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vinta/awesome-python

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TLDR

An opinionated, curated index of the best Python frameworks, libraries, and tools across AI, web development, data science, DevOps, and dozens more categories, one of GitHub's top-10 most-starred repositories, with no runnable code, just vetted links.

Mindmap

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  root((repo))
    What it is
      Curated Python index
      No runnable code
      Markdown only
    AI and ML
      Agents and orchestration
      Deep learning
      NLP and computer vision
    Web and data
      Web frameworks
      HTTP and scraping
      ORMs and databases
    Toolchain
      Environment management
      Package management
      CLI and GUI tools
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Code map

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An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

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Things people build with this

USE CASE 1

Find the best Python library for a specific task, web scraping, REST APIs, or machine learning, without sifting through all of PyPI.

USE CASE 2

Survey the AI and ML Python landscape, including orchestration frameworks like LangChain, CrewAI, and DSPy, with one-line descriptions for each.

USE CASE 3

Discover vetted Python tools for a new project area, from ORMs and caching to CLI frameworks and static site generators.

USE CASE 4

Stay current with the Python ecosystem by scanning which packages are highlighted in each themed category.

Tech stack

Python

Getting it running

Difficulty · easy Time to first run · 5min
No explicit license stated for this repository.

In plain English

This repository is an opinionated awesome-list of Python frameworks, libraries, tools, and learning resources, maintained by Vinta. It calls itself "an opinionated guide to the best Python" packages, and it is one of the most-starred lists on GitHub, with the README noting it is the #10 most-starred repo there. The repo contains no runnable code: it is a curated Markdown index that points outward to other projects on GitHub. The README opens with a short sponsor block (the current sponsor mentioned is pyr, a zero-config Python project manager) and a note about how to become a sponsor. After that, the bulk of the README is a deep table of contents grouped into high-level themes. The themes covered include AI and ML, Web Development, HTTP and Scraping, Database and Storage, Data and Science, Developer Tools, DevOps, CLI and GUI, Text and Documents, Media, Python Language, Python Toolchain, Security, and Other. Each theme then expands into many smaller categories. For example, AI and ML splits into AI and Agents, Deep Learning, Machine Learning, Natural Language Processing, Computer Vision, and Recommender Systems. Web Development splits into Web Frameworks, Web APIs, Web Servers, WebSocket, Template Engines, Web Asset Management, Authentication, Admin Panels, CMS, and Static Site Generators. Database and Storage covers ORMs, database drivers, caching, search, and serialization. Python Toolchain covers environment management, package management, package repositories, distribution, and configuration files. Inside each category sits a list of packages, with each entry showing the library name, a one-line description, and a link to its repository. The AI and Agents section, for instance, lists orchestration frameworks like autogen, crewai, dspy, langchain, openai-agents, and pydantic-ai, plus data-layer tools like instructor, llama-index, and mem0. You would use this repo as a reference: you want to do X in Python, you scan the right category, read the descriptions, and click through to the library that fits.

Copy-paste prompts

Prompt 1
I'm building a Python web scraper that needs to handle JavaScript-rendered pages. Recommend the best library from the awesome-python HTTP and Scraping section and show me a quick-start example.
Prompt 2
I want to build an AI agent in Python. Compare the top 3 orchestration frameworks in the awesome-python AI and Agents section, what are the tradeoffs for a beginner vibe coder?
Prompt 3
I'm setting up a new Python project from scratch. Which environment management and package management tools from awesome-python should I use and why?
Prompt 4
Help me pick an ORM from the awesome-python Database section for a FastAPI app that needs to work with PostgreSQL, with a focus on ease of use.
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