Engram: Hybrid Memory Plugin for OpenClaw Agents — Vector + Semantic Search with Decay

Engram is a memory plugin for OpenClaw agents that adds persistent, hybrid recall between sessions. Originally built as a hybrid memory system combining vector and semantic search with a memory decay architecture, it was broken by a recent OpenClaw update. Now fixed and released on GitHub under the name Engram (the biometric trace that enables memory).

How It Works

Engram backs agent memory with two stores:

• SQLite + FTS5 for exact, structured recall and full-text search over fact text.
• LanceDB for fuzzy semantic search over embeddings.

The two are queried together in a hybrid recall that returns both structured key/value facts and semantically similar vectors.

Features

• Hybrid recall: structured key/value facts + semantic vector search, queried together.
• FTS5 full-text search over fact text.
• Categories: preference, fact, decision, entity, other.
• Decay classes: permanent, stable, active, session, checkpoint with confidence decay.
• Auto-capture / auto-recall hooks (configurable).
• Local-first: memory stays on your machine.
• Embeddings via OpenAI (text-embedding-3-small or text-embedding-3-large).

Who It's For

Developers running OpenClaw agents who need persistent, intelligent memory that survives restarts and can distinguish between session and permanent knowledge.

Get It

Star the repo on GitHub at nanoflow-io/engram.