langgraph-human-in-the-loop

작성자: langchain-ai

그래프 실행을 일시 중지하여 사람의 검토, 승인 또는 검증을 받은 후, 입력을 받아 다시 실행합니다. 세 가지 구성 요소가 필요합니다: 체크포인터(InMemorySaver 또는 PostgresSaver), config의 스레드 ID, JSON 직렬화 가능한 인터럽트 페이로드. interrupt(value)는 데이터를 일시 중지하고 표시하며, Command(resume=value)는 다시 시작하여 일시 중지된 노드에 해당 값을 반환합니다. interrupt() 이전의 모든 코드는 다시 시작 시 재실행되므로, 부작용은 멱등성을 가져야 합니다(insert 대신 upsert 사용). 승인 워크플로우를 지원합니다,...

npx skills add https://github.com/langchain-ai/langchain-skills --skill langgraph-human-in-the-loop
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:

  1. Checkpointer — compile with checkpointer=InMemorySaver() (dev) or PostgresSaver (prod)
  2. Thread ID — pass {"configurable": {"thread_id": "..."}} to every invoke/stream call
  3. 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):
    # Pause and ask for approval
    approved = interrupt("Do you approve this action?")
    # When resumed, Command(resume=...) returns that value here
    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"}}

# Initial run — hits interrupt and pauses
result = graph.invoke({"approved": False}, config)
print(result["__interrupt__"])
# [Interrupt(value='Do you approve this action?')]

# Resume with the human's response
result = graph.invoke(Command(resume=True), config)
print(result["approved"])  # True
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) => {
  // Pause and ask for approval
  const approved = interrupt("Do you approve this action?");
  // When resumed, Command({ resume }) returns that value here
  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" } };

// Initial run — hits interrupt and pauses
let result = await graph.invoke({ approved: false }, config);
console.log(result.__interrupt__);
// [{ value: 'Do you approve this action?', ... }]

// Resume with the human's response
result = await graph.invoke(new Command({ resume: true }), config);
console.log(result.approved);  // true

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", {})

    # interrupt() must come first — any code before it will re-run on resume
    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"
    })

    # Process the human's decision
    if human_decision.get("approved"):
        return Command(
            update={"draft_response": human_decision.get("edited_response", state.get("draft_response", ""))},
            goto="send_reply"
        )
    else:
        # Rejection — human will handle directly
        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!;

  // interrupt() must come first — any code before it will re-run on resume
  const humanDecision = interrupt({
    emailId: state.emailContent,
    draftResponse: state.responseText,
    urgency: classification.urgency,
    action: "Please review and approve/edit this response",
  });

  // Process the human's decision
  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)

        # Validate the input
        if isinstance(answer, int) and answer > 0:
            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 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)
# __interrupt__: "What is your age?"

retry = graph.invoke(Command(resume="thirty"), config)
# __interrupt__: "'thirty' is not a valid age..."

final = graph.invoke(Command(resume=30), config)
print(final["age"])  # 30
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);

    // Validate the input
    if (typeof answer === "number" && answer > 0) {
      return { age: answer };
    } else {
      // Invalid input — ask again with a more specific prompt
      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"}}

# Both parallel nodes hit interrupt() and pause
result = graph.invoke({"vals": []}, config)
# result["__interrupt__"] contains both Interrupt objects with IDs

# Resume all pending interrupts at once using a map of id -> value
resume_map = {
    i.id: f"answer for {i.value}"
    for i in result["__interrupt__"]
}
result = graph.invoke(Command(resume=resume_map), config)
# result["vals"] = ["a:answer for question_a", "b:answer for question_b"]
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);

// Resume all pending interrupts at once
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);
// result.vals = ["a:answer for question_a", "b:answer for question_b"]

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).
# GOOD: Upsert is idempotent — safe before interrupt
def node_a(state: State):
    db.upsert_user(user_id=state["user_id"], status="pending_approval")
    approved = interrupt("Approve this change?")
    return {"approved": approved}

# GOOD: Side effect AFTER interrupt — only runs once
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}

# BAD: Insert creates duplicates on each resume!
def node_a(state: State):
    audit_id = db.create_audit_log({  # Runs again on resume!
        "user_id": state["user_id"],
        "action": "pending_approval",
    })
    approved = interrupt("Approve this change?")
    return {"approved": approved}
Idempotent operations before interrupt vs non-idempotent (wrong).
// GOOD: Upsert is idempotent — safe before interrupt
const nodeA = async (state: typeof State.State) => {
  await db.upsertUser({ userId: state.userId, status: "pending_approval" });
  const approved = interrupt("Approve this change?");
  return { approved };
};

// GOOD: Side effect AFTER interrupt — only runs once
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 };
};

// BAD: Insert creates duplicates on each resume!
const nodeA = async (state: typeof State.State) => {
  await db.createAuditLog({  // Runs again on resume!
    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()  # <-- Re-executes on resume
    subgraph_result = subgraph.invoke(some_input)
    # ...

def node_in_subgraph(state: State):
    some_other_code()  # <-- Also re-executes on resume
    result = interrupt("What's your name?")
    # ...
async function nodeInParentGraph(state: State) {
  someCode();  // <-- Re-executes on resume
  const subgraphResult = await subgraph.invoke(someInput);
  // ...
}

async function nodeInSubgraph(state: State) {
  someOtherCode();  // <-- Also re-executes on resume
  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.
# WRONG
graph = builder.compile()

# CORRECT
graph = builder.compile(checkpointer=InMemorySaver())
Checkpointer required for interrupt functionality.
// WRONG
const graph = builder.compile();

// CORRECT
const graph = builder.compile({ checkpointer: new MemorySaver() });
Use Command to resume from an interrupt (regular dict restarts graph).
# WRONG
graph.invoke({"resume_data": "approve"}, config)

# CORRECT
graph.invoke(Command(resume="approve"), config)
Use Command to resume from an interrupt (regular object restarts graph).
// WRONG
await graph.invoke({ resumeData: "approve" }, config);

// CORRECT
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

langchain-ai의 다른 스킬

langgraph-docs
langchain-ai
LangGraph 문서에 접근하여 상태 기반 에이전트 및 멀티 에이전트 워크플로우를 구축합니다. 공식 LangGraph Python 문서를 가져오며, 상태 머신, 그래프 기반 에이전트 설계, 인간 개입 패턴을 다룹니다. 쿼리 유형에 따라 관련 문서를 우선시합니다: 방법 질문에는 구현 가이드, 이론에는 개념 페이지, 종단 간 예제에는 튜토리얼, 기술 세부 사항에는 API 참조를 제공합니다. 자동으로 가장 관련성 높은 2~4개의 문서 URL을 선택하고 해당 콘텐츠를 검색하여 답변합니다...
official
web-research
langchain-ai
웹 리서치와 관련된 요청에 이 스킬을 사용하세요. 포괄적인 웹 리서치를 수행하기 위한 체계적인 접근 방식을 제공합니다.
official
langchain-oss-primer
langchain-ai
LangChain, Deep Agents 또는 LangGraph 에이전트 구축 프로젝트를 시작할 때는 항상 여기서 시작하세요. 다른 스킬을 선택하거나 코드를 작성하기 전에 반드시 거쳐야 하는 시작점입니다.
official
skill-creator
langchain-ai
에이전트의 기능을 확장하기 위한 효과적인 스킬을 만드는 가이드로, 특화된 지식, 워크플로우 또는 도구 통합을 포함합니다. 사용자가...
official
social-media
langchain-ai
플랫폼별 소셜 미디어 게시물을 초안 작성하며, 연구 기반 콘텐츠와 함께 생성된 보조 이미지를 제공합니다. 링크드인 게시물(1,300자, 전문적인 어조)과 트위터/X 스레드(트윗당 280자, 1/🧵 형식)를 지원합니다. 작성 전에 하위 에이전트에 연구를 위임한 후, 결과를 읽어 정확성과 관련성을 확인해야 합니다. generate_social_image 도구를 사용하여 자동으로 눈에 띄는 소셜 이미지를 생성하며, 작은 화면에 최적화된 대담하고 대비가 높은 구성을 사용합니다.
official
deep-agents-memory
langchain-ai
Deep Agents를 위한 플러그형 메모리 및 파일 백엔드로, 임시, 영구 및 하이브리드 라우팅 옵션을 제공합니다. 네 가지 백엔드 유형: StateBackend(스레드 범위, 임시), StoreBackend(세션 간 영구), FilesystemBackend(로컬 개발을 위한 실제 디스크 액세스), CompositeBackend(다른 경로를 다른 백엔드로 라우팅). FilesystemMiddleware는 ls, read_file, write_file, edit_file, glob, grep의 여섯 가지 파일 작업 도구를 제공합니다. CompositeBackend는 최장 접두사 일치를 사용하여 라우팅합니다...
official
deep-agents-orchestration
langchain-ai
서브 에이전트를 조율하고, 다단계 작업을 계획하며, 민감한 작업에 대해 인간의 승인을 요구합니다. task 도구를 통해 전문화된 서브 에이전트에 작업을 위임합니다. 맞춤형 서브 에이전트는 격리된 도구 세트와 시스템 프롬프트를 지원하며, 기본 "범용" 서브 에이전트는 메인 에이전트 구성을 상속받습니다. write_todos를 사용하여 복잡한 워크플로우를 계획 및 추적하고, 보류 중, 진행 중, 완료 상태로 작업을 구성합니다. 호출 간 지속성을 위해 thread_id가 필요합니다. 구현...
official
deep-agents-orchestration
langchain-ai
Deep Agents에서 서브 에이전트, 작업 계획 또는 인간 승인을 사용할 때 이 스킬을 호출하세요. SubAgentMiddleware, 계획을 위한 TodoList, HITL 인터럽트를 다룹니다.
official