DEV Community

Abhi A
Abhi A

Posted on • Originally published at contextcloud.pro

Best MCP Memory Servers for Teams in 2026: Context Cloud vs mem0 vs Basic Memory vs claude-mem vs MemPalace

#mcp #ai #claude #devtools

There are now dozens of MCP memory servers. I've tried most of them. They almost all solve the same problem: your AI forgets everything between sessions, so you need something to make it remember.

That problem is basically solved. Pick any of the popular options — mem0, Basic Memory, claude-mem, MemPalace — and your Claude Code or Cursor session will remember what happened last time.

But there's a different problem that none of them solve, and it's the one that actually matters if you work on a team: your AI remembers your context, but your teammate's AI doesn't.

Two engineers on the same project. Two separate AI brains. Each one learning the codebase independently. Every architecture decision, every convention, every "we tried X and it didn't work because Y" — trapped in individual sessions that never talk to each other.

I've been building Context Cloud specifically around this problem. This is an honest comparison of where things stand.

What to actually evaluate

Most comparisons focus on setup ease and basic recall. That's table stakes now. The things that actually differentiate memory servers in 2026:

Single-player vs team. Can your teammate's AI session access what yours learned? Does the system track who contributed what? Can you scope access by role?

Knowledge structure. Is it flat text, markdown files, entity graphs, or typed chunks with semantic meaning? When your AI recalls context, does it know the difference between "this is a decision we made" and "this is the current state of things"?

Storage model. Local SQLite on your machine, or cloud-hosted so it works across machines and teammates? Local is simpler. Cloud is necessary for teams.

Tool support. Claude Code only, or does it work across Cursor, Codex, Windsurf? Most teams use more than one AI tool.

Retrieval quality. Keyword matching, vector search, or hybrid? How does it handle stale context?

The comparison

Context Cloud mem0 Basic Memory claude-mem MemPalace Knowledge Graph Zep/Graphiti
Team workspaces ✅ Shared with RBAC, invites, attribution ❌ Single-user ❌ Single-user ❌ Single-user ❌ Single-user ❌ Single-user ❌ Single-user
Knowledge structure 7 typed chunks (decision, finding, convention, state, question, reference, session_summary) Flat semantic memories Markdown files Compressed observations Verbatim + vector embeddings Entity-relation graph Temporal knowledge graph
Storage Cloud (Supabase + pgvector) Cloud or self-hosted Local markdown files Local SQLite Local SQLite + ChromaDB Local JSON file Cloud or self-hosted
AI tools supported Claude (web/desktop/Code), Cursor, Codex, Windsurf Any MCP client CLI-based Claude Code, Gemini CLI Claude Code Any MCP client Any MCP client
Dashboard ✅ Web UI with knowledge graph, chunk editing, team activity ✅ Cloud dashboard ❌ Read the markdown files ✅ Local web viewer (localhost:37777) ❌ ❌ ✅ Cloud dashboard
Retrieval Hybrid RRF (vector + BM25), auto chunk-type intent detection Semantic vector search File-based lookup Compressed context injection Vector similarity via ChromaDB Graph traversal Temporal-aware graph queries
Attribution ✅ Per-chunk (who, when, which session, which tool) ❌ ❌ ❌ ❌ ❌ ❌
Auto-capture Explicit (AI extracts on commit) Explicit Manual file editing ✅ Automatic via lifecycle hooks ✅ Stores every conversation Explicit Explicit
Dedup / staleness Session-aware dedup (0.95/0.97) + topic-key supersession + bi-temporal tracking Basic dedup None (manual) Compression handles it None Manual Temporal invalidation
Server-side LLM cost $0 (extraction on client) Varies by plan $0 $0 $0 $0 Varies by plan
Price Free tier (unlimited teammates) Free / $25-$475/mo Free (OSS) Free (OSS) Free (OSS) Free (OSS) Free / $99+/mo

Deep dive on each

Context Cloud

Full disclosure: I built this. Context Cloud is an MCP memory server with shared team workspaces, typed knowledge chunks, role-based access, and cross-tool support for Claude, Cursor, and Codex.

The core idea is that knowledge has types that matter. A decision ("we're using Postgres because the DBA only supports it") is fundamentally different from state ("auth flow is 70% done") or a convention ("all API routes use camelCase"). When your AI recalls context, it should know the difference, because decisions are durable and state is temporal.

The team layer is what's different from everything else. You create a workspace, invite teammates by email, and everyone's AI sessions read and write to the same knowledge store. Every chunk tracks who committed it, when, and from which tool. Your teammate makes an architecture decision at 2am, your morning session knows about it.

Retrieval uses hybrid RRF — vector similarity plus BM25 keyword search, fused with reciprocal rank fusion. Auto chunk-type detection reads the query shape ("what decisions did we make about auth?") and filters appropriately. Session-aware deduplication prevents the same knowledge from stacking up across commits, and topic-key supersession handles evolving facts (the current state of the deployment replaces the old state, not stack on top of it).

Benchmarks: 99.4% weighted product readiness on our eval suite. 94/100 on a realistic 20-session stress test with evolved facts, stale context, and adversarial phrasing. Detail capture at 95.7–97.1%. Zero ranking failures, zero embedding failures.

Best for: Teams of 2+ using AI coding tools. Multi-tool workflows (Claude + Cursor + Codex). Projects where decisions and conventions need to persist and be shared.

Honest limitation: No ambient auto-capture yet. You have to tell your AI to "save this." The AI handles the extraction and typing, but it's not zero-friction.

mem0

mem0 is probably the most mature cloud-hosted memory option. Good semantic search, clean API, supports multiple LLM providers. The cloud dashboard is solid for browsing stored memories.

Where it falls short for teams: memories are flat — no type system, no structure beyond raw text. No shared workspaces, no RBAC, no attribution. If you're a solo dev who wants semantic recall across sessions, mem0 is a strong choice. The moment you need a second person's sessions to access the same knowledge store, it doesn't have the infrastructure.

Pricing scales with usage — the free tier is limited, and paid plans go up to $475/mo for high-volume use.

Best for: Solo developers who want cloud-hosted semantic memory with a clean API.

Basic Memory

Basic Memory is the simplest option and that's its strength. It stores knowledge as markdown files that you can read, edit, and version control yourself. No database, no cloud, no dependencies beyond the CLI.

The tradeoff is obvious: no search (beyond ctrl+F), no team features, no dashboard, no deduplication. You're maintaining text files. If you're disciplined about it and working solo on a small project, it works. It falls apart at scale or with multiple people.

Best for: Solo developers who want full control, human-readable storage, and zero infrastructure.

claude-mem

claude-mem has the most active community in this space — 1,840 commits, 109 contributors, 259 releases. That's not an accident. The hook-based auto-capture is genuinely good: it hooks into Claude Code's session lifecycle events (SessionStart, PostToolUse, Stop, etc.) and records observations automatically. You don't think about it. It just captures.

A background worker compresses observations using Claude's agent-sdk, stores them in local SQLite, and injects relevant context when a new session starts. The local web viewer at localhost:37777 gives you visibility into what's stored.

The limitation is the same as everything else: local and single-player. Your teammate can't access your SQLite database. No shared workspaces, no attribution, no cross-tool support beyond Claude Code and Gemini CLI.

Best for: Solo Claude Code users who want zero-friction auto-capture with an active community.

MemPalace

MemPalace stores every conversation verbatim, generates vector embeddings for semantic search via ChromaDB, and retrieves relevant context automatically. Three commands to install, everything runs locally, no API keys needed.

The approach of storing everything verbatim means you never lose detail — but it also means no structure, no typing, no distinction between a critical architecture decision and a throwaway debugging comment. Everything gets the same weight.

Local-only, single-player, no dashboard, no team features.

Best for: Solo Claude Code users who want full verbatim storage with semantic search and zero setup.

Knowledge Graph Memory (Official Anthropic)

The official MCP memory server from Anthropic uses an entity-relation graph model. You store entities (people, projects, concepts) and the relationships between them.

The graph model is interesting — it captures structure that flat text doesn't. But it's stored as a local JSON file (not great for scale or reliability), has no team features, no dashboard, no cloud option, and requires you to explicitly define entities and relations rather than extracting them from natural conversation.

Best for: People who want structured entity-relationship memory and are comfortable with the graph model.

Zep / Graphiti

Zep and their Graphiti engine bring something unique: temporal awareness. The system understands not just what was stored but when it was relevant and how it connects to everything else over time. This is genuinely useful for long-running projects where facts evolve.

The tradeoff is enterprise pricing and complexity. Zep is a more serious infrastructure commitment than any of the other options. No self-serve team features — it's designed for organizations that need compliance, audit trails, and managed infrastructure.

Best for: Enterprise teams that need temporal knowledge graphs and are willing to pay for managed infrastructure.

When to use what

This is the honest breakdown:

Solo dev, one project, want simplicity → Basic Memory or MemPalace. Both are free, local, and work out of the box. Basic Memory if you want human-readable markdown files. MemPalace if you want auto-storage with semantic search.

Solo dev, want zero-friction auto-capture → claude-mem. The lifecycle hooks mean you genuinely don't have to think about it. Strongest community of any option here.

Solo dev, want cloud + semantic search → mem0. Most mature cloud option, clean API, good dashboard.

Team of 2+, need shared context → Context Cloud. It's currently the only MCP memory server with shared workspaces, RBAC, attribution, and cross-tool support. This isn't a marketing claim — check the comparison table. Nobody else has team features.

Enterprise, need temporal graphs + compliance → Zep/Graphiti. Different price bracket, different use case.

The team gap

Here's the thing nobody talks about in the memory MCP space: the individual problem is solved. Multiple good options exist. Pick any of them and your sessions will remember what happened last time.

The team problem is wide open. Your teammate's Claude session still starts from zero every time, even if yours remembers everything. Architecture decisions made in your session never reach theirs. Domain expertise stays trapped in individual memory stores. Onboarding means a new engineer's AI knows nothing about months of accumulated team knowledge.

CLAUDE.md helps with static instructions ("use camelCase," "we use Postgres"), but it's not memory — it's a config file. It doesn't grow from sessions, doesn't track who added what, and pollutes git history with non-code changes.

This is the gap I'm building Context Cloud to fill. Whether you use us or not, this is the problem the MCP memory space needs to solve next.

Context Cloud is an MCP memory server with shared team workspaces, typed knowledge chunks, role-based access, and cross-tool support for Claude, Cursor, and Codex. Free to use.

Top comments (0)