cerebro-code-memory

cerebro-code-memory

Enables AI assistants to maintain persistent understanding of code projects across chat sessions via a SQLite brain that caches structural maps, summaries, and decisions. Provides tools for querying project structure, searching code, and recording insights to reduce token usage and avoid re-analysis.

Category
Visit Server

README

Cerebro 🧠

Persistent code-knowledge memory across AI chat sessions.

Every new chat re-analyzes your project's folders from scratch to understand it, burning tokens re-discovering what a previous chat already learned. Cerebro caches the understanding — not just the files — in a small SQLite "brain" that lives outside the chat. New sessions query it instead of re-reading folders.

Instead of reading 50 files (~100k tokens) to understand a project, the model makes one cerebro_map() call (~2-3k tokens) plus a few targeted lookups.

How it works

Three layers of "traces", cheapest first:

  1. Structural map (free, no LLM) — tree-sitter extracts symbols + imports and builds a dependency graph. Imports resolve both relative paths and tsconfig / jsconfig path aliases (@/...), so Next.js / NestJS monorepos get real edges. PageRank ranks the most important modules. Each file is hashed so changes are detectable. Languages: Python, JavaScript / TypeScript (incl. JSX/TSX) and Dart / Flutter.
  2. Cached summaries (the big saver) — as a chat understands a file, it calls cerebro_record(path, summary) to store a 1-3 sentence English summary (English tokenizes ~15-30% cheaper than Spanish). Future sessions reuse it.
  3. Freshness — each summary is tied to the file's hash. If the file changed, the trace is flagged stale so only that file gets re-read.

Why not Dijkstra? Code knowledge is a relevance problem, not a shortest-path one. The useful algorithms are graph traversal (BFS/DFS, for impact) and centrality (PageRank, for ranking) — not weighted routing.

MCP tools

Tool Purpose
cerebro_map(top=30) Cheap project overview, modules ranked by centrality. Call first.
cerebro_get(path) Summary + symbols + dependencies of a file, without reading it.
cerebro_search(query) Hybrid semantic + keyword search; semantic hits resolve to the exact symbol (path:line), not just the file.
cerebro_record(path, summary) Leave a trace: store your understanding of a file.
cerebro_note(content, topic?) Record a decision / domain rule / gotcha (the why).
cerebro_recall(query?) Recall decisions recorded by past sessions.
cerebro_stale() Files changed since last index + stale summaries.
cerebro_sync() Catch branch switch / git pull / external edits and reindex them.
cerebro_reindex(paths?) Refresh the structural index (only changed files).
cerebro_impact(path) Transitive blast radius: everything that (in)directly imports a file.
cerebro_cycles() Circular-import groups (architecture smell).
cerebro_orphans(prefix?) Code files nothing imports — dead-code candidates (file-level).
cerebro_dead_symbols(prefix?) Unused-export candidates: functions/classes/methods referenced nowhere in their own project, inside files that are imported (symbol-level dead code).
cerebro_callers(name) Call sites of a symbol (who calls it, with enclosing fn + line).
cerebro_calls(path) Internal functions a file calls (outgoing call edges).

Install

One command (published on PyPI) — add the MCP server to Claude Code:

claude mcp add cerebro -- uvx cerebro-code-memory

Or install the full Claude Code plugin (MCP server + session hooks + cerebro-first subagents):

/plugin marketplace add marcodavidd020/cerebro-code-memory
/plugin install cerebro@cerebro

Requires Python ≥ 3.10. Point Cerebro at a repo with CEREBRO_ROOT=/path/to/repo; it also auto-detects the nearest ancestor .cerebro/ brain (handy in monorepos).

Quick start

One command onboards any repo — it indexes and prints the exact registration line:

uv tool install --from . cerebro          # installs the `cerebro` command globally (dev)
cd /path/to/your/repo
cerebro setup --summarize --embed          # index (+ warm summaries / semantic index), then prints next steps

cerebro setup is idempotent. Then run the claude mcp add … line it prints, reload your editor, and the cerebro_* tools are available in chat.

Unified CLI

cerebro                 # no args -> MCP server (stdio); this is what the registration runs
cerebro setup           # index this repo + print MCP registration
cerebro index [--force] # build/refresh the index
cerebro search <query>  # hybrid semantic + keyword search
cerebro map             # project overview
cerebro graph           # interactive dependency-graph HTML
cerebro obsidian        # export an Obsidian vault
cerebro summarize / embed
cerebro impact / cycles / orphans / callers / calls / recall
cerebro doc-audit <vault>   # living docs: flag knowledge notes whose referenced code changed

Living documentation (doc-audit)

cerebro doc-audit <markdown-vault> cross-checks a curated knowledge vault against the code index: it parses each note's code references (path:line, backticked symbols) and the note's ultima_verificacion/fecha, then flags notes whose referenced files changed after they were verified, moved/were deleted, or mention a symbol that no longer exists. --aliases maps wiki app names to repo dirs (backend_app=fenix-store-backend,…); --fix patches stale notes' frontmatter to estado: revisar. This is the bridge between an auto-fresh code index and a human-curated wiki — documentation that can't silently rot.

cerebro doc-refresh <note> closes the loop: it re-audits one stale note against the live code and prints a briefing — current symbols, summary and dependents for each reference, plus the new location of any moved file — exactly the context an agent needs to propose the update (self-healing docs, human-reviewed).

Without a global install, prefix any command with uv run (e.g. uv run cerebro setup).

Point Cerebro at a specific repo with CEREBRO_ROOT=/path/to/repo. It honors .gitignore plus an optional .cerebroignore (same syntax) for excluding heavy non-source dirs (backup/, **/uploads/, …) without touching your VCS config. node_modules/, .next/, dist/, build/ are ignored by default.

Works on monorepos: index the whole thing at once (a single brain at the root, with cross-package alias resolution) or per sub-app (CEREBRO_ROOT per package).

Register with Claude Code

claude mcp add cerebro -- uv --directory /path/to/cerebro run cerebro

Set CEREBRO_ROOT to the project you want the brain to cover. See plugin/ for the optional Claude Code plugin that auto-injects the map at session start and flags edited files as stale.

Scope (MVP)

In: structural map, cached summaries, freshness, keyword search, tsconfig/jsconfig alias resolution, .cerebroignore, batch summary warming (cerebro-summarize, via headless claude -p — no API key), a decision log (cerebro_note / cerebro_recall, surfaced at session start), and git-aware freshness (cerebro_sync catches branch switch / pull / external edits across nested repos), and optional local semantic search (cerebro-embed + --extra semantic: model2vec embeddings — one vector per symbol — no torch, no API key, nothing leaves the machine — cerebro_search becomes hybrid semantic + keyword and lands on the exact symbol (path:line), not just the file), and visualizations (cerebro-graph → self-contained interactive HTML dependency graph; cerebro-export-obsidian → an Obsidian vault where imports are [[links]]), and architecture insights (cerebro_impact transitive blast radius, cerebro_cycles circular imports, cerebro_orphans dead-code candidates), and a symbol-level call graph (cerebro_callers / cerebro_calls, tree-sitter name-resolved). Deferred to v2: a live file watcher, and LSP-backed call graph for type-precise resolution (the current call graph resolves by name).

License

MIT © 2026 Marco Toledo — see LICENSE.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured