krippendorff
Fast computation of the Krippendorff's alpha measure.
Decision gist · record as of 2026-08-14
Yes, if you need to compute inter-rater agreement in research or annotation workflows. Low install friction, active maintenance, no security vulnerabilities, and a focused, well-scoped implementation. The GPL-3.0-or-later license requires careful review if you plan to use it in proprietary software; otherwise it is a straightforward choice for academic and open-source projects.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.10.
- Input data should be a reliability matrix where V (the number of distinct values) is reasonably small, as the implementation uses a V×V matrix internally.
- Low friction: pure Python wheel with only numpy as a runtime dependency.
License · maintenance · safety
GPL-3.0-or-later (copyleft) — GPL-3.0-or-later (copyleft): you must license any derivative work under compatible terms and disclose source. Suitable for research and open-source projects; review licensing requirements if integrating into proprietary software.
last release 2025-11-03 (284 days) · last repo commit 2026-08-02 · 160 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 123,821 downloads/mo, #11,899 on PyPI
Alternatives
Verify before relying
pip install krippendorff
import krippendorff
result = krippendorff.alpha(reliability_data=your_matrix)- Performance characteristics and typical runtime for different matrix sizes or value cardinalities.
- Whether the package handles missing data or requires complete matrices.
What it is and what it does
Krippendorff is a specialized statistics library that computes Krippendorff's alpha, a widely-used agreement coefficient in content analysis, linguistics, and social science research. It measures how consistently multiple coders or raters assign values to the same units, accounting for the possibility of incomplete or partial overlap in what each coder rated. The package is optimized for speed by avoiding nested loops over coders, making it faster than naive implementations.
The library is minimal and focused: it takes a reliability data matrix as input and returns the alpha coefficient. It depends only on numpy and runs on modern Python versions (3.10 and later). The implementation is based on Thomas Grill's algorithm and is actively maintained. It is most useful in research workflows where you need to validate inter-rater reliability before proceeding with analysis.
Use it for
- Validate agreement between multiple human annotators on a text classification or tagging task before using their labels for training.
- Measure consistency of coding in qualitative research (e.g., interview analysis, content coding) across multiple independent coders.
- Assess reliability of crowdsourced labeling by computing agreement among workers on the same items.
- Evaluate inter-rater agreement in medical or scientific studies where multiple experts independently assess the same cases.
- Benchmark annotation quality in NLP dataset creation by quantifying coder consensus.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to compute inter-rater agreement in research or annotation workflows.
Low install friction, active maintenance, no security vulnerabilities, and a focused, well-scoped implementation. The GPL-3.0-or-later license requires careful review if you plan to use it in proprietary software; otherwise it is a straightforward choice for academic and open-source projects.
Install
krippendorff on PyPI
Before you install
Low friction: pure Python wheel with only numpy as a runtime dependency. Actively maintained with recent releases; last commit 2026-08-02 and latest release 2025-11-03. Supports current Python versions (3.10–3.14).
Requires Python >= 3.10. Input data should be a reliability matrix where V (the number of distinct values) is reasonably small, as the implementation uses a V×V matrix internally.
License in practice
GPL-3.0-or-later (copyleft): you must license any derivative work under compatible terms and disclose source. Suitable for research and open-source projects; review licensing requirements if integrating into proprietary software.
Quickstart
pip install krippendorff
import krippendorff
result = krippendorff.alpha(reliability_data=your_matrix)Verify before relying
- Performance characteristics and typical runtime for different matrix sizes or value cardinalities.
- Whether the package handles missing data or requires complete matrices.
Package facts
| License | GPL-3.0-or-later copyleft |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Actively maintained 284 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 123,821 / month, #11,899 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchLicense :: OSI Approved :: GNU General Public License v3 (GPLv3)Operating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: krippendorff-0.8.2-py3-none-any.whl
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