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
autoExtractis 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, setextractionModeltoollama/<model>and keep Ollama running locally.
What It Does
- Captures every conversation turn, buffered per session
- Extracts structured facts (preferences, decisions, people, action items) via configurable LLM (opt-in — see Privacy section)
- Recalls relevant facts before each AI turn using FTS5 keyword search + optional semantic embeddings (BGE-M3)
- Respects privacy — facts are classified as
shared,private, orsecretbased on content, with hard overrides for sensitive categories (medical, financial, credentials) - 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
}
}
}
autoExtract: trueis an explicit opt-in (default:false). When enabled, conversation segments are sent to the configuredextractionModelfor LLM-based fact extraction. Omit or set tofalseto keep everything local.
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) |
The ~/.engram directory name is a legacy from when the project was called Engram. It will not change to avoid breaking existing installations.
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)
To bootstrap Memento from existing agent memory files:
- Create
~/.engram/migration-config.jsonor setMEMENTO_WORKSPACE_MAIN:
{
"agents": [
{
"agentId": "main",
"workspace": "/path/to/your-workspace",
"paths": ["MEMORY.md", "memory/*.md"]
}
]
}
- Always dry-run first to verify which files will be read:
npx tsx src/extraction/migrate.ts --all --dry-run
- Run the actual migration:
npx tsx src/extraction/migrate.ts --all
⚠️ Migration reads files from the workspace paths you specify. Review the config before running.
Architecture
- Capture layer — hooks
message:received+message:sent, buffers multi-turn segments - Extraction layer — async LLM extraction with deduplication, occurrence tracking, and temporal pattern detection
- Storage layer — SQLite (better-sqlite3) with FTS5 full-text search + optional vector embeddings
- Recall layer — multi-factor scoring (recency × frequency × category weight) injected via
before_prompt_buildhook
Requirements
- OpenClaw 2026.2.20+
- Node.js 18+
- An API key for your preferred LLM provider (for extraction — not needed if extraction is disabled or using Ollama)
- Optional: GPU for accelerated embedding search (falls back to CPU gracefully)
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.