@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