docmancer 0.5.1

Compress local documentation context for coding agents.

Local docs context for coding agents.

Install | First run | What you get | Wiki

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Docmancer turns any pile of docs into a hybrid-search index your coding agent can query through a simple CLI. Point it at a folder of Markdown / PDF / DOCX / RTF / HTML, or at a docs URL (GitBook, Mintlify, generic web, GitHub), and ask questions in natural language. Results come back as compact context packs with source attribution, sized to fit a token budget.

A fresh install ships everything you need: SQLite FTS5, a docmancer-owned local Qdrant for dense and sparse vectors, FastEmbed for embeddings (no API key), and a hybrid retriever that fuses lexical, dense, and sparse signals with Reciprocal Rank Fusion.

Install

pipx install docmancer    # Python 3.11, 3.12, or 3.13

If pipx picks an unsupported interpreter, pin one: pipx install docmancer --python python3.13.

First run

Three commands take you from a fresh install to a grounded query:

docmancer setup                                     # config + database + agent skills
docmancer ingest ./docs                             # index local files
docmancer query "How do I authenticate?" --explain  # hybrid search across the index

setup creates ~/.docmancer/ with the config and SQLite database, auto-detects installed coding agents, and installs their skill files. On the first ingest, docmancer downloads the pinned Qdrant binary (~60 MB) and the FastEmbed models (~500 MB) into ~/.docmancer/. After that, ingest stays offline.

Prefer to index a docs site instead of local files?

docmancer add https://docs.pytest.org
docmancer query "How do I parametrize a fixture?" --mode hybrid

What you get

Hybrid search by default. query fans out across SQLite FTS5 (lexical, BM25-reranked), Qdrant dense vectors (FastEmbed bge-base-en-v1.5), and SPLADE sparse vectors, then fuses results with Reciprocal Rank Fusion. The token budget keeps responses small so your agent has room for actual work:

Context pack: ~900 tokens vs ~4800 raw docs tokens (81.2% less docs overhead, 5.33x agentic runway)

No API keys required. FastEmbed runs locally. The optional OpenAI / Voyage / Cohere providers exist if you want them; if the key is missing, ingest falls back to FTS5-only and warns rather than aborting.

Inspectable. Every section is written to ~/.docmancer/extracted/ as Markdown plus JSON. docmancer inspect shows index stats. docmancer query --explain shows which signal (lexical / dense / sparse) placed each result.

Agent integration built in. docmancer setup drops skill files for Claude Code, Cursor, Codex, Cline, Claude Desktop, Gemini, GitHub Copilot, and OpenCode. Your agent can call docmancer query directly from its conversation loop.

Where to next

The wiki is the authoritative reference for everything else. Pick a page based on what you need:

Page	When to read it
Commands	Core docs commands and advanced pack commands
Configuration	All YAML keys, env vars, and the API-key reference
Architecture	How ingest, retrieval, and MCP runtime actually work
Supported Sources	What file formats and URL providers are covered
Install Targets	Where each agent's skill file lands
MCP Packs	Version-pinned API tool packs
Troubleshooting	Common errors and fixes

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