Top 10 Python Libraries for Generative AI You Need to Master in 2026

The tools behind document agents, intelligent assistants, and next-gen interfaces. Everything you need to know to build with LLMs, RAG, and production AI apps in 2026.

The 10 libraries

• 1. LangChain

  The backbone of intelligent LLM apps. Build agents that reason, use tools, remember conversations, and access APIs. If you're building anything with GPTs, LangChain is your starting point.

  www.langchain.com →

• 2. LangGraph

  LangChain + DAGs = LangGraph. It powers multi-agent workflows, conditional logic, and real-time state management. If you're serious about production AI agents, this is a must.

  www.langgraph.dev →

• 3. Docling

  Document intelligence built on LangChain. Parse, summarize, and extract structured data from PDFs, contracts, and reports. Perfect for legal, finance, and enterprise GenAI.

  docling-project.github.io/docling →

• 4. OpenAI Python SDK

  Your direct line to GPT-4o, DALL·E, Whisper, and embeddings. One SDK, endless capabilities.

  platform.openai.com →

• 5. Markitdown (by Microsoft)

  Python tool for converting files and office documents to Markdown: PDF, PowerPoint, Word, Excel, images (EXIF metadata and OCR), audio (EXIF metadata and speech transcription), HTML.

  github.com/microsoft/markitdown →

• 6. Streamlit

  Build beautiful, shareable GenAI dashboards in minutes: upload a doc, ask questions, view plots & summaries. No frontend experience needed.

  streamlit.io →

• 7. FastAPI

  Serve your models with blazing speed. Used for GenAI microservices, LLM backends, and agent APIs. It's the modern web standard for ML apps.

  fastapi.tiangolo.com →

• 8. Faiss

  FAISS = Fast Approximate Nearest Neighbor Search. Turn embeddings into semantic search, RAG systems, and instant retrieval. Facebook built it. Everyone uses it.

  github.com/facebookresearch/faiss →

• 9. SentenceTransformers

  Generate embeddings for sentences, paragraphs, and documents. Critical for clustering, similarity search, and retrieval.

  www.sbert.net →

• 10. MLflow

  Track experiments. Compare models. Deploy GenAI apps. You'll thank yourself later when you need to explain why one prompt worked better than another.

  mlflow.org →

Master these Python libraries and you’ll have the foundation for document agents, intelligent assistants, and modern GenAI interfaces. If you’d like help building AI workflows or agents for your business, get in touch.