MCP Memory Server

Persistent knowledge-graph memory for Claude and other MCP clients

Free· Free and open source (MIT). Self-hosted; no vendor charges. Runs locally via npx or Docker.MCP Servers

Best for

Developers and power users who already run Claude Desktop, Claude Code or another MCP client and want a free, local, hand-inspectable persistent memory layer they can wire up in five minutes.

Skip if

Teams that need multi-user memory, semantic search over thousands of facts, SSO or an audited managed service - reach for a hosted memory product or a real graph/vector database instead.

MCP Memory Server (published on npm as @modelcontextprotocol/server-memory) is the reference persistent-memory server maintained inside Anthropic's official modelcontextprotocol/servers repository. It gives any MCP-compatible client - Claude Desktop, Claude Code, Cursor, Zed, Windsurf and dozens of community wrappers - a local knowledge-graph store so the model can remember facts about the user, projects and prior conversations across sessions instead of losing everything at the end of a chat.

Data is modelled as a small graph of Entities (nodes with a name, an entityType and a list of atomic Observations) and Relations (directed, active-voice edges between entities). The server exposes nine tools - create_entities, create_relations, add_observations, delete_entities, delete_observations, delete_relations, read_graph, search_nodes, open_nodes - plus a memory://knowledge-graph MCP Resource that mutation tools update via notifications/resources/updated, so subscribed clients see live changes. Storage is a plain JSONL file on disk (path configurable via MEMORY_FILE_PATH), which makes the graph trivial to inspect, back up, diff in git or edit by hand.

Typical workflows: pair the server with a system prompt that tells the assistant to recall relevant nodes at the start of a chat and to write new observations at the end; use it as long-lived project memory for coding agents so they remember stack choices, coding conventions and previously fixed bugs; build a lightweight personal CRM by capturing people, companies and preferences as entities; give research agents a scratch graph they can query with search_nodes instead of re-reading the whole context. Because it is a pure MCP server, it composes cleanly with filesystem, git, database and web-fetch servers in the same client.

Editor's take

This is the memory server most people should try first: it is the official reference, it installs in one npx line, and the entity/relation/observation shape maps cleanly to how assistants actually accumulate knowledge. Treat it as durable notes for one user on one machine - not as a scalable backend - and pair it with a system prompt that reminds the model to read and write the graph.

— The AI Tool Bible editorial team

Pros

✅ Official, Anthropic-maintained reference implementation - the canonical way to add persistent memory to an MCP client
✅ Zero-config install via npx or a one-line Docker command; works out of the box with Claude Desktop's claude_desktop_config.json
✅ Simple, inspectable JSONL storage on disk that you can grep, diff, back up and edit by hand
✅ Structured entity/relation/observation model is more queryable than a raw text scratchpad and cheaper than a vector DB
✅ Nine well-scoped tools plus a live-updating knowledge-graph Resource, so agents can both read and mutate memory
✅ Fully open source (MIT) and vendor-neutral - runs against any MCP-speaking model, not just Claude
✅ Trivial to fork or wrap for team-specific schemas since the codebase is a single small TypeScript file

Cons

⚠️ Reference-quality, not production-grade: single-file JSONL storage with no concurrency control, indexing or replication
⚠️ search_nodes is a plain substring match with no embeddings or semantic ranking - large graphs degrade quickly
⚠️ No built-in multi-user, auth or per-project isolation; a shared install mixes memories from every session
⚠️ The model still has to be prompted to actually call the memory tools - forgetful assistants forget to remember
⚠️ No web UI, visualisation or admin surface; you inspect and clean the graph by editing the JSONL yourself
⚠️ Only a local filesystem backend - no Postgres, SQLite or cloud sync option is shipped

Use cases

Persistent Claude Desktop memory across chatsLong-lived coding-agent project memoryLightweight personal CRM of people and companiesResearch-agent scratch knowledge graphCross-session preference and style memoryTeam convention and past-bug recall for coding assistantsStructured note-taking backend for MCP clientsLocal-first alternative to hosted memory APIs