Create the graph workflow for multiple tools

Your building blocks for creating your chatbot graph are now ready! You'll put all of your nodes together into a single workflow using edges to manage the connections between them. To get started, your graph workflow has already been set up for you with MessagesState and the StateGraph() to track the chatbot's message updates. The display() function to render your graph as a LangGraph diagram has also been set up and the MemorySaver has been imported for you.

from langgraph.graph import StateGraph
from langgraph.checkpoint.memory import MemorySaver

workflow = StateGraph(MessagesState)

This exercise is part of the course

Designing Agentic Systems with LangChain

Exercise instructions

Add call_model as a node using the label "chatbot" and add tool_node with the label "tools".
Define an edge connecting the START node to the "chatbot" node.
Add conditional edges from the "chatbot" node to the "tools" and END nodes using should_continue, before connecting the "tools" node back to the "chatbot" node.
Create a MemorySaver() instance and compile the workflow into an application with the memory checkpointer.

Hands-on interactive exercise

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

# Add nodes for chatbot and tools
workflow.add_node("____", ____)
workflow.add_node("____", ____)

# Define an edge connecting START to the chatbot
workflow.add_edge(____, "____")

# Define conditional edges and route "tools" back to "chatbot"
workflow.add_conditional_edges("____", ____, ["____", ____])
workflow.add_edge("____", "____")

# Set up memory and compile the workflow
memory = ____()
app = workflow.____(checkpointer=____)

display(Image(app.get_graph().draw_mermaid_png()))

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 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

Current Exercise

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!