Agent memory thatsurvives the session.
Context windows reset. MemClaw gives your AI agents a durable memory layer — write once, recall by meaning, share across the fleet— with rules and provenance built in.
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Apache 2.0 · 70,000 downloads
In production at eToro — read the case study →
# Tuesday — the deploy agent learns something
memclaw_write("Postgres 16 upgrade blocked by
pgvector 0.6 — staying on 15 until Q3")
# six weeks later — a DIFFERENT agent asks
memclaw_recall("why are we still on postgres 15?")
→ "Postgres 16 upgrade blocked by pgvector 0.6…"
fact · written by deploy-bot · 41 days agoYour agents are smart. Their memory isn’t.
Three reasons “just use the context window” stops working the day you have real agents doing real work.
The context window is not memory
Everything your agent figured out this session — decisions, dead ends, tribal knowledge — evaporates when the session ends. Tomorrow it re-derives all of it, at your token cost.
RAG is not memory either
Document retrieval answers "what do the docs say?" — not "what did we decide, what did we try, and how did it turn out?". Memory needs writes, updates, and a lifecycle, not just search.
N agents = N amnesiacs
Each agent in your fleet re-learns what a teammate already paid to learn. Without a shared layer, knowledge is trapped per-agent, per-session, per-tool.
Agent memory, done properly — not a vector database with homework
MemClaw ships the whole memory layer: storage, enrichment, recall, sharing, and the governance your team will eventually wish it had from day one.
Persistent & typed
Write once — MemClaw enriches every memory with type (fact, decision, episode…), title, summary, and tags automatically.
Semantic recall
Ask in plain language, get the right memory back — by meaning, not keywords, with temporal hints ("what changed last week?").
Shared across the fleet
Agent, fleet, and tenant scopes decide who sees what. What one agent learns, every agent that should know — knows.
Governed, with provenance
Keystone policy rules every agent obeys, trust levels that gate destructive ops, supersede-don't-delete corrections, and a full audit trail of who wrote what.
Plugs into anything
MCP-native: paste one config and 12 memory tools appear in Claude Code, Claude Desktop, Cursor, Windsurf. No MCP client? Plain REST works too.
Open source, or managed
Apache 2.0 — self-host in ~5 minutes. Or use MemClaw Cloud and deploy nothing. Same API either way.
Free tier · no credit card
Long-term memory for LLMs — any model, any client
LLM memory that isn’t welded to one vendor: the store lives outside the model, so it survives session resets, model swaps, and provider changes.
Model-agnostic by design
Claude, GPT, Gemini, or local — if it speaks MCP or HTTP, it remembers. Swap models without losing a single memory.
Stop replaying transcripts
Long-term LLM memory means recalling the three facts that matter instead of re-feeding yesterday's whole conversation at token cost.
Survives everything
Session ends, context compacts, model upgrades — the memory layer doesn't notice. Write once, recall months later.
{
"mcpServers": {
"memclaw": {
"url": "https://memclaw.net/mcp",
"headers": {
"X-API-Key": "mc_your_key"
}
}
}
}Free tier · no credit card
A memory architecture with a knowledge graph inside
Three planes, one API: a typed store, an entity graph that connects what your agents know, and a governance layer that decides who may do what.
Typed memory store
Facts, decisions, episodes, actions — every write auto-enriched with title, summary, and tags, deduplicated, and superseded (never silently lost) when facts change.
Entity knowledge graph
Memories link the people, projects, and systems they mention. Ask for an entity and get its whole graph view; tune traversal depth per search profile.
Governance plane
Agent, fleet, and tenant scopes; trust levels that gate destructive operations; keystone policy rules every agent loads; full who-wrote-what provenance.
Want the internals? Read the full architecture — the memory pipeline, step by step.
Free tier · no credit card
Remembering in 30 seconds
Three steps. The longest one is typing your email.
Create a free account
Email or SSO. No credit card, no sales call.
Copy your API key
A tenant-scoped key (starts with mc_) is waiting on your dashboard.
Connect your agents
One MCP config or one REST call — your agents start remembering.
Works where your agents work
Comparing options? MemClaw vs Mem0, Zep, and Letta — honest tables.
Frequently asked questions
What is agent memory?+
Agent memory is a persistent store an AI agent writes to and recalls from across sessions — facts it learned, decisions that were made, what was tried and how it turned out. Unlike the context window (which resets) or RAG (which only reads documents), an agent memory layer supports writes, semantic recall, updates as facts change, and sharing between agents.
How is agent memory different from RAG?+
RAG retrieves passages from documents you already have. Agent memory captures what happens as your agents work: decisions, outcomes, preferences, tribal knowledge that exists in no document. MemClaw gives memories a lifecycle — they're written, enriched, recalled semantically, superseded when facts change, and audited — which document retrieval can't do.
How is MemClaw different from a vector database?+
A vector database is a building block — you'd still have to build enrichment, memory types, dedup, scopes, sharing, policy, provenance, and an agent-facing API on top. MemClaw is the finished memory layer: agents get 12 ready tools over MCP (or REST), and teams get governance — scopes, trust levels, keystone rules, audit — out of the box.
Can multiple agents share one memory?+
Yes — that's the point. Memories carry scopes (agent, fleet, tenant), so a finding written by one agent is instantly recallable by every agent that should see it, with the writer's identity attached. Per-agent API keys keep provenance real: you always know which agent learned what, and when.
How do I add memory to my agent?+
If your agent runs in an MCP client (Claude Code, Claude Desktop, Cursor, Windsurf), paste one config block and the memory tools appear. For frameworks like LangChain, LlamaIndex, CrewAI, or AutoGen there are integration guides, and the REST API works from any language. First write to first recall is typically under five minutes.
Is MemClaw open source? What does it cost?+
The engine is Apache 2.0 open source — self-host it in about five minutes. MemClaw Cloud has a free-forever tier (10K memories, unlimited agents and fleets, no credit card); Pro starts at $49/mo. Same API on both, so you can start managed and move self-hosted, or the reverse.
Give your agents a memory today.
Free tier: 10K memories, unlimited agents and fleets. No credit card.
No credit card
Apache 2.0 · 70,000 downloads