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Feature: Add OpenClaw plugin and documentation (#3964)

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This commit is contained in:
Deshraj Yadav
2026-02-02 10:04:08 -08:00
committed by GitHub
parent dba7f0458a
commit 3d3e875d21
8 changed files with 1944 additions and 13 deletions
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docs/docs.json
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{
"group": "Start Here",
"icon": "home",
"pages": ["introduction"]
"pages": [
"introduction"
]
}
]
},
@@ -112,7 +114,9 @@
{
"group": "Support & Troubleshooting",
"icon": "life-buoy",
"pages": ["platform/faqs"]
"pages": [
"platform/faqs"
]
},
{
"group": "Migration Guide",
@@ -127,7 +131,9 @@
{
"group": "Contribute",
"icon": "clipboard-list",
"pages": ["platform/contribute"]
"pages": [
"platform/contribute"
]
}
]
},
@@ -282,7 +288,10 @@
{
"group": "Community & Support",
"icon": "users",
"pages": ["contributing/development", "contributing/documentation"]
"pages": [
"contributing/development",
"contributing/documentation"
]
}
]
},
@@ -306,7 +315,9 @@
{
"group": "Getting Started",
"icon": "lightbulb",
"pages": ["cookbooks/overview"]
"pages": [
"cookbooks/overview"
]
},
{
"group": "Essentials",
@@ -378,7 +389,9 @@
{
"group": "Overview",
"icon": "plug",
"pages": ["integrations"]
"pages": [
"integrations"
]
},
{
"group": "Agent Frameworks",
@@ -409,7 +422,9 @@
{
"group": "Cloud & Infrastructure",
"icon": "cloud",
"pages": ["integrations/aws-bedrock"]
"pages": [
"integrations/aws-bedrock"
]
},
{
"group": "Developer Tools",
@@ -431,7 +446,10 @@
{
"group": "Getting Started",
"icon": "rocket",
"pages": ["api-reference", "api-reference/organizations-projects"]
"pages": [
"api-reference",
"api-reference/organizations-projects"
]
},
{
"group": "Core Memory Operations",
@@ -461,7 +479,10 @@
{
"group": "Events APIs",
"icon": "clock",
"pages": ["api-reference/events/get-events", "api-reference/events/get-event"]
"pages": [
"api-reference/events/get-events",
"api-reference/events/get-event"
]
},
{
"group": "Entities APIs",
@@ -513,12 +534,16 @@
{
"group": "Changelog",
"icon": "rocket",
"pages": ["changelog"]
"pages": [
"changelog"
]
},
{
"group": "Legacy Docs",
"icon": "archive",
"pages": ["v0x/introduction"]
"pages": [
"v0x/introduction"
]
}
]
}
@@ -539,7 +564,11 @@
{
"group": "Getting Started",
"icon": "rocket",
"pages": ["v0x/introduction", "v0x/quickstart", "v0x/faqs"]
"pages": [
"v0x/introduction",
"v0x/quickstart",
"v0x/faqs"
]
},
{
"group": "Core Concepts",
@@ -1050,4 +1079,4 @@
"destination": "/platform/features/memory-export"
}
]
}
}
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docs/integrations/openclaw.mdx
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---
title: OpenClaw
---
Add long-term memory to [OpenClaw](https://github.com/openclaw/openclaw) agents with the `@mem0/openclaw-mem0` plugin. Your agent forgets everything between sessions — this plugin fixes that by automatically watching conversations, extracting what matters, and bringing it back when relevant.
## Overview
<Frame>
<img src="/images/openclaw-architecture.png" alt="OpenClaw Mem0 Architecture" />
</Frame>
The plugin provides:
1. **Auto-Recall** — Before the agent responds, memories matching the current message are injected into context
2. **Auto-Capture** — After the agent responds, the exchange is sent to Mem0 which decides what's worth keeping
3. **Agent Tools** — Five tools for explicit memory operations during conversations
Both auto-recall and auto-capture run silently with no manual configuration required.
## Installation
```bash
openclaw plugins install @mem0/openclaw-mem0
```
## Setup and Configuration
### Platform Mode (Mem0 Cloud)
<Note>Get your API key from [app.mem0.ai](https://app.mem0.ai).</Note>
Add to your `openclaw.json`:
```json5
// plugins.entries
"openclaw-mem0": {
"enabled": true,
"config": {
"apiKey": "${MEM0_API_KEY}",
"userId": "your-user-id"
}
}
```
### Open-Source Mode (Self-hosted)
No Mem0 key needed. Requires `OPENAI_API_KEY` for default embeddings/LLM.
```json5
"openclaw-mem0": {
"enabled": true,
"config": {
"mode": "open-source",
"userId": "your-user-id"
}
}
```
Sensible defaults work out of the box. To customize the embedder, vector store, or LLM:
```json5
"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](/open-source/node-quickstart) for available providers.
## 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.
- **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.
## 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 |
The `memory_search` and `memory_list` tools 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.
## CLI Commands
```bash
# 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
# View stats
openclaw mem0 stats
```
## Configuration Options
### General Options
| Key | Type | Default | Description |
|-----|------|---------|-------------|
| `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 Options
| 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` | 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 Options
| Key | Type | Default | Description |
|-----|------|---------|-------------|
| `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.
## Key Features
1. **Zero Configuration** — Auto-recall and auto-capture work out of the box with no prompting required
2. **Dual Memory Scopes** — Session-scoped short-term and user-scoped long-term memories
3. **Flexible Backend** — Use Mem0 Cloud for managed service or self-host with open-source mode
4. **Rich Tool Suite** — Five agent tools for explicit memory operations when needed
## Conclusion
The `@mem0/openclaw-mem0` plugin gives OpenClaw agents persistent memory with minimal setup. Whether using Mem0 Cloud or self-hosting, your agents can now remember user preferences, facts, and context across sessions automatically.
<CardGroup cols={2}>
<Card title="OpenAI Agents SDK" icon="robot" href="/integrations/openai-agents-sdk">
Build agents with OpenAI's SDK and Mem0
</Card>
<Card title="LangGraph Integration" icon="diagram-project" href="/integrations/langgraph">
Create stateful agent workflows with memory
</Card>
</CardGroup>
openclaw/.gitignore
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node_modules/
package-lock.json
*.db
openclaw/README.md
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# @mem0/openclaw-mem0
Long-term memory for [OpenClaw](https://github.com/openclaw/openclaw) agents, powered by [Mem0](https://mem0.ai).
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
<p align="center">
<img src="../docs/images/openclaw-architecture.png" alt="Architecture" width="800" />
</p>
**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
```bash
openclaw plugins install @mem0/openclaw-mem0
```
### Platform (Mem0 Cloud)
Get an API key from [app.mem0.ai](https://app.mem0.ai), then add to your `openclaw.json`:
```json5
// 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.
```json5
"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:
```json5
"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](https://docs.mem0.ai/open-source/node-quickstart) 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
```bash
# 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
openclaw/index.ts
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openclaw/openclaw.plugin.json
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{
"id": "openclaw-mem0",
"kind": "memory",
"uiHints": {
"mode": {
"label": "Mode",
"help": "\"platform\" for Mem0 cloud, \"open-source\" for self-hosted"
},
"apiKey": {
"label": "Mem0 API Key",
"sensitive": true,
"placeholder": "m0-...",
"help": "API key from app.mem0.ai (or use ${MEM0_API_KEY}). Only needed for platform mode."
},
"userId": {
"label": "Default User ID",
"placeholder": "default",
"help": "User ID for scoping memories"
},
"orgId": {
"label": "Organization ID",
"placeholder": "org-...",
"advanced": true
},
"projectId": {
"label": "Project ID",
"placeholder": "proj-...",
"advanced": true
},
"autoCapture": {
"label": "Auto-Capture",
"help": "Automatically store conversation context after each agent turn"
},
"autoRecall": {
"label": "Auto-Recall",
"help": "Automatically inject relevant memories before each agent turn"
},
"customInstructions": {
"label": "Custom Instructions",
"placeholder": "Only store user preferences and important facts...",
"help": "Natural language rules for what Mem0 should store or exclude (platform mode)"
},
"customCategories": {
"label": "Custom Categories",
"advanced": true,
"help": "Map of category names to descriptions for memory tagging (platform mode only). Sensible defaults are built in."
},
"customPrompt": {
"label": "Custom Prompt (Open-Source)",
"advanced": true,
"help": "Custom prompt for open-source mode memory extraction."
},
"enableGraph": {
"label": "Enable Graph Memory",
"help": "Enable Mem0 graph memory for entity relationships (platform mode only)"
},
"searchThreshold": {
"label": "Search Threshold",
"placeholder": "0.5",
"help": "Minimum similarity score for search results (0-1). Default: 0.5"
},
"topK": {
"label": "Top K Results",
"placeholder": "5",
"help": "Maximum number of memories to retrieve"
},
"oss": {
"label": "Open-Source Configuration",
"advanced": true,
"help": "Optional. Configure custom embedder, vector store, LLM, or history DB for open-source mode. Has sensible defaults — only override what you need."
}
},
"configSchema": {
"type": "object",
"additionalProperties": false,
"properties": {
"mode": {
"type": "string",
"enum": [
"platform",
"open-source",
"oss"
]
},
"apiKey": {
"type": "string"
},
"userId": {
"type": "string"
},
"orgId": {
"type": "string"
},
"projectId": {
"type": "string"
},
"autoCapture": {
"type": "boolean"
},
"autoRecall": {
"type": "boolean"
},
"customInstructions": {
"type": "string"
},
"customCategories": {
"type": "object",
"additionalProperties": {
"type": "string"
}
},
"customPrompt": {
"type": "string"
},
"enableGraph": {
"type": "boolean"
},
"searchThreshold": {
"type": "number"
},
"topK": {
"type": "number"
},
"oss": {
"type": "object",
"properties": {
"embedder": {
"type": "object",
"properties": {
"provider": {
"type": "string"
},
"config": {
"type": "object"
}
}
},
"vectorStore": {
"type": "object",
"properties": {
"provider": {
"type": "string"
},
"config": {
"type": "object"
}
}
},
"llm": {
"type": "object",
"properties": {
"provider": {
"type": "string"
},
"config": {
"type": "object"
}
}
},
"historyDbPath": {
"type": "string"
}
}
}
},
"required": []
}
}
openclaw/package.json
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{
"name": "@mem0/openclaw-mem0",
"version": "0.1.0",
"type": "module",
"description": "Mem0 memory backend for OpenClaw — platform or self-hosted open-source",
"license": "Apache-2.0",
"keywords": [
"openclaw",
"plugin",
"memory",
"mem0",
"long-term-memory"
],
"dependencies": {
"@sinclair/typebox": "0.34.47",
"mem0ai": "^2.2.1"
},
"openclaw": {
"extensions": [
"./index.ts"
]
}
}