megabrain

One call returns all the code related to a question
— explained like a senior engineer, with the real code spliced in.

megabrain is a local code-intelligence engine. It replaces minutes of file-by-file crawling — grep, read, explore-agent chains — with a single grounded answer. Index a repo once; every later question retrieves all the related code and stitches it into a walkthrough narrated by an LLM that can only point at code, never rewrite it — so nothing is hallucinated. Retrieval itself uses no LLM (~200 ms); the one LLM call just narrates.

Languages

Code is chunked over its real AST (the cAST split-then-merge recipe), so chunks are whole functions/classes with breadcrumbs — never arbitrary line windows.

Adding a language is a LangSpec entry + pip install tree_sitter_<lang> — a config entry in a registry, not a branch in the indexer (CONTRIBUTING has the recipe). Import/call graph edges are built for Python, TS/JS and PHP (use-statement resolution) today; other languages retrieve on dense+lexical signals (no graph needed for correctness). Legacy procedural PHP (2000s-era mixed HTML/SQL pages) gets its own section-aware chunker; modern PSR/namespaced files keep the generic one.

Install

pip install megabrain                 # core: Python · TS/JS · Markdown
pip install 'megabrain[languages]'    # + Ruby · Go · Rust · PHP

From a clone, for development:

git clone https://github.com/bernatch22/megabrain.git && cd megabrain
pip install -e '.[languages]'
python3 -m pytest                      # offline test suite — no network, no key

Setup

One key, read from the environment (with a ~/.zshrc fallback):

export OPENROUTER_API_KEY=...          # embeddings + ask, all via OpenRouter

Everything runs through OpenRouter's OpenAI-compatible API, so any model works — pick per role by env (the defaults reproduce the validated stack):

export MEGABRAIN_EMBED_MODEL=perplexity/pplx-embed-v1-0.6b   # embeddings (default)
export MEGABRAIN_ASK_MODEL=qwen/qwen3-coder                  # ask narrator (default; ~5x cheaper than Haiku, on par)

Usage

megabrain index  ~/repo                                    # incremental (sha256), no daemon
megabrain ask    ~/repo "how does auth work end to end"    # walkthrough + real code (~6–20s)
megabrain ask    ~/repo/src/auth "how are tokens issued"   # scope to a sub-path (path-scope)
megabrain ask    ~/repo "how do I configure X" --docs      # explain the docs, not the code
megabrain ask    ~/repo "..." --with-docs                  # code AND docs together
megabrain query  ~/repo "request retry logic"              # raw code map, no LLM (~200ms)
megabrain query  ~/repo "..." --full                       # + RELATED code bodies (heavier; default is map-only)
megabrain query  ~/repo "..." --best                       # + LLM order-rerank (~2s, never drops files)
megabrain get    ~/repo src/x.py --symbol Class.method     # one file or symbol
megabrain chunks ~/repo src/x.py "query"                   # every chunk of one file, scored (JSON)
megabrain stats  ~/repo                                    # index stats
megabrain serve-api ~/repo --port 2134                     # long-running JSON API (warm state)

Path-scope: pass a sub-folder (~/repo/src/auth) to any of ask / query / get and retrieval is confined to files under it — the repo root (where the index lives) is auto-detected. Multi-repo works too: megabrain query ~/a/src,~/b "...".

Always fresh: ask / query / chunks auto-refresh a stale index (60 s TTL, incremental — only changed files re-embed) before answering, so results always match what's on disk. Same behavior as the MCP server.

Excluding files: build artifacts (node_modules, .venv, dist, …) are skipped by default. Add your own with megabrain index ~/repo --exclude generated --exclude '*.pb.go' or a persistent .megabrainignore at the repo root (one pattern per line; a bare name matches any path segment, a glob or path/ matches the repo-relative path):

generated/
vendor
*.min.js
docs/legacy

See it live — web demo

git clone https://github.com/bernatch22/megabrain && cd megabrain
pip install -e .                       # + OPENROUTER_API_KEY (or a local embed endpoint)
python examples/webui/server.py        # → http://localhost:8688

It ships with a 2003-style legacy-PHP sample app (indexed on the first run, ~30 s). Type a question → the real engine ranks the bundle files — CORE / RELATED, in milliseconds, no LLM → click a file → every chunk of it renders scored, with the chunks retrieval actually selected highlighted and the noise dimmed. Nothing is precomputed: every query runs the same search + chunks_for_file path agents use. Point it at your own code too:

python examples/webui/server.py ~/my/repo /tmp/click   # any repos, auto-indexed

Provider flexibility — cloud, native, local, hybrid

Embeddings and chat can each point at any OpenAI-compatible endpoint. localhost servers (Ollama / LM Studio / vLLM) need no API key:

# native provider (e.g. A/B a model directly):
export MEGABRAIN_EMBED_BASE_URL=https://api.perplexity.ai/v1   # uses PERPLEXITY_API_KEY

# hybrid — local private embeddings + cheap OpenRouter narration:
export MEGABRAIN_EMBED_BASE_URL=http://localhost:11434/v1
export MEGABRAIN_EMBED_MODEL=embeddinggemma
export MEGABRAIN_EMBED_BATCH=8            # smaller requests for local servers

# fully local (decent GPU) — nothing leaves the machine:
export MEGABRAIN_CHAT_BASE_URL=http://localhost:11434/v1
export MEGABRAIN_ASK_MODEL=qwen3-coder:30b

Changing the embed model auto-triggers a full re-embed on the next index (or force it with --force), so vectors never silently mismatch. Local-stack benchmarks live in evals/LOCAL_MODELS.md.

ask on Claude — Claude Code credits (default when installed), or the Anthropic API

The narrator (ask / --best) runs on Claude by default when the Claude Agent SDK is installed, with the same live streaming — so a Claude Code user gets subscription-credit narration with zero config:

pip install 'megabrain[claude]'        # Claude Agent SDK → ask now runs on Claude (haiku)

Credentials — the SDK drives the Claude Code CLI, so it uses whatever Claude Code already has:

• Claude Code subscription (recommended) — if the claude CLI is installed and logged in, ask runs on your plan's credits. No API key, nothing else to configure.
• Anthropic API — set ANTHROPIC_API_KEY and the same setup bills your API account.

Choosing the provider — the default is auto (Claude when its SDK is importable, else OpenRouter, so a plain pip install megabrain always works). Pin it either way, per run:

export MEGABRAIN_CHAT_PROVIDER=claude       # force Claude   (or 'openrouter' to force that)
export MEGABRAIN_ASK_MODEL=sonnet           # optional — any Claude model or alias

MEGABRAIN_CHAT_PROVIDER=openrouter megabrain ask ~/repo "..."   # one-off on OpenRouter

Embeddings are a separate lane and always need OpenRouter (or a local embed endpoint). index / query / the web demo embed text, and Anthropic has no embeddings API — so OPENROUTER_API_KEY (or MEGABRAIN_EMBED_BASE_URL → a local server like Ollama) is required regardless of the chat provider. The Claude switch only covers the chat side (ask and --best).

How it works

A three-stage pipeline. Only ask calls an LLM — and only to narrate.

Because the model only emits citations and the engine splices code from disk, code cannot be hallucinated or rewritten.

Full design doc — every stage, the locked rules, and the measurements behind them: docs/ARCHITECTURE.md.

MCP

Use it from Claude Code or any MCP client:

claude mcp add megabrain -- python3 -m megabrain.mcp_server

Tools: megabrain_ask (primary), megabrain_query, megabrain_get, megabrain_chunks, megabrain_index — ask/query take an optional scope_path for sub-path retrieval. The server auto-refreshes a stale index before answering, so results always match disk.

HTTP API

serve-api keeps the index warm in memory and serves retrieval over HTTP (stdlib only — no framework). Embed it in an app, or front a docs site with real semantic search.

megabrain serve-api ~/repo --port 2134 [--host 0.0.0.0] [--cors https://site] [--no-llm] [--token SECRET]

Binds localhost by default; --cors opts into a browser origin. Off-localhost, set --token (or MEGABRAIN_API_TOKEN) — it requires Authorization: Bearer <token> on every route except /health. /docsearch groups are configurable per deployment via .megabrain/docsearch.json ({"api/": "SDK API", …}) or MEGABRAIN_DOCSEARCH_GROUPS.

Design

Every choice below is backed by an internal golden set (30 verified queries):

Engine retrieval (internal golden set): R@1 0.86 · bundle_full 1.00 · p50 ~10 ms warm. SWE-bench Lite localization (no training): retrieval Acc@1 ≈ 0.52 / @5 ≈ 0.83 — on par with the trained CodeRankEmbed retriever.

Python API

import megabrain

megabrain.index_repo("path/to/repo")
res = megabrain.search("path/to/repo", "how are tokens issued")  # {tier1, tier2, ...}
print(megabrain.render(res))

from megabrain.ask import ask, render_ask                        # LLM walkthrough
print(render_ask(ask("path/to/repo", "how does auth work end to end")))

import megabrain is dependency-lazy (numpy/tree-sitter load on first use), and the package ships py.typed. Long-running apps: load_state() once + search_with_state() per query (that's exactly what serve-api does).

Custom chunkers — teach the engine a new content type without forking. Any object with exts + chunk_file(relpath, source) -> FileResult (chunks must be an exact line partition — check with validate_partition) plugs in via:

megabrain.index_repo("path/to/repo", strategies=[MySqlStrategy()])

Custom strategies are matched before the built-ins, so they can claim a new extension (.sql, .proto, …) or override how an existing one is chunked; everything downstream (embedding, retrieval, ask) is content-agnostic. Runnable examples — programmatic API, a complete .sql chunker, a terminal chunk heatmap, and a live web demo (python examples/webui/server.py → ask a question, watch retrieval rank the files and light up the selected chunks on a bundled legacy-PHP sample or any repo you pass) — in examples/.

Project layout

megabrain/            engine — providers, embeddings, SQLite store, graph, indexer, query, ask, serve, cli, mcp_server
megabrain/chunkers/   cAST chunkers behind one FileResult contract (python · treesitter+LangSpec · php · markdown)
docs/                 ARCHITECTURE.md — full design, locked rules, measurements
examples/             programmatic API · custom .sql chunker · chunk heatmap · web demo
tests/                offline suite (no network/key/corpus) — run with `python3 -m pytest`
evals/                golden set + model bakeoffs (maintainer-side, private corpus)

Contributions welcome — see CONTRIBUTING.md (the best first PR is a new language LangSpec). Security reports: SECURITY.md.

_{MIT · github.com/bernatch22/megabrain}