Rate limit retries not working

What engineers usually see

  • •Retry logic implemented but still hitting rate limits
  • •Retries may be too aggressive or poorly timed
  • •Cannot tell if retries respect Retry-After headers
  • •Rate limit errors persist despite backoff

Why this is hard to debug

Client-side retry code is a black box. You can't verify if it's working correctly without inspecting each request. Receipts show actual retry timing vs provider rate limit windows.

Minimal repro

from openai import OpenAI
import time

client = OpenAI(
    api_key="YOUR_OPENAI_KEY",
    base_url="https://aibadgr.com/v1",
    max_retries=0  # Manual retry logic
)

for i in range(3):
    try:
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": "test"}]
        )
        break
    except:
        time.sleep(2 ** i)  # Exponential backoff

This request routes through AI Badgr and returns a stable request ID that links to an execution record.

Note: AI Badgr is OpenAI-compatible and works as a drop-in proxy. No SDK changes required — only the base_url changes.

What a per-request execution record makes visible

  • Actual retry timing vs expected
  • Whether retries respected provider signals
  • Rate limit state during each retry
  • Retry effectiveness
  • Optimal retry strategy

Run 1 request → get receipt

Change your base URL to https://aibadgr.com/v1 and run your request.

The response includes an X-Badgr-Request-Id header that links to a receipt showing latency, retries, tokens, cost, and failure stage for that specific execution.

Get API Key

Not the engineer?
Share this page with your dev and ask them to run one request through AI Badgr. That's all that's needed to get the receipt.

This kind of thing only makes sense when you can actually see what happened to a single request from start to finish, instead of trying to piece it together from scattered logs.