# genome

Local memory for AI agents: writes with no LLM calls, semantic recall, offline operation and an auditable fact history.

- MCP server · `type=mcp` · Claude Code, Codex, Cursor, GitHub Copilot, Windsurf (Devin Desktop), Cline, Zed, Gemini CLI · ★ 10
- Author: NORTHTEKDevs
- License: Apache-2.0 · policy=link-only · description only; the file stays with the author
- Source: https://github.com/NORTHTEKDevs/genome
- Updated: 2026-10-01
- HTML: https://affpapa.org/en/nevibecode/mcp/genome

## What it does

genome keeps messages in a local store — SQLite or Postgres — and searches them by cosine similarity. The write path makes no LLM calls and touches no network: text goes through the local `all-MiniLM-L6-v2` embedder, and the same text always produces the same vector. Replay a journal and you get the same store back, which is what makes the record checkable instead of trusting whatever a model once decided mattered.

An optional bi-temporal layer files each fact under the moment it became true in the world rather than the moment it arrived. That is how point-in-time questions stay answerable — older values are not overwritten by newer ones. Belief extraction is the only step that calls an LLM, and it is opt-in.

The first run pulls the embedding model (~90 MB), so a cold machine sits quiet for a while. You can check the cost and offline claims without any API key: `python -m genome.verify` writes memories with outbound sockets blocked and prints a pass/fail receipt. Accuracy parity with Mem0 is a separate, larger check and needs an LLM key.

## Requirements
- Needs: Python and uv (commands run via uvx)

## Install

### Claude Code (native platform)

**Platform CLI**
```bash
claude mcp add genome -- uvx genome-memory
```
uvx runs the command named after the package; if the README names another one, use uvx --from genome-memory <command>

### Codex

**Platform CLI**
```bash
codex mcp add genome -- uvx genome-memory
```
uvx runs the command named after the package; if the README names another one, use uvx --from genome-memory <command>

### Cursor

**Config file**
File `.cursor/mcp.json`:
```json
{
    "mcpServers": {
        "genome": {
            "command": "uvx",
            "args": [
                "genome-memory"
            ]
        }
    }
}
```
uvx runs the command named after the package; if the README names another one, use uvx --from genome-memory <command>

### GitHub Copilot

**Config file**
File `.vscode/mcp.json`:
```json
{
    "servers": {
        "genome": {
            "type": "stdio",
            "command": "uvx",
            "args": [
                "genome-memory"
            ]
        }
    }
}
```
uvx runs the command named after the package; if the README names another one, use uvx --from genome-memory <command>

### Gemini CLI

**Platform CLI**
```bash
gemini mcp add genome uvx genome-memory
```
uvx runs the command named after the package; if the README names another one, use uvx --from genome-memory <command>

## When to use it
- carry memory across sessions without sending data off the machine
- answer what a fact was on a given date, not just what it is now
- re-derive the stored record and audit what is in it
- keep LLM calls and network traffic out of the write path
- hold several tenants' memories in one store

## Example prompts
- Remember that the client moved to Austin in February 2025
- What was the client's city in March 2024?
- List every fact about Priya that was valid in May 2023

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