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The openclaw runner is in beta and has not been fully tested. You may encounter bugs or unexpected behavior. Use with caution.
The openclaw runner connects to a locally running OpenClaw daemon that exposes an AI gateway on a configurable port (default 18789). OpenClaw manages its own configuration entirely via its own setup wizard — RemoteAgent.CHAT does not configure OpenClaw settings.

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Requirements

  • OpenClaw installed and its daemon running before remoteagent init

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Setting up OpenClaw first

Before running remoteagent init, you must set up OpenClaw independently: 
npm install -g openclaw@latest
openclaw onboard --install-daemon
openclaw onboard --install-daemon will walk you through OpenClaw’s own configuration wizard and install the background daemon. Once the daemon is running, you can proceed to initialize RemoteAgent.CHAT.
Run openclaw onboard --install-daemon before remoteagent init. The RemoteAgent.CHAT wizard checks whether the daemon is reachable and will show instructions if it is not found.

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Setup

Once the OpenClaw daemon is running, initialize the agent: 
remoteagent init --runner openclaw
Or choose openclaw when prompted by the interactive wizard. During remoteagent init, the wizard will:
  1. Ask for the gateway port (default 18789)
  2. Check whether the OpenClaw daemon is reachable on that port
  3. If the daemon is not found: display setup instructions and offer to exit or continue anyway
RemoteAgent.CHAT does not configure any OpenClaw settings — all model routing, API keys, and gateway behavior are managed through OpenClaw’s own configuration.

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How it works

The runner sends POST requests to http://localhost:{port}/v1/chat/completions using the OpenAI API format. The port is configurable and saved in ~/.remoteagent/agents/{agentId}.json. The prompt is sent as a user message. Response chunks are streamed via server-sent events (SSE) and forwarded to Telegram as they arrive. Example request body sent by the runner: 
{
  "model": "default",
  "messages": [
    { "role": "user", "content": "<your prompt here>" }
  ],
  "stream": true
}
The model field defaults to "default" — OpenClaw is responsible for routing that to the appropriate backend model.

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Use cases

  • Local model routing — run multiple models and route based on task type
  • Cost control — apply rate limiting or budget caps at the gateway layer
  • Custom middleware — inject system prompts, logging, or content filters before requests reach the model
  • Air-gapped environments — run entirely offline with no external API calls

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Pros and cons

ProsCons
Full control over the AI backendRequires running and maintaining the OpenClaw daemon
Works with any OpenAI-compatible modelOpenClaw daemon must be running before the agent starts
Air-gap compatibleMore setup complexity
No API key managed by RemoteAgent.CHAT—
OpenClaw handles all its own configuration—