bobrain 0.1.0

A local-first multi-source RAG MCP server — search across Obsidian vaults, Git repos, and PDFs from Claude / Cursor / Claude Desktop.

Bobrain

Bob, your pocket second brain.

A local-first multi-source RAG MCP server — search across multiple Obsidian vaults and the Markdown docs in your code repositories from Claude, Cursor, Claude Desktop, and any other MCP-compatible client.

Status: early prototype. Markdown-only today; PDF and code-AST chunking are on the roadmap. APIs and storage layout may change.

Landing page: https://0916shokichi-blip.github.io/bobrain/ — also browsable locally via python3 -m http.server from the repo root → http://localhost:8000/docs/.

What it is

Bobrain indexes multiple local directories into a single hybrid search layer (BM25 + dense embeddings, combined via Reciprocal Rank Fusion) and exposes a search_docs MCP tool so your AI client can retrieve relevant chunks across all of your personal knowledge sources at once.

Unlike existing RAG servers that focus on a single directory tree or require cloud embeddings, Bobrain:

• runs fully local with in-process ONNX embeddings (multilingual-e5-large)
• supports multiple independent root directories with namespace isolation
• ships Japanese-aware BM25 out of the box (MeCab via fugashi + unidic-lite)
• is designed for people whose knowledge lives in more than one place — an Obsidian vault and the README/docs folder of every active repo

Killer use case

Ask your AI:

"Where did I write about MCP chunking strategies — either in my notes or the code?"

and get a single ranked list spanning your Obsidian vault and your ~/code/ directory, cited by file path.

Install

Requires Python 3.12+.

# Recommended: install once, run from anywhere
pipx install git+https://github.com/0916shokichi-blip/bobrain
# (after PyPI release: pipx install bobrain)

# Or run a one-shot without installing (uv 0.5+)
uvx --from git+https://github.com/0916shokichi-blip/bobrain bobrain --help
# (after PyPI release: uvx bobrain --help)

Or clone and develop locally:

git clone https://github.com/0916shokichi-blip/bobrain.git
cd bobrain
uv sync

First indexing run downloads the multilingual-e5-large ONNX weights (~2.2 GB) into the fastembed cache. Subsequent runs reuse it.

Quickstart

# index a directory under a namespace
bobrain index ~/Documents/notes -n notes

# index a second namespace (they live side by side)
bobrain index ~/code/my-project -n code

# quick CLI search (BM25 + vector hybrid)
bobrain search "how did I chunk markdown" -k 5

# cross-namespace filter
bobrain search "mcp server" --ns notes --ns code

# keep the index live while you edit (Ctrl+C to stop)
bobrain watch ~/Documents/notes -n notes

(If you cloned the repo instead of installing, prefix every command with uv run.)

MCP client setup

Point your MCP client at the stdio server. If you installed via pipx:

{
  "mcpServers": {
    "bobrain": {
      "command": "bobrain",
      "args": ["serve"]
    }
  }
}

The bobrain command on PATH works the same way whether you installed from PyPI or from the git URL above.

Or, from a local clone:

{
  "mcpServers": {
    "bobrain": {
      "command": "uv",
      "args": ["run", "bobrain", "serve"],
      "cwd": "/absolute/path/to/bobrain"
    }
  }
}

Then from Claude / Cursor / Claude Desktop you can call the search_docs tool directly.

Roadmap

• Japanese-aware BM25 via MeCab (fugashi + unidic-lite)
• Upgrade embeddings to multilingual-e5-large (query/passage prefix aware)
• Incremental indexing with watchdog (bobrain watch)
• Indexing progress + per-phase wall times (scan / embed / db-write / bm25)
• PDF chunker via pymupdf (dependency already in)
• Markdown heading-aware chunker (today: fixed character window)
• Code AST-aware chunker (tree-sitter)
• LLM Wiki auto-detection (directories containing CLAUDE.md + log.md + index.md)
• Reranker integration (Voyage / Cohere)
• Pro tier with cloud sync and team sharing

License

MIT. See LICENSE.