ReAct agent implementation in LangChain - Step-by-Step Execution

Concept Flow - ReAct agent implementation

Start: Receive user query

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Agent decides: Think or Act?

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Think: Reason

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Update agent memory with observation

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Check if answer ready

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Return answer

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End

The ReAct agent loops between thinking (reasoning) and acting (calling tools), updating memory until it produces a final answer.

Execution Sample

LangChain

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

# Define tools and initialize agent
agent = initialize_agent(tools, llm, agent='zero-shot-react-description')

This code sets up a ReAct agent with tools and a language model to handle user queries by reasoning and acting.

Execution Table

Step	Agent Input	Agent Action	Tool Called	Observation	Agent Memory Update	Answer Ready?
1	User query: 'What is the capital of France?'	Think: Plans to find capital	None	None	Stores plan to use knowledge tool	No
2	Plan: Use knowledge tool	Act: Calls knowledge tool	Knowledge Tool	Paris	Stores observation 'Paris'	No
3	Observation: 'Paris'	Think: Confirms answer	None	None	Updates memory with confirmation	Yes
4	Final answer ready	Return answer: 'The capital of France is Paris.'	None	None	Ends process	Yes

💡 Agent returns final answer after confirming observation matches query.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
agent_memory	{}	{plan: 'use knowledge tool'}	{plan: 'use knowledge tool', observation: 'Paris'}	{plan: 'use knowledge tool', observation: 'Paris', confirmation: true}	{final_answer: 'The capital of France is Paris.'}

Key Moments - 2 Insights

Why does the agent alternate between thinking and acting?

The agent first thinks to decide what to do, then acts by calling a tool. This cycle repeats until it has enough info to answer, as shown in steps 1-3 in the execution table.

What happens if the tool returns no useful observation?

The agent updates memory with the observation (even if empty) and thinks again to decide next steps, continuing the loop until answer is ready.

Visual Quiz - 3 Questions

Look at the execution table, what does the agent do at step 2?

A Calls a tool to get information

B Returns the final answer

C Thinks about the next step without calling a tool

D Ends the process without answer

💡 Hint

Check the 'Agent Action' and 'Tool Called' columns at step 2.

At which step does the agent confirm it has enough information to answer?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the 'Answer Ready?' column to see when it changes to Yes.

If the tool returned no observation at step 2, how would the agent proceed?

A Return an empty answer immediately

B Update memory with empty observation and think again

C Stop and report an error

D Call a different tool without thinking

💡 Hint

Refer to the key moment about handling empty observations.

Concept Snapshot

ReAct agent loops: Think (reason) -> Act (call tool) -> Observe -> Update memory.
Repeat until answer is ready.
Use tools to get info, then reason to decide next step.
Final output returned when confident.
This cycle enables dynamic problem solving.

Full Transcript

The ReAct agent starts by receiving a user query. It thinks about how to answer, then acts by calling a tool if needed. The tool returns an observation, which the agent stores in memory. The agent thinks again to decide if it has enough info. This loop continues until the agent is confident to return a final answer. For example, when asked 'What is the capital of France?', the agent plans to use a knowledge tool, calls it, gets 'Paris', confirms the answer, and returns it. This process shows how the agent dynamically reasons and acts to solve problems.