A Step-by-Step Guide to Master LangChain
Are you ready to go beyond simple LLM apps and build powerful, stateful, and agentic workflows using LangGraph?
What you’ll learn
- What LangGraph is and how it fits into the GenAI ecosystem.
- Build your first LangGraph workflow using a state machine.
- Validate and structure your state using Pydantic models.
- Use async and streaming to build responsive applications.
- Implement conditional routing based on LLM output.
- Understand reducers and how they manage state transitions.
- Master tool calling with LangGraph’s built-in ToolNode.
- Learn about checkpointers, and apply both short-term (in-memory) and long-term (SQLite, Redis) memory storage.
- Build Agentic RAG workflows using tools and retrievers.
- Implement Human-in-the-Loop workflows using Interrupt and resume.
- Modularize complex graphs using subgraphs.
- Apply everything in a real-time Hospital Insurance Claim Management use case.
- Add tracing and observability using LangSmith.
- Explore essential agentic design patterns to scale your applications.
- All in simple steps.
Course Content
- Introduction –> 2 lectures • 2min.
- The Fundamentals –> 2 lectures • 6min.
- Langraph in action –> 6 lectures • 17min.
- Async Invocation and Streaming –> 4 lectures • 9min.
- Conditional Routing –> 3 lectures • 7min.
- Reducers –> 3 lectures • 8min.
- Tools and LLM –> 5 lectures • 18min.
- Memory –> 3 lectures • 6min.
- Agentic RAG –> 2 lectures • 7min.
- Human in the loop –> 4 lectures • 15min.
- Long Term Memory –> 3 lectures • 4min.
- Subgraphs –> 4 lectures • 15min.
- Usecase –> 12 lectures • 37min.
- Implement FastAPI –> 3 lectures • 7min.
- Agentic Patterns –> 4 lectures • 14min.
- Wrap Up –> 1 lecture • 1min.
Requirements
Are you ready to go beyond simple LLM apps and build powerful, stateful, and agentic workflows using LangGraph?
In this beginner-friendly course, you’ll master LangGraph, an open-source library built on top of LangChain, designed for orchestrating multi-agent applications using a graph-based architecture. Whether you’re building intelligent agents, dynamic RAG pipelines, or real-world enterprise solutions, this course gives you the solid foundation you need.
What You’ll Learn
• What LangGraph is and how it fits into the GenAI ecosystem
• Build your first LangGraph workflow using a state machine
• Validate and structure your state using Pydantic models
• Use async and streaming to build responsive applications
• Implement conditional routing based on LLM output
• Understand reducers and how they manage state transitions
• Master tool calling with LangGraph’s built-in ToolNode
• Learn about checkpointers, and apply both short-term (in-memory) and long-term (SQLite, Redis) memory storage
• Build Agentic RAG workflows using tools and retrievers
• Implement Human-in-the-Loop workflows using Interrupt and resume
• Modularize complex graphs using subgraphs
• Apply everything in a real-time Hospital Insurance Claim Management use case
• Add tracing and observability using LangSmith
• Explore essential agentic design patterns to scale your applications
Who This Course Is For
• AI developers looking to build production-grade agentic apps
• LangChain users who want to level up to graph-based orchestration
• Backend engineers interested in tool use, memory, and state control
• Anyone working on LLM workflows in real-world use cases
Prerequisites
• Basic Python knowledge
• Some familiarity with LangChain
By the end of this course, you’ll be able to:
• Confidently build, scale, and debug LangGraph workflows
• Integrate LLMs, tools, memory, and human feedback into your apps
• Apply LangGraph in real-world business use cases like claim processing, customer support, and document analysis
Ready to master LangGraph and take your LLM applications to the next level?
Enroll now and start building intelligent, interactive agentic systems with ease!