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motleycrew
Build Multi-agent AI Systems: Open Source, Production-Ready, Simple, and Powerful

motleycrew
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Home: https://motleycrew.ai

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What is motleycrew?

motleycrew provides an open-source framework designed for constructing sophisticated multi-agent AI systems. It empowers developers to integrate and orchestrate AI agents and tools sourced from various popular libraries, including Langchain, LlamaIndex, CrewAI, and Autogen. This flexibility allows for the creation of both simple single-task setups and complex workflows tailored to specific needs.

The framework emphasizes production readiness, simplicity, and power. Key capabilities include combining agents and tools in diverse patterns, enabling inter-agent communication by assigning agents as tools to others, and leveraging dynamic knowledge graphs for advanced task dispatch. All components adhere to Langchain's Runnable API, ensuring seamless integration with the Langchain Expression Language (LCEL). Furthermore, motleycrew incorporates built-in features like optional caching for development efficiency and observability integrations.

Features

  • Agent & Tool Integration: Mix and match AI agents (Langchain, LlamaIndex, CrewAI, Autogen) and tools (Langchain, LlamaIndex).
  • Flexible Composition: Combine agents and tools in simple and advanced patterns.
  • Inter-Agent Communication: Give any agent as a tool to any other AI agent.
  • Langchain Compatibility: All components implement Langchain's Runnable API for LCEL usage.
  • Dynamic Task Dispatch: Advanced flow design via dynamic knowledge graphs.
  • Knowledge Graph Interaction: Seamless interaction with knowledge graphs via a user-friendly OGM wrapper for Kuzu.
  • Caching: Optional caching of HTTP requests (including LLM API calls).
  • Observability: Open source observability with Lunary integration.

Use Cases

  • Developing complex AI workflows involving multiple specialized agents.
  • Building research prototypes for multi-agent systems.
  • Creating production-ready AI applications leveraging diverse AI frameworks.
  • Experimenting with advanced agent interaction patterns and dynamic task dispatch.
  • Orchestrating AI agents for collaborative problem-solving.

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