Structured step-by-step reasoning for complex problem solving.
Memory
Persistent knowledge graph memory across AI conversations.
What it does
Maintains a persistent knowledge graph of entities and relationships stored locally. The model can create, query, update, and delete memories across sessions.
Why it matters
AI models are stateless by default. Memory MCP is the simplest way to give an agent long-term knowledge that carries over between sessions.
Best for
- Long-running agent workflows
- Personal AI assistants
- Project tracking
- User preference storage
- Agent knowledge accumulation
Not ideal for
- Single-session tasks
- Structured database queries
- High-volume data storage
When to use it
When building agents that need to remember user preferences, ongoing projects, past decisions, or any information that should persist over time.
When not to use it
For single-session tasks where persistence is not needed, or when using a database MCP for structured storage is more appropriate.
Key features
- Persistent knowledge graph
- Entity and relationship storage
- Cross-session memory
- Local file storage
- Official Anthropic support
Frequently asked questions
Where is memory stored?
In a local JSON file on your machine. It persists between sessions and is only accessible to the MCP.
Can the model forget memories?
Yes. The model can delete specific entities or observations from the knowledge graph.
Install
$ npx -y @modelcontextprotocol/server-memoryScores
Details
- Pricing
- open source
- Setup time
- 2 min
- Complexity
- Low
Works with
Alternatives
- SQLite →
Local SQLite database access with full read/write support.
- Sequential Thinking →
Structured step-by-step reasoning for complex problem solving.
More AI Agents MCPs
Other tools in the same category worth evaluating.
Exploring Top MCPs for AI Agents? See all AI Agents MCPs →