OMEM

The system of record for what an AI agent believed and did. Append-only, with the evidence under every belief, both sides of every contradiction kept, and a named human behind every risky action. So when an agent acts and someone asks "why did it do that", you answer with a record instead of an investigation.

OMEM sits where a memory layer sits and does a different job. Instead of dumping text into a vector store and hoping for the best, it tracks what each agent believes over time, keeps the evidence under every belief, and handles contradictions explicitly, so an agent can reason about what it knows, when it learned it, and why it holds.

And it does not start from nothing. Installs that choose to pool what they work out about people in general, as counts that name nobody, and a young install borrows that intuition on day one instead of spending six months earning it. What you contribute is counts; what you get back is everyone else's. A borrowed pattern is born weaker than one you learned yourself, and still yields the moment a person's own evidence disagrees, so the general never overrides the individual.

The bank is empty today. No installation has contributed yet, which means the first ones decide what it learns, and it is worth knowing that a corpus of regularities about people can carry the biases of whoever filled it. The mining rule was rebuilt so that a pattern has to beat the base rate rather than ride it (Working Paper No. 1); whether the contributing population is representative is a separate question and an open one.

It runs locally with no external services and no dependencies to install.

pip install omem-infrastructure && omem-server

Or deploy a private server in one click:

The blueprint provisions one small service with a persistent disk, password auth on (the first signup is the operator account), and a generated master key. Fly.io works too: fly launch --copy-config with the included fly.toml.

Docs: infrastructure.omem-cloud.com · Quickstart · Security · Contributing

Shipping agents to clients? The audit trail and the approval gate are the point: what a client's security review asks for, and a small number of hands-on design-partner pilots are open.

Want to see the whole pattern run before reading another word? refund-desk is the reference integration: a support agent that moves money, with receipts. One file, runs in a minute, asserts every claim it makes.

_{That is scripts/demo_reasoning.py, abridged. Every line is an asserted behaviour that runs in CI, so this picture cannot quietly stop being true.}

_{Two sources disagree. Neither is overwritten. See the whole thing running.}

What makes it different

Most agent memory is a list of facts. When two facts conflict, one silently overwrites the other and the history is gone. OMEM keeps both, tracks which one is currently believed, and can tell you why. A few things it does that a plain vector store does not:

• Belief state over time. Every fact has a state (believed, contradicted, unknown) that the engine computes from the evidence, not a static row.
• Contradiction handling. Conflicting information is surfaced, not lost. Claims named X and not:X are treated as opposed automatically; for anything else, mem.contradict("prefers_annual", "prefers_monthly") says so once. OMEM never decides two claims disagree by reading them, because that judgment is what would stop the same question having the same answer a year later.
• Provenance. Ask why something is believed and get the chain that led there.
• Cross-agent memory. Memory is private to an agent by default; you choose what to share with a team or the whole project.
• Semantic recall. Finds relevant memories even when the wording differs from how they were stored. Works offline with a dependency-free embedding; set OMEM_EMBED_MODEL to use your provider's real embedding model, with cached vectors and automatic fallback if the provider is down.
• A learning loop. Memories that prove useful rank higher over time.
• Self-healing that refuses. OMEM records failures and runs repairs under policy, and will not run a repair nobody authorised. A model can propose a plan; only actions registered in code execute, and risk class comes from OMEM's registry rather than from the plan claiming its own. See Self-healing.

Quick start

You need Python 3.9 or newer. No other dependencies.

Option 1: install from PyPI (server included).

pip install omem-infrastructure
omem-server

Upgrading from an earlier version? pip install --upgrade omem-infrastructure. Plain pip install on a package you already have reports "Requirement already satisfied" and does nothing, which is a quiet way to keep running the version you were trying to leave. python -c "import omem; print(omem.__version__)" says what you actually have.

That starts the server on http://127.0.0.1:8787 and, on first run, prints a project id and an API key: no signup call, no dashboard visit, nothing to configure. Paste them straight in:

from omem import Memory

mem = Memory(api_key="omem_sk_...", base_url="http://127.0.0.1:8787",
             project="proj_...")
mem.remember(agent="support", about="customer:1", claim="prefers_annual_billing")
print(mem.believes(about="customer:1", claim="prefers_annual_billing"))
# -> BELIEVED_TRUE

QUICKSTART.md takes that to a contradiction and a provenance chain in about five minutes, which is where the difference from a vector store actually shows.

Option 2: run from this repo.

cd server
python api.py            # or: python api.py 9000 for a different port

Same server, same first-run project id and key, started from source. Setup takes about a minute either way. Two differences worth knowing:

• The database lands in a different place. From source it is server/data/omem.db; omem-server writes ./omem-data/omem.db in whatever directory you ran it from. OMEM_DB overrides either.
• The dashboard needs building once. The wheel ships a built copy; a clone does not, so the server prints "dashboard not bundled" until you run cd web && OMEM_STATIC=1 npm run build. The API is identical either way.

Option 3: Docker.

docker run -p 127.0.0.1:8787:8787 -p 127.0.0.1:3000:3000 \
  -v omem-data:/app/server/data ghcr.io/troybrandonc-bit/omem

API on 8787, dashboard on 3000, data in the named volume. The ports are published to loopback on purpose: the container runs in local mode, which has no passwords, so reachability is the access control. Putting it on a network means setting OMEM_AUTH=password and OMEM_MASTER_KEY first, and docker-compose.yml in this repo shows that shape.

Self-healing

OMEM records what breaks and repairs it under policy. This is infrastructure for your agents, not something OMEM does to itself: you register a component and the hooks it can be repaired with, and OMEM owns the memory, the safety boundary and the lifecycle.

The part that matters is what it refuses. A model may propose a repair plan; OMEM decides what is permitted. Only action types registered in code can execute, risk class comes from that registry and never from the plan, high-risk actions need explicit approval, and a repair is not successful until it verifies.

mem.healing.report_health("vector-index", "healthy", "12,400 vectors")

result = mem.healing.handle(
    error={"component": "vector-index", "error_type": "StaleShard"},
    plan={"diagnosis": "replica fell behind after a partition",
          "confidence": 0.8,
          "actions": [{"type": "rebuild_index"}, {"type": "exec_shell"}]},
)
result["status"]     # -> "denied"
result["decisions"]  # rebuild_index: permitted (low risk)
                     # exec_shell:    unknown action type (not registered)

Nothing ran. The plan is kept with the reason each action was permitted or refused, so the refusal is a record rather than a silence. Error text and model output are data here, and neither can name an action into existence.