---
id: mecab
version: "0.996.13"
license: GPL-2.0-only OR LGPL-2.1-only OR BSD-3-Clause
license_treatment: copyleft
maintenance: aging
---

# mecab — a Python binding for unofficial fork of MeCab

License: copyleft · Maintenance: aging · Downloads: 437.8K/mo

## What it is and what it does

MeCab is a Python binding to an unofficial fork of the MeCab morphological analyzer, a tool for breaking down Japanese text into its grammatical components. It tokenizes sentences into words and annotates each with part-of-speech tags, base forms, readings, and other linguistic features. The binding exposes two main APIs: a simple parse() method that returns formatted text output, and a parseToNode() method that yields individual token objects for programmatic access to morphological data.

The package is distributed as precompiled wheels for multiple Python versions (3.8–3.14) and platforms (macOS, Linux, Windows, including ARM), reducing installation friction compared to building from source. However, it carries Alpha development status and has not been updated in 243 days, suggesting either stable maturity or reduced active maintenance. The underlying MeCab library is licensed under GPL-2.0, LGPL-2.1, or BSD-3-Clause, and this binding inherits that triple-license structure.

Use it for:

- Tokenize and annotate Japanese sentences for NLP pipelines that require morphological analysis before higher-level processing.
- Extract parts of speech, base forms, and readings from Japanese text for linguistic research or language learning applications.
- Build Japanese search or indexing systems that need to segment text and normalize word variants.
- Preprocess Japanese text for machine learning models that expect tokenized and lemmatized input.
- Analyze Japanese grammar and syntax by traversing the parse tree returned by parseToNode().

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Python binding for MeCab, a morphological analyzer that tokenizes and parses Japanese text into grammatical components like parts of speech and base forms.

Yes, if you need Japanese morphological analysis and accept the copyleft licensing terms. The precompiled wheels and broad platform support make installation straightforward. However, verify that MeCab system dependencies and dictionaries are available in your environment, and be aware the package is aging—check whether it meets your stability and maintenance expectations before committing to a production dependency.

## Install

pip install mecab
uv add mecab
poetry add mecab

## Installing mecab

Before you install: Medium install friction: precompiled wheels available for Python 3.8–3.14 across macOS, Linux, Windows, and ARM platforms, but the package is aging (243 days since last release) with Alpha development status.

License in practice: Licensed under GPL-2.0-only OR LGPL-2.1-only OR BSD-3-Clause (copyleft). You may use it under any of these three terms; GPL and LGPL variants require derivative works to be similarly licensed.

Quickstart:

pip install mecab

import MeCab
t = MeCab.Tagger()
sentence = "太郎はこの本を女性に渡した。"
print(t.parse(sentence))

MeCab requires the underlying MeCab C++ library and dictionary data; installation may fail if system dependencies are missing or incompatible.

Verify before relying:

- Whether the package includes or requires separate installation of MeCab dictionary data.
- Current maintenance status and whether the aging label reflects genuine abandonment or stable maturity.
- Compatibility with Python 3.13 and 3.14 in practice, given Alpha status.

## Package facts

- License: GPL-2.0-only OR LGPL-2.1-only OR BSD-3-Clause (copyleft)
- Python support: unspecified
- Install friction: medium
- Maintenance: aging
- Downloads: 437.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

japanese text tokenization, morphological analysis japanese, mecab python binding, japanese nlp parsing, japanese word segmentation, pos tagging japanese, japanese grammar analysis, japanese-nlp, morphological-analysis, text-tokenization

[View on SkillFed](https://skillfed.io/packages/mecab) · [View on PyPI](https://pypi.org/project/mecab/)