LangChainframework~10 mins

Human-in-the-loop with LangGraph in LangChain - Step-by-Step Execution

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Concept Flow - Human-in-the-loop with LangGraph

Start: User Input

↓

LangGraph Receives Input

↓

LangGraph Processes Input & Generates Query

↓

Human Review & Feedback

↓

Approve

↓

Execute Query

↓

Back to Human Review

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Return Results

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End

The flow starts with user input, LangGraph processes it, then a human reviews and approves or requests changes before execution and returning results.

Execution Sample

LangChain

from typing import TypedDict
from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver

class State(TypedDict):
    input: str
    query: str
    results: str | None

def generate_query(state: State) -> State:
    state["query"] = "SELECT * FROM sales WHERE date > DATE_SUB(NOW(), INTERVAL 1 MONTH)"
    return state

def execute_query(state: State) -> State:
    state["results"] = f"Results from executing: {state['query']}"
    return state

workflow = StateGraph(State)
workflow.add_node("generate", generate_query)
workflow.add_node("execute", execute_query)
workflow.set_entry_point("generate")
workflow.add_edge("generate", "execute")
workflow.add_edge("execute", END)

app = workflow.compile(checkpointer=MemorySaver(), interrupt_before=["execute"])

config = {"configurable": {"thread_id": "1"}}
app.invoke({"input": "Find recent sales data"}, config)

# Human review
state = app.get_state(config).values
query = state["query"]
if human_approves(query):
    results = app.invoke(None, config)["results"]

This code shows LangGraph processing input to generate a query, interrupting for human approval via get_state/update if needed, then resuming execution with invoke(None).

Execution Table

Step	Action	Input/State	Output/State	Notes
1	Receive user input	'Find recent sales data'	Input stored	User input captured by LangGraph
2	Process input	Input stored	Generated query: 'SELECT * FROM sales WHERE date > DATE_SUB(NOW(), INTERVAL 1 MONTH)'	LangGraph creates query from input
3	Human review	Generated query	Approved or modification requested	Human checks query via app.get_state(config)
4	If approved	Approved query	Execute query	Resume with app.invoke(None, config)
5	If modification requested	Modification feedback	Query updated	LangGraph updates via app.update_state(config, updates)
6	Return results	Query executed	Results returned to user	Final output delivered
7	End	Results returned	Process complete	Cycle ends

💡 Process ends after results are returned and user is satisfied.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 5	Final
user_input	None	'Find recent sales data'	'Find recent sales data'	'Find recent sales data'	'Find recent sales data'
query	None	'SELECT * FROM sales WHERE date > DATE_SUB(NOW(), INTERVAL 1 MONTH)'	'Approved' or 'Needs change'	'Updated query if changed'	'Final approved query'
results	None	None	None	None	'Query results data'

Key Moments - 3 Insights

Why does the human need to review the query before execution?

What happens if the human requests a change to the query?

Why is the process considered 'human-in-the-loop'?

Visual Quiz - 3 Questions

Test your understanding

Look at the execution_table, what is the output after step 2?

AUser input stored

BResults returned to user

CGenerated query: 'SELECT * FROM sales WHERE date > DATE_SUB(NOW(), INTERVAL 1 MONTH)'

DQuery executed

Concept Snapshot

Human-in-the-loop with LangGraph:
- User inputs request
- LangGraph generates query
- Human reviews and approves or requests changes
- Approved query executes
- Results returned
- Loop continues until satisfied

Full Transcript

This visual execution trace shows how LangGraph works with a human in the loop. First, the user inputs a request. LangGraph processes this input and generates a query. Then, a human reviews the query to ensure it matches the intent. If approved, the query executes and results are returned. If changes are needed, the human requests modifications, and LangGraph updates the query. This loop continues until the human approves the final query, ensuring accuracy and control over automated query generation.

Practice

(1/5)

1. What is the main purpose of using Human-in-the-loop with LangGraph?

easy

A. To combine AI processing steps with human feedback for better results

B. To replace human input entirely with AI automation

C. To create static AI models without any human interaction

D. To speed up AI training by skipping validation steps

Human-in-the-loop with LangGraph in LangChain - Step-by-Step Execution

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Practice

Solution

Step 1: Understand Human-in-the-loop concept

Step 2: Role of LangGraph in this context

Final Answer:

Quick Check:

Solution

Step 1: Recall LangGraph syntax for adding nodes

Step 2: Identify correct human node creation

Final Answer:

Quick Check:

Solution

Step 1: Analyze flow node order and connections

Step 2: Understand human node behavior in flow.run

Final Answer:

Quick Check:

Solution

Step 1: Check HumanNode instantiation syntax

Step 2: Confirm connection method accepts node names as strings

Final Answer:

Quick Check:

Solution

Step 1: Identify correct node order for the flow

Step 2: Check connections match the desired order

Final Answer:

Quick Check: