model-fallback

What This Skill Does

The model-fallback skill serves as a robust safety net for OpenClaw agents by automating the selection and failover process for Large Language Models. In a production environment, reliance on a single model provider is risky due to potential downtime, sudden API rate limits, or latency spikes. This skill mitigates these issues by maintaining a user-defined fallback chain. When the primary model fails to respond—whether due to a 503 error, a timeout, or a rate limit—the agent intelligently transitions to a secondary or tertiary model in the configuration. It supports seamless integration with providers like MiniMax, Kimi, and Zhipu, ensuring your AI workflows remain uninterrupted and high-performing even when individual services falter.

Installation

To integrate the model-fallback skill into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/azure5100/model-fallback. Once installed, navigate to the config.json file in your agent's root directory to define your fallback_chain priority. Ensure you have the necessary API keys configured in your environment variables for each provider included in your chain, as the skill will reference these keys during the automatic switching process. After configuration, you can verify that the service is running by executing /scripts/model-fallback.sh --status to inspect the connectivity of your listed models.

Use Cases

This skill is ideal for mission-critical automation tasks where high availability is non-negotiable. Use it for: (1) High-traffic customer support agents that cannot afford downtime; (2) Complex research agents that require large context windows; (3) Cost-sensitive projects where you prefer to use cheaper models for mundane tasks and reserve flagship models for complex reasoning; and (4) Robust data extraction pipelines that need to handle occasional API instability gracefully without manual intervention.

Example Prompts

1. "Analyze these log files and summarize the errors using the primary model, or switch to the fallback if the primary is unresponsive."
2. "Execute the documentation generation task; if the current model hits a rate limit, please continue using the next available model in the priority list."
3. "Summarize this 100k-word document. Use the high-capacity model first, but switch to the cost-efficient model if the primary returns a 5xx error."

Tips & Limitations

To maximize the utility of this skill, prioritize your models based on both performance and cost. Place your most capable, reasoning-heavy models first, and keep faster, cheaper models as final backups. Note that switching models mid-stream may lead to slight variations in tone or style since different model architectures interpret system instructions uniquely. Always test your fallback chain by temporarily disabling your primary API to ensure the agent correctly routes tasks to your designated backups. Be aware that the skill is currently limited to OpenAI-compatible APIs; custom logic may be required for proprietary provider architectures.