mozg.

Exam-scored knowledge brains for AI coding agents. Paste one docs URL → get a searchable brain your agent queries over MCP — with a measured score and a public list of what it does not know.

Start here · Catalogue · Why not a context file · Self-host guide · Roadmap

Your agent answers from memory, and memory has a date on it. Context files rot silently, cost tokens on every session, and can never tell you what they actually cover. mozg is built on one mechanism applied everywhere:

Knowledge must be measured.

The loop

flowchart LR
    A[one docs URL] --> B[crawler<br/>github tree · llms.txt · sitemap]
    B --> C[atomic notes<br/>+ embeddings]
    C --> D{{the exam<br/>~30 questions from the goal}}
    D -->|score + failed questions| E[focused re-read<br/>chases the gaps]
    E --> C
    F[agents querying over MCP] -->|zero-hit searches| D
    F -->|corrections| G[owner review] --> C

• The exam is the product. The brain's goal becomes control questions, re-sat after every ingest. Trained 92% is a fact, not a claim — and the failures are listed publicly, so agents are told the gaps before they search. Anti-bluff questions verify it refuses what it doesn't know.

• Zero-context search. Retrieval is server-side (hybrid + reranker). A brain can hold 3,000 notes; an answer costs the three it needed.

• The collective mind. A search that returns nothing becomes an exam question. Corrections agents file become owner-reviewed notes. Nothing is ever deleted — every version is kept, and the diff between sittings shows on the brain's page.

• learn. Any brain doubles as a spaced-repetition course for humans at learn.mozg.sh — read → recall → quiz, streaks, a certificate at 80%, and a scoreboard against your own agent.

• Injection-hardened. Published notes are scanned for credential leaks, PII and prompt-injection language; third-party notes arrive framed as data, not instructions; AI training crawlers are refused in robots.txt.

Related MCP server: Educational Tutor MCP Server

Run your own, in one command

git clone https://github.com/egorfedorov/mozg.git && cd mozg
cp .env.selfhost.example .env     # fill ANTHROPIC_API_KEY + BETTER_AUTH_SECRET
docker compose -f docker-compose.selfhost.yml up

Postgres with pgvector, the embedder, the app and the worker come up together; the schema migrates itself before the app starts. Open http://localhost:3300, create an account, paste a docs URL.

First boot downloads ~2.2 GB of embedding weights into a volume — that is the slow part, and it happens once. Full operational detail, including production deploys behind nginx, lives in docs/SELFHOST.md.

Cloud, or your own metal

The deal is honest: building brains spends model tokens. On the cloud you either pay a plan (we spend), set your own API key in settings (you spend), or teach through a Claude Code subscription with the plugin's /mozg:train.

Stack

Next.js 16 · Postgres 14 + pgvector (HNSW) · pg-boss (queue in Postgres) · better-auth · bge-m3 embeddings + bge-reranker (self-hosted FastAPI) · Playwright render service for JS-shell docs sites · esbuild-bundled worker. 178 tests, CI on every push.

Contributing

Bug reports with reproduction beat everything; brain_feedback reports from real use beat those. Small PRs welcome — see CONTRIBUTING.md. New catalogue packs are data entries, not code.

License

AGPL-3.0. Run it, change it, self-host it; host it for others and your changes stay open. The hosted cloud at mozg.sh sells convenience and inference — never locks.