docs: update README for OpenClaw with AI install guide
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README_EN.md
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README_EN.md
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# TrulyMEM - WaterFlow Adapter
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# TrulyMEM - OpenClaw Adapter
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Give AI true long-term memory capability - WaterFlow framework adapter version
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Give AI true long-term memory capability - OpenClaw framework adapter version
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[中文版本](./README.md)
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---
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## 🤖 AI Self-Installation Guide
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> This section is for AI Agents, guiding them how to self-install this Skill to OpenClaw
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### Installation Steps
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#### Step 1: Locate Skill Directory
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OpenClaw Skill precedence: `/.agents/skills/` > `~/.agents/skills/` > `~/.openclaw/skills/`
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Copy the `skills/` directory to a high-priority location:
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```bash
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# Method 1: User-level installation (recommended)
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cp -r skills/ ~/.agents/skills/graph_memory
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# Method 2: System-level installation
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sudo cp -r skills/ /.agents/skills/graph_memory
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```
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#### Step 2: Verify Installation
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Invoke Skill in OpenClaw:
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```
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Remember: I like programming and am learning TypeScript
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```
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AI should receive a confirmation message.
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#### Step 3: Use the Skill
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After installation, AI can use these commands:
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| Command | Function |
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|---------|----------|
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| `commit` | Commit memories |
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| `recall` | Retrieve memories |
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| `purge` | Delete memories |
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| `introspect` | Inspect status |
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| `persona_update/clear` | Persona management |
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| `task_create/set_state/delete` | Task management |
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---
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## Introduction
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This project ports TrulyMEM's graph memory capability to TypeScript for the WaterFlow framework.
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This project ports TrulyMEM's graph memory capability to TypeScript for the OpenClaw framework.
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As a built-in module for WaterFlow, it provides graph memory functionality:
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As an OpenClaw Skill module, it provides graph memory functionality:
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- **recall**: Retrieve memories
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- **commit**: Commit memories
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@ -29,45 +74,41 @@ ts/
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│ ├── graph_memory/ # Graph memory core module
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│ │ ├── types.ts # Type definitions
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│ │ ├── graph_database.ts # Graph database
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│ │ ├── memory_service.ts # Memory service
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│ │ └── index.ts # Module exports
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│ │ ├── memory_service.ts # Memory service
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│ │ └── index.ts # Module exports
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│ └── tools/
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│ └── builtin/
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│ └── graph_memory_tool.ts # Tool implementation
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│
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├── bundled-skills/ # Skill definitions
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│ └── graph_memory/
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│ ├── SKILL.md # Memory operations
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│ ├── persona/SKILL.md # Persona management
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│ └── task/SKILL.md # Task management
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│
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├── package.json # Project config
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└── tsconfig.json # TypeScript config
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├── package.json # Project config
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└── tsconfig.json # TypeScript config
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skills/ # OpenClaw Skill definitions
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└── graph_memory/
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├── SKILL.md # Memory operations
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├── persona/SKILL.md # Persona management
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└── task/SKILL.md # Task management
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```
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---
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## Usage in WaterFlow
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## Using in OpenClaw
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This module supports two usage methods: **import as module** or **use as Skill**.
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### Method 1: As Module (for Developer Integration)
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### Method 1: Import as Module (for developer integration)
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#### Step 1: Copy Source Code
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#### Step 1: Copy source files
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Copy the `ts/` directory to your WaterFlow project, for example:
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Copy the `ts/` directory to your OpenClaw project:
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```
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your-waterflow-project/
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├── src/
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│ └── runtime/
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│ └── core/
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│ └── graph_memory/ # Copy from ts/src/runtime/core/
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└── ts/ # Or place in project root
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└── bundled-skills/ # Skill files
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yourOpenClawProject/
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└── src/
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└── runtime/
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└── core/
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└── graph_memory/ # Copy from ts/src/runtime/core/
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```
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#### Step 2: Build TypeScript
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#### Step 2: Compile TypeScript
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```bash
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cd ts/
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@ -75,104 +116,61 @@ npm install
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npm run build
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```
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Compiled files will be output to `ts/dist/`.
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Compiled files output to `ts/dist/` directory.
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#### Step 3: Import in your code
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#### Step 3: Import in Code
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```typescript
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import { createGraphMemoryTool } from './runtime/core/tools/builtin/graph_memory_tool';
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// Create tool instance, can pass sessionId to distinguish different sessions
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// Create tool instance
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const tool = createGraphMemoryTool('my-session-id');
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// Prepare execution context
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const context = {
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toolCallId: 'call-123',
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workingDirectory: '/project',
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abortController: { signal: {} },
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config: { timeout: 30000 },
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logger: {
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info: console.log,
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warn: console.warn,
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error: console.error,
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debug: console.debug
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}
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};
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// Commit memory example
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const commitResult = await tool.handler({
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action: 'commit',
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params: {
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triplets: [
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{ subject: 'User', relation: 'likes', object: 'Programming' },
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{ subject: 'User', relation: 'is learning', object: 'TypeScript' }
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{ subject: '用户', relation: '喜欢', object: '编程' },
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{ subject: '用户', relation: '正在学习', object: 'TypeScript' }
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]
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}
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}, context);
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console.log(commitResult);
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// Output: {"success":true,"data":{"createdEntities":4,"createdRelations":2}}
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// Recall memory example
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const recallResult = await tool.handler({
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action: 'recall',
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params: {
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queryIntent: 'User Programming'
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queryIntent: '用户 编程'
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}
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}, context);
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console.log(recallResult);
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// Output: {"success":true,"data":{"entities":[...],"relations":[...],"message":"Found X entities, Y relations"}}
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```
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### Method 2: Use Skill (recommended for AI Agent)
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### Method 2: Use Skill (Recommended for AI Agent)
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#### Step 1: Configure Skill source
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#### Step 1: Place Skill Files
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In your WaterFlow project, find the Skill configuration file and add bundled source pointing to this project's Skill directory:
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Copy `skills/` directory to OpenClaw's Skill directory:
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```typescript
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// skill_interface.ts or config file
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import { DEFAULT_SKILL_LOADER_CONFIG } from './skill_interface';
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const config = {
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...DEFAULT_SKILL_LOADER_CONFIG,
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sources: {
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...DEFAULT_SKILL_LOADER_CONFIG.sources,
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bundled: './ts/bundled-skills' // Point to this project's Skill directory
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},
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enabledSources: ['project', 'bundled']
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};
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```bash
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cp -r skills/ ~/.agents/skills/graph_memory
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```
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#### Step 2: Call Skill via Agent
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#### Step 2: Invoke Skill
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In your Agent or Workflow, call Skill via Tool:
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Use directly in OpenClaw:
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```
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Use skill:graph_memory for:
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1. Commit memory: I like programming, learning TypeScript
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2. Recall memory: Find memories related to me and programming
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```
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Or call via code:
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```typescript
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// Call via SkillTool
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const skillResult = await skillTool.handler({
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skill: 'graph_memory',
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args: 'recall - queryIntent: "User learning"'
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}, context);
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Use graph_memory to remember: I like programming and am learning TypeScript
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```
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#### Available Skills
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| Skill Name | Function | Use Case |
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|------------|----------|----------|
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| `graph_memory` | Memory CRUD | Read/Write/Delete memories |
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| `graph_memory_persona` | Persona management | Set AI role/personality |
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| `graph_memory_task` | Task management | Create/update long-term tasks |
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| `graph_memory` | Memory CRUD | Read/write/delete memories |
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| `graph_memory_persona` | Persona management | Set AI persona |
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| `graph_memory_task` | Task management | Create/update tasks |
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---
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@ -195,7 +193,7 @@ const tool = new GraphMemoryTool(sessionId?: string);
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| `persona_update` | Update persona | `attributes`, `mode` |
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| `persona_clear` | Clear persona | `confirm` |
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| `task_create` | Create task | `task_id`, `description`, `info_nodes` |
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| `task_set_state` | Set state | `task_id`, `state` |
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| `task_set_state` | Set task state | `task_id`, `state` |
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| `task_delete` | Delete task | `task_id` |
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---
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@ -209,8 +207,8 @@ const tool = new GraphMemoryTool(sessionId?: string);
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"action": "commit",
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"params": {
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"triplets": [
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{ "subject": "User", "relation": "likes", "object": "TypeScript" },
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{ "subject": "User", "relation": "is learning", "object": "WaterFlow" }
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{ "subject": "用户", "relation": "喜欢", "object": "TypeScript" },
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{ "subject": "用户", "relation": "正在学习", "object": "OpenClaw" }
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]
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}
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}
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{
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"action": "recall",
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"params": {
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"queryIntent": "User learning"
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"queryIntent": "用户 学习"
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}
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}
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```
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{
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"action": "task_create",
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"params": {
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"task_id": "Task_LearnTypeScript",
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"description": "Learn TypeScript and complete project",
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"info_nodes": ["Documentation", "Tutorial"]
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"task_id": "Task_学习TypeScript",
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"description": "学习 TypeScript 并完成项目",
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"info_nodes": ["文档链接", "教程链接"]
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}
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}
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```
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@ -244,4 +242,4 @@ const tool = new GraphMemoryTool(sessionId?: string);
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## License
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[GNU General Public License v3.0 (GPLv3)](LICENSE)
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[GNU General Public License v3.0 (GPLv3)](LICENSE)
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