List of the Top 25 Development Frameworks for Python in 2025

Django is a sophisticated web framework designed for Python that facilitates rapid development while prioritizing a clear and functional design. Developed by a team of experienced programmers, it addresses many of the challenges faced in web development, allowing developers to focus on creating their applications rather than starting from the ground up. This framework was specifically designed to help bring ideas to life as fully operational applications in the least amount of time. With a strong emphasis on security, Django aids developers in avoiding many common security vulnerabilities. Numerous high-traffic websites leverage Django's ability to scale quickly and adapt to changing traffic demands. Included within the framework are a variety of built-in tools aimed at addressing frequent web development issues. Additionally, Django proficiently handles critical functionalities like user authentication, content management, site maps, RSS feeds, and a host of other essential features right out of the box, establishing it as a potent resource for developers. The extensive array of tools provided ensures that developers can easily implement diverse functionalities, significantly boosting productivity and reducing project completion times. In essence, Django empowers developers to innovate and create with confidence, knowing they have a robust framework to support their efforts.

Flask is a lightweight WSGI web application framework designed with a focus on simplicity and quick development, making it user-friendly for newcomers while still being robust enough for intricate projects. Initially, it functioned as a straightforward wrapper around Werkzeug and Jinja, but it has since transformed into one of the most prominent frameworks for web development using Python. The framework offers guidance without enforcing rigid rules regarding dependencies or the organization of projects, thereby allowing developers the liberty to choose their favorite tools and libraries. Additionally, the vibrant community surrounding Flask has produced numerous extensions, which aid in seamlessly adding more features and functionalities to applications. This adaptability, combined with strong community support, has played a significant role in Flask's widespread appeal and lasting success among developers. As a result, many choose Flask not only for its ease of use but also for the extensive resources available to enhance their web development projects.

LangChain is a versatile framework that simplifies the process of building, deploying, and managing LLM-based applications, offering developers a suite of powerful tools for creating reasoning-driven systems. The platform includes LangGraph for creating sophisticated agent-driven workflows and LangSmith for ensuring real-time visibility and optimization of AI agents. With LangChain, developers can integrate their own data and APIs into their applications, making them more dynamic and context-aware. It also provides fault-tolerant scalability for enterprise-level applications, ensuring that systems remain responsive under heavy traffic. LangChain’s modular nature allows it to be used in a variety of scenarios, from prototyping new ideas to scaling production-ready LLM applications, making it a valuable tool for businesses across industries.

Streamlit serves as an incredibly efficient solution for the creation and dissemination of data applications. With this platform, users can convert their data scripts into easily shareable web apps in a matter of minutes, leveraging Python without incurring any costs, and it removes the barriers that come with needing front-end development expertise. The platform is anchored by three foundational principles: it promotes the use of Python scripting for application creation; it allows users to build applications with minimal code by utilizing a user-friendly API that automatically updates upon saving the source file; and it enhances user interaction by enabling the inclusion of widgets as effortlessly as declaring a variable, all without the need to handle backend development, define routes, or manage HTTP requests. Furthermore, applications can be deployed instantly through Streamlit’s sharing platform, which streamlines the processes of sharing, managing, and collaborating on projects. This straightforward framework allows for the development of powerful applications, such as the Face-GAN explorer that integrates Shaobo Guan’s TL-GAN project and utilizes TensorFlow and NVIDIA’s PG-GAN for generating attribute-based facial images. Another compelling example is a real-time object detection application designed as an image browser for the Udacity self-driving car dataset, demonstrating impressive capabilities in real-time object processing and recognition. Overall, Streamlit is not only beneficial for developers but also serves as a vital resource for data enthusiasts, enabling them to explore innovative projects with ease. Each of these features highlights why Streamlit has become a preferred choice for many in the data community.

Investigate libraries and frameworks that work harmoniously with Native Image to elevate your development process. By leveraging Graal, a cutting-edge optimizing compiler, you can create more efficient and streamlined code that requires fewer computational resources. Compiling Java applications into native binaries in advance allows for instantaneous startup times and peak performance without the need for warmup periods. You can also integrate the best features and libraries from various widely-used languages into a single application with minimal overhead. Moreover, it provides the ability to debug, monitor, profile, and optimize resource consumption not only in Java but across different programming languages as well. The high-performance JIT compiler integrated within GraalVM produces optimized native machine code that enhances execution speed, minimizes the generation of garbage, and lowers CPU usage through advanced compiler optimizations and aggressive inlining techniques. These improvements ultimately result in applications that run faster and utilize fewer resources, leading to a significant reduction in expenses associated with cloud services and infrastructure. This outstanding efficiency not only promotes a more sustainable methodology for software development but also contributes to better resource management practices in the industry. Overall, the combination of these tools and techniques creates a robust environment for developers seeking to maximize performance and efficiency.

The Atri framework provides a holistic approach to full-stack web development, specifically tailored for building Progressive Web Applications. Developers can considerably boost their productivity through our user-friendly visual editor, which streamlines their workflow. Moreover, the platform allows for the integration of custom React code within projects, offering flexibility and personalization. Currently, backend development is facilitated by Python, with plans underway to add NodeJS support soon. Our Command Line Interface (CLI) features a wide array of deployment options, making it simple to integrate with services like GitHub Pages and AWS, among others. Atri also comes equipped with various productivity-enhancing tools, including asset management systems, which can significantly reduce development time from months to just hours. In addition, Atri is transforming the landscape of full-stack development by inviting individuals without web development backgrounds to participate in app creation and maintenance, thus expanding the pool of contributors to application development. This inclusive methodology not only encourages teamwork but also empowers a diverse array of creators to realize their visions, fostering an environment ripe for innovation and creativity. By breaking down traditional barriers, Atri is paving the way for a more collaborative future in app development.

NiceGUI is an open-source library tailored for Python, enabling developers to build web-based graphical user interfaces (GUIs) using only Python code. With a user-friendly learning curve, it also offers advanced customization options for more experienced users. By adopting a backend-first approach, NiceGUI manages all the complexities of web development, allowing developers to focus solely on writing Python code. This framework is ideal for a variety of projects, ranging from simple scripts and dashboards to applications in robotics, IoT, smart home automation, and machine learning. Built on FastAPI for backend operations, it employs Vue.js for user interface interactions and Tailwind CSS for stylish design elements. Developers can easily create a wide array of components such as buttons, dialogs, Markdown content, 3D visualizations, and plots, all within a Python-based environment. Additionally, it supports real-time interactivity through WebSocket connections, enabling instant updates in the browser without requiring page refreshes. Moreover, NiceGUI provides an extensive selection of components and layout options, including rows and columns, which ensures users have the flexibility to design interfaces that meet their specific needs. Ultimately, this framework empowers developers with the tools and resources they need to innovate and create dynamic applications effortlessly.

Rio is a cutting-edge open-source framework designed in Python that empowers developers to build both contemporary web and desktop applications exclusively using Python. It takes cues from well-known frameworks like React and Flutter, featuring a declarative user interface model where components are structured as Python data classes that include a build() method, which allows for reactive state management to ensure fluid updates to the UI. With more than 50 ready-to-use components aligned with Google's Material Design guidelines, Rio simplifies the process of crafting professional-grade user interfaces. Its layout system is intuitive and Python-friendly, calculating the natural dimensions of each component prior to space allocation, thus eliminating the need for traditional CSS styles. Additionally, the framework provides developers the option to execute their applications locally or directly in a web browser, supported by FastAPI for backend services and WebSockets for communication. This streamlined integration not only enhances the development experience but also fosters a more productive workflow for creating rich, interactive applications. Ultimately, Rio represents a significant advancement in the realm of Python frameworks, making it easier than ever for developers to bring their ideas to life.

Tornado is a Python web framework that doubles as an asynchronous networking library, originally developed at FriendFeed. With its non-blocking network I/O, it can handle tens of thousands of concurrent connections, making it ideal for applications that rely on persistent user connections like long polling and WebSockets. Distinct from most Python web frameworks, Tornado does not adhere to the WSGI standard and typically functions on a single thread per process. While the tornado.wsgi module provides a degree of WSGI compatibility, it is not the primary focus of the framework’s development, and it is recommended that developers utilize Tornado’s dedicated interfaces, such as tornado.web, when creating applications. Additionally, it is crucial to understand that Tornado's code is generally not thread-safe. Since the release of version 5.0, Tornado has been integrated with Python's standard library asyncio module, allowing it to share the same default event loop. This integration facilitates the use of libraries built for asyncio alongside Tornado, significantly enhancing its versatility and overall functionality. Consequently, developers can construct highly efficient applications that harness both the strengths of Tornado and the capabilities of asyncio, leading to more robust and responsive user experiences. The adaptability of Tornado has made it a popular choice among developers who require high performance in their web applications.

Jinja acts as a fast, flexible, and dynamic templating engine. It includes distinctive placeholders that allow for the integration of code similar to Python syntax, which aids in generating content that is dynamic and responsive. Afterward, the template is provided with data, which is then used to create the final output document, highlighting Jinja's robust potential in the realm of web development. This adaptability makes it a popular choice among developers looking to streamline their workflow.

Horovod, initially developed by Uber, is designed to make distributed deep learning more straightforward and faster, transforming model training times from several days or even weeks into just hours or sometimes minutes. With Horovod, users can easily enhance their existing training scripts to utilize the capabilities of numerous GPUs by writing only a few lines of Python code. The tool provides deployment flexibility, as it can be installed on local servers or efficiently run in various cloud platforms like AWS, Azure, and Databricks. Furthermore, it integrates well with Apache Spark, enabling a unified approach to data processing and model training in a single, efficient pipeline. Once implemented, Horovod's infrastructure accommodates model training across a variety of frameworks, making transitions between TensorFlow, PyTorch, MXNet, and emerging technologies seamless. This versatility empowers users to adapt to the swift developments in machine learning, ensuring they are not confined to a single technology. As new frameworks continue to emerge, Horovod's design allows for ongoing compatibility, promoting sustained innovation and efficiency in deep learning projects.

Upgrade your ships with all essential tools to build a fully operational app that is ready for production. Organize your pages seamlessly, just as you would arrange files in a desktop folder. Employ simple JavaScript functions to efficiently access and store your data. Design reusable components using HTML alongside scoped CSS to enhance organization. Implement client-side interactions specifically where they enhance the user experience. Create server-rendered custom elements that can transition effortlessly into interactive web components. Enhance simplifies the workflow, guiding you to execute tasks correctly. Start with a version that caters to all users and incrementally introduce interactive features from that solid groundwork. The web platform is equipped with everything you need and maintains its timelessness. Move away from traditional throbbers and loading screens by cutting out unnecessary framework bloat, concentrating on only the vital code. As you explore Enhance, you are gaining development skills that will remain relevant as the web evolves. Applications built with Enhance are designed with deployment in mind, ensuring they are ready when the time comes. When you're set to launch, we are here to assist you with Begin, guaranteeing a smooth transition to the live environment. With Enhance, you aren't merely building apps; you are laying the groundwork for a sustainable future in web development, ensuring your projects are both innovative and adaptable. This approach not only fosters creativity but also promotes longevity in your development practices.

FastAPI stands out as a modern and rapid web framework tailored for building APIs with Python 3.7 and newer versions, utilizing standard Python type annotations. Its impressive performance is comparable to that of NodeJS and Go, primarily owing to its seamless integration with Starlette and Pydantic. Renowned for being one of the fastest frameworks within the Python landscape, it focuses on minimizing code duplication while offering a wide range of features derived from each parameter declaration. This characteristic positions FastAPI as a superb option for developers aiming for both efficiency and scalability in their API development endeavors. Additionally, its user-friendly design and comprehensive documentation further enhance its appeal to both novice and experienced programmers alike.

Modern APIs can be built with a full range of essential features such as data serialization, validation, websockets, ORM integration, session management, and authentication, among other capabilities. Litestar stands out by emphasizing both the developer experience and high performance, as it is recognized as one of the fastest ASGI frameworks on the market, all while ensuring a quick and efficient development workflow. Although it primarily operates asynchronously, it also allows for synchronous execution, ensuring that synchronous applications function without any performance penalties. Additionally, Litestar offers interfaces for various key/value stores that integrate seamlessly with your applications and support third-party extensions. Implementing response caching is straightforward and requires minimal configuration, significantly improving response times in the process. Furthermore, it includes utilities for session and JWT-based authentication, which simplifies the creation of your authentication framework. With its all-encompassing design, Litestar serves as an excellent choice for developers who want to make their API development process more efficient and effective. Ultimately, this unique combination of features and performance makes it a standout option in the realm of API frameworks.

A user-friendly API featuring sensible defaults and minimal bloat allows you to jump straight into app development effortlessly. You can build your application in your desired format without constraints from the available tools. From the outset, it has been crafted with an emphasis on both performance and scalability, making it suitable for web applications of any size. It comes with an integrated web server designed to enhance your web projects right out of the box. Sanic is recognized as one of the most popular frameworks on PyPI, especially noted for its asynchronous capabilities. The community actively contributes to the project, ensuring it stays current and advantageous for all users. This collaborative nature not only promotes ongoing enhancements but also encourages innovation within the framework, making it a compelling choice for developers. With its robust features and strong community support, Sanic continues to grow and adapt to the needs of modern web development.

The framework is built around the Model, View, and Controller (MVC) architecture, emphasizing a systematic approach that ensures long-term maintainability of the code. Unlike many popular web frameworks that prioritize quick deployment, often leading to convoluted code after several iterations, this framework focuses on stability and clarity in its design. For example, server processes like Apache and Gunicorn exemplify the role of controllers within this architecture. Upon starting, a controller process takes in a manifest, which serves as a strategic blueprint for operations. All incoming requests to the controller are directed towards specific applications detailed in the manifest. In this context, a manifest represents a collection of executable programs that can be run as needed. Users can engage with the controller through various means, such as web requests and command line commands, demonstrating its adaptability in different scenarios. This framework highlights the critical need for an organized and coherent structure in the realm of software development, showcasing how thoughtful design can enhance both usability and efficiency. Ultimately, investing in a well-structured framework can lead to more sustainable and manageable codebases over time.

Growler is a cutting-edge web framework built on asyncio, the asynchronous library introduced in PEP 3156 and included in the standard library starting with Python 3.4. It draws on the design principles of Connect and Express from the Node.js environment, employing a consolidated application object alongside a series of middleware to efficiently handle HTTP requests. This flexible middleware chain facilitates the straightforward creation of intricate applications while avoiding unnecessary complexity. With the support of the pip package manager, users can install only the optional dependencies they need, which aligns perfectly with Growler's commitment to a minimalist design that shuns superfluous installations. Additionally, the Growler community encourages developers to create and maintain supplementary packages that have been thoroughly tested for compatibility and stability with every release of Growler, ensuring that these extensions are easily accessible. By focusing on user autonomy, the framework emphasizes the inclusion of only the fundamental components required for optimal performance, thereby enhancing the overall development experience. This approach not only promotes efficiency but also fosters an ecosystem where developers can thrive and innovate.

Enhance the efficiency of API development across multiple interfaces using hug, which allows for the creation and design of your API just once, then presenting it in the most suitable format for your clients, whether that be locally, over HTTP, or through command line access. Hug is recognized as one of the fastest and most contemporary tools for building APIs in Python3, meticulously designed with an emphasis on performance. It optimally manages resources, activating them only when necessary, and employs Cython to achieve remarkable speed enhancements. As a result, hug consistently ranks among the top frameworks for Python, asserting itself as the fastest high-level framework available in the Python 3 ecosystem. Furthermore, hug makes it easy to manage different versions of your API; you can specify which version or versions an endpoint supports, ensuring that these details are automatically enforced and communicated to users. This feature not only increases the versatility of your API but also significantly improves its adaptability to the specific needs of clients, ultimately leading to a more satisfying user experience. Additionally, by streamlining the version control process, hug helps developers maintain consistency and clarity in their API offerings, further enhancing overall functionality.

Starlette includes an application class named Starlette that seamlessly combines its various features and functionalities. It employs a set list of middleware that activates with each request that comes in. Importantly, every Starlette application automatically includes two essential middleware classes: ServerErrorMiddleware, which acts as the outermost layer to capture any uncaught errors during the entire processing sequence, and ExceptionMiddleware, which is the innermost layer responsible for managing exceptions that occur during routing or endpoint operations. Additionally, both the startup and shutdown handler callables are designed to be parameter-free and can be implemented as either standard or asynchronous functions, offering flexibility in how they are deployed. This architecture guarantees that developers can effectively manage error handling and the lifecycle of their applications, ensuring a smoother operational experience. Such a structured approach allows for more resilient application performance and better debugging capabilities.

It has integrated capabilities for managing both the client and server aspects of the HTTP protocol, in addition to supporting web sockets on either side, all while successfully avoiding callback hell through middleware and adaptable routing features. This thoughtful architecture significantly improves the developer's experience, facilitating a smoother integration of various elements within web applications. Furthermore, it encourages cleaner code organization, making maintenance and updates more manageable.

Asyncio is a vital foundation for numerous asynchronous frameworks in Python, facilitating the creation of high-performance web servers, network applications, database connection libraries, distributed task queues, and beyond. Its design is especially advantageous for managing IO-bound tasks, allowing developers to construct intricate and organized network applications with ease. This adaptability positions asyncio as an indispensable resource for programmers focused on constructing scalable, responsive systems that can handle a variety of workloads. Moreover, its ability to streamline concurrency makes it an attractive choice for modern software development.

Falcon is a remarkably fast and lightweight web API framework in Python, crafted for developing robust application backends and microservices. It adeptly works with both asyncio (ASGI) and gevent/meinheld (WSGI), providing versatility across diverse scenarios. Emphasizing the REST architecture principles, the framework allows developers to create resource classes that handle HTTP method requests and facilitate state transitions. By improving upon traditional Python web frameworks, Falcon offers enhanced reliability, flexibility, and efficiency tailored to individual project requirements. Moreover, a wide array of Falcon add-ons, templates, and additional packages can be incorporated into your development efforts. For those just starting, many resources are gathered on the Falcon wiki, and browsing PyPI may reveal even more valuable tools and libraries. This rich ecosystem of resources not only supports developers effectively but also fosters growth as they expand their applications. The community surrounding Falcon continually shares insights, ensuring that developers can find assistance and best practices throughout their development journey.

CherryPy allows developers to build web applications in a manner akin to standard object-oriented Python projects, resulting in more streamlined code and faster development processes. With a history spanning over ten years, CherryPy has proven itself to be both swift and reliable. Its versatility enables it to function efficiently in a variety of production settings, from straightforward projects to highly intricate ones. To maximize your experience with CherryPy, starting with beginner tutorials that address the framework's most common features is recommended. Once these tutorials are completed, diving into both basic and advanced resources that highlight specific implementation strategies will be advantageous. Moreover, a careful review of the configuration and extension documentation can yield essential insights into the framework's powerful features, ensuring a well-rounded grasp of its capabilities. Engaging actively in community forums can further enrich your learning journey and foster connections with fellow developers, enhancing your overall experience with CherryPy. By immersing yourself in these resources and interactions, you will likely discover new techniques and best practices that can elevate your web development skills.

Bottle is a compact and efficient WSGI micro web framework crafted for Python developers. Packaged as a single-file module, it operates exclusively on the Python Standard Library, which helps to keep the number of dependencies low. The framework simplifies the process of mapping incoming requests to specific function calls and supports both clean and dynamic URL structures. Furthermore, Bottle features a fast and Pythonic built-in template engine, while also allowing for the integration of external engines such as Mako, Jinja2, and Cheetah. Developers can easily handle various types of data, such as file uploads, cookies, headers, and other elements related to HTTP requests. In addition, Bottle comes with an integrated HTTP development server and is compatible with various other WSGI-compliant servers like Paste, Bjoern, GAE, and CherryPy. Its combination of simplicity and flexibility makes it an attractive option for developers seeking to build web applications swiftly and efficiently, making it a popular choice among those who prioritize rapid development without sacrificing functionality.

Creating machine learning applications should be a smooth and straightforward process. UnionML is a Python-based open-source framework that builds upon Flyte™, simplifying the complex world of ML tools into a unified interface. It allows you to easily incorporate your preferred tools through a simple and standardized API, minimizing boilerplate code so you can focus on what truly counts: the data and the models that yield valuable insights. This framework makes it easier to merge a wide variety of tools and frameworks into a single protocol for machine learning. Utilizing established industry practices, you can set up endpoints for data collection, model training, prediction serving, and much more—all within one cohesive ML system. Consequently, data scientists, ML engineers, and MLOps experts can work together seamlessly using UnionML applications, creating a clear reference point for comprehending the dynamics of your machine learning architecture. This collaborative environment not only encourages innovation but also improves communication among team members, significantly boosting the overall productivity and success of machine learning initiatives. Ultimately, UnionML serves as a vital asset for teams aiming to achieve greater agility and productivity in their ML endeavors.