Binding multiple tools

You've just built multiple tools! Now you'll be able to add them to your chatbot by first binding them to the LLM. The tools wikipedia_tool, palindrome_checker and historical_events have all been added to your environment along with llm. You'll also create a tool node to add to your chatbot that lists all of the tools available.

This exercise is part of the course

Designing Agentic Systems with LangChain

Exercise instructions

From langgraph.prebuilt, import the necessary module for defining a tool node.
Complete the list of tools by adding the appropriate tool names present in your environment.
Pass the list of tools to the ToolNode() class and assign it to tool_node.
Bind the tools to the llm using .bind_tools().

Hands-on interactive exercise

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

# Import modules required for defining tool nodes
from ____.____ import ____

# List of tools
tools = [____, ____, ____]

# Pass the tools to the ToolNode()
____ = ____(____)

# Bind tools to the LLM
model_with_tools = ____.____(____)

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 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

Current Exercise

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!