Structured chat agent in LangChain - Step-by-Step Execution

Concept Flow - Structured chat agent

User Input

↓

Parse Input

↓

Select Tool/Chain

↓

Execute Tool/Chain

↓

Collect Output

↓

Format Response

↓

Return to User

The structured chat agent takes user input, decides which tool or chain to run, executes it, and returns the formatted answer.

Execution Sample

LangChain

from langchain.agents import StructuredChatAgent
from langchain.chat_models import ChatOpenAI

agent = StructuredChatAgent.from_llm_and_tools(ChatOpenAI(temperature=0), tools)
response = agent.invoke({'input': 'What is the weather today?'})

This code creates a structured chat agent with a chat model and tools, then sends a user question to get a response.

Execution Table

Step	Action	Input/State	Output/State
1	Receive user input	{'input': 'What is the weather today?'}	User query stored
2	Parse input	User query stored	Parsed intent and entities
3	Select tool	Parsed intent and entities	Weather tool selected
4	Execute tool	Weather tool, query	Weather data fetched
5	Format response	Weather data	Formatted answer string
6	Return response	Formatted answer string	Response sent to user

💡 Response returned to user, ending this interaction cycle

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	After Step 5	Final
user_input	None	'What is the weather today?'	'What is the weather today?'	'What is the weather today?'	'What is the weather today?'	'What is the weather today?'
parsed_intent	None	'weather_query'	'weather_query'	'weather_query'	'weather_query'	'weather_query'
selected_tool	None	None	'weather_tool'	'weather_tool'	'weather_tool'	'weather_tool'
tool_output	None	None	None	'Sunny, 25°C'	'Sunny, 25°C'	'Sunny, 25°C'
formatted_response	None	None	None	None	'The weather today is sunny with 25°C.'	'The weather today is sunny with 25°C.'

Key Moments - 2 Insights

How does the agent know which tool to use for the user's question?

The agent parses the input to find the intent and entities (see Step 2 and Step 3 in the execution table), then selects the appropriate tool based on that intent.

What happens if the tool execution fails or returns no data?

The agent would handle this by formatting a fallback response or error message during the formatting step (Step 5), ensuring the user still gets a reply.

Visual Quiz - 3 Questions

Look at the execution table, what is the output after Step 4?

A 'Sunny, 25°C'

B 'weather_tool'

C 'Parsed intent and entities'

D 'Formatted answer string'

💡 Hint

Check the 'Output/State' column for Step 4 in the execution table.

At which step does the agent decide which tool to use?

A Step 5

B Step 2

C Step 3

D Step 6

💡 Hint

Look for the step labeled 'Select tool' in the execution table.

If the user input changes, which variable in the variable tracker changes first?

A parsed_intent

B user_input

C selected_tool

D tool_output

💡 Hint

Check the variable tracker for the variable that stores the raw user question.

Concept Snapshot

Structured chat agent flow:
1. Receive user input
2. Parse input for intent
3. Select appropriate tool
4. Run tool to get data
5. Format response
6. Return answer
Use langchain's StructuredChatAgent with chat models and tools for modular, clear conversations.

Full Transcript

A structured chat agent in langchain works by taking the user's question, understanding what the user wants, choosing the right tool to answer, running that tool, and then giving back a clear answer. The process starts with receiving input, then parsing it to find the intent. Next, the agent picks the tool that fits the intent, runs it to get information, formats the answer nicely, and finally sends it back to the user. Variables like user_input, parsed_intent, selected_tool, tool_output, and formatted_response change step by step as the agent works. This flow helps keep conversations organized and easy to manage.