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klavis

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Basic Information

Klavis AI is an open source project that provides MCP (multi-tool connector protocol) integrations and hosted MCP servers to simplify building AI applications and agent workflows. The repository supplies Python and TypeScript SDKs, a REST API, example integrations and server implementations so developers can create, provision and manage MCP server instances for services such as Gmail, YouTube and many more. It centralizes authentication flows (OAuth and API key management), exposes collections of prebuilt tools in formats compatible with LLM function-calling, and includes self-hostable MCP server code so teams can run their own connectors. The project also offers documentation, tutorials and example code showing how to integrate with LLM providers and agent frameworks to enable tool calls and multi-step tool workflows.

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3947
App URL
https://github.com/Klavis-AI/klavis
Github Repository
https://github.com/Klavis-AI/klavis/blob/main/README.md

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App Details

Features
Provides instant integration via Python and TypeScript SDKs and a REST API. Built-in authentication support for OAuth flows and API key management. Hosted, production-ready MCP infrastructure designed to scale and also available as self-hostable server code. Access to 100+ prebuilt tools and integrations including CRM, GSuite, GitHub, Slack and databases. Multi-platform compatibility with any LLM provider and many agent frameworks. Tool listing and function-calling support for LLMs. Example code demonstrating creating server instances, listing tools, handling tool calls and wiring results back into conversations. Comprehensive documentation, SDK guides, MCP protocol guide and examples for platform integrations. Roadmap items include more servers, event-driven/webhook support and improved tests and docs.
Use Cases
Klavis reduces engineering effort for developers who need to connect LLMs and agent frameworks to external services by providing ready-made MCP servers and standardized tool interfaces. It handles authentication complexity so applications can provision service instances, perform OAuth or set API tokens, and call tools from LLM-driven workflows without building per-service clients. The repo supplies examples showing end-to-end flows: creating a server instance, exposing tools in OpenAI function format, invoking tools from model function calls, collecting results and returning final responses. Self-hostable servers let teams retain control over data and deployment. Documentation, tutorials and examples accelerate integration with OpenAI, Together AI and other platforms and lower the barrier to adding new MCP servers.

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