roomi-fields/rtfm

Retrieve The Forgotten Memory
### The open retrieval layer your AI agent was missing
Index everything in your project — code, docs, PDFs, legal texts, research, data — and your agent finds the right context instantly. No hallucinations. No cloud. No API costs.
Fewer calls per search: one query returns ranked hits across code, docs and PDFs — a few hundred tokens of pointers — instead of a chain of grep, glob and whole-file reads.
Free · Local · Open Source · MIT
## The problem
Your AI agent is flying blind.
It greps through thousands of files, misses the doc that answers the question, invents modules that don't exist, forgets what you decided last session. The bigger the project, the worse it gets. You've added a smarter model. It didn't help. Because the bottleneck isn't intelligence — it's retrieval.
Code indexers (Augment, Sourcegraph, Cursor) only see code. But your project isn't just code. It's specs, PRs, architecture decisions, research papers, PDFs, regulations, vault notes — the context your agent needs to stop guessing.
### Why I built this
I was writing a French tax article (~50 pages of regulatory text, cross-references between code articles, case law, administrative doctrine). Claude Code kept grep-ing the same directories in loops, running out of context, and producing confidently wrong citations. I'd added more memory, better prompts, a smarter model. None of it worked, because the agent wasn't reasoning badly — it just couldn't find the right paragraph in a 2,000-file legal corpus. So I stopped trying to make the model smarter and built the layer it was missing. That's RTFM.
## The solution
RTFM indexes everything. One command, one SQLite file, one retrieval layer your agent queries before grepping.
bash
pip install rtfm-ai && cd your-project && rtfm init
30 seconds. Claude Code now searches your indexed knowledge base — code and docs and PDFs and whatever else you drop in — with full-text, semantic, or hybrid search. The agent sees 300 tokens of metadata first, then expands only what's relevant. Progressive disclosure instead of context dumps.
> Free. Runs locally. No API keys. No cloud. Your data stays yours.
### What it looks like
text
$ rtfm search "authentication flow" --limit 3
[1] src/auth/handlers.py > authenticateuser (p.2) score 9.12
src/auth/handlers.py:147 42 lines
[2] docs/architecture/auth.md > SSO flow (p.1) score 7.84
docs/architecture/auth.md:1 23 lines
[3] docs/ADR/0007-oauth.md > Decision (p.1) score 6.90
docs/ADR/0007-oauth.md:12 18 lines
Three results, ~300 tokens. The agent decides what to read next with rtfmexpand(source, targetsection) — not a context dump, a conversation.
## Quick start
### Recommended — Claude Code plugin
In Claude Code (CLI or Desktop Code tab) :
/plugin marketplace add roomi-fields/claude-plugins
/plugin install rtfm@roomi-fields
RTFM is distributed via the roomi-fields/claude-plugins marketplace, which also ships notebooklm-mcp for citation-backed Q&A. To grab both at once:
/plugin install notebooklm@roomi-fields
That's it. The plugin auto-initializes each project on first use:
Creates .rtfm/library.db (one SQLite file)
Injects search instructions into CLAUDE.md