Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
What it is, and how people use it
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems. It sits on the MCP Servers shelf of the catalog, maintained by @deepset-ai.
Why builders keep it installedMCP Servers benefits
An MCP server gives your agent typed access to a browser, database, chart engine or cloud account. It acts through the server instead of hallucinating what the API would have returned.
Servers run on your machine or your cloud. Access is scoped per tool and can be cut mid-session, so credentials never live inside the model.
Cursor, Claude Desktop, Codex CLI and Windsurf all speak MCP. The config you write today keeps working when you switch editor or model.
Tool calls pass through the server, so you get a log of exactly what the agent read, wrote or clicked - the difference between a demo and something you'd run at work.
Contributorsloading from GitHub…
Languagesestimated from snapshot
- Python 84.6%
- Shell 11.5%
- JavaScript 3.8%