LangGraph — Visual and Conceptual Guide (With Code Examples)

This course introduces LangGraph, a graph-based framework for building controlled, deterministic, and scalable AI workflows. The focus is on visual thinking, system design, and mental models**, not implementation details.

Course Curriculum

20 modules designed to master the subject.

Module 1: Why LangGraph Exists

Understand the limits of linear chains and why graphs are needed for reliability.

Limits of Linear LLM Pipelines

Why simple chains break down when facing complex, real-world tasks.

Problems with Autonomous Agent Loops

The dangers of letting an LLM decide its own loop constraints.

Non-Deterministic Behavior

Why your prompt works today but fails tomorrow.

Tool Abuse

When agents go rogue.

Cost and Reliability

The economics of autonomous loops.

Module 2: Thinking in Graphs

Learn to visualize workflows as nodes and edges for better system design.

Nodes as Actions

Understanding the fundamental unit of work in a graph.

Edges as Decisions

How edges control the flow of execution.

Directed Graphs vs State Machines

Comparing two powerful models for system design.

Determinism through Structure

How graph shape imposes order on LLM chaos.

Module 3: Core LangGraph Concepts

Master the lifecycle of a graph: State, Nodes, and Edges.

Graph Lifecycle

The journey of a request through the graph.

Entry Points and Exit Points

Defining the boundaries of your graph.

Execution Order

How LangGraph decides what runs next.

Control Flow vs Data Flow

Separating the 'How' from the 'What'.

Module 4: State as a First-Class Citizen

How state management enables complex, multi-turn agent behaviors.

What State Represents

State is the memory of your application.

Passing State Between Nodes

How data flows through the graph.

Immutable vs Mutable State

Managing state safety in concurrent environments.

State Validation and Integrity

Ensuring your graph never processes garbage data.

Module 5: Entry and Exit Nodes

Defining boundaries: How data enters and leaves your graph safely.

Accepting Inputs

How to get external data into your graph.

Producing Outputs

Formatting the final result for the user.

Guarding Graph Boundaries

Protecting your graph from bad actors and bad logic.

Failure-Safe Exits

How to crash gracefully.

Module 6: Conditional Routing

Implementing logic gates and branching paths for dynamic workflows.

Branching Logic

How to make your graph smart.

Decision Gates

Using LLMs to make routing decisions.

Boolean and Rule-Based Paths

Combining deterministic rules with AI.

Preventing Invalid Transitions

Using the graph structure to enforce business logic.

Module 7: Loops and Repetition

Patterns for retries, feedback loops, and iterative refinement.

Controlled Iteration

How to loop safely.

Loop Guards

Preventing infinite loops.

Termination Conditions

Defining when to stop.

Avoiding Infinite Loops

Structural patterns for safety.

Module 8: Parallelism and Concurrency

Speeding up execution by running independent nodes in parallel.

Parallel Node Execution

Why and how to run things at the same time.

Fan-Out and Fan-In Patterns

The Map-Reduce of Agents.

Synchronization Points

Managing state conflicts.

Performance Tradeoffs

When parallelism hurts you.

Module 9: Tool Execution in Graphs

Safely integrating external tools and APIs as graph nodes.

Tools as Graph Nodes

Integrating APIs directly into the flow.

Controlled Tool Access

Scoped permissions for agents.

Side Effects and Isolation

Handling dangerous operations.

Retry and Fallback Paths

Resilience in tool execution.

Module 10: Retrieval-Augmented Generation (RAG)

Designing RAG pipelines as structured graph flows.

Retrieval as a Graph Step

RAG is just a node.

Context Injection

Managing the context window.

Verification and Filtering

Corrective RAG (CRAG).

Multi-Retriever Workflows

Choosing the right database.

Module 11: Agentic Workflows

Building autonomous agents with planning and execution capabilities.

Planner–Executor Patterns

The classic 'Plan and Solve' architecture.

Role-Based Agents

Assigning personas to nodes.

Memory and Feedback Loops

Learning from mistakes within a session.

Supervisor Oversight

Managed multi-agent systems.

Module 12: Human-in-the-Loop

Integrating human approval and feedback steps into automated flows.

Approval Nodes

The 'Pause' button for your graph.

Feedback Integration

Humans as editors.

Escalation Paths

Knowing when to quit.

Manual Overrides

God mode for the operator.

Module 13: Error Handling and Recovery

Designing self-healing graphs that recover from failures.

Failure Paths

Planning for the worst.

Graceful Degradation

Failing softly, not hard.

Circuit Breakers

Protecting your system from cascading failures.

Recovery Workflows

Fixing problems automatically.

Module 14: Observability and Debugging

Tracing and visualizing graph execution to fix issues.

Execution Tracing

Seeing the invisible.

State Inspection

Debugging by looking at memory.

Debugging Misroutes

Why did it go left instead of right?

Auditability

Keeping records for compliance.

Module 15: Security and Safety

Preventing infinite loops and securing sensitive data paths.

Prompt Injection Risks

Defending against semantic hacks.

Tool Misuse Prevention

Limiting the blast radius.

Secrets Management

keeping API keys out of the logs.

Access Control

Who is allowed to do what?

Module 16: Performance and Token Control

Optimizing graph efficiency and managing token usage.

Token Budgeting Per Node

Avoiding the token explosion.

Caching Strategies

Don't think twice.

Early Exits

Taking the shortcut.

Cost-Aware Routing

Model cascading.

Module 17: Versioning and Evolution

Strategies for maintaining and updating production graphs.

Versioned Graphs

Managing v1 and v2 in production.

Backward Compatibility

Non-breaking changes.

Migration Strategies

Moving users to new graphs.

Change Management

Testing before deploying.

Module 18: Deployment and Operations

Moving from prototype to production: Scaling and reliability.

Running Graphs in Production

Serving the graph API.

Scaling Execution

Handling 10,000 concurrent agents.

Long-Running Workflows

Agents that sleep for days.

Reliability Patterns

Uptime engineering.

Module 19: Real-World Use Cases

Case studies of successful LangGraph implementations.

Customer Support Automation

Case Study: Tier 1 Support.

Research and Analysis Pipelines

Case Study: Equity Research.

Enterprise Workflows

Case Study: Employee Onboarding.

Compliance-Driven Systems

Case Study: Loan Approval.

Capstone: Designing a Production LangGraph System

Apply all concepts to design a complete, robust AI workflow.

Capstone: Designing a Production LangGraph System

Apply all concepts to design a complete, robust AI workflow.

Project Planning: Building the Graph Schema

Master the first step of the capstone project. Learn how to translate a complex business problem into a robust LangGraph schema before writing a single line of logic.

Implementing the State and Logic Nodes

Cross the threshold into implementation. Learn how to write the Python functions for each node and how to manage state transitions in the capstone project.

Testing and Iterating on the Graph

Master the art of agent debugging. Learn how to trace the path of a request through your graph and how to fix 'Logic Loops' in your capstone project.

Deployment: Shipping Your Intelligent System

Cross the finish line. Learn how to wrap your capstone LangGraph in a REST API, deploy it to a server, and prepare for real-world traffic.

Course Overview

Format

Self-paced reading

Duration

Approx 6-8 hours

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