Describe it, and ClawDB drafts the memory schema instantly.
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Autonomous memory.
Re-engineered for speed.
Every new chat with an AI assistant starts from zero. It forgets your preferences, your past decisions, everything. ClawDB gives AI agents a permanent memory, so what they learn today is still there tomorrow.
Under the hood: vector search, a knowledge graph, git-like state branching, and native Model Context Protocol (MCP) support.
Three steps, then it runs itself
Connect once
Add ClawDB to your AI client or your own agent code. A dashboard config for MCP clients, or one line of code for the SDK.
It remembers as you go
Preferences, decisions, facts, and context get written to memory automatically as your agent works, no manual logging required.
It recalls what matters
The next session, the next tool, the next teammate: a query pulls back the relevant memory in well under a millisecond.
One memory layer, whoever you are
Building an agent
Drop in the SDK and give it a place to store what it learns. No vector database expertise required, and no separate service to stand up.
Using AI tools day to day
Connect Claude, Cursor, or any MCP-compatible assistant, and it stops forgetting your preferences and past conversations between chats.
Running a team on AI
One shared memory layer across every tool your team uses, with access control so the right people see the right data and nobody else does.
Every session starts from zero
Close the chat and the context is gone. Teams end up re-explaining the same preferences, re-pasting the same background, and stitching memory together with ad-hoc summaries that don't scale past one conversation or one tool.
A memory that persists, by default
ClawDB gives an agent one place to write what it learns and query it back later, across sessions, across tools, and across your whole team. Set it up once, and it just keeps working.
Three products, one memory layer
From open-source local memory to a hosted agent gateway and a decentralized memory marketplace.
Database
The free, open-source memory engine underneath everything. An embedded vector index plus graph state that you can run locally, bundle into your own service, or self-host on your own servers.
MCP
A hosted Model Context Protocol gateway for teams that don't want to run infrastructure. Point Claude, Cursor, Windsurf, or any MCP client at one URL and get persistent memory in minutes.
x402
Coming soon: a marketplace where agents pay per call for specialized memory instead of every team rebuilding the same domain knowledge from scratch, priced in micropayments rather than subscriptions.
Built for every AI agent workload
From single-session LLM memory to complex multi-agent swarms with state branching.
Memory Copilot: describe it, AI drafts it
Type what you want to remember in plain language and get a working, typed schema in seconds, or start from a template.
Autonomous AI agents
Long-term context persistence across LLM sessions, with automatic summarization so old context doesn't just pile up unread.
MCP protocol runtime
Native Model Context Protocol integration for Claude Desktop, Cursor, and Windsurf, so any of them can share the same memory.
Git-like state branching
Fork agent memory to test a reasoning path, then merge or throw it away, all without touching production state.
Import from anywhere
Already running Supabase, mem0, Zep, or something else? Bring that memory into ClawDB instead of starting over.
Enterprise and security
Encrypted memory storage, role-based access control, and workspace isolation for teams with more than one person to answer to.
Simple pricing for every scale
Start free for development, scale smoothly to production.
100,000 vector memory storage and one database. No card required.
1M vector storage, memory branching, and email support.
10M vector storage, knowledge graph, and priority support.
Dedicated clusters and a named solutions engineer.
Questions, answered
No. If you just want an AI assistant like Claude or Cursor to remember things, you connect it from the dashboard with no code: generate a key, paste a config snippet, and you're done. Writing custom logic against the SDK is only needed if you're building your own agent.
Give your AI agents permanent memory today.
Deploy in under 2 minutes. Free tier included forever.
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