LangChain- Develop AI Agents with LangChain & LangGraph (7 2025)

[0dayddl]

8.24 GB | 20min 2s | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

1 Course Introduction.mp4 (6.07 MB)
2 Course Objectives.mp4 (8.7 MB)
3 Course Structure + How to get the best of Udemy PLEASE DO NOT SKIP.mp4 (49.12 MB)
4 Course's Community.mp4 (26.72 MB)
1 What is LangChain LangChain Under 6 Minutes.mp4 (54.73 MB)
2 Project Setup (Pycharm) recommend).mp4 (104.86 MB)
3 Project Setup (vscode) - optional.mp4 (140.16 MB)
4 Environment Variables and env File.mp4 (15.14 MB)
5 Your First LangChain application - Chaining a simple prompt.mp4 (106.84 MB)
6 Using Open Source Models With LangChain (Ollama, Llama3, Mistral).mp4 (100.08 MB)
8 LangChain Version In Course (V0 3 3) - (No breaking changes in 0 3 3).mp4 (15.78 MB)
9 Which LLM to Use (OpenAI, Gemini, Anthropic, Mistral, Llama).mp4 (18.75 MB)
1 Ice Breaker- What are we building here.mp4 (34.53 MB)
10 FullsStack App- Building our LLM powered by LangChain FullStack Application.mp4 (77.79 MB)
11 Tracing application with LangSmith.mp4 (101.59 MB)
12 Real World Ice breaker Agents.mp4 (24.13 MB)
2 Integrating Linkedin Data Processing - Part 1.mp4 (278.71 MB)
3 Linkedin Data Processing - Part 2 - Agents Theory.mp4 (13.85 MB)
4 Linkedin Data Processing- Part 3 Tools, Agent Executor, create react agent.mp4 (34.75 MB)
5 Linkedin Data Processing- Part 4 Custom Search Agent Implementation.mp4 (119.94 MB)
6 Linkedin Data Processing- Part 5 Custom Search Agent Testing.mp4 (36.5 MB)
7 Optional Twitter Data Processing- Part 1- Scraping.mp4 (83.1 MB)
8 Optional Twitter Data Processing- Part 2- Agents.mp4 (93.92 MB)
9 Output Parsers- Getting Ready to work with a Frontend.mp4 (123.03 MB)
1 What are we building ReAct AgentExecutor from scratch.mp4 (32.55 MB)
2 Environment Setup + ReAct Algorithm overview.mp4 (40.9 MB)
2 react-langchain-final-0.zip (40.92 KB)
3 Defining Tools for our ReAct agent.mp4 (81.36 MB)
3 react-langchain-1.zip (41.47 KB)
4 Debugging LangChain Resolving LLM stop Token & Template Indentation Issues.mp4 (16.7 MB)
5 ReAct prompt, LLM Reasoning Engine, Output Parsing and Tool Execution.mp4 (140.54 MB)
5 react-langchain-2.zip (48.62 KB)
6 AgentAction, AgentFinish, ReAct Loop.mp4 (54.18 MB)
6 react-langchain-3.zip (48.69 KB)
7 CallbackHandlers, ReAct Prompt and finalizing the ReAct Agent loop.mp4 (90.47 MB)
7 react-langchain-final.zip (49.07 KB)
8 Recap with LangSmith.mp4 (18.93 MB)
1 Medium Analyzer- Boilerplate Project Setup.mp4 (91.42 MB)
1 rag-gist-1-setup.zip (50.08 KB)
2 Medium Analyzer- Class Review TextLoader,TextSplitter,OpenAIEmbeddings,Pinecone.mp4 (124.98 MB)
2 rag-gist-2-imports.zip (50.2 KB)
3 Medium Analyzer- Ingestion Implementation.mp4 (65.84 MB)
3 rag-gist-3-ingestion-implementation.zip (51.07 KB)
4 Medium Analyzer- Retrieval Implementation Implementation with chains.mp4 (87.63 MB)
4 rag-gist-4-retrieval-implementation-chains.zip (51.35 KB)
5 Medium Analyzer- Retrieval Implementation Implementation with LCEL.mp4 (26.14 MB)
6 Chat With Your PDF- FAISS Local Vectorstore.mp4 (68.43 MB)
1 What are we building (RAG).mp4 (73.36 MB)
10 What is Cursor.mp4 (37.06 MB)
11 Adding Sidebar and LangChain's color theme with Cursor Composer.mp4 (111.06 MB)
12 Documentation Helper In Production.mp4 (74.99 MB)
2 Environment Setup.mp4 (122.73 MB)
2 langchain-docs.zip (2.98 MB)
3 OPTIONAL Manually Scraping the LangChain Documentation.mp4 (36.41 MB)
4 Pinecone Vectorstore Ingestion.mp4 (121.5 MB)
5 Retrieval + Augmentation + Generation = RAG.mp4 (127.3 MB)
6 Building an AI LangChain Chat Assistant- Frontend with Streamlit (UI).mp4 (194.41 MB)
7 Building an AI LangChain Chat Assistant- Memory.mp4 (118.22 MB)
8 Optional RAG Pipeline Optimization featuring FireCrawl.mp4 (190.7 MB)
9 Leveraging Cursor IDE for UI Improvements.mp4 (18.06 MB)
1 What are we building (A slim Version of GPT Code-Interpreter).mp4 (90.43 MB)
2 Project Setup.mp4 (21.48 MB)
3 code-interpreter-1-python-agent.zip (44.71 KB)
3 Python Agent.mp4 (32.36 MB)
4 code-interpreter-2-csv-agent.zip (51.68 KB)
4 CSV Agent.mp4 (89.84 MB)
5 code-interpreter-3-router-agent-start-here.zip (51.83 KB)
5 code-interpreter-3-router-agent.zip (52.18 KB)
5 Wrapping Everything Router Agent.mp4 (48.85 MB)
6 Function Tool Calling in LangChain.mp4 (90.16 MB)
7 Function Calling Vs ReAct.mp4 (77.72 MB)
1 The GIST of LLMs.mp4 (23.67 MB)
2 What is a Prompt Composition of a formal prompt.mp4 (9.1 MB)
3 Zero Shot Prompting.mp4 (18.98 MB)
4 Few Shot Prompting.mp4 (66.33 MB)
5 Chain of Thought Prompting.mp4 (91.86 MB)
6 ReAct Prompting.mp4 (87.36 MB)
7 Prompt Engineering Quick Tips.mp4 (89.45 MB)
1 Have a technical issue WATCH THIS FIRST I Promise this will help!.mp4 (48.25 MB)
2 Tweet API- tweepy errors Forbidden 403 Forbidden.mp4 (29.34 MB)
3 Pinecone AttributeError init is no longer a top-level attribute of pinecone.mp4 (11.54 MB)
4 LangChain Version In Course (V0 3 3).mp4 (74.39 MB)
1 LLM Applications in Production.mp4 (60.6 MB)
2 LLM Application Development landscape.mp4 (21.05 MB)
3 LLMs in Production Privacy & Data Retention.mp4 (96.53 MB)
4 Generative UIUX featuring CopilotKit.mp4 (98.8 MB)
5 Finished course Whats next!.mp4 (161.34 MB)
6 Official LangChain Academy Courses.mp4 (24.46 MB)
7 Open Source LLMs VS Managed LLM Providers (Deepseek).mp4 (96.21 MB)
8 Confidence in AI Results By Assaf Elovic & Harrison Chase.mp4 (33.16 MB)
1 What is LangGraph.mp4 (12.13 MB)
10 Hands On Bringing Your ReAct Agent to Life Connecting Nodes into a Graph.mp4 (32.51 MB)
11 Hands On Running Our LangGraph React Agent with Function Calling.mp4 (86.05 MB)
2 Why LangGraph LangGraph VS LangChain.mp4 (57.72 MB)
3 What are Graphs.mp4 (20.08 MB)
4 LangGraph & Flow Engineering.mp4 (18.73 MB)
5 LangGraph Core Components.mp4 (46.78 MB)
6 --------- Hands On Implementing ReAct AgentExecutor with LangGraph ---------.mp4 (18.03 MB)
7 Hands On Get Started Setting Up Your ReAct Agent Project Environment.mp4 (28.83 MB)
8 Hands On Coding the Agent's Brain Implementing the ReAct Runnable.mp4 (61.45 MB)
9 Hands On 43 Building Blocks Defining Your Agent's Nodes in LangGraph.mp4 (30.81 MB)
1 What are we building.mp4 (4.4 MB)
2 Project Setup.mp4 (17.83 MB)
3 Creating the Reflector Chain and the Tweet Reviosr Chain.mp4 (23.62 MB)
4 Defining our LangGraph Graph.mp4 (164.72 MB)
5 LangSmith Tracing.mp4 (32.75 MB)
1 What are we building.mp4 (158.55 MB)
2 Project Setup.mp4 (21.43 MB)
4 Actor Agent.mp4 (155.82 MB)
5 Revisor Agent.mp4 (31.93 MB)
6 ToolNode - Executing Tools.mp4 (78 MB)
7 Building our LangGraph Graph.mp4 (72.17 MB)
1 What are Building In this Section- Agentic RAG Architecture.mp4 (30.22 MB)
10 Creating the LLM Generation Chain and Node for LangGraph.mp4 (81.37 MB)
11 Building and Running the Complete LangGraph Agent.mp4 (39.1 MB)
12 Self RAG- Intro.mp4 (17.43 MB)
13 Self RAG- Implementation.mp4 (145.06 MB)
14 Adaptive RAG.mp4 (138.85 MB)
2 Improving RAG Quality with the Corrective RAG Flow.mp4 (16.39 MB)
3 Boilerplate Setup for an Agentic RAG Agent with LangGraph.mp4 (42.4 MB)
4 Code Structure.mp4 (38.71 MB)
5 LangChain Vector Store Ingestion Pipeline (WebLoader, ChromaDB).mp4 (77.18 MB)
6 Managing Information Flow in LangGraph The GraphState.mp4 (9.1 MB)
7 Fetching Context for LLMs The LangGraph Retrieve Node.mp4 (19.81 MB)
8 Building a Relevance Filter for RAG using LangChain's Structured Output.mp4 (132.37 MB)
9 Implementing a Web Search Node in LangGraph using Tavily API.mp4 (29.32 MB)
1 Why MCP (Model Context Protocol).mp4 (53.12 MB)
2 How LLMs REALLY Use Tools Understanding Tool Calling.mp4 (53.65 MB)
3 LLM txt.mp4 (53.91 MB)
4 MCP Inspector.mp4 (32.31 MB)
5 mcpdoc.mp4 (103.83 MB)
6 Bridging the Gap The LangChain MCP Adapter Explained.mp4 (53.72 MB)
7 What are we MCBuilding.mp4 (8.94 MB)
8 Simple SSE MCP Server.mp4 (53.49 MB)
9 LangChain's MultiServerMCPClient from the LangChain MCP Adapter.mp4 (54.12 MB)
1 LangChain Hub - Downloads prompt from the community.mp4 (18.75 MB)
2 TextSplitting Playground.mp4 (37.22 MB)
3 LangChain VS LlamaIndex.mp4 (50.17 MB)
1 ChatModels.mp4 (82.21 MB)
2 Messages.mp4 (19.09 MB)
3 RecursiveCharacterTextSplitter.mp4 (13.15 MB)
4 Document.mp4 (9.48 MB)
5 LangChain Token Limitation Handeling Strategies.mp4 (168.78 MB)
6 LangChain Memory Intro- Co Reference Resolution.mp4 (25.29 MB)
7 LangChain Memory Theory Deepdive (LangGraph).mp4 (53.03 MB)
1 Bonus.mp4 (67.63 MB)

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[0dayddl]

LangChain- Develop AI Agents with LangChain & LangGraph
2025-08-03
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 8.79 GB | Duration: 15h 33m​

Learn LangChain and LangGraph by building real world AI Agents (Python, Latest Version 0.3.0+)

What you'll learn
Become proficient in LangChain
Have 3 end to end working LangChain based generative AI applications
Prompt Engineering Theory: Chain of Thought, ReAct, Few Shot prompting and understand how LangChain is build under the hood
Understand how to navigate inside the LangChain opensource codebase
Large Language Models theory for software engineers
LangChain: Lots of chains Chains, Agents, DocumentLoader, TextSplitter, OutputParser, Memory
RAG, Vectorestores/ Vector Databasrs (Pinecone, FAISS)
Model Context Protocol
LangGraph

Requirements
This is not a beginner course. Basic software engineering concepts are needed
I assume students will be familiar software engineering subjects such as: git, python, pipenv, environment variables, classes, testing and debugging
No Machine Learning experience is needed.

Description
COURSE WAS RE-RECORDED and supports- LangChain Version 0.3+**Ideal students are software developers / data scientists / AI/ML Engineers**Welcome to the AI Agents with LangChain and LangGraph Udemy course - Unleashing the Power of LLM!This course is designed to teach you how to QUICKLY harness the power the LangChain library for LLM applications. This course will equip you with the skills and knowledge necessary to develop cutting-edge LLM solutions for a diverse range of topics.Please note that this is not a course for beginners. This course 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 .What You'll Build: No fluff. No toy examples. You'll build:Ice Breaker Agent - An AI agent that searches Google, finds LinkedIn and Twitter profiles, scrapes public info, and generates personalized icebreakers.Documentation Helper - A chatbot over Python package docs (and any data you choose), using advanced retrieval and RAG.Slim ChatGPT Code Interpreter - A lightweight code execution assistant.Prompt Engineering Theory SectionIntroduction to LangGraph Introduction to Model Context Protocol (MCP)The topics covered in this course include:AI AgentsLangChain, LangGraphLLM + GenAI HistoryLLMs: Few shots prompting, Chain of Thought, ReAct promptingChat ModelsOpen Source ModelsPrompts, PromptTemplates, langchainubOutput Parsers, Pydantic Output ParsersChains: create_retrieval_chain, create_stuff_documents_chainAgents, Custom Agents, Python Agents, CSV Agents, Agent RoutersOpenAI Functions, Tool CallingTools, ToolkitsMemoryVectorstores (Pinecone, FAISS, Chroma)RAG (Retrieval Augmentation Generation)DocumentLoaders, TextSplittersStreamlit (for UI), CopilotkitLCELLangSmithLangGraphFireCrawlGIST of Cursor IDE Cursor ComposterCurser ChatMCP - Model Context Protocol & LangChain Ecosystem Introduction To LangGraphContext EngineeringThroughout the course, you will work on hands-on exercises and real-world projects to reinforce your understanding of the concepts and techniques covered. By the end of the course, you will be proficient in using LangChain to create powerful, efficient, and versatile LLM applications for a wide array of usages.Why This Course?Up-to-date: Covers LangChain v0.3+ and the latest LangGraph ecosystem.Practical: Real projects, real APIs, real-world skills.Career-boosting: Stay ahead in the LLM and GenAI job market.Step-by-step guidance: Clear, concise, no wasted time.Flexible: Use any Python IDE (Pycharm shown, but not required).DISCLAIMERSPlease note that this is not a course for beginners. This course 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.The Ice-Breaker project requires usage of 3rd party APIs-Scrapin, Tavily, Twitter API which are generally paid services.All of those 3rd parties have a free tier we will use to create stub responses development and testing.

Who this course is for:
Software Engineers that want to learn how to build Generative AI based applications with LangChain and LangGraph, Developers that want to learn how to build Generative AI based applications with LangChain and LangGraph, Engineers that want to learn how to build Generative AI based applications with LangChain and LangGraph

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