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 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
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
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 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 →
io.github.abhinavala/context-cloud
SOCIAL SHARE CARD GENERATOR