Multi-Agent Collaborative System

A decentralized multi-agent system designed for collaborative problem-solving, featuring specialized agents working together through a graph-based communication structure. We approach the problem of reproducibility of agentic conversations through our system by breaking down chain of thought through specialized instances of AI agents. We then perform a union find on extremely similar queries in order to create virtual nodes, which store multiple pathways for similar queries. Using CrossEncoder from sentence transformers we are able to rank nodes on a level by level basis to produce favourable nodes on each level. The connection of favourable levels in each level forms the critical path in the network of conversations.

Use Cases

1. Provide cachable solutions to commonly asked questions
2. Comparing adversarial patterns to a golden set for the initial query
3. Reinforcement Learning - utilizing conversation pathways for model optimization

Architecture

The system consists of three main components:

1. Chief Agent: System orchestrator

   • Implementation details in src/multiagent.py
   • Core routing and coordination logic

2. Specialized Agents: Research, Coding, and Critic agents

   • Individual agent implementations in respective modules
   • See src/multiagent.py for agent interfaces

3. Graphs: Communication optimization

   • Graph structures in graphs/conversation_graph.py
   • Virtual node implementation in src/slime_mold.py

Core Components

• Base Agent Framework: Foundation for all agent types

  • See src/multiagent.py for implementation
  • Includes agent state management and messaging system

• Response System: Manages agent interactions through Graphs

  • Graph-based routing implemented in graphs/conversation_graph.py
  • Message handling and routing logic

• Solution Architecture: Stores and retrieves solution patterns

  • Virtual node implementation in src/slime_mold.py
  • Union-find clustering for similar queries

• Safety Framework: Comprehensive security measures

  • Implementation in src/safetyframework.py
  • Pattern detection and content filtering

Testing

Test implementations are available in the tests directory:

• tests/test_conversation.py: Conversation graph tests
• tests/test_slime_mold.py: Test fixtures and utilities

Documentation

Detailed documentation:

• System Overview

Safety Features

The system includes comprehensive safety features (see src/safetyframework.py):

• Adversarial pattern detection
• Harmful content filtering
• Manipulation pattern recognition
• Privacy protection mechanisms

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Acknowledgments

• Inspired by Physarum polycephalum behavior
• Built with networkx, LangChain and Sentence Transformers

Would you like me to add any additional sections or make any modifications?

Implementation References

For detailed implementation of specific components, please refer to:

MultiAgent System: src/multiagent.py Safety Framework: src/safetyframework.py Slime Mold Algorithm: src/slime_mold.py Conversation Graphs: graphs/conversation_graph.py

Future Research Directions

Model-Based Embeddings for Syntactic Code Transformations

• Syntactic Code Understanding
• Code Equivalence Detection