Deploy Moltbot To Fly

What This Skill Does

This skill provides an automated workflow to deploy the Moltbot (Clawdbot) application to the Fly.io cloud hosting platform. It handles the critical configuration steps required to ensure the bot functions correctly with persistent storage and secure token-based authentication. By leveraging this skill, users can bypass manual infrastructure setup, ensuring that the bot remains active on a managed server with sufficient memory, volume persistence, and secret management.

Installation

To install the skill, run the following command in your terminal: clawhub install openclaw/skills/skills/hollaugo/deploy-moltbot-to-fly. Ensure you have the Fly.io CLI (flyctl) installed and authenticated on your local machine before execution, as the skill interfaces directly with your system environment to provision cloud resources.

Use Cases

This skill is ideal for developers and AI enthusiasts looking to host their Moltbot agent 24/7. It is perfect for users who want a stable, persistent, and low-latency gateway for their bot interactions without managing individual server binaries manually. It is particularly useful for deployments requiring secure API key management and persistent state handling, which are essential for long-running AI agent tasks.

Example Prompts

1. "OpenClaw, please run the deploy-moltbot-to-fly skill to set up my bot on the iad region with a 1GB persistent volume."
2. "Help me configure my Fly.io secrets for Moltbot using my Anthropic API key."
3. "Run the Moltbot deployment procedure and verify the gateway is listening on port 3000."

Tips & Limitations

Ensure your Fly.io account has sufficient billing credits or is within free tier limits, as persistent volumes and 2GB instances consume resources. Always keep your generated gateway token private, as it acts as a primary authentication mechanism for your bot's web UI access. Note that the deployment phase requires Docker engine availability; if your local environment lacks Docker, you may need to enable remote builders on Fly.io. Finally, always monitor your fly logs after deployment to ensure that memory allocation is sufficient for your specific model workloads.