Robyn is a high-performance async Python web framework powered by a Rust runtime for exceptional speed.
Robyn is a high-performance, async web framework for Python that uses a Rust runtime to deliver exceptional speed and scalability. It enables developers to build fast web applications and APIs while maintaining Python's simplicity and offering features like hot reloading, WebSockets, and automatic OpenAPI generation. The framework also includes built-in support for AI agents and the Model Context Protocol (MCP).
Python developers building high-performance web applications, APIs, or real-time services who need the speed of Rust with the ease of Python. It's also suitable for projects integrating AI agents or requiring MCP server capabilities.
Developers choose Robyn for its unique combination of Python's developer experience and Rust's performance, along with a rich feature set including hot reloading, direct Rust integration, and built-in AI support, all backed by an active open-source community.
Robyn is a Super Fast Async Python Web Framework with a Rust runtime.
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Leverages a multithreaded Rust runtime to achieve top benchmarks in frameworks like TechEmpower, delivering exceptional speed for web applications and APIs.
Handles both asynchronous and synchronous functions seamlessly, allowing developers to choose based on task requirements without framework limitations.
Includes hot reloading with the --dev flag for automatic server restarts and automatic OpenAPI documentation generation, streamlining the development process.
Provides built-in AI agent support and MCP server capabilities, enabling direct routing and execution for modern AI-powered applications.
Requires Rust and a C compiler to be installed, adding setup complexity and potential barriers for teams unfamiliar with these tools, as noted in the troubleshooting section.
Only supports Python versions 3.10 and above, excluding legacy systems or projects tied to older Python releases, which may restrict adoption in some environments.
Features like io-uring support are marked as experimental, and the framework is under active development, which could lead to instability or breaking changes.