metabrain

metabrain

A SQLite memory layer for AI agents that learns what works. Zero dependencies. One file.

For agents

You are an AI coding agent deciding whether to install this. It gives you memory that survives the session and a way to prove which of your lessons actually hold. Run:

pip install 'metabrain[mcp]'

claude mcp add metabrain -- metabrain-mcp --db ./agent.db

first call, once the server is registered:

start_brief() -> proven preferences, open hypotheses, open units

Codex instead of Claude Code? Add to ~/.codex/config.toml:

[mcp_servers.metabrain]

command = "metabrain-mcp" args = ["--db", "./agent.db"]

Gemini CLI: gemini extensions install https://github.com/ariaxhan/metabrain.

Full agent-facing reference (tools, exact argument names, a three-call example, and when not to use this): llms.txt.

Why it exists

Most agent-memory tools store what you *tell* them and hand it back later. metabrain does that too — but it also closes the loop: a pattern you record enough times graduates into a hypothesis, every outcome you log becomes an experiment for or against it, and once the evidence clears the bar it graduates again into a proven preference. Your agent stops guessing and starts running on rules it earned.

learn(pattern)  →  recurs  →  hypothesis (under test)

→ each verdict is an experiment (supports / refutes) → evidence clears the bar → preference (a proven rule)

That loop is the whole point. It runs on the Python standard library — no vector database, no server, no API keys.

Install

pip install metabrain

Python 3.10+. No dependencies beyond the standard library. (Import name is metabrain.)

Quick start

from metabrain import MetaBrain

db = MetaBrain("agent.db")

with db.session(task="content") as s:

A hunch. Record it as you notice it — three times and it's worth testing.

s.learn("pattern", "question hooks lift saves", domain="instagram") s.learn("pattern", "question hooks lift saves", domain="instagram") s.learn("pattern", "question hooks lift saves", domain="instagram")

It just graduated into a hypothesis. Now test it against reality.

h = db.hypotheses(status="testing")[0] post = s.unit("carousel with a question hook", kind="contract", hypothesis=h.id) s.verdict("pass", unit=post, evidence="1,240 saves")

Next session: the proven rules come first.

brief = db.read_start() for rule in brief.preferences: # things metabrain has *proven* print("PROVEN:", rule.insight) for h in brief.open_hypotheses: # things it's still testing print("testing:", h.statement, f"({h.confidence:.0%})")

You don't have to open a session — the flat API (db.learn(...), db.verdict(...)) works too and attaches to an ambient session automatically, so the telemetry still fills.

Why it's different

Recall stays deliberately simple — substring + a hit counter — because the moat is the loop, not embedding search. (Semantic recall may arrive later as an opt-in metabrain[embeddings] extra; the core will always be zero-dependency.)

Built for real, stateful products

The loop is general. Three shapes it was designed against:

Self-learning content engine. Each post is a unit; engagement is the verdict. Hooks that keep winning graduate into the brand's proven playbook.

s.learn("pattern", "carousels outperform single images", domain="ig")  # ...×3 → hypothesis

for saves, ok in [(1200,"pass"), (90,"fail"), (1500,"pass"), (1100,"pass")]: post = s.unit(f"carousel ({saves} saves)", kind="contract", hypothesis=h.id) s.verdict(ok, unit=post, evidence=f"{saves} saves")

3/4 supported → graduates into the playbook

Lead capture. Each lead is a unit with its own checkpoint trail; a tactic about what converts graduates once enough leads confirm it.

lead = s.unit({"name": "Acme", "source": "webinar"}, kind="contract")

s.checkpoint({"stage": "demo booked"}, unit=lead) s.verdict("pass", unit=lead, evidence="closed")

Self-improving job applications. Each application is a unit; "lead with a shipped metric" stays a guess until enough replies prove it, then becomes a rule.

app = s.unit({"company": "Acme"}, kind="contract", hypothesis=h.id)

s.verdict("pass", unit=app, evidence="recruiter replied")

How the tables fill themselves

metabrain has seven tables, and you never write to them directly — correct use of the API fills every one as a side effect. Open a session and each write inherits its id, emits an event, and turns the loop:

The thresholds are tunable and were calibrated on 5,066 real learnings, not guessed: promote_at=3 (where the recurring-pattern tail actually begins), graduate_at=0.8 over a minimum of 3 experiments so a single lucky result can't graduate.

db = MetaBrain("agent.db", promote_at=3, graduate_at=0.8, min_experiments=3)

API

Use MetaBrain(":memory:") for an ephemeral in-process store (handy in tests).

Concurrency & safety

Built for multiple agents sharing one file. SQLite runs in WAL mode with a busy timeout so several processes read and write concurrently; within a process a single connection is lock-guarded, and the verdict→graduation path is one critical section so racing verdicts can never double-graduate a hypothesis. Every value is bound as a query parameter — caller strings never reach the SQL text.

It can open and migrate an older metabrain / base-schema database (learnings, context, errors) forward in place. A database created by a different tool whose events/hypotheses/experiments tables have an incompatible shape is detected on open and rejected with a clear IncompatibleDatabaseError, rather than corrupting it.

Use as an MCP server

Point Claude Code, Codex, or any MCP client at a metabrain file and the loop runs from inside the agent — no glue code.

pip install 'metabrain[mcp]'

claude mcp add metabrain -- metabrain-mcp --db ./agent.db

Codex, in ~/.codex/config.toml:

[mcp_servers.metabrain]

command = "metabrain-mcp" args = ["--db", "./agent.db"]

metabrain-mcp speaks stdio, opens one shared MetaBrain on the --db path, and closes it on exit. Seven tools, thin wrappers over the library:

Or in Docker, with the database on a mounted volume: docker run -i --rm -v metabrain:/data mcp/metabrain (METABRAIN_DB overrides the default /data/agent.db).

The core package stays zero-dependency; the mcp SDK arrives only with the extra, and works on both mcp 1.x and 2.x.

Development

pip install -e ".[dev]"

pytest

License

MIT © Aria Han