Why?

Your AI agent is blind. It greps through thousands of files, loses context every session, hallucinates modules that don't exist. The fix isn't a smarter model — it's smarter retrieval.

Augment, Sourcegraph, and Cursor index code. RTFM indexes everything.

pip install rtfm-ai[mcp] && cd your-project && rtfm init

30 seconds. Claude Code now searches your indexed knowledge base before grepping.

Features

Search & Retrieval

• FTS5 full-text search — instant, zero-config, works out of the box
• Semantic search — optional embeddings (FastEmbed/ONNX, no GPU needed)
• Metadata-first — search returns file paths + scores (~300 tokens), not content dumps
• Progressive disclosure — the agent reads only what it needs via Read(file_path)

Indexing

• 10 parsers built-in — Markdown, Python (AST), LaTeX, YAML, JSON, Shell, PDF, XML, HTML, plain text
• Extensible — add any format in ~50 lines of Python
• Incremental sync — only re-indexes what changed
• Auto-sync — hooks keep the index fresh every prompt, zero manual work

Integration

• MCP server — works with Claude Code, Cursor, Codex, any MCP client
• CLI — rtfm search, rtfm sync, rtfm status, ...
• Python API — Library, SearchResults, custom parsers
• Non-invasive — doesn't touch your code, doesn't replace your workflow tools

Quick Start

Install

pip install rtfm-ai[mcp]

Initialize in your project

cd /path/to/your-project
rtfm init

This creates .rtfm/library.db, registers the MCP server, injects search instructions into CLAUDE.md, and installs auto-sync hooks. Done.

Then say to Claude Code: "Search for authentication flow" — it uses rtfm_search instead of grepping.

Optional extras

pip install rtfm-ai[embeddings]  # Semantic search (FastEmbed ONNX)
pip install rtfm-ai[pdf]         # PDF parsing (pdftext + marker)
pip install rtfm-ai[mcp,embeddings,pdf]  # Everything

MCP Tools

The Parser Architecture

This is what makes RTFM different. Need to index a format nobody supports?

from rtfm.parsers.base import BaseParser, ParserRegistry
from rtfm.core.models import Chunk

@ParserRegistry.register
class FHIRParser(BaseParser):
    """Parse HL7 FHIR medical records."""
    extensions = ['.fhir.json']
    name = "fhir"

    def parse(self, path, metadata=None):
        data = json.loads(path.read_text())
        for entry in data.get('entry', []):
            resource = entry.get('resource', {})
            yield Chunk(
                id=resource.get('id', str(uuid4())),
                content=json.dumps(resource, indent=2),
                book_title=f"FHIR {resource.get('resourceType', 'Unknown')}",
                book_slug=resource.get('id', 'unknown'),
                page_start=1,
                page_end=1,
            )

50 lines. Now your medical AI agent understands FHIR records.

Built-in parsers

How It Compares

Use Cases

RTFM works anywhere your project isn't just code:

• LegalTech — Code + tax law + regulatory specs. Ships with Legifrance XML and BOFiP parsers.
• Research — Code + LaTeX papers + datasets. Ships with LaTeX and PDF parsers.
• FinTech — Code + financial regulations + XBRL reports. Write an XBRL parser in 50 lines.
• HealthTech — Code + medical records (HL7/FHIR) + clinical guidelines.
• Any regulated industry — If your project mixes code with domain documents, RTFM is for you.

CLI Reference

# Search (auto-detects .rtfm/ database)
rtfm search "authentication flow"
rtfm search "article 39" --corpus cgi --limit 5

# Sync
rtfm sync                              # All registered sources
rtfm sync /path/to/docs --corpus docs  # Specific directory
rtfm sync . --force                    # Force re-index

# Source management
rtfm add /path/to/docs --corpus docs --extensions md,pdf
rtfm sources

# Status & info
rtfm status
rtfm books
rtfm tags

# Semantic search (requires embeddings)
rtfm embed                                      # Generate embeddings (one-time)
rtfm semantic-search "tax deductions" --hybrid   # Hybrid FTS + semantic

# MCP server
rtfm serve

Python API

from rtfm import Library

lib = Library("my_library.db")

# Index
stats = lib.ingest("documents/article.md", corpus="docs")
result = lib.sync(".", corpus="my-project")  # SyncResult(+3 ~1 -0 =42)

# Search
results = lib.search("depreciation", limit=10, corpus="cgi")
results = lib.hybrid_search("amortissement fiscal", limit=10)

# Export for LLM
prompt_context = results.to_prompt(max_chars=8000)

lib.close()

Works With Your Workflow Tools

RTFM isn't a task manager. It's a knowledge layer.

┌─────────────────────────────────┐
│  GSD / Taskmaster / Claude Flow │  <- Workflow
├─────────────────────────────────┤
│              RTFM               │  <- Knowledge
├─────────────────────────────────┤
│          Claude Code            │  <- Execution
└─────────────────────────────────┘

Without RTFM, your workflow tool orchestrates an agent that hallucinates. With RTFM, your agent knows what it's building on.

Contributing

Adding a parser is the easiest way to contribute — and the most impactful. See CONTRIBUTING.md.

Found a bug? Have an idea? Open an issue.

License

MIT — use it, fork it, extend it, ship it.

Author

Romain Peyrichou — @roomi-fields