Fast and accurate AI powered file content types detection
Star History
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+467
STARS · LAST 30 DAYS
16
PER DAY
#80
MOST-STARRED Rust
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +20 | +467 | +1.4K |
| Per day | 3 | 16 | 16 |
| Forks gained | +4 | +24 | +59 |
magika gained 467 stars in the last 30 days, about 16 a day, and now has 18.7K. It is about 3 years old and has averaged roughly 6.2K stars a year. It ranks #80 among Rust repositories and #623 across all languages on GitHubRepo.
Trending Record
10
DAYS ON TRENDING
#488
BEST RANK
Sep 24, 2026
FIRST APPEARANCE
Active
STATUS TODAY
magika has maintained a continuous presence across global trending indexes, peaking at #488. Below is the 30-day activity profile:
💡 Overview
magika is an open-source project written in Rust: Fast and accurate AI powered file content types detection .
Engineered for speed, consistency, and developer ease, it solves common hurdles in ai, deep-learning, filetype. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Rust for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ cargo install magika⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Rust toolchain (rustc / cargo >= 1.70)
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
magika coordinates its core functionality through a modular Rust pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Autonomous AI Agents
Orchestrate intelligent workflows and tool-calling routines with magika.
Model Inference & Prompting
Integrate fast, local or cloud-hosted generative AI models directly into production code.
Context Memory & RAG
Augment language models with dynamic vector retrieval and structured project memory.
Developer Productivity
Automate repetitive engineering tasks, code generation, and test creation using AI agents.
🚀 Getting Started
Install magika using your package manager: `cargo install magika`
Initialize your project workspace or configuration file for magika.
Import magika into your codebase or invoke it directly from your terminal.
Execute your test suite or run `magika --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Rust, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
google/magika is currently ranked #623 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
| Rank | Repository | Language | Stars | Action |
|---|---|---|---|---|
| #618 | gradle/gradle | Groovy | ★ 18.9K | Compare ↗ |
| #619 | lichess-org/lila | Scala | ★ 18.8K | Compare ↗ |
| #620 | nesquena/hermes-webui | Python | ★ 18.7K | Compare ↗ |
| #621 | tiann/KernelSU | Kotlin | ★ 18.7K | Compare ↗ |
| #622 | wled/WLED | C++ | ★ 18.7K | Compare ↗ |
| #623 | google/magika This Project | Rust | ★ 18.7K | |
| #624 | docusealco/docuseal | Ruby | ★ 18.6K | Compare ↗ |
| #625 | nukeop/nuclear | TypeScript | ★ 18.6K | Compare ↗ |
| #626 | keploy/keploy | Go | ★ 18.5K | Compare ↗ |
| #627 | NVIDIA-NeMo/Speech | Python | ★ 18.5K | Compare ↗ |
| #628 | plotly/plotly.js | JavaScript | ★ 18.4K | Compare ↗ |
Frequently Asked Questions
What does magika do? +
Fast and accurate AI powered file content types detection
What language is magika written in? +
The primary language is Rust. Topics include: ai, deep-learning, filetype, keras-classification-models, keras-models.
Is magika actively maintained? +
Yes, the last recorded push was on Oct 1, 2026 with 176 open issues being tracked.
How many stars does magika have? +
magika has 18,688 stars and 1,175 forks on GitHub.
How does magika rank among GitHub repositories? +
With 18,688 stars, google/magika is ranked #623 globally across all repositories tracked on GitHubRepo and #80 among Rust projects.
What license is magika distributed under? +
The repository reports a Apache-2.0 license. Always verify the repository LICENSE file for legal terms.
