Stateful AI Agents with LangGraph — PickAClass
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⏱ 3h 📚 30 lessons

Stateful AI Agents with LangGraph

Learn to design, build, and manage resilient multi-agent AI systems using LangGraph for complex, stateful workflows.

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About this course

Traditional LLM chains often struggle with complex, cyclical workflows and state management. LangGraph solves this by allowing developers to build robust, multi-agent systems that can plan, loop, and collaborate effectively. In this text-based course, you will transition from writing basic prompt sequences to designing sophisticated, stateful AI agents. You will understand how to model agentic workflows as graphs, manage shared state across multiple nodes, and implement robust error-handling mechanisms. What you'll learn: Understand the core concepts of LangGraph, including nodes, edges, state, and compilation; Build multi-agent architectures where specialized agents collaborate to solve complex tasks; Implement state management and persistence to track conversation history and agent decisions; Configure human-in-the-loop workflows to safely integrate human oversight into automated decision-making; Apply error handling and fallback strategies to ensure resilient agent performance; Explore modern patterns like retrieval-augmented generation integrated within agentic loops. The course begins with foundational definitions of graphs and stateful computation before guiding you through step-by-step code walkthroughs of multi-agent collaboration and persistence patterns. This course is designed for software developers, AI enthusiasts, and engineers looking to build advanced AI systems; basic familiarity with Python is recommended, but no prior experience with LangGraph is required. Start reading today to unlock the power of stateful, multi-agent AI engineering.

Course contents

6 modules · 30 lessons · 3h

1 The Graph Mindset
Understand why graphs are necessary for advanced AI and learn the basic theory of stateful computation. 5 lessons · 30 min
  1. Welcome to LangGraph
    Introduction to the course goals and the shift from linear chains to agentic graphs.    5 min
  2. Beyond Simple Chains
    Why traditional LLM chains fail at complex tasks and how graphs solve this.    6 min
  3. Graph Theory for AI
    Defining nodes and edges in the context of an AI workflow.    6 min
  4. What is Stateful Computation?
    The difference between stateless API calls and keeping memory across steps.    6 min
  5. Defining Your First State
    Using Python TypedDict to define what information your agent will track.    7 min
2 Building Your First Graph
Learn the core syntax of LangGraph to create and run a functional agent. 5 lessons · 30 min
  1. Nodes: The Workers
    Creating Python functions that act as nodes in your graph.    6 min
  2. Edges: The Paths
    Connecting nodes to define the sequence of execution.    6 min
  3. Conditional Logic
    Using conditional edges to let the agent decide which node to visit next.    7 min
  4. The Compilation Step
    Turning your graph definition into an executable application with .compile().    5 min
  5. Invoking the Graph
    Passing initial input and watching the graph execute through its nodes.    6 min
3 State Management & Persistence
Master how to store and retrieve information across long-running agent sessions. 5 lessons · 30 min
  1. How State Flows
    Understanding how the state object is passed and updated between nodes.    6 min
  2. State Reducers
    Using Annotated types to define how new data merges with existing state.    7 min
  3. Intro to Checkpointers
    Using memory savers to persist state automatically.    6 min
  4. Memory and History
    Implementing a conversation buffer within the graph state for chat-like behavior.    6 min
  5. Time Travel: State Snapshots
    Viewing and debugging the state at any specific point in the graph's history.    5 min
4 Multi-Agent Collaboration
Design systems where multiple specialized agents work together on a single task. 5 lessons · 30 min
  1. The Multi-Agent Philosophy
    Breaking down a complex task into specialized roles (e.g., Researcher, Writer).    5 min
  2. The Supervisor Pattern
    Building a 'boss' node that routes work to other agent nodes.    7 min
  3. Agent Handoffs
    How one agent signals that its work is done and passes control back or forward.    6 min
  4. Shared Scratchpads
    Managing a shared state area where multiple agents contribute findings.    6 min
  5. Building the Team
    A step-by-step code walkthrough of a two-agent collaboration graph.    6 min
5 Advanced Loops & Human Oversight
Implement complex logic like self-correction, RAG, and manual approvals. 6 lessons · 36 min
  1. Cyclical Workflows
    Creating loops that allow agents to retry or refine their work.    6 min
  2. Self-Correction Loops
    Designing a node that critiques and sends work back for revision.    6 min
  3. Agentic RAG
    Integrating retrieval tools directly into the graph's decision loop.    7 min
  4. Adaptive Retrieval
    Letting the agent decide when it needs more data vs when to answer.    6 min
  5. Human-in-the-Loop: Interrupts
    Configuring the graph to pause for human review before sensitive steps.    6 min
  6. Editing State Manually
    How a human can modify the graph's state during a pause to guide the agent.    5 min
6 Resilience & Reliability
Ensure your agents are production-ready by handling errors and edge cases. 4 lessons · 24 min
  1. Why Agents Fail
    Identifying common pitfalls like infinite loops and hallucinated tool calls.    5 min
  2. Error Handling in Nodes
    Using try-except blocks and specialized error nodes to catch failures.    6 min
  3. Fallback Strategies
    Implementing secondary LLMs or simplified logic when the primary path fails.    6 min
  4. Course Wrap-Up
    Reviewing the journey from chains to stateful multi-agent systems.    7 min
Final project

The Autonomous Research & Support Suite

Build a multi-agent system where a 'Support Agent' receives a query, a 'Researcher' fetches data via RAG, a 'Reviewer' checks the quality, and a human must approve any final external email sending. The system must persist history and handle API errors gracefully.

What you'll get

  • 📜 Certificate of completion
    Add it to your LinkedIn profile
  • 💬 Personal AI tutor
    Stuck on a lesson? Ask your built-in tutor anything, any time.
  • ♾️ Lifetime access
    Come back anytime, no expiry
  • 📱 Phone or computer
    Works anywhere, any device
  • 💸 14-day refund
    No questions asked
  • ⚡ Short & focused
    3h of practical content

Certificate of completion

Every course you complete on PickAClass issues a credential like this — original, with its own code, verifiable by URL, and detailed about what was actually demonstrated.

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PickAClass
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Certificate of Mastery
This certifies that
Name Surname
has successfully demonstrated mastery of
Stateful AI Agents with LangGraph
Skills demonstrated
✓
Behavioral pattern analysis
Foundational
1.2 hrs
✓
Decision-architecture frameworks
Proficient
1.4 hrs
✓
A/B test design
Proficient
1.7 hrs
✓
Behavioral copywriting
Advanced
1.9 hrs
P
PickAClass — Name Surname
Stateful AI Agents with LangGraph
Page 2 of 2
Performance detail
Coursework summary
Lessons completed 14 / 14
Practice questions 26 / 28
Assignments submitted 4 (avg 4.5 / 5)
Capstone project Reviewed — 4.6 / 5
Total practice 6.2 hrs
Performance benchmark
Cohort rank Top 12% of 1,625
Time to completion 11 days (median: 22)
Mastery score 91 / 100
Practice-question score 94%
Skill verification Verified Skill Path
Verify this credential
pickaclass.com/certificates/PCC-2026-X4F7-AP19
Issued under the academic standards of PickAClass. Skill levels reflect assessed performance against the course's competency rubric. This is an original credential of this platform.

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Frequently asked

What do I need to take this course? +

Just a phone or computer with internet. No installs, no special hardware.

How do I pay? +

By card via Stripe. We don’t store card details — Stripe handles them securely.

Can I get a refund? +

Yes — full refund within 14 days, no questions asked.

How long will I have access? +

Forever. Once you purchase, the course is yours to revisit anytime.

Will I get a certificate? +

Yes. On completion you'll receive a certificate you can add to your LinkedIn profile.

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