mem0/openclaw

@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.

Quick Start

openclaw plugins install @mem0/openclaw-mem0

Platform (Mem0 Cloud)

Get an API key from app.mem0.ai:

openclaw mem0 init --api-key <your-key> --user-id <your-user-id>

Or configure manually in openclaw.json:

"openclaw-mem0": {
  "enabled": true,
  "config": {
    "apiKey": "${MEM0_API_KEY}",
    "userId": "alice"
  }
}

Open-Source (Self-hosted)

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

"openclaw-mem0": {
  "enabled": true,
  "config": {
    "mode": "open-source",
    "userId": "alice"
  }
}

Customize the embedder, vector store, or LLM via the oss block:

"config": {
  "mode": "open-source",
  "userId": "alice",
  "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 the Mem0 OSS docs for supported providers.

How It Works

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

Auto-Capture — After the agent responds, the conversation is filtered through a noise-removal pipeline and sent to Mem0. New facts get stored, stale ones updated, duplicates merged.

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

Memory Scopes

Scope	Description
Session (short-term)	Memories scoped to the current conversation via run_id. Automatically recalled alongside long-term memories.
User (long-term)	Persistent memories that span all sessions. Stored via memory_add with longTerm: true (the default).

During auto-recall, both scopes are searched and presented separately — long-term first, then session — so the agent has full context.

Multi-Agent Isolation

In multi-agent setups, each agent gets its own memory namespace automatically. Session keys matching agent:<name>:<uuid> route memories to userId:agent:<name>. Single-agent deployments are unaffected.

All memory tools accept an optional agentId parameter for cross-agent queries:

memory_search({ query: "user's tech stack", agentId: "researcher" })

Agent Tools

Seven tools are available to the agent during conversations:

Tool	Description
memory_search	Search memories by natural language query. Supports scope (session, long-term, all) and agentId filtering.
memory_add	Save a fact to memory. Supports category, importance, longTerm, and agentId.
memory_get	Retrieve a specific memory by ID.
memory_list	List stored memories with optional userId, agentId, and limit filters.
memory_update	Update an existing memory's text in place. Preserves edit history.
memory_delete	Delete by ID, search query, or bulk (all: true). Requires confirm: true for bulk.
memory_history	View the edit history of a specific memory.

CLI

All commands follow the pattern openclaw mem0 <command>.

Memory Operations

# Add a memory
openclaw mem0 add "User prefers TypeScript over JavaScript"

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

# Get, list, update, delete
openclaw mem0 get <memory_id>
openclaw mem0 list --user-id alice --top-k 20
openclaw mem0 update <memory_id> "Updated preference text"
openclaw mem0 delete <memory_id>
openclaw mem0 delete --all --user-id alice --confirm

# View edit history
openclaw mem0 history <memory_id>

Management

# Authenticate and configure
openclaw mem0 init
openclaw mem0 init --api-key <key> --user-id alice

# Check connectivity
openclaw mem0 status

# Manage configuration
openclaw mem0 config show
openclaw mem0 config get api_key
openclaw mem0 config set user_id alice

# Memory consolidation (review, merge, prune)
openclaw mem0 dream
openclaw mem0 dream --dry-run

Configuration Reference

General

Key	Type	Default	Description
mode	"platform" | "open-source"	"platform"	Backend mode
userId	string	"default"	Unique identifier for the user. You define this — it's not found in any dashboard. All memories are scoped to this value.
autoRecall	boolean	true	Inject relevant memories before each turn
autoCapture	boolean	true	Extract and store facts after each turn
topK	number	5	Max memories returned per recall
searchThreshold	number	0.5	Minimum similarity score (0-1)

Platform Mode

Key	Type	Default	Description
apiKey	string	—	Required. Mem0 API key (supports ${MEM0_API_KEY})
orgId	string	—	Organization ID
projectId	string	—	Project ID
enableGraph	boolean	false	Enable entity graph for relationship tracking
customInstructions	string	(built-in)	Custom extraction rules for what to store and how to format
customCategories	object	(12 defaults)	Category name to description map for memory tagging

Open-Source Mode

All fields below are optional. Defaults use OpenAI embeddings, in-memory vector store, and OpenAI LLM.

Key	Type	Default	Description
customPrompt	string	(built-in)	Extraction prompt for memory processing
oss.embedder.provider	string	"openai"	Embedding provider
oss.embedder.config	object	—	Provider config (apiKey, model, baseURL)
oss.vectorStore.provider	string	"memory"	Vector store provider
oss.vectorStore.config	object	—	Provider config (host, port, collectionName)
oss.llm.provider	string	"openai"	LLM provider
oss.llm.config	object	—	Provider config (apiKey, model, baseURL)
oss.historyDbPath	string	—	SQLite path for memory edit history
oss.disableHistory	boolean	false	Skip history DB initialization

Supported providers: openai, anthropic, ollama, lmstudio, qdrant, chroma, and more. See the Mem0 OSS docs for the full list.

License

Apache 2.0