In-depth Comparision between most popular AI frameworks (Fixed Pipeline vs. Multi-Agent) скачать в хорошем качестве

In-depth Comparision between most popular AI frameworks (Fixed Pipeline vs. Multi-Agent)

Confused about whether to choose LangChain or Autogen for your next AI application? You're not alone! These two frameworks are the most popular in the LLM development space, but they have fundamental differences that directly impact your project's architecture. Diagram Available at - GitHub - https://github.com/kamalkrbh/langchai... Link to Series on RAG -    • How To Implement Efficient RAG   In this deep-dive comparison, we break down the core concepts of LangChain's fixed pipeline and its agentic capabilities against Autogen's dynamic, multi-agent conversational framework. Learn which architecture is best suited for your specific use case, from simple Retrieval-Augmented Generation (RAG) to complex, autonomous multi-agent systems. Don't start coding until you've seen this comparison! Chapters 00:00:00 - Introduction: LangChain vs. Autogen Confusion 00:00:41 - Core Difference: Single Agent vs. Multi-Agent 00:01:41 - Deep Dive into LangChain's Retrieval Chain Pipeline 00:10:36 - Understanding LangChain Agents (Executor & React Agent) 00:29:07 - What is Autogen? Multi-Agent Orchestration Explained 00:31:39 - Autogen: The Group Chat Manager (Coordinator) 00:36:46 - Autogen's Team: Assistant Agent (Developer) & User Proxy (Tester) 00:44:52 - Autogen Key Takeaways & When to Use Which Framework #LangChain #Autogen #LLM #AIFrameworks #MultiAgent #AgenticAI #LangChainVsAutogen #AIGeneration #KamalBhatt #RAG #OpenSourceAI #langchainvsautogen