At Mem0, we've been building memory for AI applications for the last 3 years.

After the recent launch of OpenClaw, I noticed many developers started complaining about the default memory in the AI agent.

So, I built the Mem0 memory plugin for OpenClaw that gives your AI agents persistent memory across sessions, and can be setup in less than 30 seconds.

TLDR: 

Run the command:

openclaw plugins install @mem0/openclaw-mem0 

This gives your agent persistent memory across sessions. Setup takes 30 seconds. Docs at docs.mem0.ai/integrations/openclaw.

The Problem With OpenClaw Default Memory Setup

OpenClaw agents are stateless between sessions. The default memory lives in files that must be explicitly loaded, which means continuity depends entirely on what gets re-read at startup.

Then there’s context compaction, the mechanism that summarizes older context to save tokens. When older context is summarized to save tokens, anything injected into the context window becomes lossy. Large memory files and learned facts are compressed, rewritten, or dropped entirely.

And something really interesting was, an OpenClaw agent posted this on Moltbook:

To try and fix OpenClaw memory, we saw developers on Reddit and HN building workarounds. 

Some wrote comprehensive MEMORY.md files that loaded on boot, local BM25 + vector search engines, and SQLite-backed session logs. 

OpenClaw's initial approach of manual curation and startup loading is a reasonable first step, but it eventually hits a scaling wall. And the custom solutions we saw developers build, work, but they all share a fundamental limitation: they store memory inside the context window, which means compaction or session restarts can wipe them.

How Mem0 works with OpenClaw to add memory 

Our plugin runs two processes on every conversation turn:

• Auto-Recall searches Mem0 for memories relevant to the current message before the agent responds. Matching context (preferences, past decisions, project details) gets injected into the agent's working context. This happens every turn, so even after compaction truncates the conversation window, the next response still has access to everything the agent has learned about you.

• Auto-Capture sends each exchange to Mem0 after the agent responds. Mem0's extraction layer determines what's worth persisting: new facts get stored, outdated ones get updated, duplicates get merged. There are no extraction rules to configure.

Both are enabled by default on install.

Why External Memory for OpenClaw AI Agents

Memory that lives outside the context window can't be destroyed by context management. So, compaction, token limits, session restarts don’t affect memories stored in Mem0. 

When you restart a session, Mem0 Auto-Recall pulls in what's relevant and the OpenClaw agent picks up exactly where it left off.

The plugin separates memory into two scopes. 

• Long-term memories are user-scoped and persist across all sessions: your name, your tech stack, your project structure, decisions you've made. 

• Short-term memories are session-scoped and track what you're actively working on without polluting the long-term store. 

Both scopes are searched during recall, with long-term memories surfaced first. Beyond the automatic loop, the agent gets five tools for explicit memory management: 

• memory_search for semantic queries

• memory_store for saving specific facts

• memory_list and memory_get for retrieval

• memory_forget for deletion.

Setup OpenClaw Persistent Memory with Mem0

Mem0 cloud memory:

openclaw plugins install @mem0/openclaw-mem0

Get an API key from app.mem0.ai, then add to openclaw.json:

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

Self-hosted persistent memory for OpenClaw:

Set "mode": "open-source" and bring your own embedder, vector store, and LLM. Ollama for embeddings, Qdrant for vectors, Anthropic for the LLM, whatever fits your stack:

{
  "openclaw-mem0": {
    "enabled": true,
    "config": {
      "mode": "open-source",
      "userId": "your-user-id",
      "oss": {
        "embedder": { "provider": "ollama", "config": { "model": "nomic-embed-text" } },
        "vectorStore": { "provider": "qdrant", "config": { "host": "localhost", "port": 6333 } },
        "llm": { "provider": "anthropic", "config": { "model": "claude-sonnet-4-20250514" } }
      }
    }
  }
}

No Mem0 API key needed for self-hosting and you get a fully local, fully private AI memory setup.

We've been running this internally to test and the difference is noticeable. Context carries across sessions, survives compaction, and the agent builds up a working understanding of you over time. 

Try Mem0 for OpenClaw: GitHub — npm — Docs

FAQs

1. What is the Mem0 OpenClaw plugin?

It's an OpenClaw plugin (@mem0/openclaw-mem0) that gives your agent persistent memory across sessions. It automatically captures facts from conversations and recalls relevant context on every turn, so your agent remembers your name, preferences, projects, and past decisions even after restarts or context compaction.

2. How do I install the Mem0 OpenClaw plugin?

Run openclaw plugins install @mem0/openclaw-mem0, get an API key from app.mem0.ai, and add the plugin configuration to your openclaw.json file with your API key and user ID. Auto-recall and auto-capture are enabled by default. The full setup takes about 30 seconds.

3. Why does my OpenClaw agent keep forgetting things?

OpenClaw agents are stateless between sessions, meaning every restart starts from zero. Additionally, context compaction (which summarizes older context to save tokens) can silently destroy memory files and learned facts loaded into the conversation window. The built-in memory_search tool also relies on OpenAI's embedding API, which frequently fails with 401 errors when not configured correctly.

4. How is this different from using a MEMORY.md file?

MEMORY.md files are loaded into the context window, which means context compaction can summarize or truncate them mid-conversation. Mem0 stores memories externally, outside the context window entirely. Compaction cannot affect memories stored in Mem0. On every turn, Auto-Recall injects only the relevant memories fresh, regardless of what happened to the context window.

5. Can I self-host the Mem0 OpenClaw plugin?

Yes. Set the mode to "open-source" in your plugin configuration and bring your own embedder (Ollama, OpenAI), vector store (Qdrant, in-memory), and LLM (Anthropic, OpenAI). No Mem0 API key is needed for self-hosted deployments. Everything runs locally on your infrastructure.

6. What's the difference between long-term and short-term memory in the plugin?

Long-term memories are user-scoped and persist across all sessions. They store facts like your name, tech stack, project structure, and past decisions. Short-term memories are session-scoped and track what you're actively working on in the current conversation. Both scopes are searched during recall, with long-term memories surfaced first.

7. Does the plugin work with OpenClaw's context compaction?

Yes. Since memories are stored externally in Mem0, context compaction cannot destroy them. Even if compaction truncates the entire conversation history, Auto-Recall re-injects relevant memories on the very next turn. This is the core architectural advantage over in-context memory solutions.

8. What memory tools does the agent get?

The agent receives five tools: memory_search for semantic queries across all memories, memory_store for explicitly saving facts, memory_list for viewing all stored memories, memory_get for retrieving a specific memory by ID, and memory_forget for deleting memories (GDPR-compliant).