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10 AI Frameworks and Libraries Every Developer Should Learn in 2025
My favorite Udemy courses to learn how to build RAG applications using Python and LangChain in 2025
Hello guys, Artificial Intelligence is evolving at breakneck speed. Everyday a new LLM is getting launched. Also From AI agents building apps to language models powering enterprise-grade assistants, the tools and frameworks behind the scenes are more powerful — and more accessible — than ever.
But with so many new technologies emerging, how do you know which ones to learn?
If you’re an aspiring AI engineer or a software developer diving into the AI world in 2025, here’s your roadmap: 10 essential AI frameworks and libraries to focus on.
For each, I’ll also recommend a high-quality course or book to get you started — so you don’t waste time googling the basics.
By the way, if you are new to AI world then I highly recommend you to start with The AI Engineer Course 2025: Complete AI Engineer Bootcamp, one of the most comprehensive resource to become an AI Engineer in 2025.
10 Best AI Frameworks and Libraries to Learn in 2025
Without any further ado, here are the top, in-demand AI and LLM frameworks and tools AI Engineer should learn in 2025.
1. LangChain
LangChain is the backbone of most LLM-powered apps. It helps developers chain together components like models, prompts, memory, tools, and agents to build context-aware applications.
Use Cases: Chatbots, RAG systems, custom agents, enterprise apps.
Learn It From:
LangChain for LLM Application Development on Coursera
A practical course that walks through chaining, memory, and tool usage.
2. LangGraph
LangGraph extends LangChain to build stateful, multi-step workflows for LLM agents. It’s ideal for developers who want more control over how agents behave across time.
Use Cases: Autonomous agents, recursive workflows, decision trees with memory.
Learn It From:
🎓 Master LLM Engineering & AI Agents: Build 14 Projects — 2025 (Udemy)
Includes hands-on LangGraph projects with CrewAI and HuggingFace.
3. Pinecone
Pinecone is a fully managed vector database service designed for similarity search. If you’re working with embeddings or building RAG systems, this is your go-to tool.
Use Cases: Semantic search, RAG, real-time recommendations.
Learn It From:
📘 Vector Databases: From Embeddings to Pinecone (Educative)
A deep-dive into vector stores, retrieval techniques, and Pinecone integration.
4. Pydantic (AI)
Pydantic AI is critical for data validation in Python applications. With Pydantic v2 and its growing AI ecosystem, it’s more efficient and production-ready than ever.
Use Cases: Data parsing, model serving, prompt schema validation.
Learn It From:
📘 FastAPI and Pydantic in Production (Educative)
Hands-on guidance for deploying AI APIs with strong typing and validation.
5. CrewAI
CrewAI helps orchestrate multi-agent systems, allowing agents to collaborate toward shared goals. It’s ideal for automating complex, structured tasks.
Use Cases: Research agents, collaborative AI assistants, AI orchestration.
Learn It From:
🎓 The Complete Agentic AI Engineering Course (2025) — Udemy
Builds 8 projects using CrewAI, LangGraph, and OpenAI Agents SDK.
6. LoRA (Low-Rank Adaptation)
LoRA is a technique to fine-tune LLMs efficiently with minimal compute. It’s essential if you want to personalize models without training from scratch.
Use Cases: Custom model tuning, lightweight personalization.
Learn It From:
🎓 LLM Engineering: Master AI, Large Language Models & Agents (Udemy)
Includes LoRA-based fine-tuning and deployment examples.
7. Transformers (HuggingFace)
This library powers everything from BERT to GPT to Vision Transformers. HuggingFace makes model loading, training, and inference seamless.
Use Cases: NLP, vision, model training, deployment.
Learn It From:
📕LLM Engineering Handbook by Paul Iustzin
An excellent reference for understanding transformer models in practice.
8. OpenAI Agents SDK
OpenAI’s new Agent SDK enables developers to build AI agents using GPT-4 and integrate tools, functions, and actions. It’s the future of app-like LLMs.
Use Cases: Autonomous agents, action planning, integrations with APIs and tools.
Learn It From:
🎓 Master AI Agents in 30 Days: OpenAI SDK, CrewAI, LangGraph (Udemy)
Focused on real-world agent design using OpenAI’s tools.
9. N8N + LLM Integration
N8N is a no-code automation platform that integrates beautifully with LLMs. You can automate workflows and deploy agents without writing glue code.
Use Cases: AI-powered automation, business workflow tools, AI Agents.
Learn It From:
🎓AI Automation: Build LLM Apps & AI-Agents with n8n & APIs
Shows how to use n8n with OpenAI, DeepSeek, Ollama, and more.
10. Weaviate (Alternative to Pinecone)
Weaviate is an open-source vector database gaining popularity for use in semantic search and RAG pipelines. It offers hybrid filtering, GraphQL APIs, and scalability.
Use Cases: Document search, semantic knowledge bases, hybrid queries.
Learn It From:
🎓 Generative AI Architectures with LLM, Prompt, RAG, Vector DB (Udemy)
Covers end-to-end implementation of Weaviate with LangChain and OpenAI.
Final Thoughts
That’s all about the 10 best AI and LLM libraries and frameworks you can learn as AI Engineer. In 2025, knowing Python and having AI theory alone won’t cut it. Companies and startups are demanding hands-on engineers who can build, deploy, and scale AI apps using real-world tools.
By mastering these 10 frameworks and libraries, you position yourself to build scalable, production-grade AI systems.
Pick a couple from this list based on your project needs. Learn by building. And make sure your portfolio includes LLM-powered apps, vector search, multi-agent systems, and end-to-end pipelines — because that’s what the future of AI development looks like.
By the way, if you want to join multiple course on Udemy, its may be worth getting a Udemy Personal Plan, which will give instant access of more than 11,000 top quality Udemy courses for just $30 a month.
If you got a lot of time and want to save money, Udemy Personal Plan will be perfect for you.
Other AI, LLM, and Machine Learning resources you may like
- Top 5 Courses to Prepare for AIF-C01 Exam in 2025
- 16 System Design Resources for Software Engineers
- 10 Best Udemy Courses to learn Artificial Intelligence in 2025
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- How to Prepare for AWS Solution Architect Exam in 2025
- Top 5 Udemy courses to build AI Agents in 2025
- 7 Best Courses to learn AWS S3 and DynamoDB in 2025
- Top 5 Udemy Courses for AWS Cloud Practitioner Exam in 2025
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- 8 Udemy courses to learn Prompt Engineering and ChatGPT
- 5 Best Udemy Courses to learn Building AI Agents in 2025
- Top 5 Udemy Courses to learn Large Language Model in 2025
Thanks a lot for reading this article so far, if you like these best RAG application courses on Udemy then please share with your friends and colleagues. If you have any feedback or questions then please drop a note.
Although not in your list, I suggest watching for newer courses that focus tightly on Python and LangChain with Pinecone or Chroma.
This space moves fast, and keeping an eye on the latest offerings can give you an edge.
P. S. — If you want to learn from books and looking for best AI and LLM Books then I highly recommend you to read AI Engineering by Chip Huyen and The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne, both of them are great books and my personal favorites. They are also highly ecommend on Redditt and HN.