How to Parse an LLM Response in Python

This code parses a JSON string from an LLM response, stripping code fences and handling common issues like whitespace, returning a Python dictionary.

Easy Python 3.9+ Aug 9, 2026 AI & LLM integration patterns 14 views 0 copies

llm json parsing ai-api response

Python code

9 lines

Python 3.9+

import json
from typing import Any, Dict, List


def parse_llm_response(response: str) -> Dict[str, Any]:
    """Parse a JSON string from an LLM response, handling common edge cases."""
    # Remove code fences if present
    cleaned = response.strip()
    if cleaned.startswith("

Output

stdout

{'name': 'Alice', 'age': 30}

How it works

The function first strips whitespace and removes markdown code fences (json ...) if present. It then uses json.loads() to convert the cleaned string into a Python dictionary. If the JSON string contains trailing commas or other minor issues, a fallback with strict=False is attempted. This makes the parser resilient to common variations in LLM outputs.

Common mistakes

Not removing code fences before parsing
Assuming json.loads works on any text without cleaning
Forgetting to handle empty or None responses
Ignoring potential trailing commas in LLM output

Variations

Use `json.loads(response, strict=False)` directly if trailing commas are expected
Use a regex to strip code fences more precisely

Real-world use cases

Parsing structured outputs from an LLM API (like OpenAI's response_format) for downstream processing.
Extracting configuration data from a chatbot's reply to update system settings.
Converting a language model's JSON response into Python objects for a data pipeline.

How to Parse an LLM Response in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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Python code

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How it works

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Variations

Real-world use cases

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