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/demo_timeline.py`,

captured by `tools/render_demo_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 embedderall-MiniLM-L6-v2"]
    E --> S[("local storeSQLite or Postgres")]
    M -. "optional, opt-in" .-> B["belief extraction(the only LLM call)"]
    B --> K[("bi-temporalfact log")]

    Q["query"] --> R["exact cosine searchover this tenant's rows"]
    S --> R
    R --> RR["optional cross-encoderrerank"]
    RR --> A["context for the agent"]
    Q --> PIT["as-of resolutionfacts_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

Write: embed locally, store. About 10 ms, zero LLM calls, zero network calls. The

embedding is deterministic -- the same text always yields the same vector, with no

sampled extraction step deciding what matters -- so what gets stored is a function

of the input, and replaying a journal reproduces that store exactly. (Ids and

timestamps are stamped per write, so two independent ingests of the same

conversation agree on content and vectors, not on record ids.)

Read: exact cosine search within the tenant's scope (no ANN index to build or update),

with an optional local cross-encoder reranker.

Bi-temporal layer (opt-in): records each fact at its domain time, the moment it became

true in the world, not the moment it was ingested. That is what makes point-in-time

questions answerable even when facts arrive out of order.

Why the record can be re-derived

flowchart TB
    subgraph LLM["LLM-extraction memory"]
        A1["message"] --> A2["LLM decides what matters(sampled, non-deterministic)"]
        A2 --> A3[("store")]
        A3 --> A4["replaying the same inputcan produce a different store"]
    end
    subgraph GEN["GENOME"]
        B1["message"] --> B2["local embedding(deterministic)"]
        B2 --> B3[("store")]
        B3 --> B4["replaying the same inputreproduces the same store"]
    end
    style A4 fill:#5c1f1f,color:#fff
    style B4 fill:#1f4d33,color:#fff

A record that cannot be re-derived is difficult to audit. That property, not accuracy, is

the actual argument for this design.

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

The first run downloads the local embedding model (~90 MB, one time) before printing

anything, so expect 30-120 seconds of apparent silence on a cold machine. Every run after

that is instant.

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."

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.

What determinism buys you

Because nothing on the write path interprets your content, GENOME can do things an

LLM-ingest memory system cannot do in principle:

• Memory firewall (`genome.firewall`): tag every write with where it came from

(`user`, `agent`, `tool`, `web`), quarantine low-trust origins from recall, and

enforce origin-bound authority - web content can never UPDATE or DELETE what your

user said, even when a prompt-injected conflict resolver asks for it. There is

also no extraction step for injected content to attack: the write path has no LLM.

from genome import Memory
  from genome.firewall import TrustPolicy

  m = Memory(trust_policy=TrustPolicy(recall_min_trust=1))
  m.add("I live in Anchorage", user_id="u1", provenance="user")
  m.add(scraped_page_text, user_id="u1", provenance="web")   # quarantined

• Explainable recall (`genome.explain`): `explain_search()` reports every

candidate's dense score, BM25 rank, fused score, and - when it was not returned -

the exact reason (parent-filtered, quarantined, beyond the limit). Two runs agree,

so a recall bug can be committed as a regression test instead of a shrug.

• Journal + replay (`genome.journal`): record every mutation and provably

reproduce the store - `verify_journal()` replays the history and compares

canonical hashes. Replay a prefix to roll back; replay into different storage to

branch a memory for a what-if run. The journal sits after extraction, so replay

is deterministic even if you configured an LLM extractor. Each line chains to its

predecessor, so a removed or edited line is detected even when the change cancels

out in the final state.

# Tamper-EVIDENT by default. Pass a key (kept outside the journal's directory)
  # to make it tamper-PROOF: an unkeyed chain can be recomputed by anyone with
  # write access, an HMAC chain cannot.
  m = Memory(journal="mem.journal", journal_key=os.environb[b"GENOME_JOURNAL_KEY"])

• Multi-agent belief attribution (`record_fact(..., believed_by="agent-a")`):

agents sharing a store keep their own belief timelines - agent B disagreeing does

not clobber agent A's fact - and `belief_conflicts()` surfaces disagreements for

deliberate resolution instead of silently picking a winner.

• A neutral benchmark harness (`benchmarks/neutral/`): run GENOME, Mem0, and a

full-context baseline through the same responder, judge, and embedder, with a

pairwise McNemar matrix and a full-disclosure block. GENOME is one row in the

table, not the house.

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).

mcp-name: io.github.NORTHTEKDevs/genome