Persistent memory MCP server, your AI remembers context across conversations
Visit WebsiteThe Bottom Line
Best for
Simple agent workflows that need long-term state — chat assistants, project agents, task managers. The lightweight answer when you don't want to run Pinecone or Qdrant. For production RAG use Supabase vector or Qdrant instead.
Entry price
Free, no paid tier
Biggest pro
AI remembers project decisions and preferences between sessions
Biggest con
Memory is local, not shared across devices
TL;DR - Memory MCP Server
- Persistent memory for AI assistants across conversation sessions
- Stores entities and relationships in a local knowledge graph
- No cloud dependency, data stays on your machine
What Users Say About Memory MCP Server
Memory MCP is Anthropic's reference server for giving agents persistent memory across sessions — a knowledge graph that the agent updates as it works. Users like that it's genuinely free, local, and model-agnostic. The complaints: the graph model is simple (entities + relations + observations) and power users wanting richer memory schemes often graduate to custom solutions.
Highlights
- Official and maintained by Anthropic — free to run locally
- Simple knowledge graph: entities, relations, observations
- Persists state across sessions and agent restarts
- Any MCP-compatible client can read/write the same graph
- Zero API costs — purely local storage
Limitations
- Graph model is simple — not a full vector DB or RAG stack
- No built-in observability for memory drift or conflicting entries
- No UI — you inspect the graph through the agent or raw JSON
- No built-in pruning; old memories accumulate and consume context
- Power users often graduate to custom vector stores for richer recall
Best for: Simple agent workflows that need long-term state — chat assistants, project agents, task managers. The lightweight answer when you don't want to run Pinecone or Qdrant. For production RAG use Supabase vector or Qdrant instead.
Editorial synthesis from industry coverage, product docs, and early user reports
Editorial policyWhat is Memory MCP Server?
Pros & Cons
Pros
- AI remembers project decisions and preferences between sessions
- Privacy-friendly, no data leaves your machine
Cons
- Memory is local, not shared across devices
- Graph can grow large over time without cleanup
Key Features
Pricing Plans
Pricing checked Jun 26, 2026
Open Source
Free
- Full source code access
- Community support
- Self-hosted
Reviews
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Memory MCP Server FAQ
How does Memory MCP Server enable AI assistants to retain information?
Which teams would benefit most from Memory MCP Server?
How is Memory MCP Server priced?
Can Memory MCP Server share AI context across multiple devices?
How does Memory MCP Server compare to AnythingLLM for AI memory management?
What kind of data does Memory MCP Server store for AI context?
Source: github.com