magika — What is it?

Magika is an AI-powered file content type detection tool that leverages deep learning for fast and accurate identification of file types.

⭐ 16,663 Stars 🍴 998 Forks Python Apache-2.0 Author: google
Source: per README View on GitHub →

Why it matters

Magika is gaining attention due to its high accuracy in file type detection, its near-instantaneous inference time, and its integration with major platforms like Google's services and VirusTotal. The project's unique technical choice of using a lightweight model for fast detection is particularly notable.

Source: Synthesis of README and project traits

Core Features

High Accuracy

Achieves an average of ~99% accuracy on a dataset of ~100M files across 200+ content types, outperforming existing approaches, especially on textual content types.

Source: per README
Fast Inference

Inference time is about 5ms per file after the model is loaded, even on a single CPU, with near-constant inference time independent of file size.

Source: per README
Scalability

Can process thousands of files at once and supports recursive directory scanning, making it suitable for large-scale environments.

Source: per README
Content-Type Thresholding

Utilizes a per-content-type threshold system to determine the reliability of predictions, allowing for control over the tolerance to errors.

Source: per README

Architecture

The architecture of Magika is modular, with separate components for the CLI, Python API, and bindings for other languages. It employs a custom deep learning model for file type detection, which is optimized for speed and accuracy. The project uses continuous integration and deployment workflows, and includes security scanning and documentation generation.

Source: Code tree + dependency files

Project Knowledge Graph

Knowledge graph: project (center) + core features (inner hexagons) + key dependencies (outer chips) Not enough informationNot enough inf… High Accuracy Fast Inference Scalability Content-Type ThresholdingContent-Type Thresh… magika Project Core feature Key dependency

Center: project; inner ring: core feature modules; outer ring: key dependencies. Auto-generated from core_features and tech_stack.key_deps.

Tech Stack

LanguagePythonFrameworkRust for CLI, JavaScript/TypeScript for web demo, GoLang (in development)
Not enough information
Docker, GitHub Actions for CI/CD, likely serverless or cloud-based deployment for the web demo
Source: Dependency files + code tree

Quick Start

pipx install magika magika -r * | head pip install magika npm install magika cargo install --locked magika-cli
Source: README Installation/Quick Start

Use Cases

Magika is suitable for security and content policy scanning in large organizations, such as Google, where it is used to process hundreds of billions of samples weekly. It can also be used in any scenario requiring fast and accurate file type detection, such as file servers, content management systems, or security tools.

Source: README

Strengths & Limitations

Strengths

  • Strength 1: High accuracy and speed in file type detection.
  • Strength 2: Scalable and efficient for large-scale environments.
  • Strength 3: Open-source and integrates with major platforms.

Limitations

  • Limitation 1: Limited information on key dependencies.
  • Limitation 2: Some features are still in development, such as GoLang bindings.
Source: Synthesis of README, code structure and dependencies

Latest Release

python-v1.0.2 (2026-02-27): Marked python 3.14 as supported, removed direct dependency on numpy, and removed dependency on python-dotenv. cli/v1.1.0 (2026-04-24): Latest CLI release.

Source: GitHub Releases

Verdict

Magika is a promising open-source project for organizations requiring fast and accurate file type detection. Its high accuracy, speed, and scalability make it a valuable tool for security and content policy scanning. It is particularly suitable for teams working on large-scale file processing and security applications.

Transparency Notice
This page is auto-generated by AI (a large language model) from the following public materials: GitHub README, code tree, dependency files and release notes. Analyzed at: 2026-05-22 23:27. Quality score: 85/100.

Data sources: README, GitHub API, dependency files