Table of Content
Which Python frameworks should you use?
Use Django for full web applications with an admin and database layer, FastAPI for APIs and AI services, and Flask for small, flexible apps. For data and AI work, Streamlit builds data apps, LangGraph orchestrates agents and PyTorch trains deep learning models. Scrapy handles web crawling and Tornado handles many open connections. Pick by project type, then by team skills.
- A framework sets the structure of your application and calls your code, while a library is code you call. That is why “Python frameworks” covers web tools, data app builders and AI frameworks alike.
- Eight picks cover most projects: Django, FastAPI and Flask for the web, Streamlit for data apps, LangGraph for AI agents, PyTorch for deep learning, Scrapy for crawling and Tornado for real-time connections.
- Django’s documentation covers the pieces most business sites need: models, an automatic admin interface, authentication, forms and security protections such as CSRF and clickjacking defenses.
- FastAPI is built on Starlette and Pydantic, uses standard Python type hints and generates interactive API documentation automatically. Flask describes itself as a lightweight WSGI framework that depends on Werkzeug, Jinja and Click.
- For an AI product, a common split is FastAPI to serve a model or tool, LangGraph to run agent logic and PyTorch to train models, but you do not need all three.
- Choose by what you are building, who will maintain it and who you can hire. Performance rarely decides it.
- CommercePundit builds Python web apps, APIs and AI systems for business teams.
What is a Python framework?
A Python framework is a structured toolkit that gives your application its skeleton: how requests are routed, how data is stored, where your code goes and how it is tested. The usual way to tell frameworks from libraries is who is in charge. With a library, your code calls the library when it needs something. With a framework, the framework calls your code at the right moment. Django, FastAPI and Flask are web frameworks. Streamlit, LangGraph, PyTorch and Scrapy are frameworks for specific kinds of work. The terms overlap in everyday use, so judge a tool by what it does for your project, not by its label.
What are the main Python frameworks at a glance?
| Framework | Type | Best for | Not ideal for |
|---|---|---|---|
| Django | Full-stack web framework | Business websites, portals, marketplaces and admin-heavy apps | Tiny single-endpoint services |
| FastAPI | API framework | REST APIs, microservices and AI model or tool endpoints | Server-rendered sites with an admin out of the box |
| Flask | Lightweight web framework | Small apps, prototypes and custom stacks | Large apps without strong conventions |
| Streamlit | Data app framework | Dashboards, internal tools and AI demos | Public sites with custom design and heavy traffic |
| LangGraph | Agent orchestration framework | Long-running, stateful AI agents with human review | Simple one-call AI features |
| PyTorch | Deep learning framework | Training and running neural networks | Standard business web apps |
| Scrapy | Crawling framework | Collecting structured data from many web pages | Sites where scraping is not allowed |
| Tornado | Async web framework and networking library | Apps that keep many connections open | Typical CRUD websites |
Which are the best Python frameworks? 8 picks and what each is good for
1. Django
What it is: the Django documentation describes it as “the web framework for perfectionists with deadlines.” It covers models and migrations, an automated admin interface, authentication, forms and security protections including CSRF and clickjacking protection and cryptographic signing. The documentation shown at the time of writing is for Django 6.1.
Choose it if you are building a business site, portal, marketplace or content platform and want a lot included. Watch for its conventions, which help large teams but can feel heavy for a small API.
2. FastAPI
What it is: the FastAPI documentation calls it “a modern, fast (high-performance), web framework for building APIs with Python based on standard Python type hints,” built on Starlette for the web parts and Pydantic for the data parts. It generates interactive documentation at /docs and /redoc and follows the OpenAPI and JSON Schema standards. The project says its performance is on par with Node.js and Go and cites independent TechEmpower benchmarks placing it among the fastest Python frameworks, below only Starlette and Uvicorn.
Choose it if you are building APIs, microservices or endpoints that serve AI models and tools. Watch for the need to add your own admin, authentication and templating choices.
3. Flask
What it is: the Flask documentation describes it as “a lightweight WSGI web application framework” designed to make getting started quick and easy, with the ability to scale up. It depends on Werkzeug, Jinja and Click, and community extensions add features such as database access and forms. It is released under the BSD-3-Clause license, and the current documentation covers the 3.1 series.
Choose it if you want a small app, a prototype or full control over each component. Watch for assembling many extensions yourself as the app grows. Flask supports async routes, and its docs point to Quart for fully async designs.
4. Streamlit
What it is: the Streamlit documentation calls it an open-source Python framework for data scientists and AI/ML engineers that turns scripts into interactive data apps with a few lines of code. Apps can be shared on the free Streamlit Community Cloud, and an enterprise option runs inside Snowflake.
Choose it if you need dashboards, internal tools or AI demos fast. Watch for limits on custom design and complex navigation when an app becomes a product.
5. LangGraph
What it is: the LangGraph documentation describes a “low-level orchestration framework and runtime” for long-running, stateful agents, with durable execution, streaming, persistence and human-in-the-loop controls. It is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
Choose it if you are building agents that must remember state, pause for approval and recover from failure. Watch for over-engineering: a single model call with retrieval is often enough. Our guides to building an AI agent and AI agent builders help you decide.
6. PyTorch
What it is: PyTorch is a deep learning framework governed by the PyTorch Foundation. Its site highlights production tooling such as TorchScript and TorchServe, and distributed training through torch.distributed. The latest stable release requires Python 3.10 or later.
Choose it if you train or fine-tune your own models. Watch for hardware cost and the skills needed. Most business teams call hosted models through an API instead of training their own.
7. Scrapy
What it is: the Scrapy documentation calls it “an application framework for crawling web sites and extracting structured data,” useful for data mining, information processing and archiving, and able to extract data through APIs.
Choose it if you collect structured data from many pages on a schedule. Watch for each site’s terms of service and robots rules, and for pages that change layout.
8. Tornado
What it is: the Tornado documentation describes a Python web framework paired with an asynchronous networking library that uses non-blocking I/O to keep tens of thousands of connections open at once.
Choose it if your app depends on long-lived connections such as streaming or real-time updates. Watch for a smaller ecosystem than Django or FastAPI for everyday tasks.
Django vs Flask vs FastAPI: which web framework should you choose?
Choosing a Python framework for web development, or a Python backend framework for an API, usually comes down to three names. This comparison reflects what each project’s own documentation emphasizes.
| Question | Django | Flask | FastAPI |
|---|---|---|---|
| Style | Batteries included | Lightweight, add what you need | API-first, type-hint driven |
| Admin and authentication | Built in | Through extensions or your own code | Your choice of libraries |
| Database layer | Built-in ORM and migrations | Choose your own, such as SQLAlchemy | Choose your own |
| API documentation | Add a package | Add a package | Automatic, at /docs and /redoc |
| Async support | Available | Supported, with Quart recommended for fully async apps | Core to the design |
| Best fit | Content sites, portals, admin-heavy products | Small apps, prototypes, custom stacks | APIs, microservices, AI endpoints |
The short answers: Flask vs Django is a choice between choosing your own parts and getting a complete set. FastAPI vs Flask is mostly about whether you are building an API and want validation and documentation generated for you. FastAPI vs Django is a choice between an API service and a full application with an admin. Many companies run both: Django for the main product and FastAPI for services around it. Among Python API frameworks, FastAPI and Litestar are the modern async choices, while Flask and Django (with an API add-on) are the long-established ones. For other languages, see our best backend frameworks guide.
Python frameworks for AI: which are best for AI projects?
AI projects use frameworks at different layers, and you rarely need all of them.
| Layer | Framework | What it does |
|---|---|---|
| Serve a model or tool | FastAPI | Exposes your logic as a validated, documented API |
| Orchestrate agents | LangGraph | Runs stateful, long-running agent workflows with human review |
| Train or fine-tune models | PyTorch | Builds and trains neural networks |
| Demo or internal tool | Streamlit | Wraps a model or dataset in a web interface |
| Collect data | Scrapy | Crawls and extracts structured data |
If your goal is an agent that works with your own systems, the framework matters less than the tool access and approvals around it. Our guide to MCP vs API covers how agents reach tools, and our agentic AI development services and AI development services teams build them in Python.
Which other Python web frameworks are worth knowing?
- Pyramid. Described by its project as a compact, quick and approachable Python web framework under a BSD-style license, a flexible option between Flask and Django.
- Litestar. A modern ASGI framework for scalable APIs with automatic OpenAPI documentation, dependency injection, type-hint validation, WebSockets and SQLAlchemy integration, an alternative to FastAPI.
How do you choose a Python framework?
| Question | What to look at |
|---|---|
| What are you building? | A full site or portal points to Django. An API or AI service points to FastAPI. A small app points to Flask. A data tool points to Streamlit. |
| Who will maintain it? | Pick what your team already knows, or what you can hire for. See our guide to hiring AI developers for Python-heavy teams. |
| What must be included? | Admin, authentication and forms are built into Django and must be added elsewhere. |
| How will it scale? | Most frameworks scale with good design. Choose async or message queues when you need many concurrent connections. |
| How long must it last? | Prefer active projects with clear release notes and a track record, and plan upgrades. |
| What else does it connect to? | Check support for your database, authentication provider and cloud environment. |
If you are deciding for a new product, our MVP development guide covers how to scope the first release, and hiring AI developers covers who to bring in. Container deployment basics are in Kubernetes vs Docker. Our Python development services and full-stack development services teams can build and run the application.
What are the best Python frameworks for beginners?
One scope note: Python GUI frameworks, such as Tkinter or Qt bindings, build desktop applications and are outside this list, which focuses on web, AI and data work.
Flask and Streamlit have the shortest path to a working result, which is why they are common first choices. Django takes longer to learn but teaches structure that carries over to large projects. If your goal is to get a job or ship a business product, learn Django or FastAPI. If your goal is a quick data demo, start with Streamlit.
Frequently Asked Questions
What are the libraries and frameworks in Python?
A library is code you call to do a job, such as reading files or analyzing data. A framework sets the structure of your application and calls your code, as Django, FastAPI and Flask do for web apps. In practice the two terms overlap, so judge a tool by what it gives your project.
Which is better, Django or Flask?
Neither is better in general. Django includes an ORM, admin, authentication and forms, which suits full applications. Flask is lightweight and lets you choose each part, which suits small apps and custom stacks. Choose Django for structure and Flask for flexibility.
Is FastAPI better than Flask?
FastAPI is better when you are building an API and want automatic validation and interactive documentation from type hints. Flask is simpler for small apps that serve pages. Both are widely used, so team experience should weigh heavily.
Is Django or FastAPI better?
They solve different problems. Django is a full-stack framework with an admin and database layer, while FastAPI is focused on APIs. Many teams use Django for the main product and FastAPI for services around it.
Is FastAPI a Python framework?
Yes. FastAPI is a Python web framework for building APIs, built on Starlette for the web parts and Pydantic for data validation, and it uses standard Python type hints.
Is FastAPI easy to learn?
Its documentation describes it as designed to be easy to use and learn, and type hints give editor completion and validation. You still need to understand HTTP, async basics and how to choose databases and authentication, which FastAPI leaves to you.
What is the fastest Python framework?
The FastAPI documentation says independent TechEmpower benchmarks place FastAPI under Uvicorn among the fastest Python frameworks, below only Starlette and Uvicorn themselves. Benchmarks measure narrow tests, and database and design choices usually matter more than framework speed.
What is the best Python framework for AI?
It depends on the layer. FastAPI serves models and tools, LangGraph orchestrates stateful agents, PyTorch trains models and Streamlit builds demos. Many business teams only need FastAPI plus a hosted model.
Which Python framework is best for beginners?
Flask and Streamlit give the fastest first result. Django takes longer but teaches structure used in large projects. Choose based on whether you want a quick demo or a long-term skill.
Do I need a framework to build with Python?
Not for scripts and small automations. For web apps, APIs and AI agents, a framework saves time on routing, security, validation and structure, and makes the code easier for other developers to maintain.
Bottom Line
Match the framework to the project: Django for full applications, FastAPI for APIs and AI services, Flask for small apps, Streamlit for data tools, LangGraph for agents, PyTorch for training, Scrapy for crawling and Tornado for many open connections. Prefer the tool your team can maintain and hire for, and let performance decide only when it truly limits you. If you want help choosing or building, talk to us about Python development services.
