Python is growing every year. New libraries emerge regularly, streamlining the coding workflow. In 2026, many have already caught our attention by 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 the 12 Python libraries that made waves in 2025, and every developer should try in 2026.
// 1. Mark it down
Repo: https://github.com/Microsoft/marketdown
Stars: ~86k+ on GitHub (rapid adoption in 2025)
features: MarkItDown converts documents like PDF, 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. Polar
Repo: https://github.com/pola-rs/polars
Stars: ~37k+ on GitHub
features: Pollers is a fast dataframe library written in War With Python support. It offers lazy and eager execution, multi-threading, and low memory usage. Pollers works with CSV, Parquet and JSON and is much faster than Panda For large datasets.
// 3. GPT Pilot (formerly Pythagora)
Repo: https://github.com/Pythagora-io/gpt-pilot
Stars: ~33.8k+ on GitHub
features: Pythagora uses AI to decipher code and generate documentation. GPT Pilot serves as the core technology for Pythagora VS Code ExtensionThe goal of which is to provide the first real AI developer companion who is able to write full features, debug code, discuss issues, and request reviews.
// 4. SmallAgent
Repo: https://github.com/huggingface/smolagents
Stars: ~25k+ on GitHub
features: SmallAgents is an AI agent framework hugging face. It helps you create intelligent agents that write code or call tools, supports multiple LLMs, and allows multi-step logic. It also integrates with sandboxed execution environments (Blaxel). postal worker, webassembly).
// 5. LangExtract
Repo: https://github.com/google/langextract
Stars: ~24k+ on GitHub
features: LangExtract extracts structured data from unstructured text using LLM. It can locate entities, apply schema, and visualize the results. This provider supports cloud models (like Gemini) and local models through plugins, and is optimized for handling long documents.
// 6. FastMCP
Repo: https://github.com/jlowin/fastmcp
Stars: ~22k+ on GitHub
features: FastMCP is a framework for creating Model Context Protocol (MCP) servers and clients. This simplifies connecting clients and servers and managing data changes. These integration patterns make it superior to 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 uses AI agents for data exploration through rich visualizations. It allows you to transform intent and data into charts through an interactive workflow.
// 8. Pydentic-AI
Repo: https://github.com/pydantic/pydantic-ai
Stars: ~14k+ on GitHub
features: Pydentic-AI is an agentic framework that helps create production-grade Generative AI (GenAI) applications. it combines pidentic Types with generative model patterns to ensure that outputs are valid and consistent.
// 9. Firefly
Repo: https://github.com/facebook/pyrefly
Stars: ~5k+ on GitHub
features: Pyrefly is a Python static analysis and type checking tool. It integrates with Pydentic and provides modern, fast, and accurate type checking for large projects.
// 10. Morphic-Core
Repo: https://github.com/morfik-org/morfik-core
Stars: ~3.5k+ on GitHub
features: Morphic 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 a 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 rapid engineering and hypothesis testing with LLM. It helps to compare strategies and explore model behavior.
// 12. MostAI
Repo: https://github.com/mostly-ai/mostlyai
Stars: ~700+ on GitHub
features: Generates realistic synthetic data mostly for AI testing and machine learning. This preserves the statistical properties of the actual data while keeping it private.
Kanwal Mehreen He is a machine learning engineer and a technical writer with a deep passion for the intersection of AI with data science and medicine. He co-authored the eBook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she is an advocate for diversity and academic excellence. She has also been recognized as a Teradata Diversity in Tech Scholar, a Mitex GlobalLink Research Scholar, and a Harvard VCode Scholar. Kanwal is a strong advocate for change, having founded FEMCodes to empower women in STEM fields.
