roomi-fields/rtfm — The open retrieval layer for AI coding agents.

Description and use

Here's a brief summary and explanation of the project:

**Project Summary:**
Roomi-fields/rtfm is an open-source project that serves as an open retrieval layer for AI coding agents, connecting them to real tools and data through a standard protocol. This project indexes various types of data, including code, documents, and research, using 22 parsers and a knowledge graph. It enables AI assistants to access relevant information and provide more accurate and context-aware responses.

**Value Proposition:**
The primary value proposition of roomi-fields/rtfm is to standardize integrations between AI agents and real tools and data. By providing a common protocol, this project enables developers to create more robust and context-aware AI assistants that can access relevant information and provide more accurate responses.

**Practical Adoption Path:**
To adopt roomi-fields/rtfm, developers can start by evaluating the project's API, SDK, and CLI implementation signals. They can also assess the project's language metadata and focused topics to determine its relevance to their use case. Once evaluated, developers can integrate roomi-fields/rtfm into their AI agents and tools, following the project's documentation and guidelines.

**Production Readiness:**
The production readiness of roomi-fields/rtfm is classified as medium, indicating that it is

Problem

roomi-fields/rtfm helps connect AI assistants to real tools and data through a standard protocol.

Outcome

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.

Workflow

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Evidence

The open retrieval layer for AI coding agents. Indexes code, docs, legal, research, data — 22 parsers (incl. EPUB, DOCX, ODT), FTS5 + semantic search, knowledge graph. Serves surgical context via MCP. Open source, local, free.

Source