Human-in-the-loop with LangGraph in LangChain - Mini Project: Build & Apply

Human-in-the-loop with LangGraph

📖 Scenario: You are building a simple LangGraph workflow that processes user input and allows a human to review and approve the output before finalizing it. This simulates a real-world scenario where AI suggestions need human validation.

🎯 Goal: Create a LangGraph workflow with a human-in-the-loop step that takes a user question, generates an AI response, and then waits for human approval before completing.

📋 What You'll Learn

Create a LangGraph dictionary called graph with a nodes key containing a node named input_node that accepts a question string.

Add a configuration variable called approval_required set to true to control human approval.

Add a node named ai_response_node that uses LangChain's LLMChain to generate a response from the question.

Add a human_approval_node that simulates human approval by checking approval_required and waits for approval before continuing.

💡 Why This Matters

🌍 Real World

Human-in-the-loop workflows are common in AI applications where human review improves accuracy and trust, such as customer support or content moderation.

💼 Career

Understanding how to build and configure LangGraph workflows with human steps is valuable for AI developers and data scientists working on interactive AI systems.

1

Create the initial LangGraph data structure

Create a dictionary called graph with a key nodes containing a dictionary with one node named input_node. This node should have a type of input and accept a question string.

LangChain

# Create the LangGraph dictionary with an input_node
# Your code here

Need a hint?

Think of graph as a map of steps. The input_node is where the user question enters the workflow.

2

Add a configuration variable for approval control

Add a variable called approval_required and set it to true to indicate that human approval is needed.

LangChain

graph = {
    'nodes': {
        'input_node': {
            'type': 'input',
            'inputs': ['question']
        }
    }
}

# Add approval_required variable
# Your code here

Need a hint?

This variable will help us decide if the workflow should wait for human approval.

3

Add AI response node using LLMChain

Add a node named ai_response_node inside graph['nodes'] with type set to LLMChain. It should take question as input and produce response as output.

LangChain

graph = {
    'nodes': {
        'input_node': {
            'type': 'input',
            'inputs': ['question']
        }
    }
}
approval_required = true

# Add ai_response_node to graph['nodes']
# Your code here

Need a hint?

This node uses AI to generate an answer from the question.

4

Add human approval node to control workflow

Add a node named human_approval_node inside graph['nodes'] with type set to human. It should take response as input and have a condition that checks if approval_required is true.

LangChain

graph = {
    'nodes': {
        'input_node': {
            'type': 'input',
            'inputs': ['question']
        },
        'ai_response_node': {
            'type': 'LLMChain',
            'inputs': ['question'],
            'outputs': ['response']
        }
    }
}
approval_required = true

# Add human_approval_node to graph['nodes']
# Your code here

Need a hint?

This node simulates waiting for a human to approve the AI response before continuing.