Add a tool to a graph

You've got your Wikipedia tool ready to go. Now it's time to incorporate it into your chatbot's graph workflow! You'll modify the nodes and edges in the graph workflow to incorporate the tool as an additional node. You'll set up the graph so that the chatbot can use the tool only when needed depending on the user's query. To manage your tool node and its associated edges, the following modules have been imported for you. The wikipedia_tool you built is also available in your environment. Once the graph build is complete, your graph will be visualized for you as a LangGraph diagram.

from langgraph.prebuilt import ToolNode, tools_condition

This exercise is part of the course

Designing Agentic Systems with LangChain

Exercise instructions

Use .add_node() to add the "chatbot" node to the graph, linking it to the chatbot function.
Create a ToolNode() with wikipedia_tool as tools and use .add_node() to add it to the graph as "tools".
Apply .add_conditional_edges() to route from "chatbot" based on the tools_condition.
Use .add_edge() to connect "tools" back to "chatbot", START to "chatbot", and "chatbot" to END to complete the workflow.

Hands-on interactive exercise

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

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

# Create a ToolNode to handle tool calls and add it to the graph
tool_node = ____(tools=[____])
graph_builder.____("tools", ____)

# Set up a condition to direct from chatbot to tool or END node
graph_builder.____("____", ____)

# Connect tools back to chatbot and connect START and END nodes
graph_builder.add_edge("____", "____")
graph_builder.add_edge(____, "chatbot")
graph_builder.add_edge("chatbot", ____)

graph = graph_builder.compile()

try:
    display(Image(graph.get_graph().draw_mermaid_png()))

except Exception:
    print("Plot generation failed... falling back to cached asset.")
    display_fallback()

Edit and Run Code

This exercise is part of the course

Designing Agentic Systems with LangChain

IntermediateSkill Level

4.8+

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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

Current Exercise

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!