Module 1 · AI
📖 3 min read · By ['[“HungHM15”]']

Assignment: LangGraph Foundations & State Management#

Assignment Metadata#

Field

Description

Assignment Name

LangGraph Foundations & State Management

Course

LangGraph and Agentic AI

Project Name

langgraph-chatbot-agent

Estimated Time

90 minutes

Framework

Python 3.10+, LangGraph, LangChain, OpenAI API

Learning Objectives#

By completing this assignment, you will be able to:

  • Understand LangGraph architecture and the role of Messages in State

  • Implement State Management with messages-centric pattern using TypedDict

  • Build Nodes and Edges with LangChain message types

  • Configure MemorySaver checkpointer for conversation persistence

  • Create a complete chatbot workflow with conditional routing

Problem Description#

You are tasked with building a conversational AI agent using LangGraph that can:

  1. Maintain conversation history across multiple turns

  2. Use proper message types (HumanMessage, AIMessage, SystemMessage)

  3. Implement conditional routing based on user intent

  4. Persist state using MemorySaver for multi-session conversations

Technical Requirements#

Environment Setup#

  • Python 3.10 or higher

  • Required packages:

    • langgraph >= 0.2.0

    • langchain >= 0.1.0

    • langchain-openai >= 0.1.0

API Requirements#

  • OpenAI API key configured as environment variable

Tasks#

Task 1: State Definition (20 points)#

  1. Define an AgentState using TypedDict that includes:

    • messages: Using Annotated[List[AnyMessage], add_messages] pattern

    • user_name: Context field for personalization

    • session_id: Context field for tracking

  2. Implement proper message handling that:

    • Uses add_messages reducer for automatic message merging

    • Handles all message types (HumanMessage, AIMessage, SystemMessage)

Task 2: Node Implementation (30 points)#

  1. Create a chatbot node that:

    • Reads messages from state

    • Adds a personalized system message if not present

    • Calls the LLM with message history

    • Returns new AIMessage to state

  2. Create a routing node that:

    • Analyzes the last user message

    • Routes to appropriate handler based on intent (help, general, goodbye)

Task 3: Graph Construction (30 points)#

  1. Build the StateGraph with:

    • Entry point set to chatbot node

    • Conditional edges based on routing logic

    • Proper END state configuration

  2. Configure MemorySaver checkpointer for state persistence

  3. Implement multi-turn conversation that maintains context across invocations

Task 4: Testing & Validation (20 points)#

  1. Create test conversations demonstrating:

    • Multi-turn dialogue with context retention

    • Different routing paths (help, general, goodbye)

    • Session persistence (same thread_id across calls)

  2. Debug output showing message history at each step

Submission Requirements#

Required Deliverables#

  • Source code in Python script or Jupyter notebook

  • README.md with setup instructions

  • Screenshots showing multi-turn conversation output

  • Graph visualization (using get_graph().draw_mermaid_png())

Submission Checklist#

  • State uses messages-centric pattern with add_messages

  • All node functions follow the correct signature (state) -> dict

  • Conditional routing works correctly

  • MemorySaver enables conversation persistence

  • Code runs without errors

Evaluation Criteria#

Criteria

Points

State definition with messages

20

Node implementation

30

Graph construction & routing

30

Testing & validation

15

Code quality and documentation

5

Total

100

Hints#

  • Use state["messages"][-1] to access the most recent message

  • The add_messages reducer automatically handles message deduplication

  • Use config = {"configurable": {"thread_id": "unique-id"}} for session tracking

  • Reference the knowledge document for node function patterns

References#