| name | langgraph-human-in-the-loop |
| description | INVOKE THIS SKILL when implementing human-in-the-loop patterns, pausing for approval, or handling errors in LangGraph. Covers interrupt(), Command(resume=...), approval/validation workflows, and the 4-tier error handling strategy. |
LangGraph's human-in-the-loop patterns let you pause graph execution, surface data to users, and resume with their input:
interrupt(value) — pauses execution, surfaces a value to the caller
Command(resume=value) — resumes execution, providing the value back to interrupt()
- Checkpointer — required to save state while paused
- Thread ID — required to identify which paused execution to resume
Requirements
Three things are required for interrupts to work:
- Checkpointer — compile with
checkpointer=InMemorySaver() (dev) or PostgresSaver (prod)
- Thread ID — pass
{"configurable": {"thread_id": "..."}} to every invoke/stream call
- JSON-serializable payload — the value passed to
interrupt() must be JSON-serializable
Basic Interrupt + Resume
interrupt(value) pauses the graph. The value surfaces in the result under __interrupt__. Command(resume=value) resumes — the resume value becomes the return value of interrupt().
Critical: when the graph resumes, the node restarts from the beginning — all code before interrupt() re-runs.
Pause execution for human review and resume with Command.
from langgraph.types import interrupt, Command
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
class State(TypedDict):
approved: bool
def approval_node(state: State):
approved = interrupt("Do you approve this action?")
return {"approved": approved}
checkpointer = InMemorySaver()
graph = (
StateGraph(State)
.add_node("approval", approval_node)
.add_edge(START, "approval")
.add_edge("approval", END)
.compile(checkpointer=checkpointer)
)
config = {"configurable": {"thread_id": "thread-1"}}
result = graph.invoke({"approved": False}, config)
print(result["__interrupt__"])
result = graph.invoke(Command(resume=True), config)
print(result["approved"])
Pause execution for human review and resume with Command.
import { interrupt, Command, MemorySaver, StateGraph, StateSchema, START, END } from "@langchain/langgraph";
import { z } from "zod";
const State = new StateSchema({
approved: z.boolean().default(false),
});
const approvalNode = async (state: typeof State.State) => {
const approved = interrupt("Do you approve this action?");
return { approved };
};
const checkpointer = new MemorySaver();
const graph = new StateGraph(State)
.addNode("approval", approvalNode)
.addEdge(START, "approval")
.addEdge("approval", END)
.compile({ checkpointer });
const config = { configurable: { thread_id: "thread-1" } };
let result = await graph.invoke({ approved: false }, config);
console.log(result.__interrupt__);
result = await graph.invoke(new Command({ resume: true }), config);
console.log(result.approved);
Approval Workflow
A common pattern: interrupt to show a draft, then route based on the human's decision.
Interrupt for human review, then route to send or end based on the decision.
from langgraph.types import interrupt, Command
from langgraph.graph import StateGraph, START, END
from typing import Literal
from typing_extensions import TypedDict
class EmailAgentState(TypedDict):
email_content: str
draft_response: str
classification: dict
def human_review(state: EmailAgentState) -> Command[Literal["send_reply", "__end__"]]:
"""Pause for human review using interrupt and route based on decision."""
classification = state.get("classification", {})
human_decision = interrupt({
"email_id": state.get("email_content", ""),
"draft_response": state.get("draft_response", ""),
"urgency": classification.get("urgency"),
"action": "Please review and approve/edit this response"
})
if human_decision.get("approved"):
return Command(
update={"draft_response": human_decision.get("edited_response", state.get("draft_response", ""))},
goto="send_reply"
)
else:
return Command(update={}, goto=END)
Interrupt for human review, then route to send or end based on the decision.
import { interrupt, Command, END, GraphNode } from "@langchain/langgraph";
const humanReview: GraphNode<typeof EmailAgentState> = async (state) => {
const classification = state.classification!;
const humanDecision = interrupt({
emailId: state.emailContent,
draftResponse: state.responseText,
urgency: classification.urgency,
action: "Please review and approve/edit this response",
});
if (humanDecision.approved) {
return new Command({
update: { responseText: humanDecision.editedResponse || state.responseText },
goto: "sendReply",
});
} else {
return new Command({ update: {}, goto: END });
}
};
Validation Loop
Use interrupt() in a loop to validate human input and re-prompt if invalid.
Validate human input in a loop, re-prompting until valid.
from langgraph.types import interrupt
def get_age_node(state):
prompt = "What is your age?"
while True:
answer = interrupt(prompt)
if isinstance(answer, int) and answer > 0:
break
else:
prompt = f"'{answer}' is not a valid age. Please enter a positive number."
return {"age": answer}
Each Command(resume=...) call provides the next answer. If invalid, the loop re-interrupts with a clearer message.
config = {"configurable": {"thread_id": "form-1"}}
first = graph.invoke({"age": None}, config)
retry = graph.invoke(Command(resume="thirty"), config)
final = graph.invoke(Command(resume=30), config)
print(final["age"])
Validate human input in a loop, re-prompting until valid.
import { interrupt } from "@langchain/langgraph";
const getAgeNode = (state: typeof State.State) => {
let prompt = "What is your age?";
while (true) {
const answer = interrupt(prompt);
if (typeof answer === "number" && answer > 0) {
return { age: answer };
} else {
prompt = `'${answer}' is not a valid age. Please enter a positive number.`;
}
}
};
Multiple Interrupts
When parallel branches each call interrupt(), resume all of them in a single invocation by mapping each interrupt ID to its resume value.
Resume multiple parallel interrupts by mapping interrupt IDs to values.
from typing import Annotated, TypedDict
import operator
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.graph import START, END, StateGraph
from langgraph.types import Command, interrupt
class State(TypedDict):
vals: Annotated[list[str], operator.add]
def node_a(state):
answer = interrupt("question_a")
return {"vals": [f"a:{answer}"]}
def node_b(state):
answer = interrupt("question_b")
return {"vals": [f"b:{answer}"]}
graph = (
StateGraph(State)
.add_node("a", node_a)
.add_node("b", node_b)
.add_edge(START, "a")
.add_edge(START, "b")
.add_edge("a", END)
.add_edge("b", END)
.compile(checkpointer=InMemorySaver())
)
config = {"configurable": {"thread_id": "1"}}
result = graph.invoke({"vals": []}, config)
resume_map = {
i.id: f"answer for {i.value}"
for i in result["__interrupt__"]
}
result = graph.invoke(Command(resume=resume_map), config)
Resume multiple parallel interrupts by mapping interrupt IDs to values.
import { Command, END, MemorySaver, START, StateGraph, interrupt, isInterrupted, INTERRUPT, Annotation } from "@langchain/langgraph";
const State = Annotation.Root({
vals: Annotation<string[]>({
reducer: (left, right) => left.concat(Array.isArray(right) ? right : [right]),
default: () => [],
}),
});
function nodeA(_state: typeof State.State) {
const answer = interrupt("question_a") as string;
return { vals: [`a:${answer}`] };
}
function nodeB(_state: typeof State.State) {
const answer = interrupt("question_b") as string;
return { vals: [`b:${answer}`] };
}
const graph = new StateGraph(State)
.addNode("a", nodeA)
.addNode("b", nodeB)
.addEdge(START, "a")
.addEdge(START, "b")
.addEdge("a", END)
.addEdge("b", END)
.compile({ checkpointer: new MemorySaver() });
const config = { configurable: { thread_id: "1" } };
const interruptedResult = await graph.invoke({ vals: [] }, config);
const resumeMap: Record<string, string> = {};
if (isInterrupted(interruptedResult)) {
for (const i of interruptedResult[INTERRUPT]) {
if (i.id != null) {
resumeMap[i.id] = `answer for ${i.value}`;
}
}
}
const result = await graph.invoke(new Command({ resume: resumeMap }), config);
User-fixable errors use interrupt() to pause and collect missing data — that's the pattern covered by this skill. For the full 4-tier error handling strategy (RetryPolicy, Command error loops, etc.), see the fundamentals skill.
Side Effects Before Interrupt Must Be Idempotent
When the graph resumes, the node restarts from the beginning — ALL code before interrupt() re-runs. In subgraphs, BOTH the parent node and the subgraph node re-execute.
Do:
- Use upsert (not insert) operations before
interrupt()
- Use check-before-create patterns
- Place side effects after
interrupt() when possible
- Separate side effects into their own nodes
Don't:
- Create new records before
interrupt() — duplicates on each resume
- Append to lists before
interrupt() — duplicate entries on each resume
Idempotent operations before interrupt vs non-idempotent (wrong).
def node_a(state: State):
db.upsert_user(user_id=state["user_id"], status="pending_approval")
approved = interrupt("Approve this change?")
return {"approved": approved}
def node_a(state: State):
approved = interrupt("Approve this change?")
if approved:
db.create_audit_log(user_id=state["user_id"], action="approved")
return {"approved": approved}
def node_a(state: State):
audit_id = db.create_audit_log({
"user_id": state["user_id"],
"action": "pending_approval",
})
approved = interrupt("Approve this change?")
return {"approved": approved}
Idempotent operations before interrupt vs non-idempotent (wrong).
const nodeA = async (state: typeof State.State) => {
await db.upsertUser({ userId: state.userId, status: "pending_approval" });
const approved = interrupt("Approve this change?");
return { approved };
};
const nodeA = async (state: typeof State.State) => {
const approved = interrupt("Approve this change?");
if (approved) {
await db.createAuditLog({ userId: state.userId, action: "approved" });
}
return { approved };
};
const nodeA = async (state: typeof State.State) => {
await db.createAuditLog({
userId: state.userId,
action: "pending_approval",
});
const approved = interrupt("Approve this change?");
return { approved };
};
Subgraph re-execution on resume
When a subgraph contains an interrupt(), resuming re-executes BOTH the parent node (that invoked the subgraph) AND the subgraph node (that called interrupt()):
def node_in_parent_graph(state: State):
some_code()
subgraph_result = subgraph.invoke(some_input)
def node_in_subgraph(state: State):
some_other_code()
result = interrupt("What's your name?")
async function nodeInParentGraph(state: State) {
someCode();
const subgraphResult = await subgraph.invoke(someInput);
}
async function nodeInSubgraph(state: State) {
someOtherCode();
const result = interrupt("What's your name?");
}
Command(resume) Warning
Command(resume=...) is the only Command pattern intended as input to invoke()/stream(). Do NOT pass Command(update=...) as input — it resumes from the latest checkpoint and the graph appears stuck. See the fundamentals skill for the full antipattern explanation.
Fixes
Checkpointer required for interrupt functionality.
graph = builder.compile()
graph = builder.compile(checkpointer=InMemorySaver())
Checkpointer required for interrupt functionality.
const graph = builder.compile();
const graph = builder.compile({ checkpointer: new MemorySaver() });
Use Command to resume from an interrupt (regular dict restarts graph).
graph.invoke({"resume_data": "approve"}, config)
graph.invoke(Command(resume="approve"), config)
Use Command to resume from an interrupt (regular object restarts graph).
await graph.invoke({ resumeData: "approve" }, config);
await graph.invoke(new Command({ resume: "approve" }), config);
### What You Should NOT Do
- Use interrupts without a checkpointer — will fail
- Resume without the same thread_id — creates a new thread instead of resuming
- Pass
Command(update=...) as invoke input — graph appears stuck (use plain dict)
- Perform non-idempotent side effects before
interrupt() — creates duplicates on resume
- Assume code before
interrupt() only runs once — it re-runs every resume