You’re building an OpenClaw agent and you want it to remember things between sessions. You search “AI agent memory” and immediately hit three names: Mem0, Zep, and MemoClaw. All three promise persistent memory for agents. But they solve different problems, charge differently, and require very different amounts of setup. Here’s an honest breakdown from someone who’s actually wired these things up.

The MEMORY.md problem

If you’re running an OpenClaw agent today, you probably have a MEMORY.md file in your workspace. It works. Sort of. Your agent reads it on startup, appends to it during the session, and hopes the file doesn’t balloon to 40KB and eat your entire context window. There’s no search. No prioritization. No way to say “find that thing the user told me about their database setup three weeks ago” without scanning the whole file.

That’s the gap all three services fill. But they fill it differently.

Mem0

Mem0 (mem0.ai) positions itself as a “universal memory layer for LLM applications.” It’s a solid product with good Python and JavaScript SDKs. You sign up for an account, get an API key, and integrate through their client libraries.

The free tier gives you 10,000 memories and 1,000 retrieval API calls per month. Their paid plans start at $19/month (50K memories, 5K retrievals) and jump to $249/month for unlimited memories with graph memory support.

Here’s where it gets tricky for OpenClaw users: Mem0 is built around traditional API key authentication. You create an account, manage API keys, and integrate through their SDK. There’s no OpenClaw skill. No CLI that plugs into your agent workflow out of the box. You’d need to write custom integration code, manage credentials in your environment, and handle the SDK yourself.

Mem0 also decides what to extract from conversations automatically. That’s useful if you want hands-off memory management, but it means less control over what gets stored and how. If your agent stores something with high importance, Mem0 doesn’t really have that concept natively.

Zep

Zep (getzep.com) is the heavyweight in this space. It bills itself as a “context engineering and agent memory platform” and it’s not kidding about the scope. Graph RAG, custom entity extraction, webhook support, structured knowledge graphs. This is enterprise tooling.

Pricing starts at $25/month for 20,000 credits on their Flex plan, with auto-topup when you run low. Flex Plus runs $475/month. There’s a free tier with 1,000 credits. They also have enterprise options with BYOK (bring your own key) and on-prem deployment.

For an OpenClaw agent that just needs to remember “user prefers dark mode” or “last deployment was on Railway,” Zep is overkill. You’re paying for a knowledge graph engine when you need a notepad with search. The setup requires account creation, project configuration, and learning their credit system. Like Mem0, there’s no native OpenClaw integration.

That said, if you’re building something complex where your agent needs to track relationships between entities, understand conversation context over long sessions, or integrate with enterprise auth, Zep handles that well. It’s just a different class of tool.

MemoClaw

MemoClaw takes the opposite approach: do less, but make it dead simple.

Install the skill and you’re storing memories in under a minute:

# Install
clawhub install anajuliabit/memoclaw

# Store something
memoclaw store "user prefers Railway for deployments" --importance 0.8 --tags infra

# Find it later
memoclaw recall "where does the user deploy?"

No account creation. No API keys. Your wallet address is your identity. The first 100 API calls are free, and after that it’s $0.005 per store or recall operation, paid via x402 with USDC on Base.

That pricing model matters more than it looks. With Mem0, you’re paying $19/month whether you make 50 calls or 5,000. With MemoClaw, 1,000 calls in a month costs you $5. If your agent is quiet for a week, you pay nothing. For personal agents and small projects, pay-per-call beats subscriptions every time.

MemoClaw also gives you explicit control. You set importance scores (0 to 1), add tags, organize memories into namespaces. When your agent stores a user correction with importance 0.95, that memory gets prioritized in recall. You decide what matters, not an extraction algorithm.

The OpenClaw integration is where it really pulls ahead. Add a few lines to your AGENTS.md and your agent uses MemoClaw automatically:

# In your agent's workflow
memoclaw store "Ana prefers direct answers, no fluff" --importance 0.9 --tags preferences
memoclaw recall "communication style" --tags preferences

Namespaces let you isolate memories per project. Multiple agents sharing the same wallet can access the same memory pool, which is handy if you run specialized subagents that need shared context.

What MemoClaw doesn’t do

Being honest here: MemoClaw is not a RAG solution. It won’t ingest your entire codebase or process PDFs. Each memory tops out at 8,192 characters. It uses OpenAI embeddings only, so you can’t bring your own embedding model. And it’s cloud-only, no self-hosting.

If you need to index large documents, look at a proper RAG setup or Zep’s Graph RAG. If you need on-prem for compliance, Zep Enterprise or a self-hosted solution is your path.

The actual decision

Think about what your agent needs to remember and how often.

If you’re building an enterprise product with complex entity relationships and long conversation threads, Zep is the right tool. Budget $25-475/month and plan for integration work.

If you’re already deep in the Python/JS ecosystem and want automatic memory extraction with a managed service, Mem0 works well at $19-249/month.

If you’re running an OpenClaw agent and want persistent memory that works in five minutes with no accounts, no API keys, and costs proportional to what you actually use: that’s MemoClaw.

For most OpenClaw users I’ve talked to, the answer is straightforward. They don’t need knowledge graphs. They don’t need enterprise SSO. They need their agent to remember things between sessions without eating their context window. MemoClaw does that for half a cent per operation.

clawhub install anajuliabit/memoclaw
memoclaw store "this is my first memory" --importance 0.5

Two commands. You’re done.

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MemoClaw docs: https://docs.memoclaw.com | CLI: npm install -g memoclaw | Skill: https://clawhub.ai/anajuliabit/memoclaw