MCP Memory Server vs Qdrant MCP Server

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	MCP Memory Server MCP Servers	Qdrant MCP Server MCP Servers
Tagline	Persistent knowledge-graph memory for Claude and other MCP clients	Official Qdrant MCP server that turns a vector database into a semantic memory layer for Claude, Cursor, Windsurf, and any MCP client.
Category	MCP Servers	MCP Servers
Pricing	Free· Free and open source (MIT). Self-hosted; no vendor charges. Runs locally via npx or Docker.	Free· Free and open source (Apache-2.0). Qdrant itself can be self-hosted for free or used via Qdrant Cloud (free tier available, paid plans from ~$25/mo for managed clusters).
Model	—	FastEmbed (default: sentence-transformers/all-MiniLM-L6-v2); pairs with any MCP-capable LLM such as Claude 3.5/4, GPT-4o, or local models
Editorial score	—	—
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	Persistent memory for Claude Desktop agentsSemantic code snippet search in Cursor and WindsurfPrivate documentation retrieval for internal LLM copilotsTeam knowledge base backed by Qdrant CloudLocal offline vector memory via QDRANT_LOCAL_PATHRead-only knowledge lookup skill for customer-support agentsCross-session context store for autonomous coding agents
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	Official, actively maintained by the Qdrant team with 1.4k+ stars and frequent releases Two-tool surface (store/find) is small enough that models actually use it correctly Bundled FastEmbed means no separate OpenAI/Cohere embedding key is required to get started Configurable tool descriptions let you rebrand the same server as memory, code search, or docs lookup Works across the major MCP clients: Claude Desktop, Cursor, Windsurf, VS Code, and custom agents Apache-2.0 with local, Docker, and uvx install paths including a fully offline QDRANT_LOCAL_PATH mode Read-only mode makes it safe to expose a curated knowledge base without letting the model write to it
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	Only FastEmbed is supported today, so you cannot plug in OpenAI, Cohere, or Voyage embeddings without forking Just two tools: no filtering, hybrid search, payload updates, or collection management surfaced to the model Single active collection per server process; multi-collection agents need multiple server instances or wrapping Assumes you already run and secure a Qdrant instance (self-hosted or Cloud) — not a turnkey managed product Chunking, ingestion pipelines, and re-ranking are entirely your problem; this is a thin bridge, not a RAG framework
Website	github.com	github.com

Pick MCP Memory Server if

✅ 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

Pick Qdrant MCP Server if

✅ Official, actively maintained by the Qdrant team with 1.4k+ stars and frequent releases
✅ Two-tool surface (store/find) is small enough that models actually use it correctly
✅ Bundled FastEmbed means no separate OpenAI/Cohere embedding key is required to get started
✅ Configurable tool descriptions let you rebrand the same server as memory, code search, or docs lookup

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