llmclean 0.1.0

Utilities for cleaning and normalizing raw LLM output

llmclean

A zero-dependency Python library for cleaning and normalizing raw LLM output.

LLMs are inconsistent: they wrap JSON in markdown fences, add prose around code, repeat themselves, and produce subtly broken JSON. llmclean handles all of that with three focused utilities.

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Install

pip install llmclean

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Quick start

from llmclean import strip_fences, enforce_json, trim_repetition

# Remove ```json ... ``` wrappers
strip_fences('```json\n{"name": "Alice"}\n```')
# → '{"name": "Alice"}'

# Extract valid JSON from messy output
enforce_json('Here you go: {"ok": True, "items": [1,2,3,]}')
# → '{\n  "ok": true,\n  "items": [1, 2, 3]\n}'

# Remove repeated sentences/paragraphs at the end
trim_repetition("The answer is 42. This is final. This is final.")
# → 'The answer is 42. This is final.'

For full examples and edge cases see USAGE.md.

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Functions

Function	What it fixes
strip_fences(text)	Removes ```lang / ``` / ~~~ code fences
enforce_json(text)	Extracts valid JSON from fences, prose, trailing commas, Python literals, unquoted keys, unclosed brackets
trim_repetition(text)	Removes repeated sentences, near-duplicates, and repeated paragraphs from the tail

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Design principles

• Zero dependencies — pure Python standard library
• Never throws — every function returns the original input if cleaning fails
• Non-destructive — unchanged input when nothing needs cleaning
• Composable — chain freely

# Full pipeline
data = enforce_json(trim_repetition(strip_fences(raw_output)))

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Running tests

# With pytest
pip install "llmclean[dev]"
pytest -v

# Without pytest
python run_tests.py

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License

MIT