AI product Open source

Ratel

Ratel is an in-process context-engineering platform for AI agents. It catalogs tools, skills, and persistent facts, then searches the catalog on each turn and progressively discloses only the capabilities relevant to the request instead of placing every tool schema and instruction in the context window. Its retrieval supports BM25, semantic, and hybrid ranking without requiring a vector database.

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Overview

The project includes a Rust core, TypeScript and Python SDKs, an MCP server, and a CLI. Tools can be registered directly or ingested from an MCP server; skills can associate instructions with tools, while registered facts are re-injected when they are no longer fresh in the conversation. The repository describes the system as reducing token usage and mitigating tool overload.

What Ratel is used for

2 uses taken from transcripts — each links to the moment in the video.

  • An in-process context-engineering platform that selects which tools enter an AI agent's context window on each turn. It can register tools or ingest an MCP server and includes a Rust core, TypeScript SDK, MCP server, and CLI.

  • Reduces agent tool overload by indexing tool schemas and skill metadata, searching them, and injecting only relevant tools or instructions for the current turn. It supports BM25 search with optional semantic and hybrid ranking.

Videos mentioning Ratel

2 in the library.