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SKILL.md

SKILL.md

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SKILL.md

Using gpt4free as an LLM Server for Bots (Clawbot/OpenClaw)

Overview

This skill covers running gpt4free as a local LLM server with an OpenAI-compatible REST API, custom model routing (config.yaml), and integration with bots like Clawbot or OpenClaw.

Best Practices

  • Start the API server with: python -m g4f --port 8080 (or use g4f api --debug --port 8080)
  • Use the /v1 endpoint for OpenAI-compatible requests (e.g., POST to http://localhost:8080/v1/chat/completions)
  • Define custom model routes in config.yaml to aggregate/fallback across providers
  • Place config.yaml in your cookies directory (e.g., ~/.g4f/cookies/config.yaml)
  • For Clawbot/OpenClaw, patch their config to point to your gpt4free server (see patch-openclaw.py)
  • Test with: g4f client "Hello" --model openclaw or Python client

Common Pitfalls

  • Not starting the server before connecting bots
  • Incorrect config.yaml path or syntax errors
  • Missing required Python dependencies (install with pip install -r requirements.txt)
  • Not exposing the correct port (default 8080)
  • Forgetting to patch bot configs to use your local endpoint

Workflow Steps

  1. Install and set up gpt4free (see README)
  2. Start the API server: python -m g4f --port 8080
  3. (Optional) Create or edit config.yaml for custom model routing:
    yaml
    models:
    - name: "openclaw"
   	 providers:
   		 - provider: "GeminiCLI"
   			 model: "gemini-3-flash-preview"
   			 condition: "quota.models.gemini-3-flash-preview.remainingFraction > 0 and error_count < 3"
   		 - provider: "Antigravity"
   			 model: "gemini-3-flash"
   		 - provider: "PollinationsAI"
   			 model: "openai"
  4. Patch your bot config (e.g., OpenClaw) to use http://localhost:8080/v1 as the base URL (see scripts/patch-openclaw.py)
  5. Start your bot and verify it connects to gpt4free
  6. Monitor logs and test with the Python client or CLI

References