Magika Python Package

Magika is a novel AI-powered file type detection tool that relies on the recent advance of deep learning to provide accurate detection. Under the hood, Magika employs a custom, highly optimized model that only weighs about a few MBs, and enables precise file identification within milliseconds, even when running on a single CPU. Magika has been trained and evaluated on a dataset of ~100M samples across 200+ content types (covering both binary and textual file formats), and it achieves an average ~99% accuracy on our test set.

Use Magika as a command line client or in your Python code!

You can find more information on which content types are supported, extended documentation, and bindings for other languages on Magika's website at https://securityresearch.google/magika/.

Installing Magika

Magika is available as magika on PyPI:

To install the most recent stable version:

pip install magika

If you intend to use Magika only as a command line, you may want to use pipx install magika instead.

If you want to test out the latest release candidate, you can install it with pip install --pre magika.

Using Magika as a command-line tool

Beginning with version 0.6.0, the magika Python package includes a pre-compiled Rust-based command-line tool, replacing the previous Python version. This binary is distributed as platform-specific wheels for most common architectures. For unsupported platforms, a pure-Python wheel is also available, providing the legacy Python client as a fallback.

$ cd tests_data/basic && magika -r *
asm/code.asm: Assembly (code)
batch/simple.bat: DOS batch file (code)
c/code.c: C source (code)
css/code.css: CSS source (code)
csv/magika_test.csv: CSV document (code)
dockerfile/Dockerfile: Dockerfile (code)
docx/doc.docx: Microsoft Word 2007+ document (document)
epub/doc.epub: EPUB document (document)
epub/magika_test.epub: EPUB document (document)
flac/test.flac: FLAC audio bitstream data (audio)
handlebars/example.handlebars: Handlebars source (code)
html/doc.html: HTML document (code)
ini/doc.ini: INI configuration file (text)
javascript/code.js: JavaScript source (code)
jinja/example.j2: Jinja template (code)
jpeg/magika_test.jpg: JPEG image data (image)
json/doc.json: JSON document (code)
latex/sample.tex: LaTeX document (text)
makefile/simple.Makefile: Makefile source (code)
markdown/README.md: Markdown document (text)
[...]

$ magika ./tests_data/basic/python/code.py --json
[
  {
    "path": "./tests_data/basic/python/code.py",
    "result": {
      "status": "ok",
      "value": {
        "dl": {
          "description": "Python source",
          "extensions": [
            "py",
            "pyi"
          ],
          "group": "code",
          "is_text": true,
          "label": "python",
          "mime_type": "text/x-python"
        },
        "output": {
          "description": "Python source",
          "extensions": [
            "py",
            "pyi"
          ],
          "group": "code",
          "is_text": true,
          "label": "python",
          "mime_type": "text/x-python"
        },
        "score": 0.753000020980835
      }
    }
  }
]

$ cat doc.ini | magika -
-: INI configuration file (text)

$ magika --help
Determines the content type of files with deep-learning

Usage: magika [OPTIONS] [PATH]...

Arguments:
  [PATH]...
          List of paths to the files to analyze.

          Use a dash (-) to read from standard input (can only be used once).

Options:
  -r, --recursive
          Identifies files within directories instead of identifying the directory itself

      --no-dereference
          Identifies symbolic links as is instead of identifying their content by following them

      --colors
          Prints with colors regardless of terminal support

      --no-colors
          Prints without colors regardless of terminal support

  -s, --output-score
          Prints the prediction score in addition to the content type

  -i, --mime-type
          Prints the MIME type instead of the content type description

  -l, --label
          Prints a simple label instead of the content type description

      --json
          Prints in JSON format

      --jsonl
          Prints in JSONL format

      --format <CUSTOM>
          Prints using a custom format (use --help for details).

          The following placeholders are supported:

            %p  The file path
            %l  The unique label identifying the content type
            %d  The description of the content type
            %g  The group of the content type
            %m  The MIME type of the content type
            %e  Possible file extensions for the content type
            %s  The score of the content type for the file
            %S  The score of the content type for the file in percent
            %b  The model output if overruled (empty otherwise)
            %%  A literal %

  -h, --help
          Print help (see a summary with '-h')

  -V, --version
          Print version

Check the Rust CLI docs for more information.

Using Magika as a Python module

Here is a few examples on how to use the Magika Python module:

>>> from magika import Magika
>>> m = Magika()
>>> res = m.identify_bytes(b'function log(msg) {console.log(msg);}')
>>> print(res.output.label)
javascript

>>> from magika import Magika
>>> m = Magika()
>>> res = m.identify_path('./tests_data/basic/ini/doc.ini')
>>> print(res.output.label)
ini

>>> from magika import Magika
>>> m = Magika()
>>> with open('./tests_data/basic/ini/doc.ini', 'rb') as f:
>>>     res = m.identify_stream(f)
>>> print(res.output.label)
ini

Core Concepts

To get the most out of Magika, it's worth learning about its core concepts. You can read about how Magika works, the models, the supported content types, the prediction modes, and notes on how to understand all parts of the output in the Core Concepts section of Magika's website.

API documentation

The Python API is documented in the Python bindings section of Magika's website, and via docstrings in the Magika module source code.

Research Paper and Citation

We describe how we developed Magika and the choices we made in our research paper, which was accepted at the International Conference on Software Engineering (ICSE) 2025. You can find a copy of the paper here.

If you use this software for your research, please cite it as:

@InProceedings{fratantonio25:magika,
  author = {Yanick Fratantonio and Luca Invernizzi and Loua Farah and Kurt Thomas and Marina Zhang and Ange Albertini and Francois Galilee and Giancarlo Metitieri and Julien Cretin and Alexandre Petit-Bianco and David Tao and Elie Bursztein},
  title = {{Magika: AI-Powered Content-Type Detection}},
  booktitle = {Proceedings of the International Conference on Software Engineering (ICSE)},
  month = {April},
  year = {2025}
}