> ## Documentation Index > Fetch the complete documentation index at: https://docs.langchain.com/llms.txt > Use this file to discover all available pages before exploring further. # Interrupts 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](/oss/python/langgraph/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. ## Pause using `interrupt` The [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) function pauses graph execution and returns a value to the caller. When you call [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) within a node, LangGraph saves the current graph state and waits for you to resume execution with input. To use [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt), you need: 1. A **checkpointer** to persist the graph state (use a durable checkpointer in production) 2. A **thread ID** in your config so the runtime knows which state to resume from 3. To call `interrupt()` where you want to pause (payload must be JSON-serializable) ```python theme={null} from langgraph.types import interrupt def 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`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt), here's what happens: 1. **Graph execution gets suspended** at the exact point where [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) is called 2. **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) 3. **Value is returned** to the caller under `__interrupt__`; it can be any JSON-serializable value (string, object, array, etc.) 4. **Graph waits indefinitely** until you resume execution with a response 5. **Response is passed back** into the node when you resume, becoming the return value of the `interrupt()` call ## Resuming interrupts 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. ```python theme={null} 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 node graph.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`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) call * The node restarts from the beginning of the node where the [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) was called when resumed, so any code before the [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) runs again * You can pass any JSON-serializable value as the resume value ## Common patterns 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: * [Approval workflows](#approve-or-reject): Pause before executing critical actions (API calls, database changes, financial transactions) * [Review and edit](#review-and-edit-state): Let humans review and modify LLM outputs or tool calls before continuing * [Interrupting tool calls](#interrupts-in-tools): Pause before executing tool calls to review and edit the tool call before execution * [Validating human input](#validating-human-input): Pause before proceeding to the next step to validate human input ### Stream with human-in-the-loop (HITL) interrupts When building interactive agents with human-in-the-loop workflows, you can stream both message chunks and node updates simultaneously to provide real-time feedback while handling interrupts. Use multiple stream modes (`"messages"` and `"updates"`) with `subgraphs=True` (if subgraphs are present) to: * Stream AI responses in real-time as they're generated * Detect when the graph encounters an interrupt * Handle user input and resume execution seamlessly ```python theme={null} async for metadata, mode, chunk in graph.astream( initial_input, stream_mode=["messages", "updates"], subgraphs=True, config=config ): if mode == "messages": # Handle streaming message content msg, _ = chunk if isinstance(msg, AIMessageChunk) and msg.content: # Display content in real-time display_streaming_content(msg.content) elif mode == "updates": # Check for interrupts if "__interrupt__" in chunk: # Stop streaming display interrupt_info = chunk["__interrupt__"][0].value # Handle user input user_response = get_user_input(interrupt_info) # Resume graph with updated input initial_input = Command(resume=user_response) break else: # Track node transitions current_node = list(chunk.keys())[0] ``` * **`stream_mode=["messages", "updates"]`**: Enables dual streaming of both message chunks and graph state updates * **`subgraphs=True`**: Required for interrupt detection in nested graphs * **`"__interrupt__"` detection**: Signals when human input is needed * **`Command(resume=...)`**: Resumes graph execution with user-provided data ### Approve or reject 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. ```python theme={null} from typing import Literal from langgraph.types import interrupt, Command def 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: ```python theme={null} # To approve graph.invoke(Command(resume=True), config=config) # To reject graph.invoke(Command(resume=False), config=config) ``` ```python theme={null} from typing import Literal, Optional, TypedDict from langgraph.checkpoint.memory import MemorySaver from langgraph.graph import StateGraph, START, END from langgraph.types import Command, interrupt class 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("proceed", END) builder.add_edge("cancel", END) # Use a more durable checkpointer in production checkpointer = 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 cancel resumed = graph.invoke(Command(resume=True), config=config) print(resumed["status"]) # -> "approved" ``` ### Review and edit state 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. ```python theme={null} from langgraph.types import interrupt def 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: ```python theme={null} graph.invoke( Command(resume="The edited and improved text"), # Value becomes the return from interrupt() config=config ) ``` ```python theme={null} import sqlite3 from typing import TypedDict from langgraph.checkpoint.memory import MemorySaver from langgraph.graph import StateGraph, START, END from langgraph.types import Command, interrupt class ReviewState(TypedDict): generated_text: str def 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 reviewer final_state = graph.invoke( Command(resume="Improved draft after review"), config=config, ) print(final_state["generated_text"]) # -> "Improved draft after review" ``` ### Interrupts in tools 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`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt): ```python theme={null} from langchain.tools import tool from langgraph.types import interrupt @tool def 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. ```python theme={null} import sqlite3 from typing import TypedDict from langchain.tools import tool from langchain_anthropic import ChatAnthropic from langgraph.checkpoint.sqlite import SqliteSaver from langgraph.graph import StateGraph, START, END from langgraph.types import Command, interrupt class AgentState(TypedDict): messages: list[dict] @tool def 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-4-5-20250929").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 arguments resumed = graph.invoke( Command(resume={"action": "approve", "subject": "Updated subject"}), config=config, ) print(resumed["messages"][-1]) # -> Tool result returned by send_email ``` ### Validating human input Sometimes you need to validate input from humans and ask again if it's invalid. You can do this using multiple [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) calls in a loop. ```python theme={null} from langgraph.types import interrupt def 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. ```python theme={null} import sqlite3 from typing import TypedDict from langgraph.checkpoint.sqlite import SqliteSaver from langgraph.graph import StateGraph, START, END from langgraph.types import Command, interrupt class FormState(TypedDict): age: int | None def 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-prompts retry = 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 updates final = graph.invoke(Command(resume=30), config=config) print(final["age"]) # -> 30 ``` ## Rules of interrupts When you call [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.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`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) was called. This means any code that ran before the [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.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. ### Do not wrap `interrupt` calls in try/except The way that [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) pauses execution at the point of the call is by throwing a special exception. If you wrap the [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.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`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) calls from error-prone code * ✅ Use specific exception types in try/except blocks ```python Separating logic theme={null} 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 ``` ```python Explicit exception handling theme={null} def node_a(state: State): # ✅ Good: catching specific exception types # will not catch the interrupt exception try: name = interrupt("What's your name?") fetch_data() # This can fail except NetworkException as e: print(e) return state ``` * 🔴 Do not wrap [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) calls in bare try/except blocks ```python theme={null} 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 ``` ### Do not reorder `interrupt` calls within a node 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`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) calls consistent across node executions ```python theme={null} 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`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) calls within a node * 🔴 Do not loop [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) calls using logic that isn't deterministic across executions ```python Skipping interrupts theme={null} 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} ``` ```python Looping interrupts theme={null} def node_a(state: State): # ❌ Bad: looping based on non-deterministic data # The number of interrupts changes between executions results = [] for item in state.get("dynamic_list", []): # List might change between runs result = interrupt(f"Approve {item}?") results.append(result) return {"results": results} ``` ### Do not return complex values in `interrupt` calls 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`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) * ✅ Pass dictionaries/objects with simple values ```python Simple values theme={null} 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} ``` ```python Structured data theme={null} def node_a(state: State): # ✅ Good: passing dictionaries with simple values response = interrupt({ "question": "Enter user details", "fields": ["name", "email", "age"], "current_values": state.get("user", {}) }) return {"user": response} ``` * 🔴 Do not pass functions, class instances, or other complex objects to [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) ```python Functions theme={null} def validate_input(value): return len(value) > 0 def 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} ``` ```python Class instances theme={null} class DataProcessor: def __init__(self, config): self.config = config def node_a(state: State): processor = DataProcessor({"mode": "strict"}) # ❌ Bad: passing a class instance to interrupt # The instance cannot be serialized response = interrupt({ "question": "Enter data to process", "processor": processor # This will fail }) return {"result": 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`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.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`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.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`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) * ✅ Place side effects after [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) calls * ✅ Separate side effects into separate nodes when possible ```python Idempotent operations theme={null} 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} ``` ```python Side effects after interrupt theme={null} def node_a(state: State): # ✅ Good: placing side effect after the interrupt # This ensures it only runs once after approval is received approved = interrupt("Approve this change?") if approved: db.create_audit_log( user_id=state["user_id"], action="approved" ) return {"approved": approved} ``` ```python Separating into different nodes theme={null} def approval_node(state: State): # ✅ Good: only handling the interrupt in this node approved = interrupt("Approve this change?") return {"approved": approved} def notification_node(state: State): # ✅ Good: side effect happens in a separate node # This runs after approval, so it only executes once if (state.approved): send_notification( user_id=state["user_id"], status="approved" ) return state ``` * 🔴 Do not perform non-idempotent operations before [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) * 🔴 Do not create new records without checking if they exist ```python Creating records theme={null} 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} ``` ```python Appending to lists theme={null} def node_a(state: State): # ❌ Bad: appending to a list before interrupt # This will add duplicate entries on each resume db.append_to_history(state["user_id"], "approval_requested") approved = interrupt("Approve this change?") return {"approved": approved} ``` ## Using with subgraphs called as functions 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`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) was triggered. Similarly, the **subgraph** will also resume from the beginning of the node where [`interrupt`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) was called. ```python theme={null} 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) # ... def node_in_subgraph(state: State): some_other_code() # <-- This will also re-execute when resumed result = interrupt("What's your name?") # ... ``` ## Debugging with interrupts 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`](https://reference.langchain.com/python/langgraph/types/#langgraph.types.interrupt) function instead. ```python theme={null} graph = builder.compile( interrupt_before=["node_a"], # [!code highlight] interrupt_after=["node_b", "node_c"], # [!code highlight] checkpointer=checkpointer, ) # Pass a thread ID to the graph config = { "configurable": { "thread_id": "some_thread" } } # Run the graph until the breakpoint graph.invoke(inputs, config=config) # [!code highlight] # Resume the graph graph.invoke(None, config=config) # [!code highlight] ``` 1. The breakpoints are set during `compile` time. 2. `interrupt_before` specifies the nodes where execution should pause before the node is executed. 3. `interrupt_after` specifies the nodes where execution should pause after the node is executed. 4. A checkpointer is required to enable breakpoints. 5. The graph is run until the first breakpoint is hit. 6. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit. ```python theme={null} config = { "configurable": { "thread_id": "some_thread" } } # Run the graph until the breakpoint graph.invoke( inputs, interrupt_before=["node_a"], # [!code highlight] interrupt_after=["node_b", "node_c"], # [!code highlight] config=config, ) # Resume the graph graph.invoke(None, config=config) # [!code highlight] ``` 1. `graph.invoke` is called with the `interrupt_before` and `interrupt_after` parameters. This is a run-time configuration and can be changed for every invocation. 2. `interrupt_before` specifies the nodes where execution should pause before the node is executed. 3. `interrupt_after` specifies the nodes where execution should pause after the node is executed. 4. The graph is run until the first breakpoint is hit. 5. The graph is resumed by passing in `None` for the input. This will run the graph until the next breakpoint is hit. ### Using LangSmith Studio You can use [LangSmith Studio](/langsmith/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. *** [Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/langgraph/interrupts.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose). [Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.