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LangChain & LangGraph for AI Agents

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Go beyond single-call LLM apps. Build production-grade agentic systems with LangChain (LCEL composition, tools) and LangGraph (state machines, conditional routing, persistence, human-in-the-loop). Multi-agent patterns, memory architectures, observability via LangSmith, and evaluation. Six end-to-end cases from support agents to multi-agent research systems. Builds on LLMs & GenAI for Practitioners; assumes Python comfort and basic LLM knowledge.

LangChain & LangGraph for AI Agents

Duration

4 weeks

Content

4 modules, 32 lessons

Students

2

Total Time

~7 hours

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Course Curriculum

1

Agent Foundations and LangChain BasicsFREE PREVIEW

What an AI agent actually is (vs chains, vs workflows); the LangChain ecosystem in 2026 (LCEL, Hub, Smith); building your first chains with LCEL; tools, toolkits, and tool calling.

85 min8 lessons

What an AI Agent Actually Is

Recap — Agents vs Chains vs Workflows

LangChain in 2026 — What's Changed, What's Stable

Recap — The LangChain Ecosystem (LCEL, Hub, Smith)

Building Your First Chain with LCEL

Recap — LCEL Patterns You'll Use Constantly

Tools, Toolkits, and Tool Calling in LangChain

Recap — Designing Tools for Agent Use

2

LangGraph for Stateful AgentsFREE PREVIEW

Why LangGraph beats linear chains for real agents; nodes, edges, and state; conditional routing; persistence and checkpointing for resumable agents; human-in-the-loop interrupts and approvals.

110 min10 lessons

Why LangGraph Exists — The Limits of Linear Chains

Recap — Graphs Beat Chains for Real Agents

Nodes, Edges, and State in LangGraph

Recap — Building Your First Graph

Conditional Edges and Routing

Recap — Branching and Decision Patterns

Persistence and Checkpointing

Recap — Making Agents Resumable and Stateful

Human-in-the-Loop and Interrupts

Recap — Adding Human Oversight to Agents

3

Production Agent PatternsFREE PREVIEW

Multi-agent systems (supervisor / hierarchical patterns); memory architectures (short-term, long-term, semantic); streaming and observability with LangSmith; evaluation that catches real agent bugs.

90 min8 lessons

Multi-Agent Systems and Supervisor Patterns

Recap — When to Split Into Multiple Agents

Memory — Short-term, Long-term, Semantic

Recap — Designing Agent Memory

Streaming, Observability, and LangSmith

Recap — Debugging and Monitoring Agents in Production

Evaluation and Testing for Agents

Recap — Agent Eval Beyond Simple Metrics

4

Live Agent CasesFREE PREVIEW

Six end-to-end walkthroughs: customer support agent with LangGraph, multi-agent research system, long-term memory for conversational agent, human-in-the-loop approval for high-stakes actions, production deployment with LangSmith observability, migrating a linear chain to a state machine.

150 min6 lessons

Case 1 — Build a Customer Support Agent with LangGraph

Case 2 — Multi-Agent Research System (Researcher + Writer + Reviewer)

Case 3 — Add Long-Term Memory to a Conversational Agent

Case 4 — Human-in-the-Loop Approval Flow for High-Stakes Actions

Case 5 — Deploy a LangGraph Agent to Production with Observability

Case 6 — Migrate a Linear Chain to a LangGraph State Machine

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