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Agentic Track
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CagriCatik/Agentic-Track
Agentic Track
CagriCatik/Agentic-Track
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01. Introduction
01. Introduction
Overview
Overview
Table of contents
Overview
Lessons
Lessons
01. Introduction
02. Objectives
03. Resources
02. The Essentials of LangChain - Hello World Chain
02. The Essentials of LangChain - Hello World Chain
Overview
Lessons
Lessons
01. What is LangChain?
02. What are we building? LangChain Hello World Chain
03. Project Setup
04. LangChain Fundamentals: Prompt Templates, ChatModels, and Chains
05. Building a LangChain Chain to Summarize Text
06. Debugging and Tracing Our LangChain Chain
07. Using Local Open-Weights Models with LangChain and Ollama
08. Integrating LangSmith for LangChain Application Tracing
09. LangChain LCEL Fundamentals Quiz
10. LangChain Model Switching: Groq API Integration
11. LangChain Version in Course V1
12. Which LLM to Use? (OpenAI, Gemini, Anthropic, Mistral, Llama)
03. The Essentials of AI Agents
03. The Essentials of AI Agents
Overview
Lessons
Lessons
01. What are AI Agents? A High-Level Overview
02. Creating an AI Job Search Agent: A Practical Demo
03. The Evolution of LangChain ReAct Agents
04. Setting Up the Environment for a LangChain Search Agent
05. Creating Your First LangChain Agent: Tools and LLMs
06. From Query to Answer: How a LangChain Agent Thinks
07. Integrating Real-World Search with Tavily and LangChain Tools
08. Structured Output with LangChain Agents Using Pydantic
09. Predictable Agent Responses with LangChain Structured Output
04. Agents Under the Hood
04. Agents Under the Hood
Overview
Lessons
Lessons
01. Introduction to the Core Architecture of AI Agents
02. Project Theme: The E-Commerce Agent
03. The Essentials of ReAct
04. Setup
05. Layer 1: The ReAct Loop
06. Tool Binding and Defensive Prompting
07. Understanding the ReAct Agent Loop in LangChain
08. Model Switch
09. Quiz: AI Agent Loop with LangChain Tool Calling
10. Layer 2: Manual JSON Schemas vs. LangChain Tool Abstraction
11. Building a ReAct Agent Loop with the Raw Ollama SDK
12. Recap
13. Quiz: Raw Function Calling - AI Agent Without LangChain
14. Layer 3: The ReAct Prompt
15. Generating Dynamic Tool Descriptions in Python
16. Understanding the ReAct Prompt
17. Implementing Manual Tool Calling for LLMs
18. Agent Loop With ReAct Prompt
05. Function Calling
05. Function Calling
Overview
Lessons
Lessons
01. Intro
02. Understanding Function Calling for LLMs
06. The Essentials of RAG - Embeddings, Vector Databases, Retrieval
06. The Essentials of RAG - Embeddings, Vector Databases, Retrieval
Overview
Lessons
Lessons
01. Introduction to Retrieval Augmentation Generation
02. Introduction to RAG Implementation
03. Medium Analyzer - Boilerplate Setup
04. Medium Analyzer - Class Review
05. Medium Analyzer - Ingestion Implementation
06. Recap
07. Medium Analyzer - Naive Retrieval
08. Medium Analyzer - 2 Step RAG
09. LangChain RAG Documentation
10. RAG Implementation with Vector Stores Quiz
07. Building a Documentation Assistant (RAG)
07. Building a Documentation Assistant (RAG)
Overview
Lessons
Lessons
01. What are we building?
02. Pipenv vs uv
03. Environment Setup
04. Ingestion Pipeline Intro
05. Imports
06. Tavily Crawling
07. TavilyMap / TavilyExtract
08. Crawling Deep Dive
09. Recap
10. Chunking (Text Splitting)
11. Batch Indexing
12. Retrieval Agent Implementation
13. Run, Debug, Trace RAG Agent
14. Frontend with Streamlit
15. Documentation Helper in Production
16. RAG Architecture
08. Prompt Engineering Theory
08. Prompt Engineering Theory
Overview
Lessons
Lessons
01. The Essentials of LLMs
02. What is a Prompt?
03. Zero Shot Prompting
04. Few Shot Prompting
05. Chain of Thought Prompting
06. ReAct Prompting
07. Prompt Engineering Quick Tips
08. Context Engineering
09. Context Engineering a System Prompt
09. LLM Applications in Production
09. LLM Applications in Production
Overview
Lessons
Lessons
01. LLM Applications in Production
02. LLM Application Development Landscape
03. LLMs in Production: Privacy & Data Retention
04. Generative UI/UX with CopilotKit
05. Official LangChain Academy Courses
06. Open Source LLMs vs Managed Providers
07. Confidence in AI Results
08. AI FOMO is the New Normal
09. Finished Course? What's Next?
10. Introduction to LangGraph
10. Introduction to LangGraph
Overview
Lessons
Lessons
01. What is LangGraph?
02. Why LangGraph?
03. What are Graphs?
04. LangGraph & Flow Engineering
05. LangGraph Core Components
06. Implementing ReAct AgentExecutor with LangGraph
07. Poetry vs uv
08. Setting Up ReAct Agent Project
09. Coding the Agent's Brain
10. Defining Agent Nodes in LangGraph
11. Connecting Nodes into a Graph
12. Running LangGraph ReAct Agent with Function Calling
13. Building Modern LLM Agents
11. Reflection Agent
11. Reflection Agent
Overview
Lessons
Lessons
01. What are we building?
02. Project Setup
03. Creating the Reflector Chain and Tweet Revisor
04. Defining our LangGraph Graph
05. LangSmith Tracing
12. Reflexion Agent
12. Reflexion Agent
Overview
Lessons
Lessons
01. What are we building
02. Project Setup
03. Section Resources
04. Actor Agent V2
05. Revisor Agent
06. ToolNode - Executing Tools
07. Building our LangGraph Graph
08. Tracing our Graph
13. Agentic RAG
13. Agentic RAG
Overview
Lessons
Lessons
01. Agentic RAG Architecture
02. Improving RAG with Corrective Flow
03. Boilerplate Setup
04. Code Structure
05. LangChain Vector Store Ingestion Pipeline
06. Managing Information Flow in LangGraph
07. LangGraph Retrieve Node
08. Relevance Filter for RAG
09. Web Search Node with Tavily API
10. LLM Generation Node
11. Running the Complete LangGraph Agent
12. Self RAG
13. Adaptive RAG
14. Model Context Protocol (MCP)
14. Model Context Protocol (MCP)
Overview
Lessons
Lessons
01. Why MCP
02. How LLMs Use Tools
03. Essentials of the Protocol with Tool Calling
04. MCP Architecture
05. MCP Servers
06. LangChain MCP Adapter
07. MCP Quiz
15. Using a Prebuilt MCP Server
15. Using a Prebuilt MCP Server
Overview
Lessons
Lessons
01. What are we building?
02. MCP Inspector
03. LLM.txt
04. mcpdoc
16. Building MCP Servers and Clients
16. Building MCP Servers and Clients
Overview
Lessons
Lessons
01. Intro
02. Boilerplate
03. Servers
04. What are we building?
05. Simple MCP Server
06. LangChain MultiServer MCP Client
17. Useful Tools for LLM Applications
17. Useful Tools for LLM Applications
Overview
Lessons
Lessons
01. Stop Writing Deprecated Code
02. LangChain Hub
03. TextSplitting Playground
04. LangChain vs LlamaIndex
18. Deep Agents
18. Deep Agents
Overview
Lessons
Lessons
01. Introduction
02. Taxonomy of Agents
03. Dynamic To-Do Lists
04. Sub-Agents and Hierarchical Delegation
05. Subagent Context Flow
06. Deep Agent File Systems
19. LangChain Glossary
19. LangChain Glossary
Overview
Lessons
Lessons
01. ChatModels
02. Messages
03. RecursiveCharacterTextSplitter
04. Document
05. LangChain Token Limitation Handling
06. LangChain Memory Intro
07. LangChain Memory Theory Deepdive
20. Agent Security
20. Agent Security
Overview
Lessons
Lessons
01. What is LLM App Security?
02. The Bad of Agentic Coding
Table of contents
Overview
01. Introduction
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Overview
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Introduction
Objectives
Structure
Resources