Python continues to grow every year. New libraries emerge regularly, streamlining coding workflows. In 2026, several have already caught our attention, offering tools for data, AI agents, code analysis, documentation, and synthetic data. Most are open-source and accessible.
# 12 Python Libraries for 2026
These are 12 Python libraries that made waves in 2025, and that every developer should try in 2026.
// 1. MarkItDown
Repo: https://github.com/microsoft/markitdown
Stars: ~86k+ on GitHub (rapid adoption in 2025)
Features: MarkItDown converts documents like PDFs, Word, Excel, and PowerPoint into Markdown. It preserves structure such as headings, tables, and lists and is designed for large language model (LLM) workflows.
// 2. Polars
Repo: https://github.com/pola-rs/polars
Stars: ~37k+ on GitHub
Features: Polars is a fast DataFrame library written in Rust with Python support. It offers lazy and eager execution, multi-threading, and low memory usage. Polars works with CSV, Parquet, and JSON and is much faster than Pandas for large datasets.
// 3. GPT Pilot (Previously Pythagora)
Repo: https://github.com/Pythagora-io/gpt-pilot
Stars: ~33.8k+ on GitHub
Features: Pythagora uses AI to explain code and generate documentation. GPT Pilot serves as the core technology for the Pythagora VS Code extension, which aims to provide the first real AI developer companion capable of writing full features, debugging code, discussing issues, and requesting reviews.
// 4. Smolagents
Repo: https://github.com/huggingface/smolagents
Stars: ~25k+ on GitHub
Features: Smolagents is an AI agent framework from Hugging Face. It helps you build intelligent agents that write code or call tools, supports multiple LLMs, and allows multi-step reasoning. It also integrates with sandboxed execution environments (Blaxel, Docker, WebAssembly).
// 5. LangExtract
Repo: https://github.com/google/langextract
Stars: ~24k+ on GitHub
Features: LangExtract extracts structured data from unstructured text using LLMs. It can detect entities, apply schemas, and visualize results. It supports cloud models (e.g. Gemini) and local models via provider plugins, and is optimized to handle long documents.
// 6. FastMCP
Repo: https://github.com/jlowin/fastmcp
Stars: ~22k+ on GitHub
Features: FastMCP is a framework for building Model Context Protocol (MCP) servers and clients. It simplifies connecting clients and servers and managing data transformations. These integration patterns make it better than raw MCP implementations.
// 7. Data-Formulator
Repo: https://github.com/microsoft/data-formulator
Stars: ~15k+ on GitHub
Features: Data Formulator is a Microsoft Research project that utilizes AI agents for data exploration via rich visualizations. It allows you to turn intent and data into charts through an interactive workflow.
// 8. Pydantic-AI
Repo: https://github.com/pydantic/pydantic-ai
Stars: ~14k+ on GitHub
Features: Pydantic-AI is an agentic framework that helps build production-grade generative AI (GenAI) applications. It combines Pydantic types with generative model patterns to ensure outputs are validated and consistent.
// 9. Pyrefly
Repo: https://github.com/facebook/pyrefly
Stars: ~5k+ on GitHub
Features: Pyrefly is a Python static analysis and type checking tool. It integrates with Pydantic and provides modern, fast, and accurate type checking for large projects.
// 10. Morphik-Core
Repo: https://github.com/morphik-org/morphik-core
Stars: ~3.5k+ on GitHub
Features: Morphik is an AI toolset for working with visually rich and multimodal documents. It lets developers store, search, and analyze PDFs, images, videos, and more, with Python software development kit (SDK) and web console support.
// 11. ChainForge
Repo: https://github.com/ianarawjo/ChainForge
Stars: ~2.9k+ on GitHub
Features: ChainForge is a visual toolkit for prompt engineering and hypothesis testing with LLMs. It helps compare strategies and explore model behavior.
// 12. MostlyAI
Repo: https://github.com/mostly-ai/mostlyai
Stars: ~700+ on GitHub
Features: MostlyAI generates realistic synthetic data for testing and machine learning. It preserves statistical properties of real data while keeping it private.
Kanwal Mehreen is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She’s also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.
