Python is developing every year. Recent libraries are released regularly to streamline your coding workflow. In 2026, several solutions offering tools for data handling, AI agents, code analysis, documentation and synthetic data have already caught our attention. Most of them are open source and available.
# 12 Python Libraries for 2026
Here are 12 Python libraries that made waves in 2025 and that every developer should try in 2026.
// 1. Mark it down
Repository: https://github.com/microsoft/markitdown
Stars: ~86k+ on GitHub (speedy adoption in 2025)
Characteristics: MarkItDown converts documents such as PDF, Word, Excel and PowerPoint files to Markdown. It preserves structure such as headings, tables, and lists, and is intended for gigantic language model (LLM) workflows.
// 2. Poles
Repository: https://github.com/pola-rs/polars
Stars: ~37k+ on GitHub
Characteristics: Polars is a speedy DataFrame library written Rust with Python support. It offers idle and speedy execution, multi-threading and low memory consumption. Polars works with CSV, Parquet and JSON files and is much faster than Pandas for large data.
// 3. GPT Pilot (formerly Pitagora)
Repository: https://github.com/Pythagora-io/gpt-pilot
Stars: ~33.8k+ on GitHub
Characteristics: Pythagora uses artificial intelligence to explain code and generate documentation. GPT Pilot serves as the core technology for Pythagoras VS Code extensionwhich aims to provide the first true AI developer companion capable of writing full functions, debugging code, discussing issues and asking for reviews.
// 4. Smolagents
Repository: https://github.com/huggingface/smolagents
Stars: ~25k+ on GitHub
Characteristics: Smolagents is the company’s AI agent platform Face Hugging. It helps create smart agents that write code or invoke tools, supports multiple LLMs, and enables multi-step reasoning. It also integrates with sandboxed runtime environments (Blaxel, Docker, Website Team).
// 5. Lang extract
Repository: https://github.com/google/langextract
Stars: ~24k+ on GitHub
Characteristics: LangExtract extracts structured data from unstructured text using LLM. It can detect entities, apply patterns, and visualize results. It supports cloud models (e.g. Gemini) and on-premises models via vendor plugins and is optimized for long document handling.
// 6.FastMCP
Repository: https://github.com/jlowin/fastmcp
Stars: ~22k+ on GitHub
Characteristics: 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
Repository: https://github.com/microsoft/data-formulator
Stars: ~15k+ on GitHub
Characteristics: Data Formulator is a Microsoft research project that uses AI agents to explore data with prosperous visualizations. It enables you to transform intent and data into charts through an interactive workflow.
// 8. Pydantic-AI
Repository: https://github.com/pydantic/pydantic-ai
Stars: ~14k+ on GitHub
Characteristics: Pydantic-AI is an agent platform that helps you build production-grade generative artificial intelligence (GenAI) applications. It connects Pydantic types with generative model patterns to ensure validation and consistency of results.
// 9. Pyrefly
Repository: https://github.com/facebook/pyrefly
Stars: ~5k+ on GitHub
Characteristics: Pyrefly is a Python stagnant analysis and type checking tool. Integrates with Pydantic and provides state-of-the-art, speedy and right type checking for gigantic projects.
// 10. Morphik Core
Repository: https://github.com/morphik-org/morphik-core
Stars: ~3.5k+ on GitHub
Characteristics: Morphik is a set of AI tools for working with visually prosperous and multimodal documents. It enables developers to store, search, and analyze PDF files, images, videos, and more with a Python software development kit (SDK) and web console support.
// 11. Chain Forge
Repository: https://github.com/ianarawjo/ChainForge
Stars: ~2.9k+ on GitHub
Characteristics: ChainForge is a visual toolkit for quickly designing and testing hypotheses with LLM. It helps you compare strategies and examine model behavior.
// 12. MainlyAI
Repository: https://github.com/mostly-ai/mostlyai
Stars: ~700+ on GitHub
Characteristics: MostlyAI generates realistic, synthetic data for testing and machine learning. It preserves the statistical properties of real data while maintaining its privacy.
Kanwal Mehreen is a machine learning engineer and technical writer with a deep passion for data science and the intersection of artificial intelligence and medicine. She is co-author of the e-book “Maximizing Productivity with ChatGPT”. As a 2022 Google Generation Scholar for APAC, she promotes diversity and academic excellence. She is also recognized as a Teradata Diversity in Tech Scholar, a Mitacs Globalink Research Scholar, and a Harvard WeCode Scholar. Kanwal is a staunch advocate for change and founded FEMCodes to empower women in STEM fields.