---
id: guessit
version: "4.4.0"
license: LGPL-3.0-or-later
license_treatment: copyleft
maintenance: active
---

# guessit — GuessIt - a library for guessing information from video filenames.

License: copyleft · Maintenance: active · Downloads: 164.4K/mo

## What it is and what it does

GuessIt is a Python library that parses video filenames to extract structured metadata. Given a filename like 'Shameless.US.S05E10.720p.HDTV.x264-KILLERS.mkv', it returns a dictionary with title, season, episode, resolution, source, codec, release group, container, and media type. It works with both movie and TV episode filenames, handling a wide variety of naming conventions and abbreviations.

The library relies on pattern matching and rule-based extraction via its dependency on Rebulk. It is maintained actively, supports modern Python versions, and has no known security vulnerabilities. The three runtime dependencies (babelfish, python-dateutil, rebulk) are lightweight and add minimal installation overhead. GuessIt is useful for media center applications, download managers, and any tool that needs to organize or catalog video files by their filenames.

Use it for:

- Organize downloaded TV episodes and movies into a media library by automatically extracting season/episode numbers and titles.
- Build a download manager that renames files consistently based on parsed metadata (title, year, resolution, codec).
- Populate a media database by bulk-parsing a folder of video files to extract and validate metadata.
- Filter or sort video files by properties like codec, resolution, or release group extracted from filenames.
- Validate that a video file's name matches expected metadata before processing or archiving it.

## Worth the install?

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

GuessIt extracts structured metadata from video filenames, parsing titles, seasons, episodes, codecs, resolutions, and release information from both movies and TV show files.

Yes. GuessIt is actively maintained, has low install friction, carries no known vulnerabilities, and solves a concrete problem for media-handling tools. The LGPL-3.0-or-later license is permissive for most use cases but requires attention if you are building a proprietary application. Install it if you need to parse video filenames; skip it if your filenames follow a rigid, custom format you can parse with simpler regex.

## Install

pip install guessit
uv add guessit
poetry add guessit

## Installing guessit

Before you install: Low install friction with a pure-Python wheel and only three runtime dependencies. Actively maintained with a release 14 days old and recent commits; the project has 929 GitHub stars and supports current Python versions (3.10 through 3.14).

License in practice: Licensed under LGPL-3.0-or-later (copyleft). You may use and modify GuessIt freely, but any derivative work must also be distributed under a compatible copyleft license; proprietary applications cannot link it without legal review.

Quickstart:

pip install guessit

from guessit import guessit
result = guessit('The.Matrix.1999.1080p.BluRay.x264-GROUP.mkv')
print(result['title'], result['year'], result['video_codec'])

Requires Python 3.10 or later.

Verify before relying:

- Accuracy and coverage of filename patterns across different regional naming conventions and edge cases.
- Performance characteristics when processing large batches of filenames.
- Whether the library handles non-English or mixed-language filenames reliably.

## Package facts

- License: LGPL-3.0-or-later (copyleft)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 164.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

video filename parser, extract metadata from video names, tv episode title parser, movie filename metadata, parse video release info, season episode number extraction, video codec detection from filename, media-metadata, filename-parsing

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