Retrieve The Forgotten Memory

Name: Retrieve The Forgotten Memory
Author: roomi-fields

@roomi-fields

关于 Retrieve The Forgotten Memory

The open retrieval layer for AI coding agents. Indexes code, docs, legal, research, data — 15 parsers, FTS5 + semantic search, knowledge graph. Serves surgical context via MCP. Open source, local, free.

基本信息

分类

记忆与知识

传输方式

stdio

发布者

roomi-fields

提交者

roomi-fields

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "rtfm": {
      "command": "rtfm-serve",
      "args": [],
      "env": {
        "RTFM_DB": ".rtfm/library.db"
      }
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Retrieve The Forgotten Memory?

Retrieve The Forgotten Memory (RTFM) is a local, free knowledge-base indexer for AI agents. It indexes all project files—code, specs, PRs, PDFs, regulations, vault notes—into a single SQLite file and provides full-text, semantic, or hybrid search so agents find the right context before resorting to grep.

How to use Retrieve The Forgotten Memory?

Install with pip install rtfm-ai, then run rtfm init inside your project directory. Agents (e.g., Claude Code) query the indexed knowledge base automatically, seeing a 300-token metadata snippet before expanding only relevant content.

Key features of Retrieve The Forgotten Memory

Indexes code, docs, PDFs, and any other file type
Full-text, semantic, and hybrid search modes
Progressive disclosure: 300-token metadata snippets
100% local—no cloud, no API keys, no cost
Single SQLite file per project
Runs in ~30 seconds to index a typical project

Use cases of Retrieve The Forgotten Memory

AI coding agents that need access to project documentation, not just source code
Legal or regulatory research across thousands of files and cross-references
Large monorepos where standard grep searches miss the right file
Keeping agent context efficient by expanding only relevant content
Offline-first knowledge retrieval for sensitive or air-gapped projects

FAQ from Retrieve The Forgotten Memory

What makes RTFM different from code indexers like Augment, Sourcegraph, or Cursor?

Code indexers only see code. RTFM indexes everything—specs, PRs, architecture decisions, research papers, PDFs, regulations, vault notes—so the agent can find non-code context it needs to stop guessing.

What are the runtime dependencies and installation requirements?

RTFM requires Python. Installation is a single pip install rtfm-ai command with no API keys, cloud accounts, or external services.

Where does the indexed data live?

All data is stored in a single local SQLite file inside your project directory. Nothing is sent to any server.

What search methods does RTFM support?

It supports full-text search, semantic search, and hybrid search (combining both).

Does RTFM require internet or any authentication?

No. It runs entirely locally with no API keys, no cloud dependency, and no internet connection needed.

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