domains / families / agent-tooling / mesh-memory
mesh-memory
authentic
Self-hosted semantic memory for AI agents via MCP. Save worklogs, decisions, and notes, then recall them across sessions by meaning, not keyword. Postgres + pgvector with auto-tagging.
When to use
- Saving a session worklog, decision, or research note so a later session can find it.
- Recalling past work by topic when you do not remember the exact words you used.
- Sharing a long-lived knowledge base across multiple agents, terminals, or teammates.
- Organizing context by role or project through workspaces (one workspace per role/project).
- Looking up structured tags (e.g. all `type:decision` entries from one project).
When not to use
- Mesh is a knowledge store, not a chat memory. Long conversation transcripts should be summarized before being saved.
- Vector similarity is robust but not perfect; for high-precision structured lookups, prefer `mesh_bytag` over `mesh_search`.
- Embeddings run on CPU by default; very large corpora (hundreds of thousands of documents) benefit from a dedicated instance and pgvector tuning, not covered here.
- The optional AI categorizer requires an OpenAI-compatible LLM endpoint and is disabled by default.
Skill page
Source