BrainX V6

Persistent semantic memory is added to OpenClaw agents by capturing, embedding, and storing operational learnings in PostgreSQL with pgvector. This enables cross-session recall and shared knowledge retrieval for ongoing workflows.

Yes, using pgvector for agent memory requires PostgreSQL with the pgvector extension and OpenAI embeddings to store and retrieve durable vector memories. These dependencies are strictly enforced for semantic search operations.

You store a durable decision memory using the CLI add command with context and importance flags, then retrieve and inject it via semantic search that formats selected memories for prompt injection during agent runtime.

The best way to manage agent memory lifecycle is using built-in maintenance automation commands for deduplication, contradiction handling, and diagnostics. These tools support ongoing memory health and backup or restore operations.

Sensitivity control and PII scrubbing work during recall by enforcing verification states and conservative rollout of runtime surfaces. This governance ensures safe prompt injection and prevents sensitive data from leaking into agent contexts.

OpenClaw agent operational learnings get lost across sessions because the agents lack long-term, searchable memory. Storing these learnings as pgvector embeddings in PostgreSQL solves this by enabling reliable semantic recall later.