Build an AI Error Explainer in Python

Telnyx released a Python example that turns stack traces into structured debugging JSON using its AI Inference API. The Flask app accepts a stack trace and optional context, then returns a predictable object with root cause, severity, suggested fix, and other fields. This enables developers to route, store, or display error explanations programmatically.

Stack traces are useful, but they are not always easy to act on quickly. When something breaks, you usually want more than the exception name. You want to know the likely root cause, how serious it is, where to look, and what fix to try first. This Python example turns a stack trace into structured debugging JSON using Telnyx AI Inference. The Flask app exposes: POST /explain GET /analyses GET /analyses/<id GET /health POST /explain accepts a stack trace, plus optional language and runtime context: { "language": "python", "context": "Flask production server with gunicorn", "stack trace": "Traceback most recent call last :\n File \"app.py\", line 42..." } The app sends the trace to Telnyx AI Inference and asks for structured JSON: { "root cause": "The outbound HTTP call is failing because the downstream service is unreachable.", "severity": "high", "confidence": 0.91, "likely culprit": "app.py:42", "suggested fix": "Add timeout handling and retry logic around the request.", "fix snippet": "resp = requests.post url, timeout=15 ", "related errors": "requests.exceptions.Timeout" , "prevention": "Set explicit timeouts for outbound HTTP calls." } Plain text explanations are nice for humans. Structured output is useful for apps. With this response shape, you could: The example stores recent analyses in memory and lets you retrieve them by ID. Clone the examples repo: git clone https://github.com/team-telnyx/telnyx-code-examples.git cd telnyx-code-examples git switch feat/error-explainer-python cd error-explainer-python Create your .env file: cp .env.example .env Add your Telnyx API key: TELNYX API KEY=your telnyx api key AI MODEL=moonshotai/Kimi-K2.6 HOST=127.0.0.1 Install and start: pip install -r requirements.txt python app.py Try the health endpoint: curl http://localhost:5000/health Explain an error: curl -X POST http://localhost:5000/explain \ -H "Content-Type: application/json" \ -d '{ "language": "python", "context": "Flask production server", "stack trace": "KeyError: user id" }' | python3 -m json.tool This is a small example, but it maps pretty cleanly to real developer workflows: The useful part is not only that a model can explain an error. It is that the app gets back a predictable object it can route, store, display, or review. Telnyx AI skills and toolkits: https://github.com/team-telnyx/ai https://github.com/team-telnyx/ai Telnyx AI Inference docs: https://developers.telnyx.com/docs/inference https://developers.telnyx.com/docs/inference Chat Completions API: https://developers.telnyx.com/api/inference/chat-completions https://developers.telnyx.com/api/inference/chat-completions Telnyx Portal: https://portal.telnyx.com/ https://portal.telnyx.com/