Mesh Memory

Mesh Memory is a self-hosted semantic memory service with a built-in MCP server. It stores documents (worklogs, decisions, notes, research) in PostgreSQL with pgvector and retrieves them by meaning, so a query like "what database did we pick?" surfaces a saved note that says "chose Redis for caching" even with zero keyword overlap. Embeddings are generated locally with multilingual-e5-base (768 dimensions); the core flow requires no external API keys.

Use this skill when an agent needs persistent memory across sessions: saving its own work, recalling prior decisions, or building a project knowledge base shared between multiple agents.

When to Use This Skill

• 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).

Prerequisites

• A running Mesh Memory instance reachable from the MCP server. Local Docker is the common path -- docker compose up -d in the upstream repo brings it up; see https://github.com/dklymentiev/mesh-memory for the full Quick Start.
• The MCP server (mcp_server.py) registered with your client (Claude Code, Cursor, Claude Desktop, or any other MCP-aware agent).
• MESH_API_URL pointing at the running instance (default: http://localhost:8000).