LangGraph is LangChain’s open-source, graph-based framework for building stateful AI agents that branch, loop, and recover from errors instead of following one fixed path. This LangChain course walks you through nodes, edges, state schemas, checkpointing, and human-in-the-loop controls, then puts them to work in real multi-agent projects. By the end of the program, you’ll be able to design, debug with LangSmith, and deploy production-ready agentic applications on your own, backed by kodestree’s mentor support and hands-on project feedback.
Prerequisites
You don’t need prior agent-building experience, but the following will help you keep pace with the live sessions:
- Working knowledge of Python (functions, classes, dictionaries)
- Basic understanding of REST APIs and JSON
- Familiarity with any LLM API (OpenAI, Anthropic, Gemini, etc.) is helpful but not mandatory
- Prior exposure to LangChain basics is a plus- a quick refresher is built into Module 1 for those without it
- A laptop with Python 3.10+ and an IDE such as VS Code installed
Why Learn LangGraph?
AI agents in 2026 don’t run on a
single prompt-and-response loop anymore. They branch, retry, wait for human
approval, and pick up exactly where they left off after a crash and LangGraph
is the framework most teams reach for to build that kind of behavior. Because
LangChain 1.0 now runs on LangGraph under the hood, learning LangGraph is no
longer a niche add-on skill; it has effectively become the default
orchestration layer across the LangChain ecosystem.
Organizations including Uber,
JPMorgan, BlackRock, Cisco, Klarna, CyberArk, and Replit already run
LangGraph-based agents in production, and the open-source library has crossed
roughly 31,000 GitHub stars, a sign of how quickly adoption is moving. With LangGraph
1.0 adding durable execution, time-travel debugging, and enterprise-grade
checkpointing, the framework has matured well past the experimentation stage.
For developers, that shift shows up as a fast-growing list of job titles- Agentic AI Engineer, LLM Application Developer, AI Workflow Architect- that
now specifically call out LangGraph experience, making it one of the more
future-proof additions to a GenAI resume today.
Course Objectives
By the end of this course, you
will be able to:
- Explain how LangGraph’s graph-based model differs from
linear LangChain chains - Design and compile StateGraphs using nodes, edges, and
conditional routing - Build agents that loop, retry, and recover using cycles
and checkpoints - Add persistent memory and human-in-the-loop approval
steps to any workflow - Coordinate multiple specialized agents inside one
orchestrated graph - Trace, debug, and monitor agent runs using LangSmith
- Deploy a LangGraph application to a production-style
environment
What You Will Learn
Across the training, you will
work through these core building blocks of LangGraph:
- Graphs, Nodes, Edges, and the StateGraph API
- TypedDict and Pydantic-based state schemas
- Conditional edges, branching, and cyclic workflows
- Checkpointers and persistence for long-running agents
- Short-term and long-term memory patterns
- Human-in-the-loop interrupts and approval gates
- Multi-agent architectures like supervisor, hierarchical,
and swarm patterns - Streaming tokens, state updates, and intermediate steps
- Tool calling, MCP (Model Context Protocol) integration,
and external APIs - LangSmith tracing, evaluation, and observability
- Deploying agents with LangSmith Deployment (formerly
LangGraph Platform)
Who Is this Course For?
This course is built for:
- Python developers ready to move from scripts to
production AI agents - LangChain users who need to handle branching, loops,
and multi-step workflows - ML and Data Science professionals adding agentic AI to
their skill set - Backend and full-stack engineers building AI-powered
products - Solution architects and tech leads evaluating agent
frameworks - Anyone preparing for AI Engineer or LLM Developer
interviews
Tools You Will Work With
- Python 3.10+
- LangGraph & LangChain (v1.0)
- LangSmith (tracing, evaluation, deployment)
- OpenAI, Anthropic, and Google Gemini APIs
- Vector databases- Pinecone, Chroma, FAISS
- FastAPI for serving agents
- Git & GitHub
- Jupyter Notebook / VS Code
- Docker (for the deployment module)
Skills You Will Gain
On completion, you’ll walk away
with these practical, job-ready skills:
- Graph-based agent architecture design
- State management and persistence engineering
- Multi-agent orchestration and coordination
- Human-in-the-loop workflow design
- LLM tool integration and function calling
- Agent debugging, tracing, and evaluation
- Production deployment of AI agents
Career Outcomes
LangGraph skills open doors to
roles such as:
- Agentic AI Engineer
- LLM Application Developer
- AI Workflow / Orchestration Engineer
- Generative AI Engineer
- Machine Learning Engineer (Agent Systems)
- AI Solutions Architect
- Conversational AI / Chatbot Developer
LangGraph Professional Salary in India and USA
| Experience Level | India Salary (Annual CTC) | USA Salary (Annual) |
|---|---|---|
| Beginner (0-2 Years) | ₹3.5 – ₹8 LPA | $98,945 – $112,424 |
| Intermediate (2-5 Years) | ₹8 – ₹16 LPA | $123,128 – $145,000 |
| Experienced (5+ Years) | ₹20 – ₹45 LPA | $154,000 – $270,015 |
Why Choose kodestree for This Training?
Here’s what makes kodestree’s LangGraph training different:
- Live, instructor-led sessions with practicing GenAI
engineers - Curriculum benchmarked against official LangGraph and
LangChain documentation - Small batch sizes with 1-on-1 mentoring available
- A real multi-agent capstone project you can add to your
portfolio - Lifetime access to session recordings and course
material - Course completion certificate with lifetime validity
- Resume building, interview preparation, and job
assistance support - 24×7 learner support with flexible weekday and weekend batches
