Creating a ReAct agent

Now that you've learned the basic components of LangChain, you'll jump right in and create a ReAct agent that can count how many 'r's there are in any word with the tool count_r_in_word.

The following have been loaded for you: tool, ChatOpenAI, create_react_agent, math, and model.

This exercise is part of the course

Designing Agentic Systems with LangChain

Exercise instructions

Set up the agent app using create_react_agent() by passing in the model and the count_r_in_word to the list of tools.
Define a query variable that accepts the user's question as a string.
Invoke the app with .invoke() and pass a dictionary with a "messages" key, labeling the query as "human".
Access the last message in the response and print its .content attribute to get the agent's answer.

Hands-on interactive exercise

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

# Create the agent
____ = ____(model=____, tools=[____])

# Create a query
____ = "How many r's are in the word 'Terrarium'?"

# Invoke the agent and store the response
response = app.____({"messages": [("human", ____)]})

# Print the agent's response
print(____['messages'][____].____)

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

Current Exercise

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 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!