Memento v1.0: Local Persistent Memory for AI Coding Agents
What Memento v1.0 Does
Memento v1.0 provides a local-first persistent memory layer for AI coding agents. Everything runs on your machine — embeddings, storage, and search — with no cloud requirements or API keys needed after setup.
Key Technical Details
Embeddings: Uses all-MiniLM-L6-v2 via @xenova/transformers (384 dimensions) running fully offline. Optional cloud embeddings via environment variables for OpenAI (text-embedding-3-small) or Gemini (embedding-001).
Storage: Local JSON + HNSW index by default. Optional ChromaDB or Neo4j support.
Search: HNSW index for approximate nearest neighbor search (<50ms on 2000+ memories). Full BM25 implementation with k1=1.2, b=0.75 for keyword search. Hybrid mode combining 70% cosine similarity + 30% BM25.
Deduplication: SHA-256 + 0.92 cosine threshold.
Resilience features: Circuit breaker, write-ahead log, LRU cache.
Memory management: 347-day exponential decay on importance scores.
Setup and Usage
Install with: npx memento-memory setup
Migration tool: memory_migrate re-embeds your entire store when switching embedding providers — no data loss.
IDE Support and Tools
Multi-IDE compatibility: Claude Code, Cursor, Windsurf, OpenCode — all share the same local store.
17 MCP tools across save/recall/search/export/import/ingest/compact/graph/session lifecycle.
Privacy and Licensing
Zero telemetry — your architectural decisions and code patterns never leave your machine. Works without internet after setup. AGPL-3.0 licensed and self-hostable in one command.
📖 Read the full source: r/LocalLLaMA
👀 See Also
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