Programming Tutorials

  Nothing kills a background job or an interactive chat session faster than an unhandled   429 Rate limit exceeded   error. If you are building with the Anthropic API, particularly on a  Tier 1 or Tier 2 account , you have likely encountered this wall. Tier 1 accounts are restricted to strictly low Request Per Minute (RPM) and Token Per Minute (TPM) limits. A single heavy prompt or a small burst of concurrent users is often enough to crash an application that lacks robust retry logic. This guide provides production-grade strategies to handle Anthropic rate limits using  Exponential Backoff with Jitter  in both Python and Node.js. Understanding the Root Cause: RPM vs. TPM Before implementing a fix, you must distinguish between the two types of limits Anthropic enforces. The  429  error usually occurs due to one of the following: RPM (Requests Per Minute):  The number of HTTP requests you send. TPM (Tokens Per Minute):  The total volume of...

  It is 2:00 AM. Your monitoring dashboard lights up with a spike in 5xx errors. Your LLM-powered feature—the core of your application—is failing. Upon inspecting the logs, you don't see the standard "Service Unavailable" errors. Instead, you are met with   529 Overloaded   or   429 Too Many Requests . For Site Reliability Engineers (SREs) and Backend Developers integrating the Anthropic API (Claude), these two errors are the primary adversaries of uptime. While the official SDKs provide basic retry mechanisms, they are often insufficient for high-throughput production environments facing genuine traffic spikes. This guide details the root causes of these errors and provides production-grade, copy-pasteable implementation patterns for Node.js and Python to handle them gracefully using Exponential Backoff with Jitter. Root Cause Analysis: Why Your Requests Fail To fix the crash, we must understand the architecture of the failure. The 429 Error (Rate Limit Exceeded) Th...