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Basic Information

Composio is a public GitHub repository that provides a large catalog of ready-made integrations to extend the capabilities of AI agents and large language models via function calling. It is intended as a connector layer so agents can invoke external services, access data, and perform real-world actions without each project reimplementing individual API integrations. The project emphasizes high-quality, curated integrations and aims to let developers and teams plug external functionality into LLM-driven workflows. The repository serves as a central place to discover, reuse, and maintain many integrations that agents and LLMs can call programmatically, reducing duplication and accelerating agent development.

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Features
The repository advertises over 100 high-quality integrations that are exposed through a function-calling interface for use by agents and LLMs. It focuses on curated connectors that standardize how external services are invoked by language models, enabling consistent function schemas and predictable behavior. The project is presented as a catalog of integrations rather than a single end-user agent. The README and project signals emphasize interoperability with LLMs, a broad set of external service bindings, and a maintaned collection intended for reuse across agent projects.
Use Cases
Composio helps developers and organizations quickly add external service access to AI agents without building each connector from scratch. By providing a catalog of integrations accessible via function calling, it reduces engineering effort, speeds prototyping, and enables agents to perform tasks that require external data or actions. The collection makes it easier to scale agent capabilities, to standardize integrations across projects, and to maintain and update connectors centrally. This lowers the barrier to productionizing LLM-based workflows that need reliable interactions with third-party APIs and services.

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