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langgraph-human-in-the-looplanggraph 人类在循环中

Agent Skill

langgraph-human-in-the-loop 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

465

周安装

19

GitHub Stars

95

下载量

150
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:langgraph-human-in-the-loop(langgraph 人类在循环中)
来源仓库:https://github.com/langchain-ai/skills-benchmarks
仓库路径:skills/langgraph-human-in-the-loop
安装命令:
npx skills add https://github.com/langchain-ai/skills-benchmarks --skill langgraph-human-in-the-loop
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

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

简介

langgraph-human-in-the-loop 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态、代码变更或协作事项进行整理。
  • 通过 npx skills add 命令从指定仓库安装并使用该技能。
  • 安装前需确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

  • 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.

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

</python>
<typescript>
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.

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)

</python> <typescript> 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.

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


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.; }


}};

</typescript> </ex-validation-loop>


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.

<ex-multiple-interrupts> <python> 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"]

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"]

</typescript>
</ex-multiple-interrupts>

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.

<idempotency-rules>

**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

</idempotency-rules>

<ex-idempotent-patterns>
<python>
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}


// 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};};

</typescript> </ex-idempotent-patterns>

<subgraph-interrupt-re-execution>

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()):

<python>

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 nodeInSubgraph(state: State) {someOtherCode(); // <-- Also re-executes on resume const result = interrupt("What's your name?"); //...}

</typescript>
</subgraph-interrupt-re-execution>

---

## 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

<fix-checkpointer-required-for-interrupts>
<python>
Checkpointer required for interrupt functionality.

WRONG

graph = builder.compile()

CORRECT

graph = builder.compile(checkpointer=InMemorySaver())


// CORRECT const graph = builder.compile({checkpointer: new MemorySaver()});

</typescript> </fix-checkpointer-required-for-interrupts>

<fix-resume-with-command> <python> 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)

// CORRECT await graph.invoke(new Command({resume: "approve"}), config);

</typescript>
</fix-resume-with-command>

<boundaries>
### 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
</boundaries>

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02

需要根据任务场景推荐可安装能力包时

03

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.96%
按下载量换算57

Claude

29.23%
按下载量换算44

Cursor

18.24%
按下载量换算27

Gemini CLI

9.56%
按下载量换算14

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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