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How OpenClaw Works: A Technical Overview

February 17, 20267 min readReviewed March 8, 2026

In this article: We'll break down the technical architecture of OpenClaw, explain the Gateway pattern, and show you exactly how messages flow from your chat app to the AI and back.

The Big Picture: Gateway Architecture

At its core, OpenClaw uses a Gateway architecture pattern. The Gateway is a single process that runs on your machine and acts as the central hub for all AI interactions[1].

                Chat Apps

                WhatsApp • Telegram • Discord
Slack • iMessage • etc.
            

⬇️

                OpenClaw Gateway

                Routing • Sessions • Memory

⬇️

                AI Provider

                Claude • GPT • Local Models

The Gateway serves as the single source of truth for sessions, routing, and channel connections[1]. All messages flow through this central hub.

Key Components

1. Channel Adapters

Channel adapters are the connectors that link OpenClaw to different messaging platforms. Each adapter:

Authenticates with the platform (API keys, webhooks, etc.)
Receives incoming messages from users
Sends outgoing messages back to the platform
Handles platform-specific features (mentions, groups, attachments)

Supported platforms include WhatsApp, Telegram, Discord, Slack, iMessage, Google Chat, Signal, Microsoft Teams, Matrix, Zalo, and WebChat[1].

2. Message Router

The router determines which agent should handle each incoming message. Routing decisions are based on:

Channel source: Different platforms can route to different agents
Sender identity: Individual users or groups can have dedicated agents
Content analysis: Message content can trigger specialized agents
Custom rules: User-defined routing logic

3. Session Manager

Each conversation has a session that maintains:

Context history: All messages in the conversation
Memory: Long-term information across conversations
State: Current mode, settings, and preferences
Isolation: Separate contexts for different senders

4. Agent Engine

The agent engine handles:

LLM communication: API calls to Anthropic, OpenAI, or local models
Tool use: Executing skills and plugins
Prompt construction: Building context-aware prompts
Response parsing: Extracting structured outputs from AI responses

Message Flow: Step by Step

Here's what happens when you send a message to OpenClaw:

Step 1: Message Arrives

Your message arrives from a chat platform (e.g., Telegram). The channel adapter receives it and extracts:

Sender identity (phone number, username, etc.)
Message content
Metadata (timestamp, group info, attachments)

Step 2: Routing Decision

The router examines the message and determines which agent should handle it based on your routing configuration[2].

Step 3: Session Lookup

The session manager finds or creates the appropriate session:

If this sender has an existing session, it's loaded
If not, a new session is created with default settings
Conversation history is attached to the context

Step 4: AI Processing

The agent engine constructs a prompt containing:

System prompt (personality, capabilities)
Conversation history
Available tools/skills
The current message

This is sent to your configured LLM (Claude, GPT, or local model) via API[3].

Step 5: Tool Execution

If the AI decides to use a tool, the agent engine:

Parses the tool call from the AI response
Executes the skill/plugin code
Returns the result to the AI for further processing

Step 6: Response Delivery

The final response is sent back through the channel adapter to your chat platform. You see it as a normal message in your conversation.

Multi-Agent Routing

One of OpenClaw's most powerful features is multi-agent routing. This allows you to run multiple specialized AI agents simultaneously, each with its own context and purpose[2].

Use cases include:

Work vs Personal: Separate agents for professional and private conversations
Team collaboration: Different agents for different team channels
Specialized tasks: Dedicated agents for coding, writing, research, etc.
Testing: Sandbox agent for trying new configurations

Routing Configuration Example

            {
  "routing": {
    "channels": {
      "telegram:work_chat": "work_agent",
      "telegram:personal": "personal_agent",
      "discord:server_1": "coding_agent"
    },
    "default": "personal_agent"
  }
}
        

Persistent Memory

OpenClaw maintains persistent memory across conversations. This means the AI remembers:

Information you've shared in previous conversations
Your preferences and settings
Context from different channels (if configured)
Long-term projects and ongoing tasks

Memory is stored locally on your machine in ~/.openclaw/ and never sent to external servers except through AI API calls[1].

Daemon Operation

OpenClaw runs as a daemon (background service), ensuring it's always available to respond to messages[4].

Daemon Commands

            # Start the daemon
openclaw daemon start

# Check status
openclaw daemon status

# Stop the daemon
openclaw daemon stop

# Restart
openclaw daemon restart
        

The daemon handles:

Maintaining all channel connections
Processing incoming messages asynchronously
Scheduled tasks and cron jobs
Heartbeat checks for proactive notifications

Dashboard & Control UI

OpenClaw includes a web-based dashboard for monitoring and control:

Local access: http://127.0.0.1:18789/
Remote access: Via Tailscale or tunneling

The dashboard provides:

Active session monitoring
Channel connection status
Message logs and debugging
Configuration editing

Skills & Plugins

OpenClaw's capabilities can be extended through skills — reusable packages of functionality that add new features. Skills can:

Make API calls to external services
Process and transform data
Interact with local files and applications
Automate complex workflows

The community contributes skills, and the AI can even help create new skills through conversation[5].

Security Architecture

OpenClaw's design prioritizes security:

Local-first: Data stays on your machine
Access controls: allowFrom lists restrict who can interact
Group chat safety: Mention patterns prevent unwanted triggers
API key security: Keys stored locally, never exposed to chat platforms

Security Note: OpenClaw requires broad system access to function. Always review skill code before installation and keep your system updated[4].

Conclusion

OpenClaw's Gateway architecture provides a clean, scalable way to integrate AI into all your messaging platforms. By centralizing routing, sessions, and memory in a single locally-running process, it gives you unprecedented control over your AI assistant while maintaining privacy and extensibility.

Understanding how the pieces fit together helps you configure OpenClaw effectively and build powerful automations that work seamlessly across your digital life.

References

OpenClaw Documentation - https://docs.openclaw.ai - Accessed February 2026
Multi-Agent Routing - Complete Guide to Multi-Agent Routing - February 2026
LLM Provider Configuration - Model Selection Guide - February 2026
OpenClaw Security Guide - Security Best Practices - February 2026
Creating Custom Skills - Skills Development Guide - February 2026

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How OpenClaw Works: A Technical Overview

The Big Picture: Gateway Architecture

Key Components

1. Channel Adapters

2. Message Router

3. Session Manager

4. Agent Engine

Message Flow: Step by Step

Step 1: Message Arrives

Step 2: Routing Decision

Step 3: Session Lookup

Step 4: AI Processing

Step 5: Tool Execution

Step 6: Response Delivery

Multi-Agent Routing

Routing Configuration Example

Persistent Memory

Daemon Operation

Daemon Commands

Dashboard & Control UI

Skills & Plugins

Security Architecture

Conclusion

References

Ready to dive deeper?

Reference Trail

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