How to Serialize an Exception to a JSON-Safe Dict in Python

Convert any Python exception into a JSON-safe dictionary with type, message, and the last few traceback lines for logging.

Easy Python 3.9+ Aug 9, 2026 Errors & debugging 15 views 0 copies

exceptions json logging debugging

Python code

20 lines

Python 3.9+

import json
import traceback
from typing import Any


def exception_to_dict(exc: Exception) -> dict[str, Any]:
    """Convert an exception into a JSON-safe dictionary."""
    return {
        "type": type(exc).__name__,
        "message": str(exc),
        "traceback": traceback.format_exc().strip().split("\n")[-3:],
    }


if __name__ == "__main__":
    try:
        value = 10 / 0
    except ZeroDivisionError as e:
        result = exception_to_dict(e)
        print(json.dumps(result, indent=2))

Output

stdout

{
  "type": "ZeroDivisionError",
  "message": "division by zero",
  "traceback": [
    "    value = 10 / 0",
    "ZeroDivisionError: division by zero"
  ]
}

How it works

The exception_to_dict function uses type(exc).__name__ to capture the exception class name and str(exc) for a human-readable message. traceback.format_exc() returns the full formatted traceback of the currently-handled exception, and splitting on newlines lets us keep the last few lines so logs stay compact. Because the result is a plain dict of strings and lists, json.dumps can serialize it without custom encoders. This pattern is useful for structured logging or sending error details to a monitoring service.

Common mistakes

Calling `traceback.format_exc()` outside an `except` block returns an empty string.
Using `str(exc)` on exceptions with no message yields an empty string; combine with the type for context.
Including the full traceback can bloat JSON logs — trim it to a manageable number of lines.

Variations

Use `exc.__traceback__` with `traceback.extract_tb` for a more structured list of frames instead of raw lines.
Add a `datetime.utcnow().isoformat()` timestamp key so logs are time-stamped for correlation.

Real-world use cases

Logging structured error payloads to a JSON-based monitoring service like Datadog or Sentry.
Serializing exception details to JSON for an API error response so clients can parse and display issues.
Sending failed job metadata to a message queue for a retry pipeline or dead-letter analysis.

How to Serialize an Exception to a JSON-Safe Dict in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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