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

	metabrain	typical vector-memory store
Remembers what you tell it	✅	✅
Proves which memories actually work	✅ the learn→experiment→graduate loop	❌
Working state + telemetry, not just recall	✅ units, checkpoints, sessions, events	❌
Infrastructure	a single SQLite file	vector DB / server / API key
Dependencies	none (stdlib sqlite3)	several

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:

Table	Filled by	When
sessions	db.session() open/close	every run
events	every write method	always (telemetry is automatic)
learnings	learn() — preference rows are graduated	always
context	unit(), checkpoint(), handoff(), verdict()	always
hypotheses	a pattern crossing promote_at (default 3 hits)	automatic
experiments	a verdict() on a unit/hypothesis under test	automatic
errors	capture_error(), and any exception inside a session	automatic

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

Method	What it does
session(*, task, tier, agent, meta)	Open a session (context manager); records the outcome on close
learn(type, insight, *, evidence, domain, ...)	Record/reinforce a lesson; recurring patterns graduate to hypotheses
recall(query, *, limit)	Substring-search lessons; bumps hit count (can trigger graduation)
learnings(*, type, domain, limit)	Fetch lessons, newest first
forget(id)	Delete a lesson
unit(statement, *, kind, acceptance, hypothesis)	Open a unit of work; kind="spec" requires acceptance=[...]
checkpoint(content, *, unit, agent)	Record progress mid-work
handoff(content, *, unit, agent)	Record a brief for the next session
verdict(result, *, unit, hypothesis, evidence)	"pass"/"fail"; becomes an experiment when a hypothesis is in play
hypotheses(*, status, limit) / experiments(*, hypothesis)	Inspect the loop
context(*, type, unit, limit)	Fetch work-state entries
read_start(*, learnings_limit)	The "what to know" digest — proven preferences first
capture_error(tool, error, ...) / errors(*, limit)	Record / fetch failures
prune(*, keep) / stats()	Trim old checkpoints / row counts per table

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:

Tool	Calls
start_brief()	read_start() — proven preferences first; run it before you work
recall(query, limit=20)	recall()
learn(type, insight, domain?, context?)	learn(); type is failure / pattern / gotcha / preference
hypotheses(status?)	hypotheses()
verdict(result, unit?, evidence?, hypothesis?)	verdict() — closes the loop
stats()	stats()
capture_error(tool, error, context?)	capture_error()

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