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.

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 (two independent benchmark runs confirm parity). The advantage is cost, speed, offline operation, and a temporal/auditable record Mem0 can't produce.

Don't believe it? Prove it yourself

The cost, speed, and offline claims need no API key — measure them on your machine in 60 seconds:

git clone https://github.com/NORTHTEKDevs/genome && cd genome
pip install -e . && python -m genome.verify

It writes memories with your outbound network physically blocked and prints a live pass/fail receipt — 0 network calls, 0 LLM calls, single-digit-ms writes, retrieval that works:

  [PASS] Air-gapped write path: wrote 200 memories with every outbound socket blocked -> 0 network attempts, 0 LLM calls
  [PASS] Write latency: 7.1 ms/message  (Mem0's measured write path: ~2,055 ms + 1 LLM call/message)
  [PASS] Retrieval works: top hit score 0.598

That receipt covers the cost/speed/offline story only. The accuracy-parity with Mem0 claim is a separate, larger check that needs an LLM key — reproduce it head-to-head on the same questions with your own key via python benchmarks/head_to_head.py (one OpenRouter key works; see benchmarks/RESULTS.md for the n=90 / n=205 runs, the paired significance tests, and the published nulls). The full test suite runs in public CI (badge above). The pitch isn't "trust me" — it's "run it."

Related MCP server: MCP Memento

Add persistent memory to your agent in one line (MCP)

GENOME ships a fully-local MCP server — cross-session memory for Claude Desktop, Claude Code, or Cursor with no API key and no data leaving your machine:

pip install "genome-memory[mcp]"

{ "mcpServers": { "genome": { "command": "genome-mcp" } } }

Or zero-install via uv: { "command": "uvx", "args": ["--from", "genome-memory[mcp]", "genome-mcp"] }

Tools the agent gets: remember, recall, forget, reset_memories. Memories persist locally in ~/.genome/memories.db. Full MCP details ↓

GENOME vs Mem0 at a glance

Every number is measured within one harness — same responder, judge, embedder, and top-k; only the memory layer changes — with paired significance tests. Full detail and per-number provenance: benchmarks/RESULTS.md. Formatted report: benchmarks/GENOME-LoCoMo-Report.pdf.

Why it's ~1,000× cheaper: it never calls an LLM to remember

Storing one message costs one LLM call in Mem0, zero in GENOME (just a local embedding). That's not a benchmark you can argue with — it's arithmetic, and it holds no matter which LLM you price it against. At 10,000 users × 50 messages/day (15M messages/month):

The gap survives the cheapest model and grows in production (Mem0 re-sends stored memories to the LLM as the store fills). Reproduce: python benchmarks/tco_project.py (no API key).

It runs air-gapped

GENOME's default embedder is local. We proved the write path is genuinely offline by blocking all network during writes — they still succeed:

• ~10 ms/message, 0 network calls, 0 LLM calls (python benchmarks/local_writepath.py)

• Mem0 can't do this — it needs an LLM API call to ingest.

That makes GENOME usable on-prem, in regulated environments, or fully offline. It's a yes/no capability, not a price point.

How it works

• Write: embed the message locally and store it. No LLM, no network. (~10 ms)

• Read: vector search over your memories, with an optional local cross-encoder reranker for harder queries.

• Optional bi-temporal layer: track how facts change over time and answer "what was true at time T" — see below.

Install

pip install genome-memory

The default embedder is local (sentence-transformers/all-MiniLM-L6-v2) — no API key, works offline; the first run downloads the ~90 MB model once. OpenAI embeddings are optional for higher-dimensional retrieval.

Dependency footprint, honestly: the core install is numpy, sentence-transformers, scikit-learn, and rank-bm25. Local embeddings run on PyTorch (pulled in by sentence-transformers), so it isn't a tiny install — that's the deliberate tradeoff for offline, zero-cost embedding. Plotting/benchmark-chart deps live in an optional [viz] extra, not the core. Migrating from Mem0? See docs/migrating_from_mem0.md.

Quickstart (fully local, no API key)

from genome import Memory

mem = Memory(storage="genome.db")   # local embedder by default; ":memory:" for ephemeral

# Store a message -- embedded locally, no LLM call, no network
mem.add("Ada met Lin at the robotics summit in Berlin.", user_id="u1")
mem.add("They are collaborating on an open-source planning library.", user_id="u1")

# Retrieve the most relevant memories
for hit in mem.search("Where did Ada meet Lin?", user_id="u1", limit=5):
    print(f"{hit.score:.3f}  {hit.content}")

Memory mirrors Mem0's API (add / search / get / delete / reset) — a near drop-in swap. To use OpenAI embeddings instead (set OPENAI_API_KEY):

from genome import Memory, EmbeddingProvider
mem = Memory(storage="genome.db",
             embedding_provider=EmbeddingProvider(model_name="openai:text-embedding-3-small"))

Use it as an MCP server (fully-local memory for any agent)

GENOME ships an MCP server, so any MCP client (Claude Desktop, Claude Code, Cursor, …) gets persistent cross-session memory that runs entirely on the local machine — no LLM calls, no API keys, no data leaves the box. Most memory MCPs can't say that.

Install with the mcp extra, then add it to your client's config:

pip install "genome-memory[mcp]"

{
  "mcpServers": {
    "genome": { "command": "genome-mcp" }
  }
}

Tools the agent gets: remember (store a fact/preference, local + 0 LLM), recall (semantic search), forget (delete the memory matching a query), reset_memories (clear a user's memories). Memories persist in ~/.genome/memories.db (override with the GENOME_MCP_DB env var). Run standalone with genome-mcp or python -m genome.mcp.server.

Run it as an HTTP API

Prefer HTTP? GENOME ships a FastAPI server that mirrors the library 1:1 (add / search / get / update / delete / reset / synthesize), with an auto-generated OpenAPI spec at /docs.

pip install "genome-memory[fastapi]"

Try it locally (keyless, loopback only — one flag makes the "no auth" intent explicit):

GENOME_ALLOW_NO_AUTH=1 python -m genome.server        # serves on 127.0.0.1:8080

curl -X POST localhost:8080/v1/memories \
  -H 'Content-Type: application/json' \
  -d '{"text": "Ada met Lin at the robotics summit in Berlin.", "user_id": "u1"}'

curl -X POST localhost:8080/v1/search \
  -H 'Content-Type: application/json' \
  -d '{"query": "Where did Ada meet Lin?", "user_id": "u1", "limit": 5}'

Safe by default. The server refuses to serve unauthenticated unless you opt in as above, and it will not bind a non-loopback interface without a key. To expose it, set an API key (sent as X-API-Key) — required to bind beyond localhost:

GENOME_API_KEY=$(openssl rand -hex 32) GENOME_HOST=0.0.0.0 python -m genome.server
# then add:  -H "X-API-Key: $GENOME_API_KEY"  to every request

For multi-tenant deployments, set GENOME_REQUIRE_SCOPE=1 to require user_id/agent_id on every call and disable the global reset. Docker: docker-compose up (needs GENOME_API_KEY and POSTGRES_PASSWORD; Postgres is published on loopback only). Full guide, including the Postgres backend and every env var: docs/tutorial_quickstart.md.

TypeScript / JavaScript client

@northtek/genome-memory mirrors the Python Memory API shape against this server (ESM, Node 20+ or browser):

npm install @northtek/genome-memory

import { Memory } from "@northtek/genome-memory";

const mem = new Memory({ baseUrl: "http://localhost:8080" });
await mem.add({ text: "Ada met Lin in Berlin.", userId: "u1" });
const hits = await mem.search({ query: "Where did Ada meet Lin?", userId: "u1" });

Full client docs: sdks/typescript/README.md.

The honest results

Same responder + judge + embedder for every system; only the memory layer changes.

What we tested that didn't help (so you don't have to)

We publish our nulls — it's how you know the wins are real:

• Synthesis / consolidation: accuracy-neutral at equal token budget (p = 0.86).

• Hybrid (BM25 + dense) and graph retrieval: hybrid underperformed plain dense on LoCoMo; graph was not validated here.

• Reranking's accuracy gain is embedder-dependent: it reliably improves retrieval hit-rate, but its effect on final answer accuracy depends on the embedder — treat it as a retrieval-quality tool, not a guaranteed accuracy win.

Bi-temporal memory: "what was true at time T"

GENOME can track how facts change over time and answer point-in-time questions — something overwrite-based memory structurally can't do (it only keeps the latest value):

from genome.memory.belief import ingest_belief_turn, answer_belief_context

mem = Memory(storage="genome.db", llm_call=my_llm_fn)

# facts land at their DOMAIN time (parsed from the text), not wall-clock ingest time
ingest_belief_turn(mem, "In March 2024, Jordan moved to Seattle.", session_time=t0, user_id="u")
ingest_belief_turn(mem, "Jordan just moved to Austin.", session_time=t2, user_id="u")

answer_belief_context(mem, "Where does Jordan live now?", user_id="u")            # -> Austin
answer_belief_context(mem, "Where did Jordan live in early 2024?", user_id="u")   # -> Seattle
answer_belief_context(mem, "List every city Jordan has lived in.", user_id="u")   # -> Seattle; Austin

On the TempBelief benchmark it answers as-of queries at 0.870 vs Mem0's 0.676, with the knowledge graph audited at 0.97 precision / 0.96 recall. Caveat: TempBelief is synthetic text with explicit dates; the edge shrinks on natural speech. Real capability, bounded proof.

Optional features

Opt-in; the default path stays LLM-free and local at ingest.

mem = Memory(
    storage="genome.db",
    llm_call=my_llm_fn,             # LLM-based fact extraction on add()
    resolve_conflicts=True,         # ADD/UPDATE/DELETE vs existing memories
    auto_extract_entities=True,     # entity graph for graph retrieval
    auto_consolidate_threshold=200, # summarize-or-prune when a scope grows past N
)
mem.search("...", user_id="u1", mode="hybrid")   # modes: "dense" (default), "hybrid", "graph"

Reranking (local, free, no API):

from genome.memory.rerank import CrossEncoderReranker
mem = Memory(storage="genome.db", reranker=CrossEncoderReranker())   # lazy-loaded
mem.search("Where did the user go on vacation?", user_id="u1", limit=5)  # reranked

Reproduce the benchmarks

The LoCoMo and LongMemEval datasets are not bundled (they carry their own licenses — LoCoMo is CC BY-NC 4.0). See benchmarks/data/README.md to download them. The first two lines need no dataset and no API keys:

python benchmarks/local_writepath.py        # local write path: ~10ms/msg, 0 network
python benchmarks/tco_project.py            # deployment cost projection
python benchmarks/verdict.py                # in-window accuracy + McNemar
python benchmarks/haystack_report.py        # overflow / context-window crossover
python benchmarks/ingest_cost.py --n 80     # measured ingestion cost vs Mem0
python benchmarks/lme_qa.py --n 90          # LongMemEval head-to-head vs Mem0
python benchmarks/tempbelief_run.py --convs 6   # bi-temporal point-in-time vs baselines

Support and commercial tier

Bugs and questions: issues and discussions. Community support is best-effort — see SUPPORT.md.

GENOME Enterprise is a separate commercial product for regulated and on-premise buyers who have to answer to an auditor for what an AI system knew and when: a tamper-evident hash-chained audit record, point-in-time reconstruction, compliance reports, retention with erasure proofs, RBAC and SSO. Self-hosted and licensed per deployment — there is no hosted version, deliberately, because the value is that your data never leaves. That tier is what funds this one. Evaluating it, or want commercial support on the open core? info@northtek.io

License

Apache License 2.0 — see LICENSE and NOTICE.

GENOME is free and open source: read it, modify it, self-host it, and embed it in your own applications — commercial use included — under the terms of Apache 2.0. There is no "open core bait and switch" planned: the core stays Apache-2.0.

The Apache-2.0 grant covers the code, not the name — see TRADEMARKS.md, which leads with what you may do without asking. Questions: info@northtek.io.

Copyright 2026 Northtek (FrostByte Digital LLC).