Add human-in-the-loop

interrupt

The interrupt function 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.

The graph is resumed using a Command object that provides the human's response.

^{API Reference: interrupt | Command}

from langgraph.types import interrupt, Command

def human_node(state: State):
   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']
# >    )
# > ] 

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.

Extended example: using interrupt

from typing import TypedDict
import uuid

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

class State(TypedDict):
   some_text: str

def human_node(state: State):
   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']
# >    )
# > ] 

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.

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. Please read the resuming from an interrupt section for more details.

Requirements

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 Design 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).

Design patterns

There are typically three different actions that you can do with a human-in-the-loop workflow:

1. Approve or Reject: 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. This pattern often involve routing the graph based on the human's input.
2. Edit Graph State: Pause the graph to review and edit the graph state. This is useful for correcting mistakes or updating the state with additional information. This pattern often involves updating the state with the human's input.
3. Get Input: Explicitly request human input at a particular step in the graph. This is useful for collecting additional information or context to inform the agent's decision-making process.

Below we show different design patterns that can be implemented using these actions.

Approve or reject

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.

^{API Reference: interrupt | Command}

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)

Extended example: approve or reject with interrupt

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)

See how to review tool calls for a more detailed example.

Review & edit state

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

^{API Reference: interrupt}

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
)

Extended example: edit state with interrupt

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

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.

def human_review_node(state) -> Command[Literal["call_llm", "run_tool"]]:
    # This is the value we'll be providing via Command(resume=<human_review>)
    human_review = interrupt(
        {
            "question": "Is this correct?",
            # Surface tool calls for review
            "tool_call": tool_call
        }
    )

    review_action, review_data = human_review

    # Approve the tool call and continue
    if review_action == "continue":
        return Command(goto="run_tool")

    # Modify the tool call manually and then continue
    elif review_action == "update":
        ...
        updated_msg = get_updated_msg(review_data)
        # Remember that to modify an existing message you will need
        # to pass the message with a matching ID.
        return Command(goto="run_tool", update={"messages": [updated_message]})

    # Give natural language feedback, and then pass that back to the agent
    elif review_action == "feedback":
        ...
        feedback_msg = get_feedback_msg(review_data)
        return Command(goto="call_llm", update={"messages": [feedback_msg]})

See how to review tool calls for a more detailed example.

Validating 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.

^{API Reference: interrupt}

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
    }

Extended example: validating user input

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

Resume using the Command primitive

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

To resume execution, use the Command primitive, which can be supplied via the invoke, ainvoke, stream, or astream methods.

Providing a response to the interrupt: To continue execution, pass the user's input using Command(resume=value). 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.

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

How does resuming from an interrupt work?

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.

A critical aspect of using interrupt is understanding how resuming works. When you resume execution after an interrupt, graph execution starts from the beginning of the graph node where the last interrupt was triggered.

All code from the beginning of the node to the interrupt will be re-executed.

counter = 0
def node(state: State):
    # All the code from the beginning of the node to the interrupt will be re-executed
    # when the graph resumes.
    global counter
    counter += 1
    print(f"> Entered the node: {counter} # of times")
    # Pause the graph and wait for user input.
    answer = interrupt()
    print("The value of counter is:", counter)
    ...

Upon resuming the graph, the counter will be incremented a second time, resulting in the following output:

> Entered the node: 2 # of times
The value of counter is: 2

Resuming 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 values to resume multiple interrupts 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 pitfalls

Side-effects

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

Side effects before interrupt (BAD)Side effects after interrupt (OK)Side effects in a separate node (OK)

This code will re-execute the API call another time when the node is resumed from the interrupt.

This can be problematic if the API call is not idempotent or is just expensive.

from langgraph.types import interrupt

def human_node(state: State):
    """Human node with validation."""
    api_call(...) # This code will be re-executed when the node is resumed.
    answer = interrupt(question)

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

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

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 (and where an interrupt was triggered). Similarly, the subgraph, will resume from the beginning of the node where the interrupt() function was called.

For example,

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)
    ...

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.

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

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.

Extended example: incorrect code that introduces non-determinism

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)

{'__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'}}