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Overview

Mnemo MCP – Overview

Section titled “Mnemo MCP – Overview”

Persistent, searchable long-term memory for AI agents. Store facts, preferences, and decisions once and recall them across sessions with hybrid natural-language and entity-graph search.

What it does

Section titled “What it does”
  • Store and recall: add memories with categories and tags; search by natural-language query, browse, update, and delete.
  • Entity knowledge graph: relate memories to entities, walk a neighbourhood subgraph, view an entity timeline, and take a point-in-time (as_of) view of what was known at a given moment.
  • Lifecycle: archive and restore, LLM-based consolidation and compression, and JSONL export/import for portability.
  • Optional encrypted sync keeps a memory store consistent across machines.

Tools

Section titled “Tools”

Eleven single-purpose memory tools (the primary interface) plus a consolidated memory dispatcher that mirrors them and adds the entity graph, timeline, and point-in-time actions. Every server in this stack also ships the two universal tools – config (status, sync, runtime settings, credential setup) and help; config__open_relay reopens the browser setup form in HTTP mode. Every action and parameter is listed in the tools reference.

Clients

Section titled “Clients”

Runs over stdio with any MCP client – Claude Code, Codex, Gemini CLI, Cursor, and Windsurf. Self-host the HTTP build for claude.ai web compatibility and multi-user, per-user isolation. See the modes overview.

Configuration

Section titled “Configuration”

Works out of the box with a bundled local embedding model – no keys required. Supply your own provider keys (JINA_AI_API_KEY, GEMINI_API_KEY, OPENAI_API_KEY, COHERE_API_KEY) as environment variables to use cloud embeddings or LLM-based consolidation. Use placeholders in shared config. Full walkthrough in setup.

Next steps

Section titled “Next steps”