skillfed

edlib

Lightweight, super fast library for sequence alignment using edit (Levenshtein) distance.

edlib v1.3.9.post1 273.6K downloads/30d#8,199 on PyPI604
Permissive license MIT AGING released

What it is and what it does

Edlib is a Python wrapper around a C/C++ library that computes edit distance and sequence alignment using Myers's bit-vector algorithm. It takes two sequences (strings, bytes, or iterables of hashable objects) and returns the edit distance, alignment locations, and optionally the alignment path in CIGAR format. The package supports three alignment modes: global (NW), prefix (SHW), and infix (HW), each suited to different use cases. You can also define custom character equalities to handle case-insensitive matching, wildcards, or degenerate nucleotides.

Common use cases include aligning DNA sequences in bioinformatics, calculating text or word similarity, and finding optimal subsequence matches. The library is designed for speed and can handle both small and large sequences efficiently. It has no runtime dependencies and is available as pre-built wheels for modern Python versions on standard platforms.

Use it for:

  • Align DNA or protein sequences in bioinformatics pipelines to find mutations or similarities.
  • Calculate edit distance between user-provided strings to detect typos or find similar text.
  • Find the best alignment location of a query sequence within a larger target sequence using infix mode.
  • Implement fuzzy string matching with custom equality rules (e.g., case-insensitive or with wildcards).
  • Benchmark or validate sequence alignment algorithms in research or testing workflows.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Edlib calculates edit distance (Levenshtein distance) between two sequences and optionally finds the optimal alignment path, supporting global, prefix, and infix alignment modes.

Yes, if you need fast edit distance or sequence alignment. The package is lightweight, has no runtime dependencies, and offers good performance via compiled bindings. The aging maintenance status (last release 709 days ago) is a minor concern but not a blocker; the repository is still active and there are no known vulnerabilities. The constraint that alphabet length must be ≤ 256 may limit use cases with very large character sets.

Install

edlib on PyPI

pip

pip install edlib

uv

uv add edlib

poetry

poetry add edlib

Installing edlib

Before you install

Medium install friction due to compiled C/C++ bindings, but pre-built wheels are available for common Python versions (3.10–3.13) and platforms (macOS, Linux, musl). Last release was 709 days ago; repository is active but aging.

License in practice

MIT license is permissive; you may use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.

Quickstart

pip install edlib

import edlib

result = edlib.align("elephant", "telephone")
print(result)  # {'editDistance': 3, 'alphabetLength': 8, 'locations': [(None, 8)], 'cigar': None}

# For alignment path visualization:
result = edlib.align("elephant", "telephone", task="path")
nice = edlib.getNiceAlignment(result, "elephant", "telephone")
print("\n".join(nice.values()))

Alphabet length (unique characters across both sequences) must be <= 256.

Verify before relying

  • Whether the package supports Python versions earlier than 3.10 or later than 3.13.
  • Performance characteristics on sequences larger than the benchmark examples shown in the description.

Package facts

License MIT (permissive)
Python support not specified
Install friction medium — platform-specific wheel
Runtime dependencies none
Maintenance aging — 709 days since the last release
Last repo commit
First released
Downloads 273,649/month — #8,199 on PyPI (30-day window, as of 2026-08-14)
Known vulnerabilities none known (OSV.dev, checked 2026-08-14)

Evidence: edlib-1.3.9.post1-cp310-cp310-macosx_10_9_universal2.whl; edlib-1.3.9.post1-cp310-cp310-macosx_10_9_x86_64.whl; edlib-1.3.9.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; edlib-1.3.9.post1-cp310-cp310-musllinux_1_2_i686.whl; edlib-1.3.9.post1-cp310-cp310-musllinux_1_2_x86_64.whl; edlib-1.3.9.post1-cp311-cp311-macosx_10_9_universal2.whl; edlib-1.3.9.post1-cp311-cp311-macosx_10_9_x86_64.whl; edlib-1.3.9.post1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; edlib-1.3.9.post1-cp311-cp311-musllinux_1_2_i686.whl; edlib-1.3.9.post1-cp311-cp311-musllinux_1_2_x86_64.whl; edlib-1.3.9.post1-cp312-cp312-macosx_10_9_universal2.whl; edlib-1.3.9.post1-cp312-cp312-macosx_10_9_x86_64.whl; edlib-1.3.9.post1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; edlib-1.3.9.post1-cp312-cp312-musllinux_1_2_i686.whl; edlib-1.3.9.post1-cp312-cp312-musllinux_1_2_x86_64.whl; edlib-1.3.9.post1-cp313-cp313-macosx_10_13_universal2.whl; edlib-1.3.9.post1-cp313-cp313-macosx_10_13_x86_64.whl; edlib-1.3.9.post1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; edlib-1.3.9.post1-cp313-cp313-musllinux_1_2_i686.whl; edlib-1.3.9.post1-cp313-cp313-musllinux_1_2_x86_64.whl

Keywords: edit, distance, levenshtein, align, sequence, bioinformatics

Tags

edit distance calculationlevenshtein distancesequence alignmentdna sequence alignmentstring similaritysequence matchingalignment path
bioinformaticssequence-alignmentstring-similarity

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