Exam-scored knowledge brains for AI agents: paste a docs URL, get a searchable brain over MCP with a measured score and known gaps. AGPL.
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:
- 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.
From the project README.
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