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OpenClaw Architecture

理解 OpenClaw 的技术架构，用「餐厅模型」轻松掌握各组件如何协同工作

V
Victor.Chen
2026年3月19日

Source: OpenClaw Orange Paper v1.2.0, Chapters 2-9

Technical Architecture: The Restaurant Model

It's easier to understand OpenClaw's components if you picture it as a restaurant:

Component Reference Table

Component Restaurant Role Description
🧠 AI Model (LLM) The chef Does the "thinking" and "content generation"
🔧 Skills Kitchen tools Search skill = search engine; email skill = email client
💬 Channels The ordering counter Chat with the AI via WeChat, Feishu, a web page, and other channels
💾 Memory Customer profiles Remembers earlier conversations and understands you better over time
👤 User The customer You just tell the AI what you want

Core Components in Detail

1. AI Model (LLM)

OpenClaw has a model-agnostic architecture and supports multiple AI models:

Type Models Notes
International models Claude, GPT-4, Gemini Best quality; need a VPN or proxy from mainland China
Chinese models DeepSeek, Kimi, ERNIE Bot Friendly to mainland networks, affordable
Local models Ollama, LM Studio Completely free and private, but need a GPU

Model recommendations:

  • First-time users: Google Gemini (free tier available)
  • Users in mainland China: DeepSeek / Kimi
  • Quality first: Claude / GPT-4
  • Cost-sensitive: local models (Ollama)

2. Skills

Skills are OpenClaw's "hands and eyes" — they let the AI perform real actions.

Skill categories:

Category Example skills Purpose
Information retrieval Search, news, weather Fetch real-time information
Content processing Email, documents, translation Work with text content
Automation Scheduled tasks, workflows Run tasks automatically
Data processing Spreadsheets, charts, databases Analyze and process data
Browser Web scraping, screenshots Operate the browser

Where skills come from:

  • ClawHub official marketplace: 13,729+ skills
  • Community contributions: open-source skills on GitHub
  • Self-built skills: develop your own as needed

3. Channels

OpenClaw supports talking to users through a variety of channels:

International platforms: Telegram, Discord, WhatsApp, Slack, web UI

Mainland-China platforms: Feishu, DingTalk, WeCom, WeChat (requires extra configuration)

4. Memory

The memory system lets OpenClaw remember your preferences and past conversations.

Memory types:

Type Description Storage
Short-term memory Current conversation context RAM
Long-term memory User preferences and history Local database
Vector memory Semantic memory retrieval Vector database

What memory does: remembers your preferences, refers back to earlier conversations, and understands you better the more you use it

Data Flow

User message → Channel (Feishu/DingTalk) → Gateway → LLM (thinking)
                                    ↓
                              Skills (execution)
                                    ↓
User response ← Channel (Feishu/DingTalk) ← Gateway ← results

Security Architecture

Local Deployment Security

  • All data is stored locally
  • API keys live only on your own server
  • Nothing passes through third-party servers

Network Security

  • HTTPS encryption supported
  • Configurable firewall rules
  • VPN deployment supported

Access Control

  • Admin password protection
  • Multi-user permission management
  • Sandboxed skill execution

Resource Requirements

Minimum Setup

Resource Minimum Recommended
CPU 2 cores 4+ cores
RAM 2GB 4GB+
Storage 10GB 20GB+
Network Stable connection Broadband

Setups by Scenario

Scenario Suggested Setup
Personal learning A local computer with 4GB RAM
Light use 2-core 2GB cloud server
Medium use 2-core 4GB cloud server
Heavy use 4-core 8GB cloud server
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