ReAct agent implementation in LangChain

Introduction

The ReAct agent helps a program think and act step-by-step by combining reasoning and actions. It makes the program smarter by letting it decide what to do next based on what it learns.

When you want a chatbot to answer complex questions by searching and reasoning.

When building a helper that needs to gather information before giving a final answer.

When you want to combine multiple tools or APIs in a smart, stepwise way.

When you want your program to explain its thinking while solving a problem.

Syntax

LangChain

from langchain.agents import initialize_agent, AgentType
from langchain.llms import OpenAI
from langchain.tools import Tool

# Define your tools
tools = [Tool(name="Search", func=search_function, description="Useful for searching the web")]

# Initialize your language model
llm = OpenAI(temperature=0)

# Create the ReAct agent
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

The initialize_agent function sets up the agent with tools and a language model.

The AgentType.ZERO_SHOT_REACT_DESCRIPTION tells LangChain to use the ReAct pattern for reasoning and acting.

Examples

This example shows how to set up a ReAct agent with one simple search tool.

LangChain

from langchain.agents import initialize_agent, AgentType
from langchain.llms import OpenAI
from langchain.tools import Tool

# Simple tool example
def search_function(query: str) -> str:
    return f"Searching for {query}..."

tools = [Tool(name="Search", func=search_function, description="Search the web")]
llm = OpenAI(temperature=0)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

This example adds a calculator tool so the agent can do math and search.

LangChain

from langchain.agents import initialize_agent, AgentType
from langchain.llms import OpenAI
from langchain.tools import Tool

# Multiple tools example

def search_function(query: str) -> str:
    return f"Searching for {query}..."

def calculator(input: str) -> str:
    return str(eval(input))

tools = [
    Tool(name="Search", func=search_function, description="Search the web"),
    Tool(name="Calculator", func=calculator, description="Do math calculations")
]

llm = OpenAI(temperature=0)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

Sample Program

This program creates a ReAct agent with two tools: a search and a calculator. It asks the agent to do a math calculation and then search for a topic. The agent thinks step-by-step and uses the right tool for each part.

LangChain

from langchain.agents import initialize_agent, AgentType
from langchain.llms import OpenAI
from langchain.tools import Tool

# Define a simple search tool

def search_function(query: str) -> str:
    return f"Result for '{query}'"

# Define a calculator tool

def calculator(input: str) -> str:
    try:
        return str(eval(input))
    except Exception:
        return "Error in calculation"

# Create tools list
tools = [
    Tool(name="Search", func=search_function, description="Search the web"),
    Tool(name="Calculator", func=calculator, description="Perform math calculations")
]

# Initialize language model
llm = OpenAI(temperature=0)

# Initialize ReAct agent
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False)

# Run the agent with a question
question = "What is 12 * 12 and then search for 'Python programming'?"
response = agent.run(question)
print(response)

Important Notes

Make sure your tools have clear descriptions so the agent knows when to use them.

Set verbose=True during development to see the agent's thought process in the console.

Use a low temperature (like 0) in the language model for more predictable answers.

Summary

The ReAct agent combines thinking and acting to solve problems step-by-step.

You set it up by giving it tools and a language model in LangChain.

It is useful for tasks that need reasoning and multiple actions, like searching and calculating.

Practice

1. What is the main purpose of a ReAct agent in LangChain?

easy

A. To store data without processing or reasoning

B. To only perform simple, single-step actions without reasoning

C. To replace language models with rule-based systems

D. To combine reasoning and actions step-by-step to solve complex tasks

Solution

1. Step 1: Understand the ReAct agent concept

   The ReAct agent is designed to think (reason) and act (perform tasks) in steps.

2. Step 2: Identify its main use

   It helps solve problems that need multiple steps, like searching and calculating.

3. Final Answer:

   To combine reasoning and actions step-by-step to solve complex tasks -> Option D

4. Quick Check:

   ReAct agent = reasoning + actions [OK]

Hint: ReAct means think and act together step-by-step [OK]

Common Mistakes:

• Thinking it only acts without reasoning
• Confusing it with simple action-only agents
• Assuming it replaces language models

2. Which of the following is the correct way to create a ReAct agent in LangChain?

easy

A. agent = ReActAgent(llm=llm, tools=tools)

B. agent = ReActAgent(tools=llm, llm=tools)

C. agent = ReActAgent()

D. agent = ReActAgent(llm)

Solution

1. Step 1: Recall ReAct agent constructor parameters

   The ReAct agent requires a language model (llm) and a list of tools (tools) as named arguments.

2. Step 2: Check each option for correct syntax

   agent = ReActAgent(llm=llm, tools=tools) correctly passes llm and tools by name. agent = ReActAgent(tools=llm, llm=tools) swaps them incorrectly. agent = ReActAgent() misses required arguments. agent = ReActAgent(llm) passes only llm without tools.

3. Final Answer:

   agent = ReActAgent(llm=llm, tools=tools) -> Option A

4. Quick Check:

   Correct parameters = llm and tools [OK]

Hint: Pass llm and tools as named parameters to ReActAgent [OK]

Common Mistakes:

• Swapping llm and tools arguments
• Omitting required parameters
• Passing parameters positionally without names

3. Given this code snippet, what will be the output behavior of the ReAct agent?

from langchain.agents import ReActAgent
from langchain.llms import OpenAI

llm = OpenAI(temperature=0)
tools = [search_tool, calculator_tool]
agent = ReActAgent(llm=llm, tools=tools)

response = agent.run('What is the capital of France and what is 5 plus 3?')

medium

A. The agent will first search for the capital of France, then calculate 5 plus 3, returning both answers.

B. The agent will only perform the search and ignore the calculation.

C. The agent will return an error because multiple tools cannot be used.

D. The agent will calculate 5 plus 3 first, then search for the capital.

Solution

1. Step 1: Understand ReAct agent multi-tool usage

   The ReAct agent can use multiple tools and decides which to use based on the question.

2. Step 2: Analyze the question and agent behavior

   The question asks two things: capital of France (search) and 5 plus 3 (calculator). The agent will perform both actions step-by-step.

3. Final Answer:

   The agent will first search for the capital of France, then calculate 5 plus 3, returning both answers. -> Option A

4. Quick Check:

   Multi-tool agent answers multi-part questions [OK]

Hint: ReAct agents use all needed tools for multi-part questions [OK]

Common Mistakes:

• Thinking agent uses only one tool per run
• Assuming order is reversed without reason
• Believing multiple tools cause errors

4. What is the likely cause of this error when running a ReAct agent?

TypeError: ReActAgent.__init__() missing 1 required positional argument: 'llm'

medium

A. The ReActAgent does not accept an llm argument.

B. The tools list was empty, causing the error.

C. The ReActAgent was created without passing the required language model (llm) argument.

D. The run method was called with an invalid input string.

Solution

1. Step 1: Interpret the error message

   The error says the __init__ method is missing the required 'llm' argument.

2. Step 2: Identify correct constructor usage

   ReActAgent requires an llm parameter when created. Missing it causes this TypeError.

3. Final Answer:

   The ReActAgent was created without passing the required language model (llm) argument. -> Option C

4. Quick Check:

   Missing llm argument = TypeError [OK]

Hint: Always pass llm when creating ReActAgent [OK]

Common Mistakes:

• Forgetting to pass llm argument
• Confusing tools argument with llm
• Misreading error as related to run method

5. You want to build a ReAct agent that can handle a question requiring web search, math calculation, and database lookup. Which setup correctly supports this multi-step reasoning and acting?

hard

A. Create separate agents for each tool and run them independently without combining

B. Create a ReActAgent with llm and a tools list including search_tool, calculator_tool, and db_tool

C. Use only the calculator_tool since it can handle all tasks internally

D. Create a ReActAgent with llm but no tools, relying on the model alone

Solution

1. Step 1: Identify the need for multiple tools

   The question requires web search, math calculation, and database lookup, so multiple tools are needed.

2. Step 2: Choose the correct agent setup

   ReActAgent supports multiple tools passed as a list along with the llm to reason and act step-by-step.

3. Step 3: Evaluate other options

   Separate agents won't combine reasoning easily; calculator_tool alone can't do all tasks; no tools means no external actions.

4. Final Answer:

   Create a ReActAgent with llm and a tools list including search_tool, calculator_tool, and db_tool -> Option B

5. Quick Check:

   Multi-tool ReActAgent = best for multi-step tasks [OK]

Hint: Pass all needed tools in one ReActAgent for multi-step tasks [OK]

Common Mistakes:

• Trying to run separate agents instead of one combined
• Assuming one tool can do all tasks
• Not passing any tools to the agent