Develop LLM Powered Applications with LangChain and LangGraph


Welcome to an engaging and intensive hands-on workshop designed to unleash the power of LLM agents using the LangGraph library by LangChain. This two-hour session is crafted for AI practitioners who already possess a background in Python and familiarity with LangChain, aiming to elevate their skills in developing cutting-edge LLM agentic applications.
In this workshop, we will dive deep into the advanced capabilities of LangGraph, exploring its integration with LangChain to create robust, efficient, and versatile LLM solutions. Our agenda includes a comprehensive introduction to key components such as LCEL, multi-agents, reflection agents, Reflexion agents, and more. Participants will also get hands-on experience with advanced RAG architectures.

Key Learning Outcomes:

Introduction to LangGraph: Understand the fundamental concepts and applications of LangGraph in the context of LLM agents.
Advanced Agent Techniques: Implement multi-agents, reflection agents, and Reflexion agents to enhance the functionality and efficiency of LLM applications.

RAG Methodologies: Explore and apply advanced RAG techniques such as Corrective RAG, Self RAG, and Adaptive RAG for improved retrieval and generation processes.

Practical Application: Work on hands-on exercises and real-world projects to reinforce your understanding and skills.

Target Audience:
This workshop is not for beginners. It is tailored for professionals with a solid foundation in software engineering and proficiency in Python. Attendees should be comfortable using IDEs such as PyCharm or any preferred editor for debugging and running scripts.
By the end of this workshop, participants will be equipped with the expertise to leverage LangGraph by LangChain for developing sophisticated LLM agents, ready to tackle a diverse range of applications in production environments.

Session Outline:





Multi Agents

Reflection Agents

Reflexion Agents


CrewAI VS LangGraph

Advanced RAG

Corrective RAG

Self RAg

Adaptive RAG

Background Knowledge:

Please note that this is not a workshop for beginners. This workshop assumes that you have a background in software engineering and are proficient in Python. I will be using Pycharm IDE but you can use any editor you'd like since we only use basic feature of the IDE like debugging and running scripts.


Eden is a seasoned backend software engineer with deep expertise in generative AI, cloud, and cybersecurity. With years of experience in backend development, he currently works at Google as an LLM Specialist, assisting customers in implementing complex generative AI solutions on GCP using open-source frameworks like LangChain and Google's generative AI services. Additionally, he is an educator, instructor and creator of best-selling Udemy courses on LangChain, LlamaIndex, LangGraph, and pytest. As an educator at heart, he is passionate about sharing knowledge and helping others learn.

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