What engineers usually see
- •Multiple retry attempts all timeout
- •No successful response after retry logic
- •Cannot determine if timeouts are consistent or random
- •Cost and latency keep accumulating
Why this is hard to debug
When retries all timeout, you lose visibility into whether each attempt behaved differently. Were they all slow? Did they hit different rate limits? Receipts track each retry attempt separately.
Minimal repro
from openai import OpenAI
client = OpenAI(
api_key="YOUR_OPENAI_KEY",
base_url="https://aibadgr.com/v1",
max_retries=3,
timeout=15.0
)
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": "test"}]
)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
- Each retry attempt timing
- Why each attempt failed
- Rate limit signals per attempt
- Cumulative latency and cost
- Whether retries made progress
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.
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.