---
id: krippendorff
version: "0.8.2"
license: GPL-3.0-or-later
license_treatment: copyleft
maintenance: active
---

# krippendorff — Fast computation of the Krippendorff's alpha measure.

License: copyleft · Maintenance: active · Downloads: 123.8K/mo

## 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 above — verify before relying.

Computes Krippendorff's alpha, a statistical measure of inter-rater agreement, with optimized performance for reliability data matrices.

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

pip install krippendorff
uv add krippendorff
poetry add krippendorff

## Installing krippendorff

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).

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)

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.

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_current
- Install friction: low
- Maintenance: active
- Downloads: 123.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

krippendorff alpha agreement, inter-rater reliability measure, coding agreement statistics, inter-coder agreement calculation, reliability data analysis, agreement coefficient computation, statistical agreement measure, statistics, annotation-validation, research

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