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Basic Information

motleycrew is a framework for building multi-agent AI systems that orchestrates and composes agents and tools from popular frameworks. It is designed to let developers mix and match agents and tools from Langchain, LlamaIndex, CrewAI, and Autogen while providing building blocks for constructing workflows and dynamic knowledge graphs. The project stores tasks and execution data in a knowledge graph so tasks can use shared context and control flow. Components implement Langchain's Runnable API for compatibility with LCEL. The repository includes agent types, task primitives like SimpleTask, operators to chain tasks, and integrations for caching and observability, enabling users to focus on high-level system design while the framework manages execution details.

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App Details

Features
Integration with multiple agent and tool ecosystems including Langchain and LlamaIndex. Flexibility to provide agents with arbitrary tools or even other agents. All components implement Langchain"s Runnable API to support LCEL compatibility. Advanced flow design via task chaining and a knowledge graph backend used for control flow and as a universal data store. SimpleTask primitive and a >> operator for chaining tasks in pipelines. Built-in HTTP and LLM request caching through motleycache. Open-source observability support via Lunary for monitoring and visualizing agent performance and system flows. Documentation, quickstart, and examples illustrate common usage patterns.
Use Cases
motleycrew reduces the engineering effort needed to build complex multi-agent AI systems by supplying reusable primitives, integrations, and orchestration logic. Developers can prototype and run chains of tasks without wiring low-level plumbing, enabling focus on high-level design. The knowledge graph centralizes task data and execution metadata so tasks can share context or be coordinated programmatically. Caching of HTTP and LLM calls lowers costs and stabilizes tests, while Lunary observability helps debug and monitor runtime behavior. Compatibility with existing tool ecosystems and a pip-installable package plus example projects and documentation make it straightforward to adopt and extend.

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