langgraph/docs/docs/how-tos/human_in_the_loop/add-human-in-the-loop.md

search, tags, hide

search	tags	hide
boost 2	human-in-the-loop hil interrupt	tags

Enable human intervention

To review, edit, and approve tool calls in an agent or workflow, use LangGraph's human-in-the-loop features.

Pause using interrupt

The [interrupt function][langgraph.types.interrupt] in LangGraph enables human-in-the-loop workflows by pausing the graph at a specific node, presenting information to a human, and resuming the graph with their input. It's useful for tasks like approvals, edits, or gathering additional context.

To use interrupt in your graph, you need to:

1. Specify a checkpointer to save the graph state after each step.
2. Call interrupt() in the appropriate place. See the Common Patterns section for examples.
3. Run the graph with a thread ID until the interrupt is hit.
4. Resume execution using invoke/ainvoke/stream/astream (see The Command primitive).

# highlight-next-line
from langgraph.types import interrupt, Command

def human_node(state: State):
    # highlight-next-line
    value = interrupt( # (1)!
        {
            "text_to_revise": state["some_text"] # (2)!
        }
    )
    return {
        "some_text": value # (3)!
    }


graph = graph_builder.compile(checkpointer=checkpointer) # (4)!

# Run the graph until the interrupt is hit.
config = {"configurable": {"thread_id": "some_id"}}
result = graph.invoke({"some_text": "original text"}, config=config) # (5)!
print(result['__interrupt__']) # (6)!
# > [
# >    Interrupt(
# >       value={'text_to_revise': 'original text'}, 
# >       resumable=True,
# >       ns=['human_node:6ce9e64f-edef-fe5d-f7dc-511fa9526960']
# >    )
# > ] 

# highlight-next-line
print(graph.invoke(Command(resume="Edited text"), config=config)) # (7)!
# > {'some_text': 'Edited text'}

1. interrupt(...) pauses execution at human_node, surfacing the given payload to a human.
2. Any JSON serializable value can be passed to the interrupt function. Here, a dict containing the text to revise.
3. Once resumed, the return value of interrupt(...) is the human-provided input, which is used to update the state.
4. A checkpointer is required to persist graph state. In production, this should be durable (e.g., backed by a database).
5. The graph is invoked with some initial state.
6. When the graph hits the interrupt, it returns an Interrupt object with the payload and metadata.
7. The graph is resumed with a Command(resume=...), injecting the human's input and continuing execution.

??? example "Extended example: using interrupt"

```python
from typing import TypedDict
import uuid

from langgraph.checkpoint.memory import InMemorySaver
from langgraph.constants import START
from langgraph.graph import StateGraph
# highlight-next-line
from langgraph.types import interrupt, Command

class State(TypedDict):
    some_text: str

def human_node(state: State):
    # highlight-next-line
    value = interrupt( # (1)!
        {
            "text_to_revise": state["some_text"] # (2)!
        }
    )
    return {
        "some_text": value # (3)!
    }


# Build the graph
graph_builder = StateGraph(State)
graph_builder.add_node("human_node", human_node)
graph_builder.add_edge(START, "human_node")

checkpointer = InMemorySaver() # (4)!

graph = graph_builder.compile(checkpointer=checkpointer)

# Pass a thread ID to the graph to run it.
config = {"configurable": {"thread_id": uuid.uuid4()}}

# Run the graph until the interrupt is hit.
result = graph.invoke({"some_text": "original text"}, config=config) # (5)!

print(result['__interrupt__']) # (6)!
# > [
# >    Interrupt(
# >       value={'text_to_revise': 'original text'}, 
# >       resumable=True,
# >       ns=['human_node:6ce9e64f-edef-fe5d-f7dc-511fa9526960']
# >    )
# > ] 

# highlight-next-line
print(graph.invoke(Command(resume="Edited text"), config=config)) # (7)!
# > {'some_text': 'Edited text'}
```

1. `interrupt(...)` pauses execution at `human_node`, surfacing the given payload to a human.
2. Any JSON serializable value can be passed to the `interrupt` function. Here, a dict containing the text to revise.
3. Once resumed, the return value of `interrupt(...)` is the human-provided input, which is used to update the state.
4. A checkpointer is required to persist graph state. In production, this should be durable (e.g., backed by a database).
5. The graph is invoked with some initial state.
6. When the graph hits the interrupt, it returns an `Interrupt` object with the payload and metadata.
7. The graph is resumed with a `Command(resume=...)`, injecting the human's input and continuing execution.

!!! tip "New in 0.4.0"

  `__interrupt__` is a special key that will be returned when running the graph if the graph is interrupted. Support for `__interrupt__` in `invoke` and `ainvoke` has been added in version 0.4.0. If you're on an older version, you will only see `__interrupt__` in the result if you use `stream` or `astream`. You can also use `graph.get_state(thread_id)` to get the interrupt value.

!!! warning

  Interrupts are both powerful and ergonomic. However, while they may resemble Python's input() function in terms of developer experience, it's important to note that they do not automatically resume execution from the interruption point. Instead, they rerun the entire node where the interrupt was used. For this reason, interrupts are typically best placed at the start of a node or in a dedicated node.

Resume using the Command primitive

!!! warning

Resuming from an `interrupt` is different from Python's `input()` function, where execution resumes from the exact point where the `input()` function was called.

When the interrupt function is used within a graph, execution pauses at that point and awaits user input.

To resume execution, use the [Command][langgraph.types.Command] primitive, which can be supplied via the invoke, ainvoke, stream, or astream methods. The graph resumes execution from the beginning of the node where interrupt(...) was initially called. This time, the interrupt function will return the value provided in Command(resume=value) rather than pausing again. All code from the beginning of the node to the interrupt will be re-executed.

# Resume graph execution by providing the user's input.
graph.invoke(Command(resume={"age": "25"}), thread_config)

Resume multiple interrupts with one invocation

If you have multiple interrupts in the task queue, you can use Command.resume with a dictionary mapping of interrupt ids to resume with a single invoke / stream call.

For example, once your graph has been interrupted (multiple times, theoretically) and is stalled:

resume_map = {
    i.interrupt_id: f"human input for prompt {i.value}"
    for i in parent.get_state(thread_config).interrupts
}

parent_graph.invoke(Command(resume=resume_map), config=thread_config)

Common patterns

Below we show different design patterns that can be implemented using interrupt and Command.

Approve or reject

{: style="max-height:400px"}

Depending on the human's approval or rejection, the graph can proceed with the action or take an alternative path.

Pause the graph before a critical step, such as an API call, to review and approve the action. If the action is rejected, you can prevent the graph from executing the step, and potentially take an alternative action.

from typing import Literal
from langgraph.types import interrupt, Command

def human_approval(state: State) -> Command[Literal["some_node", "another_node"]]:
    is_approved = interrupt(
        {
            "question": "Is this correct?",
            # Surface the output that should be
            # reviewed and approved by the human.
            "llm_output": state["llm_output"]
        }
    )

    if is_approved:
        return Command(goto="some_node")
    else:
        return Command(goto="another_node")

# Add the node to the graph in an appropriate location
# and connect it to the relevant nodes.
graph_builder.add_node("human_approval", human_approval)
graph = graph_builder.compile(checkpointer=checkpointer)

# After running the graph and hitting the interrupt, the graph will pause.
# Resume it with either an approval or rejection.
thread_config = {"configurable": {"thread_id": "some_id"}}
graph.invoke(Command(resume=True), config=thread_config)

??? example "Extended example: approve or reject with interrupt"

```python
from typing import Literal, TypedDict
import uuid

from langgraph.constants import START, END
from langgraph.graph import StateGraph
from langgraph.types import interrupt, Command
from langgraph.checkpoint.memory import MemorySaver

# Define the shared graph state
class State(TypedDict):
    llm_output: str
    decision: str

# Simulate an LLM output node
def generate_llm_output(state: State) -> State:
    return {"llm_output": "This is the generated output."}

# Human approval node
def human_approval(state: State) -> Command[Literal["approved_path", "rejected_path"]]:
    decision = interrupt({
        "question": "Do you approve the following output?",
        "llm_output": state["llm_output"]
    })

    if decision == "approve":
        return Command(goto="approved_path", update={"decision": "approved"})
    else:
        return Command(goto="rejected_path", update={"decision": "rejected"})

# Next steps after approval
def approved_node(state: State) -> State:
    print("✅ Approved path taken.")
    return state

# Alternative path after rejection
def rejected_node(state: State) -> State:
    print("❌ Rejected path taken.")
    return state

# Build the graph
builder = StateGraph(State)
builder.add_node("generate_llm_output", generate_llm_output)
builder.add_node("human_approval", human_approval)
builder.add_node("approved_path", approved_node)
builder.add_node("rejected_path", rejected_node)

builder.set_entry_point("generate_llm_output")
builder.add_edge("generate_llm_output", "human_approval")
builder.add_edge("approved_path", END)
builder.add_edge("rejected_path", END)

checkpointer = MemorySaver()
graph = builder.compile(checkpointer=checkpointer)

# Run until interrupt
config = {"configurable": {"thread_id": uuid.uuid4()}}
result = graph.invoke({}, config=config)
print(result["__interrupt__"])
# Output:
# Interrupt(value={'question': 'Do you approve the following output?', 'llm_output': 'This is the generated output.'}, ...)

# Simulate resuming with human input
# To test rejection, replace resume="approve" with resume="reject"
final_result = graph.invoke(Command(resume="approve"), config=config)
print(final_result)
```

Review and edit state

{: style="max-height:400px"}

A human can review and edit the state of the graph. This is useful for correcting mistakes or updating the state with additional information.

from langgraph.types import interrupt

def human_editing(state: State):
    ...
    result = interrupt(
        # Interrupt information to surface to the client.
        # Can be any JSON serializable value.
        {
            "task": "Review the output from the LLM and make any necessary edits.",
            "llm_generated_summary": state["llm_generated_summary"]
        }
    )

    # Update the state with the edited text
    return {
        "llm_generated_summary": result["edited_text"] 
    }

# Add the node to the graph in an appropriate location
# and connect it to the relevant nodes.
graph_builder.add_node("human_editing", human_editing)
graph = graph_builder.compile(checkpointer=checkpointer)

...

# After running the graph and hitting the interrupt, the graph will pause.
# Resume it with the edited text.
thread_config = {"configurable": {"thread_id": "some_id"}}
graph.invoke(
    Command(resume={"edited_text": "The edited text"}), 
    config=thread_config
)

??? example "Extended example: edit state with interrupt"

```python
from typing import TypedDict
import uuid

from langgraph.constants import START, END
from langgraph.graph import StateGraph
from langgraph.types import interrupt, Command
from langgraph.checkpoint.memory import MemorySaver

# Define the graph state
class State(TypedDict):
    summary: str

# Simulate an LLM summary generation
def generate_summary(state: State) -> State:
    return {
        "summary": "The cat sat on the mat and looked at the stars."
    }

# Human editing node
def human_review_edit(state: State) -> State:
    result = interrupt({
        "task": "Please review and edit the generated summary if necessary.",
        "generated_summary": state["summary"]
    })
    return {
        "summary": result["edited_summary"]
    }

# Simulate downstream use of the edited summary
def downstream_use(state: State) -> State:
    print(f"✅ Using edited summary: {state['summary']}")
    return state

# Build the graph
builder = StateGraph(State)
builder.add_node("generate_summary", generate_summary)
builder.add_node("human_review_edit", human_review_edit)
builder.add_node("downstream_use", downstream_use)

builder.set_entry_point("generate_summary")
builder.add_edge("generate_summary", "human_review_edit")
builder.add_edge("human_review_edit", "downstream_use")
builder.add_edge("downstream_use", END)

# Set up in-memory checkpointing for interrupt support
checkpointer = MemorySaver()
graph = builder.compile(checkpointer=checkpointer)

# Invoke the graph until it hits the interrupt
config = {"configurable": {"thread_id": uuid.uuid4()}}
result = graph.invoke({}, config=config)

# Output interrupt payload
print(result["__interrupt__"])
# Example output:
# Interrupt(
#   value={
#     'task': 'Please review and edit the generated summary if necessary.',
#     'generated_summary': 'The cat sat on the mat and looked at the stars.'
#   },
#   resumable=True,
#   ...
# )

# Resume the graph with human-edited input
edited_summary = "The cat lay on the rug, gazing peacefully at the night sky."
resumed_result = graph.invoke(
    Command(resume={"edited_summary": edited_summary}),
    config=config
)
print(resumed_result)
```

Review tool calls

{: style="max-height:400px"}

A human can review and edit the output from the LLM before proceeding. This is particularly critical in applications where the tool calls requested by the LLM may be sensitive or require human oversight.

To add a human approval step to a tool:

1. Use interrupt() in the tool to pause execution.
2. Resume with a Command(resume=...) to continue based on human input.

from langgraph.checkpoint.memory import InMemorySaver
from langgraph.types import interrupt
from langgraph.prebuilt import create_react_agent

# An example of a sensitive tool that requires human review / approval
def book_hotel(hotel_name: str):
    """Book a hotel"""
    # highlight-next-line
    response = interrupt(  # (1)!
        f"Trying to call `book_hotel` with args {{'hotel_name': {hotel_name}}}. "
        "Please approve or suggest edits."
    )
    if response["type"] == "accept":
        pass
    elif response["type"] == "edit":
        hotel_name = response["args"]["hotel_name"]
    else:
        raise ValueError(f"Unknown response type: {response['type']}")
    return f"Successfully booked a stay at {hotel_name}."

# highlight-next-line
checkpointer = InMemorySaver() # (2)!

agent = create_react_agent(
    model="anthropic:claude-3-5-sonnet-latest",
    tools=[book_hotel],
    # highlight-next-line
    checkpointer=checkpointer, # (3)!
)

1. The [interrupt function][langgraph.types.interrupt] pauses the agent graph at a specific node. In this case, we call interrupt() at the beginning of the tool function, which pauses the graph at the node that executes the tool. The information inside interrupt() (e.g., tool calls) can be presented to a human, and the graph can be resumed with the user input (tool call approval, edit or feedback).
2. The InMemorySaver is used to store the agent state at every step in the tool calling loop. This enables short-term memory and human-in-the-loop capabilities. In this example, we use InMemorySaver to store the agent state in memory. In a production application, the agent state will be stored in a database.
3. Initialize the agent with the checkpointer.

Run the agent with the stream() method, passing the config object to specify the thread ID. This allows the agent to resume the same conversation on future invocations.

config = {
   "configurable": {
      # highlight-next-line
      "thread_id": "1"
   }
}

for chunk in agent.stream(
    {"messages": [{"role": "user", "content": "book a stay at McKittrick hotel"}]},
    # highlight-next-line
    config
):
    print(chunk)
    print("\n")

You should see that the agent runs until it reaches the interrupt() call, at which point it pauses and waits for human input.

Resume the agent with a Command(resume=...) to continue based on human input.

from langgraph.types import Command

for chunk in agent.stream(
    # highlight-next-line
    Command(resume={"type": "accept"}),  # (1)!
    # Command(resume={"type": "edit", "args": {"hotel_name": "McKittrick Hotel"}}),
    config
):
    print(chunk)
    print("\n")

1. The [interrupt function][langgraph.types.interrupt] is used in conjunction with the [Command][langgraph.types.Command] object to resume the graph with a value provided by the human.

Add interrupts to any tool

You can create a wrapper to add interrupts to any tool. The example below provides a reference implementation compatible with Agent Inbox UI and Agent Chat UI.

from typing import Callable
from langchain_core.tools import BaseTool, tool as create_tool
from langchain_core.runnables import RunnableConfig
from langgraph.types import interrupt 
from langgraph.prebuilt.interrupt import HumanInterruptConfig, HumanInterrupt

def add_human_in_the_loop(
    tool: Callable | BaseTool,
    *,
    interrupt_config: HumanInterruptConfig = None,
) -> BaseTool:
    """Wrap a tool to support human-in-the-loop review.""" 
    if not isinstance(tool, BaseTool):
        tool = create_tool(tool)

    if interrupt_config is None:
        interrupt_config = {
            "allow_accept": True,
            "allow_edit": True,
            "allow_respond": True,
        }

    @create_tool(  # (1)!
        tool.name,
        description=tool.description,
        args_schema=tool.args_schema
    )
    def call_tool_with_interrupt(config: RunnableConfig, **tool_input):
        request: HumanInterrupt = {
            "action_request": {
                "action": tool.name,
                "args": tool_input
            },
            "config": interrupt_config,
            "description": "Please review the tool call"
        }
        # highlight-next-line
        response = interrupt([request])[0]  # (2)!
        # approve the tool call
        if response["type"] == "accept":
            tool_response = tool.invoke(tool_input, config)
        # update tool call args
        elif response["type"] == "edit":
            tool_input = response["args"]["args"]
            tool_response = tool.invoke(tool_input, config)
        # respond to the LLM with user feedback
        elif response["type"] == "response":
            user_feedback = response["args"]
            tool_response = user_feedback
        else:
            raise ValueError(f"Unsupported interrupt response type: {response['type']}")

        return tool_response

    return call_tool_with_interrupt

1. This wrapper creates a new tool that calls interrupt() before executing the wrapped tool.
2. interrupt() is using special input and output format that's expected by Agent Inbox UI:
   • a list of [HumanInterrupt][langgraph.prebuilt.interrupt.HumanInterrupt] objects is sent to AgentInbox render interrupt information to the end user
   • resume value is provided by AgentInbox as a list (i.e., Command(resume=[...]))

You can use the add_human_in_the_loop wrapper to add interrupt() to any tool without having to add it inside the tool:

from langgraph.checkpoint.memory import InMemorySaver
from langgraph.prebuilt import create_react_agent

# highlight-next-line
checkpointer = InMemorySaver()

def book_hotel(hotel_name: str):
   """Book a hotel"""
   return f"Successfully booked a stay at {hotel_name}."


agent = create_react_agent(
    model="anthropic:claude-3-5-sonnet-latest",
    tools=[
        # highlight-next-line
        add_human_in_the_loop(book_hotel), # (1)!
    ],
    # highlight-next-line
    checkpointer=checkpointer,
)

config = {"configurable": {"thread_id": "1"}}

# Run the agent
for chunk in agent.stream(
    {"messages": [{"role": "user", "content": "book a stay at McKittrick hotel"}]},
    # highlight-next-line
    config
):
    print(chunk)
    print("\n")

1. The add_human_in_the_loop wrapper is used to add interrupt() to the tool. This allows the agent to pause execution and wait for human input before proceeding with the tool call.

You should see that the agent runs until it reaches the interrupt() call, at which point it pauses and waits for human input.

Resume the agent with a Command(resume=...) to continue based on human input.

from langgraph.types import Command 

for chunk in agent.stream(
    # highlight-next-line
    Command(resume=[{"type": "accept"}]),
    # Command(resume=[{"type": "edit", "args": {"args": {"hotel_name": "McKittrick Hotel"}}}]),
    config
):
    print(chunk)
    print("\n")

Validate human input

If you need to validate the input provided by the human within the graph itself (rather than on the client side), you can achieve this by using multiple interrupt calls within a single node.

from langgraph.types import interrupt

def human_node(state: State):
    """Human node with validation."""
    question = "What is your age?"

    while True:
        answer = interrupt(question)

        # Validate answer, if the answer isn't valid ask for input again.
        if not isinstance(answer, int) or answer < 0:
            question = f"'{answer} is not a valid age. What is your age?"
            answer = None
            continue
        else:
            # If the answer is valid, we can proceed.
            break
            
    print(f"The human in the loop is {answer} years old.")
    return {
        "age": answer
    }

??? example "Extended example: validating user input"

```python
from typing import TypedDict
import uuid

from langgraph.constants import START, END
from langgraph.graph import StateGraph
from langgraph.types import interrupt, Command
from langgraph.checkpoint.memory import MemorySaver

# Define graph state
class State(TypedDict):
    age: int

# Node that asks for human input and validates it
def get_valid_age(state: State) -> State:
    prompt = "Please enter your age (must be a non-negative integer)."

    while True:
        user_input = interrupt(prompt)

        # Validate the input
        try:
            age = int(user_input)
            if age < 0:
                raise ValueError("Age must be non-negative.")
            break  # Valid input received
        except (ValueError, TypeError):
            prompt = f"'{user_input}' is not valid. Please enter a non-negative integer for age."

    return {"age": age}

# Node that uses the valid input
def report_age(state: State) -> State:
    print(f"✅ Human is {state['age']} years old.")
    return state

# Build the graph
builder = StateGraph(State)
builder.add_node("get_valid_age", get_valid_age)
builder.add_node("report_age", report_age)

builder.set_entry_point("get_valid_age")
builder.add_edge("get_valid_age", "report_age")
builder.add_edge("report_age", END)

# Create the graph with a memory checkpointer
checkpointer = MemorySaver()
graph = builder.compile(checkpointer=checkpointer)

# Run the graph until the first interrupt
config = {"configurable": {"thread_id": uuid.uuid4()}}
result = graph.invoke({}, config=config)
print(result["__interrupt__"])  # First prompt: "Please enter your age..."

# Simulate an invalid input (e.g., string instead of integer)
result = graph.invoke(Command(resume="not a number"), config=config)
print(result["__interrupt__"])  # Follow-up prompt with validation message

# Simulate a second invalid input (e.g., negative number)
result = graph.invoke(Command(resume="-10"), config=config)
print(result["__interrupt__"])  # Another retry

# Provide valid input
final_result = graph.invoke(Command(resume="25"), config=config)
print(final_result)  # Should include the valid age
```

Considerations

When using human-in-the-loop, there are some considerations to keep in mind.

Using with code with side-effects

Place code with side effects, such as API calls, after the interrupt or in a separate node to avoid duplication, as these are re-triggered every time the node is resumed.

=== "Side effects after interrupt"

```python
from langgraph.types import interrupt

def human_node(state: State):
    """Human node with validation."""
    
    answer = interrupt(question)
    
    api_call(answer) # OK as it's after the interrupt
```

=== "Side effects in a separate node"

```python
from langgraph.types import interrupt

def human_node(state: State):
    """Human node with validation."""
    
    answer = interrupt(question)
    
    return {
        "answer": answer
    }

def api_call_node(state: State):
    api_call(...) # OK as it's in a separate node
```

Using with subgraphs called as functions

When invoking a subgraph as a function, the parent graph will resume execution from the beginning of the node where the subgraph was invoked where the interrupt was triggered. Similarly, the subgraph will resume from the beginning of the node where the interrupt() function was called.

def node_in_parent_graph(state: State):
    some_code()  # <-- This will re-execute when the subgraph is resumed.
    # Invoke a subgraph as a function.
    # The subgraph contains an `interrupt` call.
    subgraph_result = subgraph.invoke(some_input)
    ...

??? example "Extended example: parent and subgraph execution flow"

Say we have a parent graph with 3 nodes:

**Parent Graph**: `node_1` → `node_2` (subgraph call) → `node_3`

And the subgraph has 3 nodes, where the second node contains an `interrupt`:

**Subgraph**: `sub_node_1` → `sub_node_2` (`interrupt`) → `sub_node_3`

When resuming the graph, the execution will proceed as follows:

1. **Skip `node_1`** in the parent graph (already executed, graph state was saved in snapshot).
2. **Re-execute `node_2`** in the parent graph from the start.
3. **Skip `sub_node_1`** in the subgraph (already executed, graph state was saved in snapshot).
4. **Re-execute `sub_node_2`** in the subgraph from the beginning.
5. Continue with `sub_node_3` and subsequent nodes.

Here is abbreviated example code that you can use to understand how subgraphs work with interrupts.
It counts the number of times each node is entered and prints the count.

```python
import uuid
from typing import TypedDict

from langgraph.graph import StateGraph
from langgraph.constants import START
from langgraph.types import interrupt, Command
from langgraph.checkpoint.memory import MemorySaver


class State(TypedDict):
    """The graph state."""
    state_counter: int


counter_node_in_subgraph = 0

def node_in_subgraph(state: State):
    """A node in the sub-graph."""
    global counter_node_in_subgraph
    counter_node_in_subgraph += 1  # This code will **NOT** run again!
    print(f"Entered `node_in_subgraph` a total of {counter_node_in_subgraph} times")

counter_human_node = 0

def human_node(state: State):
    global counter_human_node
    counter_human_node += 1 # This code will run again!
    print(f"Entered human_node in sub-graph a total of {counter_human_node} times")
    answer = interrupt("what is your name?")
    print(f"Got an answer of {answer}")


checkpointer = MemorySaver()

subgraph_builder = StateGraph(State)
subgraph_builder.add_node("some_node", node_in_subgraph)
subgraph_builder.add_node("human_node", human_node)
subgraph_builder.add_edge(START, "some_node")
subgraph_builder.add_edge("some_node", "human_node")
subgraph = subgraph_builder.compile(checkpointer=checkpointer)


counter_parent_node = 0

def parent_node(state: State):
    """This parent node will invoke the subgraph."""
    global counter_parent_node

    counter_parent_node += 1 # This code will run again on resuming!
    print(f"Entered `parent_node` a total of {counter_parent_node} times")

    # Please note that we're intentionally incrementing the state counter
    # in the graph state as well to demonstrate that the subgraph update
    # of the same key will not conflict with the parent graph (until
    subgraph_state = subgraph.invoke(state)
    return subgraph_state


builder = StateGraph(State)
builder.add_node("parent_node", parent_node)
builder.add_edge(START, "parent_node")

# A checkpointer must be enabled for interrupts to work!
checkpointer = MemorySaver()
graph = builder.compile(checkpointer=checkpointer)

config = {
    "configurable": {
      "thread_id": uuid.uuid4(),
    }
}

for chunk in graph.stream({"state_counter": 1}, config):
    print(chunk)

print('--- Resuming ---')

for chunk in graph.stream(Command(resume="35"), config):
    print(chunk)
```

This will print out

```pycon
Entered `parent_node` a total of 1 times
Entered `node_in_subgraph` a total of 1 times
Entered human_node in sub-graph a total of 1 times
{'__interrupt__': (Interrupt(value='what is your name?', resumable=True, ns=['parent_node:4c3a0248-21f0-1287-eacf-3002bc304db4', 'human_node:2fe86d52-6f70-2a3f-6b2f-b1eededd6348'], when='during'),)}
--- Resuming ---
Entered `parent_node` a total of 2 times
Entered human_node in sub-graph a total of 2 times
Got an answer of 35
{'parent_node': {'state_counter': 1}}
```

Using multiple interrupts

Using multiple interrupts within a single node can be helpful for patterns like validating human input. However, using multiple interrupts in the same node can lead to unexpected behavior if not handled carefully.

When a node contains multiple interrupt calls, LangGraph keeps a list of resume values specific to the task executing the node. Whenever execution resumes, it starts at the beginning of the node. For each interrupt encountered, LangGraph checks if a matching value exists in the task's resume list. Matching is strictly index-based, so the order of interrupt calls within the node is critical.

To avoid issues, refrain from dynamically changing the node's structure between executions. This includes adding, removing, or reordering interrupt calls, as such changes can result in mismatched indices. These problems often arise from unconventional patterns, such as mutating state via Command(resume=..., update=SOME_STATE_MUTATION) or relying on global variables to modify the node’s structure dynamically.

??? example "Extended example: incorrect code that introduces non-determinism"

```python
import uuid
from typing import TypedDict, Optional

from langgraph.graph import StateGraph
from langgraph.constants import START 
from langgraph.types import interrupt, Command
from langgraph.checkpoint.memory import MemorySaver


class State(TypedDict):
    """The graph state."""

    age: Optional[str]
    name: Optional[str]


def human_node(state: State):
    if not state.get('name'):
        name = interrupt("what is your name?")
    else:
        name = "N/A"

    if not state.get('age'):
        age = interrupt("what is your age?")
    else:
        age = "N/A"
        
    print(f"Name: {name}. Age: {age}")
    
    return {
        "age": age,
        "name": name,
    }


builder = StateGraph(State)
builder.add_node("human_node", human_node)
builder.add_edge(START, "human_node")

# A checkpointer must be enabled for interrupts to work!
checkpointer = MemorySaver()
graph = builder.compile(checkpointer=checkpointer)

config = {
    "configurable": {
        "thread_id": uuid.uuid4(),
    }
}

for chunk in graph.stream({"age": None, "name": None}, config):
    print(chunk)

for chunk in graph.stream(Command(resume="John", update={"name": "foo"}), config):
    print(chunk)
```

```pycon
{'__interrupt__': (Interrupt(value='what is your name?', resumable=True, ns=['human_node:3a007ef9-c30d-c357-1ec1-86a1a70d8fba'], when='during'),)}
Name: N/A. Age: John
{'human_node': {'age': 'John', 'name': 'N/A'}}
```