AgentConnect
A Decentralized Framework for Autonomous Agent Collaboration
Build and connect independent AI agents that discover, interact, and collaborate securely.
Overview¶
AgentConnect is a revolutionary framework for building and connecting independent AI agents. Unlike traditional multi-agent systems that operate within a single, centrally controlled environment, AgentConnect enables the creation of a decentralized network of autonomous agents that can:
Operate Independently: Each agent is a self-contained system with its own internal logic
Discover Each Other Dynamically: Agents find each other based on capabilities, not pre-defined connections
Communicate Securely: Built-in message signing, verification, and standardized protocols
Collaborate on Complex Tasks: Request services, exchange data, and work together to achieve goals
Scale Horizontally: Support thousands of independent agents in a decentralized ecosystem
Why AgentConnect?¶
Beyond Hierarchies: Break free from centrally controlled multi-agent systems
True Agent Autonomy: Build agents that are independent and interact with any agent in the network
Dynamic Discovery: The network adapts as agents join, leave, and update capabilities
Secure Interactions: Cryptographic verification ensures trustworthy communication
Unprecedented Scalability: Designed for thousands of interconnected agents
Extensible Architecture: Easily integrate custom agents, capabilities, and protocols
Key Features¶
🤖 Dynamic Agent Discovery
- Capability-based matching
- Flexible agent network
- No pre-defined connections
⚡ Decentralized Communication
- Secure message routing
- No central control
- Reliable message delivery
⚙️ Autonomous Agents
- Independent operation
- Own processing loop
- Complex internal structure
🔒 Secure Communication
- Message signing
- Identity verification
- Standardized protocols
🔌 Multi-Provider Support
- OpenAI
- Anthropic
- Groq
- Google AI
📊 Monitoring & Tracing
- LangSmith integration
- Comprehensive tracing
- Performance analysis
Architecture¶
AgentConnect is built on three core pillars that enable decentralized agent collaboration:
1. Decentralized Agent Registry
A registry that allows agents to publish capabilities and discover other agents. This is not a central controller, but a directory service that agents can query to find collaborators that meet their needs.
2. Communication Hub
A message routing system that facilitates secure peer-to-peer communication. The hub ensures reliable message delivery but does not dictate agent behavior or control the network.
3. Independent Agent Systems
Each agent is a self-contained unit built using tools and frameworks of your choice. Agents interact through standardized protocols, while their internal operations remain independent.
Installation¶
Attention
AgentConnect is currently available from source only. Direct installation via pip will be available soon.
Prerequisites¶
Python 3.11 or higher
Poetry (Python package manager)
Redis server
Node.js 18+ and npm (for frontend)
Development Installation¶
To install AgentConnect from source:
# Clone the repository
git clone https://github.com/AKKI0511/AgentConnect.git
cd AgentConnect
# Using Poetry (Recommended)
# Install all dependencies (recommended)
poetry install --with demo,dev
# For production only
poetry install --without dev
Environment Setup¶
# Copy environment template
copy example.env .env # Windows
cp example.env .env # Linux/Mac
Configure API keys in the .env file:
DEFAULT_PROVIDER=groq
GROQ_API_KEY=your_groq_api_key
For monitoring and additional features, you can configure optional settings:
# LangSmith for monitoring (recommended)
LANGSMITH_TRACING=true
LANGSMITH_API_KEY=your_langsmith_api_key
LANGSMITH_PROJECT=AgentConnect
# Additional providers
OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
GOOGLE_API_KEY=your_google_api_key
For more detailed installation instructions, see the Installation guide.
Quick Start¶
import asyncio
from agentconnect.agents import AIAgent, HumanAgent
from agentconnect.core.registry import AgentRegistry
from agentconnect.communication import CommunicationHub
from agentconnect.core.types import ModelProvider, ModelName, AgentIdentity, InteractionMode
async def main():
# Create registry and hub
registry = AgentRegistry()
hub = CommunicationHub(registry)
# Create and register an AI agent
ai_agent = AIAgent(
agent_id="assistant",
name="AI Assistant",
provider_type=ModelProvider.OPENAI,
model_name=ModelName.GPT4O,
api_key="your-openai-api-key",
identity=AgentIdentity.create_key_based(),
interaction_modes=[InteractionMode.HUMAN_TO_AGENT]
)
await hub.register_agent(ai_agent)
# Create and register a human agent
human = HumanAgent(
agent_id="human-user",
name="Human User",
identity=AgentIdentity.create_key_based()
)
await hub.register_agent(human)
# Start interaction between human and AI
await human.start_interaction(ai_agent)
if __name__ == "__main__":
asyncio.run(main())
For more detailed examples, check out our Quickstart guide.
Documentation¶
Getting Started
User Guides
Reference
Monitoring with LangSmith¶
AgentConnect integrates with LangSmith for comprehensive monitoring:
Set up LangSmith
Create an account at LangSmith
Add your API key to .env:
LANGSMITH_TRACING=true LANGSMITH_API_KEY=your_langsmith_api_key LANGSMITH_PROJECT=AgentConnect
Monitor agent workflows
View detailed traces of agent interactions
Debug complex reasoning chains
Analyze token usage and performance
Roadmap¶
✅ MVP with basic agent-to-agent interactions
✅ Autonomous communication between agents
✅ Capability-based agent discovery
⬜ Secure data exchange between agents
⬜ Decentralized payment integration
⬜ Additional AI providers and protocols
⬜ Advanced memory systems (Redis, PostgreSQL)
⬜ Federated learning capabilities
⬜ Cross-chain communication
⬜ Marketplace for agent capabilities
Built with ❤️ by the AgentConnect team