Adding nodes and edges

Now that your StateGraph() is ready, it's time to add your chatbot's nodes to the graph! The pre-built START and END nodes are directly imported from LangGraph, so you'll have just one chatbot node to create. You'll also define the edges that determine the direction of your chatbot's conversation, from start to finish. Once your nodes and edges are added, you'll compile the graph to get it ready to run with a query.

This exercise is part of the course

Designing Agentic Systems with LangChain

Exercise instructions

Define the chatbot function by using the llm.invoke() method on the current "messages" in state and return its response.
Use .add_node() to add a node named "chatbot" to the graph that references the chatbot function.
Connect the START node to the "chatbot" node and "chatbot" to the END node using .add_edge() to define the edges for the conversation.
Compile the graph using .compile() to prepare it for execution.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Define chatbot function to respond with the model
def chatbot(state: State):
    return {"messages": [llm.____(____["____"])]}

# Add chatbot node to the graph
graph_builder.____("____", ____)

# Define the start and end of the conversation flow
graph_builder.____(____, "____")
graph_builder.____("____", ____)

# Compile the graph to prepare for execution
graph = graph_builder.____()

Edit and Run Code

This exercise is part of the course

Designing Agentic Systems with LangChain

IntermediateSkill Level

4.8+

Start Course for Free

Build intelligent agentic systems! Discover the key components of LangChain agents, including how prompts, LLMs, and tools work together for reasoning and action. You'll set up an agent with OpenAI's API, define custom tools, and tackle real-world tasks like math calculations. Plus, explore how LangChain organizes data using graphs, nodes, and edges.

Exercise 1: Agents in LangChain Exercise 2: Creating a ReAct agent Exercise 3: Components of LangChain agents Exercise 4: Building custom tools Exercise 5: Create a tool for math calculations Exercise 6: Integrating custom tools and queries Exercise 7: Conversation with a ReAct agent Exercise 8: Conversation setup Exercise 9: Ask questions about conversation history

Build dynamic, tool-augmented chatbots with LangChain and LangGraph! You’ll explore how to create a chatbot that adapts based on user input by defining states and integrating external APIs for real-time information retrieval. You'll connect these components into a responsive graph structure, enabling smooth transitions between conversation and tool-assisted responses. By the end, you’ll have a visually represented chatbot framework with enhanced reasoning and multi-step workflows.

Exercise 1: Building graphs for chatbots Exercise 2: Graph and agent states Exercise 3: Adding nodes and edges

Current Exercise

Exercise 4: Generating chatbot responses Exercise 5: Queries and responses Exercise 6: Generate a graph diagram Exercise 7: Adding external tools to a chatbot Exercise 8: Build a Wikipedia tool Exercise 9: Add a tool to a graph Exercise 10: Adding memory and conversation Exercise 11: Visualize the graph and return tool outputs Exercise 12: Adding graph memory Exercise 13: Using graph memory for conversation

Expand your chatbot with dynamic tools and memory! Define and integrate multiple tools into flexible workflows, build functions for dynamic tool calling, and configure your chatbot for multiple-tool handling. Organize memory and outputs to enable interleaved, multi-turn conversations. By the end, you'll have created a sophisticated chatbot capable of complex interactions.

Exercise 1: Defining multiple tools Exercise 2: Build a tool that invokes the LLM Exercise 3: Build a tool with Python code Exercise 4: Binding multiple tools Exercise 5: Defining nodes and edges for flexible function calling Exercise 6: Define a function that stops the chatbot Exercise 7: Create a function to return an LLM response Exercise 8: Create the graph workflow for multiple tools Exercise 9: Organize chatbot outputs with memory Exercise 10: Configure outputs for multiple tools Exercise 11: Enable multi-turn conversation with memory Exercise 12: Congratulations!