LLM providers enforce rate limits to prevent abuse and ensure fair resource allocation. When OpenClaw exceeds these limits, you'll see 429 Too Many Requests errors. This can happen from burst traffic, aggressive polling, running multiple OpenClaw instances with the same API key, or simply hitting tier limits. This guide shows you how to implement proper retry logic, request queuing, and key distribution.
Why This Is Hard to Do Yourself
These are the common pitfalls that trip people up.
Provider tier rate limits
Free tier: 10 req/min, Paid tier: 100 req/min, hitting caps during peak usage
Burst traffic spikes
Sudden influx of requests overwhelming available quota
No retry logic or exponential backoff
Failed requests not retried, or retry storms making rate limiting worse
Multiple instances sharing one key
Load balancer or horizontal scaling using same API key, multiplying request rates
Step-by-Step Guide
Identify which provider is rate limiting
Check logs and error responses to determine if it's Anthropic, OpenAI, or another provider.
Check current usage against limits
Review your provider dashboard to see quota utilization and tier limits.
Implement retry with exponential backoff
Configure OpenClaw to automatically retry failed requests with increasing delays.
Configure request queuing and throttling
Limit concurrent requests to stay under provider rate limits.
Distribute load across multiple API keys
Use key rotation to spread requests across multiple provider accounts.
Set up rate limit monitoring and alerts
Track rate limit hits and get notified before hitting hard limits.
Constant Rate Limit Headaches?
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