I burned my Anthropic org cap and waited 3 days. Then I built llmfleet.
Mukunda Rao Katta experienced issues with rate limits while using Anthropic's API, leading to a three-day wait for support to clear his token cap. In response, he developed llmfleet, a pooled dispatcher that manages message requests while respecting rate limits. The tool aims to prevent future frustrations by optimizing request handling and minimizing unnecessary retries.
- ▪Katta faced a 429 error due to exceeding the daily token budget while using Anthropic's API.
- ▪After three days of waiting for support to clear the cap, he created llmfleet to manage message requests more effectively.
- ▪llmfleet allows users to set concurrency limits and respects the API's rate limit headers to avoid excessive retries.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3915555) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Mukunda Rao Katta Posted on May 21 I burned my Anthropic org cap and waited 3 days. Then I built llmfleet. #hermesagent #ai #llm #python Tuesday afternoon I kicked off a re-grading job. About 18,000 prompts against claude-opus-4-7, eight workers, each one looping messages.create as fast as it could. Forty minutes in, every call started coming back with a 429 and a header that said anthropic-ratelimit-tokens-remaining: 0. Fine, I thought. Back off. I cut workers to four and waited.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
Discussion
0 commentsMore from DEV.to (Top)
