Redlines

Redlines compares two strings/text and produces structured output showing their differences. Changes are represented with strike-throughs and highlights, similar to Microsoft Word's track changes. The output includes detailed change information, positions, and statistics for programmatic use.

Supports multiple output formats: JSON (default, with structured change data and statistics), Markdown, HTML, and rich (terminal display).

# Install
pip install redlines

# CLI: Compare two texts (outputs JSON by default)
redlines "The quick brown fox jumps over the lazy dog." "The quick brown fox walks past the lazy dog."

# Python: Compare and get markdown
from redlines import Redlines
test = Redlines(
    "The quick brown fox jumps over the lazy dog.",
    "The quick brown fox walks past the lazy dog.",
    markdown_style="none"
)
print(test.output_markdown)
# Output: The quick brown fox <del>jumps over </del><ins>walks past </ins>the lazy dog.

Supported: Python 3.10 - 3.14 (Python 3.8 and 3.9 support dropped)

Optional dependencies:

• pip install redlines[pdf] for PDF file comparison
• pip install redlines[nupunkt] for advanced sentence boundary detection (Python 3.11+, handles abbreviations, citations, URLs)
• pip install redlines[levenshtein] for additional statistics

The library contains one class: Redlines, which is used to compare text.

Basic comparison:

from redlines import Redlines

test = Redlines(
    "The quick brown fox jumps over the lazy dog.",
    "The quick brown fox walks past the lazy dog.",
    markdown_style="none"
)
assert (
    test.output_markdown
    == "The quick brown fox <del>jumps over </del><ins>walks past </ins>the lazy dog."
)

Multiple comparisons with one source:

from redlines import Redlines

test = Redlines("The quick brown fox jumps over the lazy dog.", markdown_style="none")
assert (
    test.compare("The quick brown fox walks past the lazy dog.")
    == "The quick brown fox <del>jumps over </del><ins>walks past </ins>the lazy dog."
)

assert (
    test.compare("The quick brown fox jumps over the dog.")
    == "The quick brown fox jumps over the <del>lazy </del>dog."
)

JSON output with structured data:

from redlines import Redlines

test = Redlines(
    "The quick brown fox jumps over the lazy dog.",
    "The quick brown fox walks past the lazy dog."
)

# Get JSON with changes, positions, and statistics
print(test.output_json(pretty=True))

Basic usage (outputs JSON by default):

redlines "old text" "new text"
redlines file1.txt file2.txt --pretty

Output formats:

redlines text "source" "test"              # Rich terminal display
redlines markdown file1.txt file2.txt      # Markdown output
redlines stats old.txt new.txt             # Statistics only

Run redlines --help or redlines guide for the Agent Integration Guide. See also: redlines-textual.

Use NupunktProcessor for sentence-level tokenization with intelligent boundary detection:

from redlines import Redlines
from redlines.processor import NupunktProcessor

processor = NupunktProcessor()
test = Redlines("Dr. Smith said hello.", "Dr. Smith said hi.", processor=processor)

Sentence mode preserves the input's paragraph boundaries (fixed in 0.6.2): sentences are anchored within their paragraph, so the output is not reflowed one sentence per paragraph.

Use NupunktProcessor for: Legal/technical documents with abbreviations, URLs, citations, decimals Use WholeDocumentProcessor (default) for: Simple documents, speed-critical tasks (5-6x faster), paragraph-level granularity

See demo comparison for benchmarks.

🤖 Using with AI coding agents? See the Agent Integration Guide for JSON schemas, automation patterns, error handling, and runnable examples. Agents fetching documentation as text can start at llms.txt.

Full Documentation: https://houfu.github.io/redlines

Example Use Cases:

• View and mark changes in legislation: PLUS Explorer
• Visualise changes after ChatGPT transforms a text: ChatGPT Prompt Engineering for Developers Lesson 6

MIT License