Memory LanceDB Pro MCP Server
Memory LanceDB Pro MCP server providing AI agents with persistent semantic memory via LanceDB — storing and retrieving memories with vector embeddings for semantic search, managing long-term agent memory across sessions, enabling context-aware memory retrieval, and integrating LanceDB's serverless vector database into agent-driven memory and knowledge management workflows.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Local LanceDB. No auth. Embedding API keys in env var. Filesystem permissions. Community MCP. No network exposure.
⚡ Reliability
Best When
An agent needs persistent semantic memory with vector similarity search — LanceDB's embedded, serverless model makes it ideal for local agent deployments without external vector DB infrastructure.
Avoid When
You need a managed cloud vector database, or your memory requirements are simple enough for key-value storage.
Use Cases
- • Persisting agent memories across sessions with semantic search retrieval
- • Building RAG systems with LanceDB as the vector store from knowledge agents
- • Storing and retrieving user preferences and facts from personal assistant agents
- • Maintaining conversation context across long agent workflows
- • Building knowledge bases with semantic search from research agents
- • Implementing episodic memory for autonomous agents
Not For
- • Teams needing cloud-hosted vector databases (LanceDB is serverless/local)
- • Simple keyword-based memory (use SQLite or key-value MCPs)
- • Teams preferring Pinecone, Weaviate, or other managed vector DBs
Interface
Authentication
No authentication — local LanceDB database. Access controlled by filesystem permissions. Optional embedding model API key (OpenAI, etc.) for generating embeddings.
Pricing
LanceDB open source is free. Community MCP is free. Embedding model costs may apply (OpenAI API for embeddings). LanceDB Cloud available for managed deployments.
Agent Metadata
Known Gotchas
- ⚠ Embedding model API key required for semantic search (OpenAI, Cohere, etc.)
- ⚠ LanceDB storage path must be configured — data persists at this path
- ⚠ Embedding dimensions must be consistent across stored and queried vectors
- ⚠ Community MCP from coleam00 — active in MCP community, reasonable quality
- ⚠ Memory deduplication is the agent's responsibility — no automatic dedup
- ⚠ LanceDB versioning may cause compatibility issues with older stored data
Alternatives
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Scores are editorial opinions as of 2026-03-07.