megabrain 0.1.2

Local code-intelligence engine: one call returns all the code related to a question, explained with the real code spliced in.

megabrain

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

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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.

Install

No packaging step — runs straight from a clone:

git clone https://github.com/pinecall/megabrain.git
cd megabrain
pip install numpy                                # core (Python indexing)
pip install tree_sitter tree_sitter_typescript  # TS/JS (+ tree_sitter_ruby tree_sitter_go for Ruby/Go)
alias megabrain='python3 -m megabrain.cli'       # optional: clean invocation

Keys are read from the environment (with a ~/.zshrc fallback):

export PERPLEXITY_API_KEY=...   # required — embeddings
export ANTHROPIC_API_KEY=...    # only for `ask` and `--best`

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 "how do I configure X" --docs        # explain the docs instead of code
megabrain query  ~/repo "request retry logic"                # raw code map, no LLM (~200ms)
megabrain get    ~/repo src/x.py --symbol Class.method       # one file or symbol

Indexes code (.py · .ts · .tsx · .js · .jsx · .mjs · .cjs · Ruby · Go) and markdown (.md · .markdown · .mdx) through a strategy registry — adding a language or content type is a config entry, not a branch in the indexer.

How it works

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

stage	what it does
index	cAST chunk → Perplexity embed (int8, L2-normalized) → SQLite. Incremental by sha256, no watcher.
query	No-LLM retrieval (~200ms): dense-chunk + file-skeleton fusion, with import/call-graph candidates. Returns a map — CORE (full code of the top files) + RELATED (every connected file with its best chunk).
ask	One streamed Haiku call writes the walkthrough and cites code as [[k]]; the engine replaces each citation with the verbatim block (real file, real line numbers). Non-cited related files are listed at the end. Fail-open: any API error falls back to the full query bundle.

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

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_index. The server auto-refreshes a stale index before answering, so results always match disk.

Design

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

decision	evidence
cAST chunking (4K nws chars, breadcrumbs, partition-guaranteed)	unit-tested; every line lands in exactly one chunk — no gaps, no overlaps
pplx-embed-v1 (1024-d, int8 wire, L2-normalized)	beats openai-3-large on code; ~$0.0016/repo
dense chunk + 0.5 × file-skeleton score	dual-granularity; precision up, no downside
graph (import + call edges) for candidates only	PageRank-as-ranking rejected by data (Acc@1 0.91 → 0.73)
no LLM in the retrieval path	every LLM prune variant cost completeness; ask explains, it never prunes

Engine retrieval (internal golden set): R@1 0.86 · bundle_full 1.00 · p50 8 ms warm. SWE-bench Lite localization (no training): retrieval Acc@1 ≈ 0.52 / @5 ≈ 0.83 — on par with the trained CodeRankEmbed retriever.

Project layout

megabrain/   engine — chunkers, embeddings, SQLite store, graph, indexer, query, ask, cli, mcp_server
evals/       golden.json (30 verified queries) + swebench harness
tests/       engine + chunker gates

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_{github.com/pinecall/megabrain}