Alpha Notice: These docs cover the v1-alpha release. Content is incomplete and subject to change.For the latest stable version, see the current LangGraph Python or LangGraph JavaScript docs.
Interrupts allow you to pause graph execution at specific points and wait for external input before continuing. This enables human-in-the-loop patterns where you need external input to proceed. When an interrupt is triggered, LangGraph saves the graph state using its persistence layer and waits indefinitely until you resume execution.Interrupts work by calling the interrupt() function at any point in your graph nodes. The function accepts any JSON-serializable value which is surfaced to the caller. When you’re ready to continue, you resume execution by re-invoking the graph using Command, which then becomes the return value of the interrupt() call from inside the node.Unlike static breakpoints (which pause before or after specific nodes), interrupts are dynamic—they can be placed anywhere in your code and can be conditional based on your application logic.
Checkpointing keeps your place: the checkpointer writes the exact graph state so you can resume later, even when in an error state.
thread_id is your pointer: set config={"configurable": {"thread_id": ...}} to tell the checkpointer which state to load.
Interrupt payloads surface as __interrupt__: the values you pass to interrupt() return to the caller in the __interrupt__ field so you know what the graph is waiting on.
The thread_id you choose is effectively your persistent cursor. Reusing it resumes the same checkpoint; using a new value starts a brand-new thread with an empty state.
The interrupt function pauses graph execution and returns a value to the caller. When you call interrupt within a node, LangGraph saves the current graph state and waits for you to resume execution with input.To use interrupt, you need:
A checkpointer to persist the graph state (use a durable checkpointer in production)
A thread ID in your config so the runtime knows which state to resume from
To call interrupt() where you want to pause (payload must be JSON-serializable)
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from langgraph.types import interruptdef approval_node(state: State): # Pause and ask for approval approved = interrupt("Do you approve this action?") # When you resume, Command(resume=...) returns that value here return {"approved": approved}
When you call interrupt, here’s what happens:
Graph execution gets suspended at the exact point where interrupt is called
State is saved using the checkpointer so execution can be resumed later, In production, this should be a persistent checkpointer (e.g. backed by a database)
Value is returned to the caller under __interrupt__; it can be any JSON-serializable value (string, object, array, etc.)
Graph waits indefinitely until you resume execution with a response
Response is passed back into the node when you resume, becoming the return value of the interrupt() call
After an interrupt pauses execution, you resume the graph by invoking it again with a Command that contains the resume value. The resume value is passed back to the interrupt call, allowing the node to continue execution with the external input.
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from langgraph.types import Command# Initial run - hits the interrupt and pauses# thread_id is the persistent pointer (stores a stable ID in production)config = {"configurable": {"thread_id": "thread-1"}}result = graph.invoke({"input": "data"}, config=config)# Check what was interrupted# __interrupt__ contains the payload that was passed to interrupt()print(result["__interrupt__"])# > [Interrupt(value='Do you approve this action?')]# Resume with the human's response# The resume payload becomes the return value of interrupt() inside the nodegraph.invoke(Command(resume=True), config=config)
Key points about resuming:
You must use the same thread ID when resuming that was used when the interrupt occurred
The value passed to Command(resume=...) becomes the return value of the interrupt call
The node restarts from the beginning of the node where the interrupt was called when resumed, so any code before the interrupt runs again
You can pass any JSON-serializable value as the resume value
The key thing that interrupts unlock is the ability to pause execution and wait for external input. This is useful for a variety of use cases, including:
One of the most common uses of interrupts is to pause before a critical action and ask for approval. For example, you might want to ask a human to approve an API call, a database change, or any other important decision.
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from typing import Literalfrom langgraph.types import interrupt, Commanddef approval_node(state: State) -> Command[Literal["proceed", "cancel"]]: # Pause execution; payload shows up under result["__interrupt__"] is_approved = interrupt({ "question": "Do you want to proceed with this action?", "details": state["action_details"] }) # Route based on the response if is_approved: return Command(goto="proceed") # Runs after the resume payload is provided else: return Command(goto="cancel")
When you resume the graph, pass true to approve or false to reject:
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# To approvegraph.invoke(Command(resume=True), config=config)# To rejectgraph.invoke(Command(resume=False), config=config)
Full example
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import sqlite3from typing import Literal, Optional, TypedDictfrom langgraph.checkpoint.memory import MemorySaverfrom langgraph.graph import StateGraph, START, ENDfrom langgraph.types import Command, interruptclass ApprovalState(TypedDict): action_details: str status: Optional[Literal["pending", "approved", "rejected"]]def approval_node(state: ApprovalState) -> Command[Literal["proceed", "cancel"]]: # Expose details so the caller can render them in a UI decision = interrupt({ "question": "Approve this action?", "details": state["action_details"], }) # Route to the appropriate node after resume return Command(goto="proceed" if decision else "cancel")def proceed_node(state: ApprovalState): return {"status": "approved"}def cancel_node(state: ApprovalState): return {"status": "rejected"}builder = StateGraph(ApprovalState)builder.add_node("approval", approval_node)builder.add_node("proceed", proceed_node)builder.add_node("cancel", cancel_node)builder.add_edge(START, "approval")builder.add_edge("approval", "proceed")builder.add_edge("approval", "cancel")builder.add_edge("proceed", END)builder.add_edge("cancel", END)# Use a more durable checkpointer in productioncheckpointer = MemorySaver()graph = builder.compile(checkpointer=checkpointer)config = {"configurable": {"thread_id": "approval-123"}}initial = graph.invoke( {"action_details": "Transfer $500", "status": "pending"}, config=config,)print(initial["__interrupt__"]) # -> [Interrupt(value={'question': ..., 'details': ...})]# Resume with the decision; True routes to proceed, False to cancelresumed = graph.invoke(Command(resume=True), config=config)print(resumed["status"]) # -> "approved"
Sometimes you want to let a human review and edit part of the graph state before continuing. This is useful for correcting LLMs, adding missing information, or making adjustments.
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from langgraph.types import interruptdef review_node(state: State): # Pause and show the current content for review (surfaces in result["__interrupt__"]) edited_content = interrupt({ "instruction": "Review and edit this content", "content": state["generated_text"] }) # Update the state with the edited version return {"generated_text": edited_content}
When resuming, provide the edited content:
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graph.invoke( Command(resume="The edited and improved text"), # Value becomes the return from interrupt() config=config)
Full example
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import sqlite3from typing import TypedDictfrom langgraph.checkpoint.memory import MemorySaverfrom langgraph.graph import StateGraph, START, ENDfrom langgraph.types import Command, interruptclass ReviewState(TypedDict): generated_text: strdef review_node(state: ReviewState): # Ask a reviewer to edit the generated content updated = interrupt({ "instruction": "Review and edit this content", "content": state["generated_text"], }) return {"generated_text": updated}builder = StateGraph(ReviewState)builder.add_node("review", review_node)builder.add_edge(START, "review")builder.add_edge("review", END)checkpointer = MemorySaver()graph = builder.compile(checkpointer=checkpointer)config = {"configurable": {"thread_id": "review-42"}}initial = graph.invoke({"generated_text": "Initial draft"}, config=config)print(initial["__interrupt__"]) # -> [Interrupt(value={'instruction': ..., 'content': ...})]# Resume with the edited text from the reviewerfinal_state = graph.invoke( Command(resume="Improved draft after review"), config=config,)print(final_state["generated_text"]) # -> "Improved draft after review"
You can also place interrupts directly inside tool functions. This makes the tool itself pause for approval whenever it’s called, and allows for human review and editing of the tool call before it is executed.First, define a tool that uses interrupt:
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from langchain.tools import toolfrom langgraph.types import interrupt@tooldef send_email(to: str, subject: str, body: str): """Send an email to a recipient.""" # Pause before sending; payload surfaces in result["__interrupt__"] response = interrupt({ "action": "send_email", "to": to, "subject": subject, "body": body, "message": "Approve sending this email?" }) if response.get("action") == "approve": # Resume value can override inputs before executing final_to = response.get("to", to) final_subject = response.get("subject", subject) final_body = response.get("body", body) return f"Email sent to {final_to} with subject '{final_subject}'" return "Email cancelled by user"
This approach is useful when you want the approval logic to live with the tool itself, making it reusable across different parts of your graph. The LLM can call the tool naturally, and the interrupt will pause execution whenever the tool is invoked, allowing you to approve, edit, or cancel the action.
Full example
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import sqlite3from typing import TypedDictfrom langchain.tools import toolfrom langchain_anthropic import ChatAnthropicfrom langgraph.checkpoint.sqlite import SqliteSaverfrom langgraph.graph import StateGraph, START, ENDfrom langgraph.types import Command, interruptclass AgentState(TypedDict): messages: list[dict]@tooldef send_email(to: str, subject: str, body: str): """Send an email to a recipient.""" # Pause before sending; payload surfaces in result["__interrupt__"] response = interrupt({ "action": "send_email", "to": to, "subject": subject, "body": body, "message": "Approve sending this email?", }) if response.get("action") == "approve": final_to = response.get("to", to) final_subject = response.get("subject", subject) final_body = response.get("body", body) # Actually send the email (your implementation here) print(f"[send_email] to={final_to} subject={final_subject} body={final_body}") return f"Email sent to {final_to}" return "Email cancelled by user"model = ChatAnthropic(model="claude-sonnet-4-5").bind_tools([send_email])def agent_node(state: AgentState): # LLM may decide to call the tool; interrupt pauses before sending result = model.invoke(state["messages"]) return {"messages": state["messages"] + [result]}builder = StateGraph(AgentState)builder.add_node("agent", agent_node)builder.add_edge(START, "agent")builder.add_edge("agent", END)checkpointer = SqliteSaver(sqlite3.connect("tool-approval.db"))graph = builder.compile(checkpointer=checkpointer)config = {"configurable": {"thread_id": "email-workflow"}}initial = graph.invoke( { "messages": [ {"role": "user", "content": "Send an email to alice@example.com about the meeting"} ] }, config=config,)print(initial["__interrupt__"]) # -> [Interrupt(value={'action': 'send_email', ...})]# Resume with approval and optionally edited argumentsresumed = graph.invoke( Command(resume={"action": "approve", "subject": "Updated subject"}), config=config,)print(resumed["messages"][-1]) # -> Tool result returned by send_email
Sometimes you need to validate input from humans and ask again if it’s invalid. You can do this using multiple interrupt calls in a loop.
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from langgraph.types import interruptdef get_age_node(state: State): prompt = "What is your age?" while True: answer = interrupt(prompt) # payload surfaces in result["__interrupt__"] # Validate the input if isinstance(answer, int) and answer > 0: # Valid input - continue break else: # Invalid input - ask again with a more specific prompt prompt = f"'{answer}' is not a valid age. Please enter a positive number." return {"age": answer}
Each time you resume the graph with invalid input, it will ask again with a clearer message. Once valid input is provided, the node completes and the graph continues.
Full example
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import sqlite3from typing import TypedDictfrom langgraph.checkpoint.sqlite import SqliteSaverfrom langgraph.graph import StateGraph, START, ENDfrom langgraph.types import Command, interruptclass FormState(TypedDict): age: int | Nonedef get_age_node(state: FormState): prompt = "What is your age?" while True: answer = interrupt(prompt) # payload surfaces in result["__interrupt__"] if isinstance(answer, int) and answer > 0: return {"age": answer} prompt = f"'{answer}' is not a valid age. Please enter a positive number."builder = StateGraph(FormState)builder.add_node("collect_age", get_age_node)builder.add_edge(START, "collect_age")builder.add_edge("collect_age", END)checkpointer = SqliteSaver(sqlite3.connect("forms.db"))graph = builder.compile(checkpointer=checkpointer)config = {"configurable": {"thread_id": "form-1"}}first = graph.invoke({"age": None}, config=config)print(first["__interrupt__"]) # -> [Interrupt(value='What is your age?', ...)]# Provide invalid data; the node re-promptsretry = graph.invoke(Command(resume="thirty"), config=config)print(retry["__interrupt__"]) # -> [Interrupt(value="'thirty' is not a valid age...", ...)]# Provide valid data; loop exits and state updatesfinal = graph.invoke(Command(resume=30), config=config)print(final["age"]) # -> 30
When you call interrupt within a node, LangGraph suspends execution by raising an exception that signals the runtime to pause. This exception propagates up through the call stack and is caught by the runtime, which notifies the graph to save the current state and wait for external input.When execution resumes (after you provide the requested input), the runtime restarts the entire node from the beginning—it does not resume from the exact line where interrupt was called. This means any code that ran before the interrupt will execute again. Because of this, there’s a few important rules to follow when working with interrupts to ensure they behave as expected.
The way that interrupt pauses execution at the point of the call is by throwing a special exception. If you wrap the interrupt call in a try/except block, you will catch this exception and the interrupt will not be passed back to the graph.
✅ Separate interrupt calls from error-prone code
✅ Use specific exception types in try/except blocks
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def node_a(state: State): # ✅ Good: interrupting first, then handling # error conditions separately interrupt("What's your name?") try: fetch_data() # This can fail except Exception as e: print(e) return state
🔴 Do not wrap interrupt calls in bare try/except blocks
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def node_a(state: State): # ❌ Bad: wrapping interrupt in bare try/except # will catch the interrupt exception try: interrupt("What's your name?") except Exception as e: print(e) return state
It’s common to use multiple interrupts in a single node, however this 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 important.
✅ Keep interrupt calls consistent across node executions
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def node_a(state: State): # ✅ Good: interrupt calls happen in the same order every time name = interrupt("What's your name?") age = interrupt("What's your age?") city = interrupt("What's your city?") return { "name": name, "age": age, "city": city }
🔴 Do not conditionally skip interrupt calls within a node
🔴 Do not loop interrupt calls using logic that isn’t deterministic across executions
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def node_a(state: State): # ❌ Bad: conditionally skipping interrupts changes the order name = interrupt("What's your name?") # On first run, this might skip the interrupt # On resume, it might not skip it - causing index mismatch if state.get("needs_age"): age = interrupt("What's your age?") city = interrupt("What's your city?") return {"name": name, "city": city}
Depending on which checkpointer is used, complex values may not be serializable (e.g. you can’t serialize a function). To make your graphs adaptable to any deployment, it’s best practice to only use values that can be reasonably serialized.
✅ Pass simple, JSON-serializable types to interrupt
✅ Pass dictionaries/objects with simple values
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def node_a(state: State): # ✅ Good: passing simple types that are serializable name = interrupt("What's your name?") count = interrupt(42) approved = interrupt(True) return {"name": name, "count": count, "approved": approved}
🔴 Do not pass functions, class instances, or other complex objects to interrupt
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def validate_input(value): return len(value) > 0def node_a(state: State): # ❌ Bad: passing a function to interrupt # The function cannot be serialized response = interrupt({ "question": "What's your name?", "validator": validate_input # This will fail }) return {"name": response}
Side effects called before interrupt must be idempotent
Because interrupts work by re-running the nodes they were called from, side effects called before interrupt should (ideally) be idempotent. For context, idempotency means that the same operation can be applied multiple times without changing the result beyond the initial execution.As an example, you might have an API call to update a record inside of a node. If interrupt is called after that call is made, it will be re-run multiple times when the node is resumed, potentially overwriting the initial update or creating duplicate records.
✅ Use idempotent operations before interrupt
✅ Place side effects after interrupt calls
✅ Separate side effects into separate nodes when possible
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def node_a(state: State): # ✅ Good: using upsert operation which is idempotent # Running this multiple times will have the same result db.upsert_user( user_id=state["user_id"], status="pending_approval" ) approved = interrupt("Approve this change?") return {"approved": approved}
🔴 Do not perform non-idempotent operations before interrupt
🔴 Do not create new records without checking if they exist
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def node_a(state: State): # ❌ Bad: creating a new record before interrupt # This will create duplicate records on each resume audit_id = db.create_audit_log({ "user_id": state["user_id"], "action": "pending_approval", "timestamp": datetime.now() }) approved = interrupt("Approve this change?") return {"approved": approved, "audit_id": audit_id}
When invoking a subgraph within a node, the parent graph will resume execution from the beginning of the node where the subgraph was invoked and the interrupt was triggered. Similarly, the subgraph will also resume from the beginning of the node where interrupt was called.
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def node_in_parent_graph(state: State): some_code() # <-- This will re-execute when resumed # Invoke a subgraph as a function. # The subgraph contains an `interrupt` call. subgraph_result = subgraph.invoke(some_input)async function node_in_subgraph(state: State) { someOtherCode(); # <-- This will also re-execute when resumed result = interrupt("What's your name?") ...}
To debug and test a graph, you can use static interrupts as breakpoints to step through the graph execution one node at a time. Static interrupts are triggered at defined points either before or after a node executes. You can set these by specifying interrupt_before and interrupt_after when compiling the graph.
Static interrupts are not recommended for human-in-the-loop workflows. Use the interrupt method instead.
At compile time
At run time
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graph = builder.compile( interrupt_before=["node_a"], interrupt_after=["node_b", "node_c"], checkpointer=checkpointer,)# Pass a thread ID to the graphconfig = { "configurable": { "thread_id": "some_thread" }}# Run the graph until the breakpointgraph.invoke(inputs, config=config)# Resume the graphgraph.invoke(None, config=config)
The breakpoints are set during compile time.
interrupt_before specifies the nodes where execution should pause before the node is executed.
interrupt_after specifies the nodes where execution should pause after the node is executed.
A checkpointer is required to enable breakpoints.
The graph is run until the first breakpoint is hit.
The graph is resumed by passing in None for the input. This will run the graph until the next breakpoint is hit.
You can use LangGraph Studio to set static interrupts in your graph in the UI before running the graph. You can also use the UI to inspect the graph state at any point in the execution.