Memento

Memento — Local Persistent Memory for OpenClaw Agents

Memento gives your agents long-term memory. It captures conversations, extracts structured facts using an LLM, and auto-injects relevant knowledge before each AI turn.

All stored data stays on your machine — no cloud sync, no subscriptions. Extraction uses your configured LLM provider; use a local model (Ollama) for fully air-gapped operation.

> ⚠️ Privacy note: When autoExtract is enabled, conversation segments are sent to your configured LLM provider for fact extraction. If you use a cloud provider (Anthropic, OpenAI, Mistral), that text leaves your machine. For fully local operation, set extractionModel to ollama/ and keep Ollama running locally.

What It Does

1. Captures every conversation turn, buffered per session 2. Extracts structured facts (preferences, decisions, people, action items) via configurable LLM (opt-in — see Privacy section) 3. Recalls relevant facts before each AI turn using FTS5 keyword search + optional semantic embeddings (BGE-M3) 4. Respects privacy — facts are classified as shared, private, or secret based on content, with hard overrides for sensitive categories (medical, financial, credentials) 5. Cross-agent knowledge — shared facts flow between agents with provenance tags; private/secret facts never cross boundaries

Quick Start

Install the plugin, restart your gateway, and Memento starts capturing automatically. Extraction is off by default — enable it explicitly when ready.

Optional: Semantic Search

Download a local embedding model for richer recall:

mkdir -p ~/.node-llama-cpp/models
curl -L -o ~/.node-llama-cpp/models/bge-m3-Q8_0.gguf \
  "https://huggingface.co/gpustack/bge-m3-GGUF/resolve/main/bge-m3-Q8_0.gguf"

Environment Variables

All environment variables are optional — you only need the one matching your chosen LLM provider:

| Variable | When Needed | |----------|-------------| | ANTHROPIC_API_KEY | Using anthropic/* models for extraction | | OPENAI_API_KEY | Using openai/* models for extraction | | MISTRAL_API_KEY | Using mistral/* models for extraction | | MEMENTO_API_KEY | Generic fallback for any provider | | MEMENTO_WORKSPACE_MAIN | Migration only: path to agent workspace for bootstrapping |

No API key needed for ollama/* models (local inference).

Configuration

Add to your openclaw.json under plugins.entries.memento.config:

{
  "memento": {
    "autoCapture": true,
    "extractionModel": "anthropic/claude-sonnet-4-6",
    "extraction": {
      "autoExtract": true,
      "minTurnsForExtraction": 3
    },
    "recall": {
      "autoRecall": true,
      "maxFacts": 20,
      "crossAgentRecall": true,
      "autoQueryPlanning": false
    }
  }
}

> autoExtract: true is an explicit opt-in (default: false). When enabled, conversation segments are sent to the configured extractionModel for LLM-based fact extraction. Omit or set to false to keep everything local.

> autoQueryPlanning: true is an explicit opt-in (default: false). When enabled, a fast LLM call runs before each recall search to expand the query with synonyms and identify relevant categories — improving precision at the cost of one extra LLM call per turn.

Data Storage

Memento stores all data locally:

| Path | Contents | |------|----------| | ~/.engram/conversations.sqlite | Main database: conversations, facts, embeddings | | ~/.engram/segments/*.jsonl | Human-readable conversation backups | | ~/.engram/migration-config.json | Optional: migration workspace paths (only for bootstrapping) |

Privacy & Data Flow

| Feature | Data leaves machine? | Details | |---------|---------------------|---------| | autoCapture (default: true) | ❌ No | Writes to local SQLite + JSONL only | | autoExtract (default: false) | ⚠️ Yes, if cloud LLM | Sends conversation text to configured provider. Use ollama/* for local. | | autoRecall (default: true) | ❌ No | Reads from local SQLite only | | Secret facts | ❌ Never | Filtered from extraction context — never sent to any LLM | | Migration | ❌ No | Reads local workspace files, writes to local SQLite |

Migration (Bootstrap from Existing Memory Files)

Migration is an optional, one-time process to seed Memento from existing agent memory/markdown files. It is user-initiated only — never runs automatically.

What it reads

Migration reads only the files you explicitly list in the config. It does not scan your filesystem, read arbitrary files, or access anything outside the configured paths.

Setup

1. Create ~/.engram/migration-config.json or set MEMENTO_WORKSPACE_MAIN:

{
  "agents": [
    {
      "agentId": "main",
      "workspace": "/path/to/your-workspace",
      "paths": ["MEMORY.md", "memory/*.md"]
    }
  ]
}

2. Always dry-run first to verify exactly which files will be read:

npx tsx src/extraction/migrate.ts --all --dry-run

The dry-run prints every file path it would read — review this before proceeding.

3. Run the actual migration:

npx tsx src/extraction/migrate.ts --all

Security notes

Architecture

Requirements

Install

# From ClawHub
clawhub install memento
Or for local development
git clone https://github.com/braibaud/Memento
cd Memento
npm install

Note: better-sqlite3 includes native bindings that compile during npm install. This is expected behavior for SQLite access.

Install the plugin, restart your gateway, and Memento starts capturing automatically. Extraction is off by default — enable it explicitly when ready.

Optional: Semantic Search

Download a local embedding model for richer recall:

mkdir -p ~/.node-llama-cpp/models
curl -L -o ~/.node-llama-cpp/models/bge-m3-Q8_0.gguf \
  "https://huggingface.co/gpustack/bge-m3-GGUF/resolve/main/bge-m3-Q8_0.gguf"

Add to your openclaw.json under plugins.entries.memento.config:

{
  "memento": {
    "autoCapture": true,
    "extractionModel": "anthropic/claude-sonnet-4-6",
    "extraction": {
      "autoExtract": true,
      "minTurnsForExtraction": 3
    },
    "recall": {
      "autoRecall": true,
      "maxFacts": 20,
      "crossAgentRecall": true,
      "autoQueryPlanning": false
    }
  }
}

> autoExtract: true is an explicit opt-in (default: false). When enabled, conversation segments are sent to the configured extractionModel for LLM-based fact extraction. Omit or set to false to keep everything local.

> autoQueryPlanning: true is an explicit opt-in (default: false). When enabled, a fast LLM call runs before each recall search to expand the query with synonyms and identify relevant categories — improving precision at the cost of one extra LLM call per turn.