Context deadline exceeded (OpenAI)

What engineers usually see

  • •Request times out with "context deadline exceeded" error
  • •Common in Go-based applications using context timeouts
  • •No visibility into how much processing completed
  • •Unclear if retrying is safe or will duplicate work

Why this is hard to debug

Context deadlines are client-side timeouts. They don't tell you what happened on the provider. The request might have completed, partially processed, or never started. Receipts show actual provider execution regardless of client timeout.

Minimal repro

curl -m 5 https://aibadgr.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_OPENAI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "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

  • Actual provider processing time
  • Whether request completed despite deadline
  • Tokens consumed before timeout
  • Cost incurred
  • Safe to retry indicator

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.