Model Failover Guard

What This Skill Does

The OpenClaw Model Failover Guard, authored by BovmantH, serves as a high-availability resilience layer for AI agent infrastructure. In mission-critical deployments, relying on a single Large Language Model (LLM) can lead to complete service disruption if that specific provider experiences downtime, rate limiting, or API instability. This skill addresses that vulnerability by implementing a robust health monitoring loop. It continuously polls your primary model's status and, upon detecting a configurable number of consecutive failures, triggers an automated transition to a standby model. Once the system detects that the primary model has returned to a healthy state for a specified number of check intervals, it intelligently attempts to restore the primary service, ensuring your infrastructure is always optimized for cost and capability without sacrificing uptime.

Installation

To integrate this skill into your local OpenClaw environment, use the command-line interface as follows:

1. Install via the CLI: clawhub install openclaw/skills/skills/bovmanth/model-failover-guard
2. Navigate to the skill directory: cd skills/model-failover-guard/
3. Initialize your configuration by copying the template: cp config.example.json config.json
4. Modify config.json to define your primaryModel, failThreshold, and recoverThreshold parameters to match your specific SLA requirements.

Use Cases

This skill is essential for developers running AI agents in production environments where downtime is unacceptable. It is particularly valuable for:

• Enterprise customer support bots that must remain available 24/7.
• Automated data processing pipelines where an API timeout could result in significant data bottlenecks.
• Prototyping and testing new model providers by using a stable, cheaper model as a fallback for high-latency or experimental primary models.

Example Prompts

1. "OpenClaw, initiate the model failover guard in daemon mode to ensure our customer support agent remains active during the system upgrade."
2. "Check the current status of my primary model and list which fallback is currently active according to the model failover guard settings."
3. "Restart the failover monitoring script to apply the updated thresholds in my config.json file."

Tips & Limitations

• Threshold Tuning: Be careful not to set failThreshold too low, as transient network blips might trigger unnecessary switches, leading to "flapping" between models. A value of 3-5 is usually the sweet spot.
• Fallback Costs: Ensure that your designated fallback model does not have significantly higher costs than your primary, or you may face unexpected budget spikes during a primary service outage.
• Latency Differences: Remember that switching models may alter the response time of your agent. The guard is intended for stability, but users should calibrate expectations regarding the performance of secondary providers.