Memory MCP Server — Persistent Agent Memory
Memory MCP Server providing persistent memory storage for AI agents — storing facts, preferences, conversation summaries, and contextual information across sessions, enabling agents to remember user preferences, past conversations, and learned information beyond the context window through structured memory operations.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Local storage. No credentials. Community MCP. Protect stored memory. Don't store secrets in memory.
⚡ Reliability
Best When
An AI agent needs to persist facts, preferences, and summaries across multiple sessions — providing memory continuity beyond the LLM context window.
Avoid When
You need full-text search, vector similarity search, or multi-user memory — use ChromaDB, Neo4j memory, or LanceDB MCPs.
Use Cases
- • Storing and retrieving user preferences from personalization agents
- • Persisting conversation history summaries across sessions from assistant agents
- • Building user-specific knowledge bases from personal assistant agents
- • Remembering project context across separate coding sessions from development agents
- • Accumulating learned facts about users and tasks from continuous learning agents
- • Creating agent memory that survives context window resets
Not For
- • High-volume data storage (this is agent context memory, not a database)
- • Shared memory across multiple users (single-user memory store)
- • Teams with specific vector search requirements (use vector DB MCPs)
Interface
Authentication
No external authentication — local SQLite or file-based storage. Data stored locally on the machine.
Pricing
Free, open source community MCP.
Agent Metadata
Known Gotchas
- ⚠ Memory grows unboundedly without pruning — implement memory management strategies
- ⚠ No semantic search — retrieval is keyword or key-based, not vector similarity
- ⚠ Data is local machine only — not shared across machines or users
- ⚠ Memory structure and schema should be designed before heavy use
- ⚠ Community MCP — storage format may change between versions
- ⚠ Agent may confuse old memories with current context — recency weighting important
Alternatives
Full Evaluation Report
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Scores are editorial opinions as of 2026-03-06.