Photo by editorThe python grows every year. New libraries emerge regularly, streamlining the coding workflow. In 2026, many have already captured 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 every developer should try in 2026.
// 1. MarkItDown
Repo: https://github.com/microsoft/markitdown
Stars: ~86k+ on GitHub (fast adoption in 2025)
Features: MarkItDown converts documents such as PDFs, Word, Excel, and PowerPoint to Markdown. It preserves structure such as headings, tables and lists and is designed for large language model (LLM) workflows.
// 2. Poles
Repo: https://github.com/pola-rs/polars
Stars: ~37k+ on GitHub
Features: Pollers is a fast dataframe library written in rust With Python support. It offers slow and eager execution, multi-threading, and low memory usage. Pollers works with CSV, Parquet, and JSON and is much faster. Panda. For large data sets.
// 3. GPT Pilot (formerly Pythagoras)
Repo: https://github.com/Pythagora-io/gpt-pilot
Stars: ~33.8k+ on GitHub
Features: Pythagora uses AI to define code and generate documentation. GPT Pilot serves as the underlying technology for this. Pythagora VS code extensionwhich aims to provide the first true AI developer companion capable of writing complete features, debugging code, discussing issues and requesting reviews.
// 4. Smolagants
Repo: https://github.com/huggingface/smolagents
Stars: ~25k+ on GitHub
Features: is an AI agent framework from Smolagents. A huggable face. It helps you build intelligent agents that write code or call tools, supports multiple LLMs, and allows multistep reasoning. It also integrates with sandboxed execution environments (Blexail, Docker, Web assembly).
// 5. Lang extract
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 view results. It supports cloud models (e.g. Gemini) and local models via provider plugins, and is optimized for handling long documents.
// 6. Fast MCP
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 changes. These integration patterns make it better than the raw MCP implementation.
// 7. Data Formulator
Repo: https://github.com/microsoft/data-formulator
Stars: ~15k+ on GitHub
Features: Data Formulator is a Microsoft research project that uses AI agents to explore data through rich visualization. It allows you to transform intent and data into charts through an interactive workflow.
// 8. Pedantic-AI
Repo: https://github.com/pydantic/pydantic-ai
Stars: ~14k+ on GitHub
Features: Pydantic-AI is an agent framework that helps build production-grade generative AI (GenAI) applications. It combines. Pedantic Types with production model samples to ensure that the output is correct and consistent.
// 9. Pyrifly
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 advanced, fast and accurate type checking for large projects.
// 10. The morphic core
Repo: https://github.com/morphik-org/morphik-core
Stars: ~3.5k+ on GitHub
Features: Morphic is an AI toolset that works 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. Chain Forge
Repo: https://github.com/ianarawjo/ChainForge
Stars: ~2.9k+ on GitHub
Features: ChainForge is a visual toolkit for rapid engineering and hypothesis testing with LLMs. It helps to compare strategies and explore model behavior.
// 12. Most AI
Repo: https://github.com/mostly-ai/mostlyai
Stars: ~700+ on GitHub
Features: Most generate realistic synthetic data for AI testing and machine learning. It preserves the statistical properties of real data while keeping it private.
Kanwal Mehreen is a machine learning engineer and a technical writer with a deep passion for AI along with data science and medicine. He co-authored the e-book “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she is a champion of diversity and academic excellence. She has also been recognized as a Teradata Diversity in Tech Scholar, a Mitacs Globalink Research Scholar, and a Harvard WeCode Scholar. Kanwal is a passionate advocate for change, having founded FEMCodes to empower women in STEM fields.