Background LLM task failed

What engineers usually see

  • •Background task using LLM fails silently
  • •No user-facing error but task didn't complete
  • •Difficult to debug without request context
  • •Cannot link task failure to specific LLM request

Why this is hard to debug

Background tasks don't have user context. Traditional logs don't link task execution to LLM requests. Receipts provide stable request IDs for correlation.

Minimal repro

curl https://aibadgr.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_OPENAI_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Task-ID: task-123" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [{"role": "user", "content": "background task"}]
  }'

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

  • Background task request ID
  • Task execution status
  • Failure reason
  • Task-to-request correlation
  • Task retry history

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.