Chroma MCP vs MCP Memory Server

Chroma MCP

MCP Servers

MCP Memory Server

MCP Servers

Tagline

Chroma MCP

Official MCP server that gives LLM clients direct access to the Chroma vector database.

MCP Memory Server

Persistent knowledge-graph memory for Claude and other MCP clients

Pricing

Chroma MCP

Free· Open source (Apache 2.0). Free to run locally or self-hosted; embedding-function API keys (OpenAI, Cohere, Jina, VoyageAI, Roboflow) billed by those providers. Chroma Cloud pricing set separately by Chroma.

MCP Memory Server

Free· Free and open source (MIT). Self-hosted; no vendor charges. Runs locally via npx or Docker.

API

Chroma MCP

Yes

MCP Memory Server

Yes

Open source

Chroma MCP

Yes · Apache-2.0

MCP Memory Server

Yes · NOASSERTION

GitHub stars

Chroma MCP

600

checked 2026-10-29

MCP Memory Server

91,651

checked 2026-10-29

Last GitHub push

Chroma MCP

2025-09-17

MCP Memory Server

2026-10-29

First commit

Chroma MCP

2025-02

MCP Memory Server

2024-11

Package downloads (30 d)

Chroma MCP

—

MCP Memory Server

175,042 · pypi mcp-server-git

checked 2026-10-15

Best for

Chroma MCP

Developers who already use Chroma (or want to) and need to give Claude Desktop, Cursor, or another MCP client durable vector memory or a searchable knowledge base with minimal glue code.

MCP Memory Server

Developers and power users who already run Claude Desktop, Claude Code or another MCP client and want a free, local, hand-inspectable persistent memory layer they can wire up in five minutes.

Not for

Chroma MCP

Teams needing hosted, multi-tenant vector search with auth and quotas out of the box, or anyone who wants an opinionated end-to-end RAG framework rather than a database bridge.

MCP Memory Server

Teams that need multi-user memory, semantic search over thousands of facts, SSO or an audited managed service - reach for a hosted memory product or a real graph/vector database instead.

Editorial score

Chroma MCP

—

MCP Memory Server

—

Use cases

Chroma MCP

Long-term memory for Claude DesktopTeam knowledge base shared across agentsRAG over local documentsSemantic code search in Cursor or ContinueVector store for custom MCP agentsPersonal notes and journal recallMetadata-filtered document retrievalChroma Cloud access from LLM clients

MCP Memory Server

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

Pros

Chroma MCP

• Official server from the Chroma team, tracks the database's features closely
• Four deployment modes (ephemeral, persistent, HTTP, Chroma Cloud) from one binary
• Twelve well-scoped MCP tools cover the full collection and document lifecycle
• Supports six embedding functions including OpenAI, Cohere, Jina, and VoyageAI with per-collection persistence
• Zero-config install via uvx - drops into Claude Desktop or Cursor in a couple of JSON lines
• Apache 2.0 licensed and readable Python source; easy to fork or extend
• Query tools expose HNSW tuning and metadata/full-text filters, not just naive semantic search

MCP Memory Server

• 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

Cons

Chroma MCP

• You still have to run and back up the Chroma store yourself unless you pay for Chroma Cloud
• Command-line API key arguments are convenient but leak into process lists; env/.env path is safer but extra setup
• No built-in access control or multi-tenant isolation - anything the MCP client sees, it can delete
• Embedding-function persistence only works for collections created on Chroma v1.0.0+; older stores need migration
• Python-only server; teams on pure Node stacks add a runtime dependency
• MCP tool surface is CRUD-shaped - no higher-level RAG primitives like chunking, re-ranking, or hybrid fusion

MCP Memory Server

• 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

Website

Chroma MCP

github.com

MCP Memory Server

github.com

Editorial score: rule-based, 0–10, from AI-assisted profile inputs (see /methodology) — not a user rating; “—” means unscored. “Not listed” means we have no record of it, not that it is absent. GitHub figures and prices carry the date they were checked or captured; prices are shown as published, unconverted.