🤖 Python libraries for AI agents — what to study

If you want to develop AI agents in Python, it's important to understand the order of studying libraries.

Start with LangChain, CrewAI or SmolAgents — they allow you to quickly assemble simple agents, connect tools, and test ideas.

The next level is LangGraph, LlamaIndex and Semantic Kernel. These tools are already used for production systems: RAG, orchestration, and complex workflows.

The most complex level is AutoGen, DSPy and A2A. They are needed for autonomous multi-agent systems and optimizing LLM pipelines.

LangChain — simple agents, tools, and memory 
github.com/langchain-ai/langchain

CrewAI — multi-agent systems with roles 
github.com/joaomdmoura/crewAI

SmolAgents — lightweight agents for quick experiments 
github.com/huggingface/smolagents

LangGraph — orchestration and stateful workflow 
github.com/langchain-ai/langgraph

LlamaIndex — RAG and knowledge-agents 
github.com/run-llama/llama_index

Semantic Kernel — AI workflow and plugins 
github.com/microsoft/semantic-kernel

AutoGen — autonomous multi-agent systems 
github.com/microsoft/autogen

DSPy — optimizing LLM pipelines 
github.com/stanfordnlp/dspy

A2A — protocol for interaction between agents 
github.com/a2aproject/A2A

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