metadata

title: Collective Intelligence Orchestrator
emoji: 🐢
colorFrom: purple
colorTo: indigo
sdk: gradio
sdk_version: 5.33.1
app_file: app.py
pinned: true
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

This Gradio-based interface coordinates a network of autonomous AI agents using Hugging Face's MCP protocol.

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
Media Monitor
Public Health
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:

Input:

“Massive flooding in coastal Bangladesh, affecting over 200,000 residents. Power outages, displacement, and rising waterborne diseases reported.”

Expected output:

Climate Sensor: Critical anomaly detected.
Policy Modeler: Emergency zoning, sanitation relief, medical deployment.
Economic Forecast: Estimated GDP loss, recovery timeline.
Media Monitor: High sentiment panic, moderate misinfo risk.
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