Update README, update README link, update logs

README.md

@@ -8,14 +8,33 @@ sdk_version: 5.33.1
 app_file: app.py
 pinned: false
 license: apache-2.0
+tags:
+  - mcp
+  - agents
+  - gradio
+  - hackathon
+  - agent-demo-track
+  - collective-intelligence
+  - mcp-server-track
 short_description: Gradio-based interface coordinates a network of agents
 ---
 
-# Collective Intelligence Orchestrator
+# 🧠 Collective Intelligence Orchestrator
 
 This Gradio-based interface coordinates a network of autonomous AI agents using Hugging Face's MCP protocol.
 
-## Agents in the Network:
+Collective Intelligence Orchestrator is a Gradio-powered interactive application that coordinates a swarm of autonomous AI agents using the Hugging Face Model Context Protocol (MCP). It simulates real-time, cross-domain collaboration between specialized agents for responding to real-world challenges.
+
+This project is built for the Hugging Face + Open Source AI Hackathon and explores how autonomous model-to-model communication can drive situational understanding, policy modeling, and collective action at scale.
+
+# 🚀 What It Does
+You provide a real-world scenario — such as a humanitarian crisis, environmental disruption, or geopolitical event.
+The orchestrator routes this context to a network of specialized MCP-compliant agents. These agents independently process the scenario and return responses from their unique domain lenses.
+
+The orchestrator aggregates their outputs and presents a coordinated, multi-perspective analysis.
+
+## 🤖 Agents in the Swarm:
+Each agent specializes in a unique area of knowledge. Current agents include:
 - Climate Sensor
 - Policy Modeler
 - Economic Forecast
@@ -24,8 +43,9 @@ This Gradio-based interface coordinates a network of autonomous AI agents using
 - NGO Matcher
 
 Submit a real-world scenario, and watch the swarm collaborate in real time.
+All agents comply with MCP and are auto-discoverable, allowing seamless plug-and-play integration of new models.
 
-## Test Case Example:
+## 📊 Test Case Example:
 Input:
 
 - “Massive flooding in coastal Bangladesh, affecting over 200,000 residents. Power outages, displacement, and rising waterborne diseases reported.”
@@ -39,4 +59,10 @@ Expected output:
 - Public Health: Cholera risk, hospital overload.
 - NGO Matcher: Suggest 2–3 orgs that can help.
 
+## 🛠 Architecture
+- Frontend: Gradio interface for input and real-time orchestration feedback
+- Orchestrator Logic: app.py routes user scenarios to agents based on roles and context
+- Agent Discovery: MCP auto-discovers agent endpoints via metadata (.well-known/mcp.yaml)
+- Model Interfacing: Each agent may wrap a hosted LLM (Claude, GPT-4, etc.) or a local model with domain-aligned prompts
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -273,7 +273,7 @@ if __name__ == "__main__":
         Enter a real-world scenario (e.g., natural disaster, policy failure, humanitarian crisis), and let the orchestrator dynamically coordinate a swarm response using multiple autonomous MCP agents.
 
         **Author**: [@dtka](https://huggingface.co/dtka)  
-        **Project Docs**: [GitHub Repo](https://github.com/dtka/collective-intelligence-networks)  
+        **Project Docs**: [HF Repo README](https://huggingface.co/spaces/Agents-MCP-Hackathon/collective-intelligence-orchestrator/resolve/main/README.md)  
         **Hackathon**: [Hugging Face MCP Hackathon](https://huggingface.co/Agents-MCP-Hackathon)
         """)
 

swarm_log.jsonl

@@ -0,0 +1,4 @@
+{"timestamp": "2025-06-09T22:01:00Z", "input": "How can we adapt economic policy for climate impact?", "agents": ["Climate Sensor", "Economic Forecast", "Policy Modeler"], "response": "Generated plan combining climate data with economic models."}
+{"timestamp": "2025-06-09T22:05:00Z", "input": "What are public health risks post-disaster?", "agents": ["Public Health", "Media Monitor"], "response": "Identified risks using media and health databases."}
+{"timestamp": "2025-06-09T22:09:00Z", "input": "Which NGOs can help in this region?", "agents": ["NGO Matcher", "Public Health"], "response": "Matched suitable NGOs based on medical need."}
+{"timestamp": "2025-06-09T22:13:00Z", "input": "Can we predict social unrest due to inflation?", "agents": ["Economic Forecast", "Media Monitor", "Policy Modeler"], "response": "Predicted unrest hotspots by merging trends and forecasts."}