We recently built memory for OpenClaw and received a large number of people testing OpenClaw with Mem0.

If you haven't tried that setup yet, this tutorial is for you.

When you run OpenClaw for the first time, it collects information about you and writes it into memory files that the agent can reference during conversations. The agent may ask about your work, your preferences, or how you want it to behave. As you keep using it, responses may begin to reflect that information, which makes the agent feel like it is learning over time.

But as conversations grow longer or span multiple sessions, that expectation starts to break down. Details you shared earlier stop showing up in responses. Information that felt important to the task is no longer recalled. In some cases, the agent behaves as if the information was never provided at all.

This happens because OpenClaw’s default memory system does not guarantee persistence or meomory recall. Memory storage and retrieval are left to the LLM, guided by prompts, heuristics, and a small set of markdown files.

The model decides what to save, when to search memory, and whether previously stored information is relevant enough to be loaded back into the current context.

There is simply no guarantee that information will be persisted or reloaded when needed.

This article shows how to add enforced, persistent memory to OpenClaw using the Mem0 plugin, @mem0/openclaw-mem0.

TLDR

• OpenClaw provides memory files and memory tools, but it does not guarantee when information is saved or recalled

• Memory persistence and retrieval are optional behaviors controlled by prompts and model heuristics

• Long conversations and context compaction reduce the reliability of recall

• @mem0/openclaw-mem0 enforces automatic memory capture outside the agent lifecycle

• Relevant memory is injected into every response automatically.

• Memory survives restarts and session boundaries, making agents reliable across runs

What persistent memory means in OpenClaw agents

In OpenClaw, persistent memory refers to memory that is stored outside the agent’s execution lifecycle and can be reintroduced after a session ends or the process restarts.

Agents do not run forever. Sessions end. Context gets trimmed. Processes restart. If memory only lives inside the active prompt, it will disappear.

Persistent memory solves that by living outside the agent lifecycle and being reintroduced when needed.

Without it, agents rely on short term context and best effort recall. With it, agents can actually build on past interactions over time.

How OpenClaw’s memory system works, and why it fails for long-term recall

Out of the box, OpenClaw stores memory as markdown files on disk.

ls ~/.openclaw/workspace

You will see files like:

AGENTS.md
IDENTITY.md
MEMORY.md
USER.md

As you talk to your agent, OpenClaw gives the LLM access to memory tools such as:

memory_search
memory_get

At first glance, this looks reasonable. Memory exists and tools exist.

The problem is how those tools are used.

Saving memory depends on the LLM

When you tell your agent something important, OpenClaw does not force it into memory.

Before responding, the agent does this internally:

• The LLM decides if the information is worth saving

• If it decides yes, it writes it to disk

• If it decides no, the information is ignored forever

There is no guarantee it will be saved.

This is one of the first places where AI agents' long term memory breaks down in OpenClaw. Memory exists, but it is optional.

Recall depends on the LLM too

Even when something was saved, recall is still not guaranteed.

OpenClaw provides tools like memory_search, but the agent must decide to call them.

User: I usually build backend APIs in Python
Agent: Okay, noted

Later in the same session or a new one:

User: Suggest a project idea for me

At this point, the agent has two choices:

• Look into memory

• Answer from its training data

Most of the time, it chooses the second option.

You then get answers like:

Agent: You could build a mobile app or a game

This is how LLM agent memory feels broken even though memory files exist.

Context compaction makes it worse

To avoid hitting token limits, OpenClaw compacts context.

Older messages are summarized or removed from the active conversation.

User: I have a meeting with my boss on Friday
Agent: Got it

After many turns, that message is no longer in the context.

Later in the same session:

User: What is my schedule this week

If the agent does not decide to search memory again, it answers without context.

From your point of view, the agent forgot something important mid conversation. In reality, the information was removed from context and never reloaded.

This is one of the biggest weaknesses of relying on prompt level memory for long running agents.

Built in memory search can still miss

OpenClaw builds a vector index over markdown memory files.

In practice:

• Search results are inconsistent

• Search calls may fail silently

• The agent may not call search at all

By now, the pattern should be obvious.

All these behaviors make memory unreliable in practice.

Information may exist on disk, but there is no guarantee it will be saved, searched, or reintroduced when needed.

For short demos, the built in memory is usually fine. But once you start doing real work, long sessions, or agents that run across days, things fall apart quickly.

At that point, you need a better memory system. By better, I mean memory that:

• is always called

• does not fail silently

• is not affected by context compaction

That is where @mem0/openclaw-mem0 comes in.

How Mem0 adds persistent memory to OpenClaw agents

@mem0/openclaw-mem0 moves memory control out of the agent loop and into the system layer.

It does this by handling memory outside the agent lifecycle and making memory part of every turn.

Memory is captured automatically

With @mem0/openclaw-mem0, memory capture does not depend on the agent deciding what is important.

Relevant user information is detected and stored automatically as structured memory outside the session.

User: I usually build backend APIs in Python

What happens:

• The information is detected as a user preference

• It is stored as user scoped memory

• It lives outside the agent session

Memory capture and recall are enforced by the integration layer rather than being left to the agent’s discretion.

Memory is added before every response

With the built in OpenClaw memory, the agent must decide to call memory_search.

With @mem0/openclaw-mem0, this does not happen.

Before every response:

• Relevant memory is retrieved

• Memory is injected directly into the context

So when the agent reasons, the memory is already there.

Memory survives sessions

With @mem0/openclaw-mem0:

• You can stop the agent

• Restart it later

• Continue the conversation

The memory still exists because it lives outside the session.

This is what makes it a real memory system for AI agents.

Setting up @mem0/openclaw-mem0 step by step

Now let’s wire this up properly.

The goal is simple.
Replace OpenClaw’s default memory behavior with enforced persistent memory backed by Mem0, then confirm that it actually works.

Step 1: Get a Mem0 API key

First, create a Mem0 account and copy your API key.

Expose it as an environment variable.

export MEM0_API_KEY=your_api_key_here

Everything else depends on this being set, so make sure it is available in your shell.

Step 2: Install the plugin

Install the integration using the OpenClaw CLI.

openclaw plugins add @mem0/openclaw-mem0

At this point, the plugin is installed, let’s verify.

Step 3: Verify the plugin is registered

Before restarting anything, confirm that OpenClaw sees the plugin.

cat ~/.openclaw/openclaw.json

You should see openclaw-mem0 listed under plugins.entries.

If it is there, the plugin is wired correctly..

Step 4: Edit the OpenClaw config

Open your OpenClaw config file.

nano ~/.openclaw/openclaw.json

Inside the file, locate plugins.entries and add the Mem0 configuration.

"openclaw-mem0": {
  "enabled": true,
  "config": {
    "apiKey": "${MEM0_API_KEY}",
    "userId": "your-user-id"
  }
}

Save the file and exit the editor.

Step 5: Restart the OpenClaw gateway

For the changes to take effect, restart the gateway.

openclaw gateway

This reloads the configuration and activates the Mem0 integration.

Using the open source mode instead of Mem0 Cloud

If you want to run everything locally, you can swap the cloud configuration for open source mode.

Edit the same config file:

nano ~/.openclaw/openclaw.json

Replace the Mem0 config with:

"openclaw-mem0": {
  "enabled": true,
  "config": {
    "mode": "open-source",
    "userId": "your-user-id"
  }
}

In this mode, no Mem0 API key is required. Memory is still captured and recalled automatically.

For more details on the integration, configuration options, and examples, visit the OpenClaw integration documentation and repository for @mem0/openclaw-mem0

What tools your agent now has access to

Once the plugin is enabled, your agent gains additional memory tools automatically:

• memory_search

• memory_list

• memory_store

• memory_get

• memory_forget

You do not need to call these for normal usage.
Auto capture and auto recall handle most cases.

They are there when you need more advanced control.

Verifying that persistent memory works

At this point, everything should be wired up. The only thing left is to see if memory actually persists.

Start by telling your agent something that is clearly worth remembering.

User: I usually build backend APIs in Python
Agent: Got it. I've noted that you build backend APIs in Python.

You should already be seeing things like this in your logs

21:49:09 [plugins] openclaw-mem0: auto-captured 1 memories

Stop the agent.

Start it again so you are definitely in a new session.

openclaw gateway

Now ask a question that depends on that memory.

User: Suggest a project idea for me
Agent: Since you build backend APIs in Python, you could build a small API gateway with rate limiting and API key support

This confirms that the agent responded using information from a previous session rather than general knowledge.

But you can go one step further and confirm that the memory actually exists.

From your terminal, search the stored memories.

openclaw mem0 search "backend APIs in Python"

You should see something like this:

Found 1 memory
- User usually builds backend APIs in Python

You can also search more loosely.

openclaw mem0 search "what does the user usually build"

If the same memory shows up, then it is clearly stored and retrievable.

At this point, there is nothing left to assume.
The memory exists. It survives restarts. It is injected into every response. The agent is no longer guessing.

Start building agents that actually remember

OpenClaw agents forget because memory is treated as a suggestion, not a requirement.

Facts may or may not be saved. Memory may or may not be searched. Context may disappear at any time due to compaction. When all of that is left to the LLM, forgetting is the expected outcome.

@mem0/openclaw-mem0 changes this by enforcing memory capture and recall at the system layer rather than leaving it to the prompt.

Memory is captured outside the agent session. Relevant memory is reintroduced on every turn. Restarts do not matter. Long conversations do not matter. The agent reasons with the same facts every time.

You do not need to rewrite prompts or change how your agent works. You only replace the memory layer.

If you are building agents that run across sessions, handle real user preferences, or are expected to behave consistently over time, persistent memory is not optional. It is the foundation.

The simplest next step is to install the plugin, restart your agent, and watch it stop guessing and start remembering.

FAQs

Do I need to change my agent prompts to use @mem0/openclaw-mem0?

No. The plugin works at the memory layer. Your prompts and agent logic stay the same.

Does this replace OpenClaw’s built in memory tools?

Yes. The plugin replaces the default memory behavior with persistent memory backed by Mem0.

Is this ready for real projects?

Yes, for real agents that need memory across sessions. The integration is still evolving, but the core behavior is stable and already useful.

When should I not use Mem0?

If your agent only runs short, one off demos and never needs to remember anything across sessions, the built in memory is usually enough.