@mem0/openclaw-mem0

Long-term memory for OpenClaw agents, powered by Mem0.

Your agent forgets everything between sessions. This plugin fixes that. It watches conversations, extracts what matters, and brings it back when relevant — automatically.

How it works

Auto-Recall — Before the agent responds, the plugin searches Mem0 for memories that match the current message and injects them into context.

Auto-Capture — After the agent responds, the plugin sends the exchange to Mem0. Mem0 decides what's worth keeping — new facts get stored, stale ones updated, duplicates merged.

Both run silently. No prompting, no configuration, no manual calls.

Short-term vs long-term memory

Memories are organized into two scopes:

• Session (short-term) — Auto-capture stores memories scoped to the current session via Mem0's run_id / runId parameter. These are contextual to the ongoing conversation and automatically recalled alongside long-term memories.

• User (long-term) — The agent can explicitly store long-term memories using the memory_store tool (with longTerm: true, the default). These persist across all sessions for the user.

During auto-recall, the plugin searches both scopes and presents them separately — long-term memories first, then session memories — so the agent has full context.

The agent tools (memory_search, memory_list) accept a scope parameter ("session", "long-term", or "all") to control which memories are queried. The memory_store tool accepts a longTerm boolean (default: true) to choose where to store.

All new parameters are optional and backward-compatible — existing configurations work without changes.

Setup

openclaw plugins install @mem0/openclaw-mem0

Platform (Mem0 Cloud)

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

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

Open-Source (Self-hosted)

No Mem0 key needed. Requires OPENAI_API_KEY for default embeddings/LLM.

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

Sensible defaults out of the box. To customize the embedder, vector store, or LLM:

"config": {
  "mode": "open-source",
  "userId": "your-user-id",
  "oss": {
    "embedder": { "provider": "openai", "config": { "model": "text-embedding-3-small" } },
    "vectorStore": { "provider": "qdrant", "config": { "host": "localhost", "port": 6333 } },
    "llm": { "provider": "openai", "config": { "model": "gpt-4o" } }
  }
}

All oss fields are optional. See Mem0 OSS docs for providers.

Agent tools

The agent gets five tools it can call during conversations:

| Tool | Description | |------|-------------| | memory_search | Search memories by natural language | | memory_list | List all stored memories for a user | | memory_store | Explicitly save a fact | | memory_get | Retrieve a memory by ID | | memory_forget | Delete by ID or by query |

CLI

# Search all memories (long-term + session)
openclaw mem0 search "what languages does the user know"

# Search only long-term memories
openclaw mem0 search "what languages does the user know" --scope long-term

# Search only session/short-term memories
openclaw mem0 search "what languages does the user know" --scope session

# Stats
openclaw mem0 stats

Options

General

| Key | Type | Default | | |-----|------|---------|---| | mode | "platform" | "open-source" | "platform" | Which backend to use | | userId | string | "default" | Scope memories per user | | autoRecall | boolean | true | Inject memories before each turn | | autoCapture | boolean | true | Store facts after each turn | | topK | number | 5 | Max memories per recall | | searchThreshold | number | 0.3 | Min similarity (0–1) |

Platform mode

| Key | Type | Default | | |-----|------|---------|---| | apiKey | string | — | Required. Mem0 API key (supports ${MEM0_API_KEY}) | | orgId | string | — | Organization ID | | projectId | string | — | Project ID | | enableGraph | boolean | false | Entity graph for relationships | | customInstructions | string | (built-in) | Extraction rules — what to store, how to format | | customCategories | object | (12 defaults) | Category name → description map for tagging |

Open-source mode

Works with zero extra config. The oss block lets you swap out any component:

| Key | Type | Default | | |-----|------|---------|---| | customPrompt | string | (built-in) | Extraction prompt for memory processing | | oss.embedder.provider | string | "openai" | Embedding provider ("openai", "ollama", etc.) | | oss.embedder.config | object | — | Provider config: apiKey, model, baseURL | | oss.vectorStore.provider | string | "memory" | Vector store ("memory", "qdrant", "chroma", etc.) | | oss.vectorStore.config | object | — | Provider config: host, port, collectionName, dimension | | oss.llm.provider | string | "openai" | LLM provider ("openai", "anthropic", "ollama", etc.) | | oss.llm.config | object | — | Provider config: apiKey, model, baseURL, temperature | | oss.historyDbPath | string | — | SQLite path for memory edit history |

Everything inside oss is optional — defaults use OpenAI embeddings (text-embedding-3-small), in-memory vector store, and OpenAI LLM. Override only what you need.

License

Apache 2.0