OpenClaw for Developers: Building Custom AI Agents

OpenClaw's Architecture for Developers

OpenClaw is a Node.js 22+ application structured around three core components:

• The Gateway — The central message broker that handles routing, sessions, and channel connections. All messages flow through the Gateway.
• Channel adapters — Plugins that connect the Gateway to messaging platforms (Telegram, WhatsApp, Discord, Slack, Signal, iMessage).
• Skills — Modular capabilities that give the AI agent access to tools and services. This is where most developer customization happens.

Building OpenClaw Skills

Skills are the extension point of OpenClaw. Think of them like VS Code extensions, but for an AI agent. Each skill defines:

• Tools — Functions the AI model can call. These are the actions your skill enables.
• Capabilities — A description of what the skill does, so the AI knows when to use it.
• Permissions — What system access the skill requires (file system, network, etc.).
• Configuration — User-configurable options for the skill.

A skill is a JavaScript module that exports these definitions. The OpenClaw runtime loads skills at startup and makes their tools available to the AI model during conversation.

Example: A Custom OpenClaw Skill

Let's say you want to build a skill that checks your deployment status from a CI/CD pipeline. The skill would:

1. Define a check_deployment tool that hits your CI/CD API.
2. Describe the capability: "Check the status of recent deployments."
3. Require network permissions to reach your CI/CD endpoint.
4. Accept configuration for the API URL and authentication token.

Once installed, you can ask your OpenClaw agent "What's the status of the latest deploy?" through Telegram, and it will call your skill, query the API, and return the result — all through a chat message.

OpenClaw Multi-Model Routing for Developers

The model layer is fully pluggable. OpenClaw supports Anthropic (Claude Sonnet 4.5, Opus 4.6, Haiku 4.5), OpenAI (GPT-5.2, GPT-5 Mini), Google (Gemini 3 Flash, Gemini 3 Pro), and local models via compatible APIs.

The community-built ClawRouter takes this further with automatic model selection. It analyzes the incoming request and routes it to the optimal model — fast models for simple queries, powerful models for complex reasoning. You can configure routing rules or let the router decide autonomously.

The OpenClaw Developer Ecosystem

OpenClaw has a growing ecosystem of community-built tools:

• Skills Library — A shared repository of community skills covering everything from smart home control to financial tracking.
• Mission Control — A multi-agent orchestration dashboard for managing multiple OpenClaw instances.
• ClawRouter — Intelligent model routing that optimizes cost and performance.
• Channel plugins — Community adapters for platforms like Mattermost and DingTalk.

Development Workflow

The typical OpenClaw developer workflow:

1. Run locally. Install OpenClaw on your machine, point it at a test messaging channel, and iterate quickly.
2. Build skills. Write your custom skills as JavaScript modules. Test them by chatting with your local agent.
3. Deploy. Push to your production VPS — or use OneClickClaw for managed deployment. Your skills and configuration transfer directly.
4. Monitor. Use the built-in control dashboard to watch conversations, debug tool calls, and refine your agent's behavior.

Why Developers Choose OpenClaw

OpenClaw appeals to developers because it's genuinely hackable:

• MIT license. Use it however you want. No restrictions.
• JavaScript/Node.js native. No new language to learn. The entire stack runs on Node.js 22+.
• Plugin architecture. Extend without forking. Skills are loaded at runtime.
• Model agnostic. Swap models without changing your code.
• Full source access. Read the code, understand the internals, contribute upstream.