memento

name	memento
description	Local persistent memory for OpenClaw agents. Captures conversations, extracts structured facts via LLM, and auto-recalls relevant knowledge before each turn. Privacy-first, all stored data stays local in SQLite.
metadata	{"version":"0.6.0","author":"braibaud","license":"MIT","repository":"https://github.com/braibaud/Memento","openclaw":{"emoji":"🧠","kind":"plugin","requires":{"node":">=18.0.0","optionalEnv":"[Truncated]","dataFiles":"[Truncated]"},"install":["[Truncated]"],"extensions":["./src/index.ts"]},"keywords":["memory","knowledge-base","recall","conversation","extraction","embeddings","sqlite","privacy","local","cross-agent"]}

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/<model> 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

• Migration only reads files matching the glob patterns you configure
• Extracted facts inherit visibility classification (shared/private/secret)
• Secret-classified facts are never sent to cloud LLM providers
• Migration config file is optional — if absent, migration is completely inert
• The migration script has no network access beyond the configured extraction LLM

Architecture

• Capture layer — hooks message:received + message:sent, buffers multi-turn segments
• Extraction layer — async LLM extraction with deduplication, occurrence tracking, temporal state transitions (previous_value), and knowledge graph relations (including causal edges with causal_weight)
• Storage layer — SQLite schema v7 (better-sqlite3) with FTS5 full-text search + optional vector embeddings; knowledge graph (fact_relations with causal_weight), multi-layer clusters, and temporal transition tracking (previous_value)
• Recall layer — optional LLM query planning pre-pass (autoQueryPlanning), multi-factor scoring (recency × frequency × category weight), 1-hop graph traversal with causal edge 1.5× boost, injected via before_prompt_build hook

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.