Genome MCP server

GENOME

Open memory for AI agents. Same answer accuracy as Mem0 - but ~1,000× cheaper to store, runs fully offline, and keeps an auditable record.

Papers: Do Agents Need an LLM to Remember? (the core evaluation, 2026) and What Does Each Memory Feature Buy? (a measured audit of all five optional features, wins and failures alike, 2026). PDFs in papers/; result tables in benchmarks/AUDIT-RESULTS.md.

Most agent-memory tools (like Mem0) call an LLM on every message to decide what to remember. That's the slow, expensive part - and GENOME's bet is that you don't need it. GENOME just embeds each message locally: no LLM, no API, no network in the write path.

Benchmarked honestly on public datasets (LoCoMo, LongMemEval), GENOME answers just as accurately as Mem0 - while storing memories for a tiny fraction of the cost and running completely offline.

Honest up front: on answer accuracy, GENOME ties Mem0 - we do not claim to beat

it there (six independent benchmark configurations confirm parity, none significant in

either direction). The advantage is cost, speed, offline operation, and a

temporal/auditable record Mem0 can't produce.

See it work

Every frame is real output from examples/demotimeline.py, captured by tools/renderdemo_gif.py. Run it yourself, no API key required:

python examples/demo_timeline.py

The interesting part is step 3. The same question gets three different correct answers depending on when you ask about, because the store keeps when each fact became true rather than overwriting it:

The "thinking about maybe moving to Denver, nothing decided" turn is stored but never becomes an answer: it is a plan, not a durable fact.

How it works

The write path is deliberately dumb and cheap. All the intelligence happens at read time, when there is a query to focus it.

flowchart LR
    M["incoming message"] --> E["local embedder<br/>all-MiniLM-L6-v2"]
    E --> S[("local store<br/>SQLite or Postgres")]
    M -. "optional, opt-in" .-> B["belief extraction<br/>(the only LLM call)"]
    B --> K[("bi-temporal<br/>fact log")]

    Q["query"] --> R["exact cosine search<br/>over this tenant's rows"]
    S --> R
    R --> RR["optional cross-encoder<br/>rerank"]
    RR --> A["context for the agent"]
    Q --> PIT["as-of resolution<br/>facts_valid_at(entity, T)"]
    K --> PIT
    PIT --> A

    style E fill:#0A84FF,color:#fff
    style S fill:#1c2530,color:#fff
    style K fill:#1c2530,color:#fff
    style B fill:#3a3a3a,color:#fff

Shortened. The full README is on GitHub.