. Develops learning solutions
+    3. Creates content strategy
+    4. Manages user engagement
+    5. Scales platform
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate EdTech strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_edtech_market(query, context)
+            
+            # Solution development
+            solution = await self._develop_learning_solution(market, context)
+            
+            # Content strategy
+            content = await self._create_content_strategy(solution, context)
+            
+            # Engagement strategy
+            engagement = await self._manage_engagement(content, context)
+            
+            # Scale strategy
+            scale = await self._plan_edtech_scaling(engagement, context)
+            
+            return {
+                "success": scale["annual_revenue"] >= 1_000_000,
+                "market": market,
+                "solution": solution,
+                "content": content,
+                "engagement": engagement,
+                "scale": scale
+            }
+        except Exception as e:
+            logging.error(f"Error in EdTech strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class BlockchainStrategy(ReasoningStrategy):
+    """
+    Blockchain venture strategy that:
+    1. Identifies blockchain opportunities
+    2. Develops blockchain solutions
+    3. Ensures security
+    4. Manages tokenomics
+    5. Scales network
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate blockchain strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_blockchain_market(query, context)
+            
+            # Solution development
+            solution = await self._develop_blockchain_solution(market, context)
+            
+            # Security strategy
+            security = await self._ensure_blockchain_security(solution, context)
+            
+            # Tokenomics
+            tokenomics = await self._design_tokenomics(security, context)
+            
+            # Scale strategy
+            scale = await self._plan_blockchain_scaling(tokenomics, context)
+            
+            return {
+                "success": scale["annual_revenue"] >= 1_000_000,
+                "market": market,
+                "solution": solution,
+                "security": security,
+                "tokenomics": tokenomics,
+                "scale": scale
+            }
+        except Exception as e:
+            logging.error(f"Error in blockchain strategy: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+class AIMarketplaceStrategy(ReasoningStrategy):
+    """
+    AI marketplace venture strategy that:
+    1. Creates AI model marketplace
+    2. Manages model deployment
+    3. Handles transactions
+    4. Ensures quality
+    5. Scales platform
+    """
+    
+    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate AI marketplace strategy."""
+        try:
+            # Market analysis
+            market = await self._analyze_ai_marketplace(query, context)
+            
+            # Platform development
+            platform = await self._develop_marketplace_platform(market, context)
+            
+            # Quality strategy
+            quality = await self._ensure_model_quality(platform, context)
+            
+            # Transaction system
+            transactions = await self._design_transaction_system(quality, context)
+            
+            # Scale strategy
+            scale = await self._plan_marketplace_scaling(transactions, context)
+            
+            return {
+                "success": scale["annual_revenue"] >= 1_000_000,
+                "market": market,
+                "platform": platform,
+                "quality": quality,
+                "transactions": transactions,
+                "scale": scale
+            }
+        except Exception as e:
+            logging.error(f"Error in AI marketplace strategy: {str(e)}")
+            return {"success": False, "error": str(e)}

requirements.txt

@@ -0,0 +1,35 @@
+# Core dependencies
+fastapi>=0.68.0
+uvicorn>=0.15.0
+gradio==4.44.1
+pydantic>=2.0.0
+python-dotenv>=0.19.0
+
+# API and networking
+httpx>=0.24.0
+requests>=2.31.0
+aiohttp>=3.8.0
+urllib3>=2.0.7
+websockets>=10.0
+
+# ML and data processing
+numpy>=1.24.0
+pandas>=2.1.0
+scikit-learn>=1.3.2
+plotly>=5.18.0
+
+# Model integration
+huggingface-hub>=0.19.4
+groq>=0.4.1
+
+# Utilities
+typing-extensions>=4.0.0
+asyncio>=3.4.3
+tqdm>=4.66.0
+joblib==1.3.2
+
+# Development
+pytest>=7.0.0
+black>=22.0.0
+isort>=5.0.0
+mypy>=1.0.0