langchain-middleware（LangChain middleware 命令行）

• HumanInTheLoopMiddleware / humanInTheLoopMiddleware: Pause before dangerous tool calls for human approval
• Custom middleware: Intercept tool calls for error handling, logging, retry logic
• Command resume: Continue execution after human decisions (approve, edit, reject)

Requirements: Checkpointer + thread_id config for all HITL workflows.

Human-in-the-Loop

@tool def send_email(to: str, subject: str, body: str) -> str: """Send an email.""" return f"Email sent to {to}"

agent = create_agent(model="gpt-4.1", tools=[send_email], checkpointer=MemorySaver(), # Required for HITL middleware=[HumanInTheLoopMiddleware(interrupt_on={"send_email": {"allowed_decisions": ["approve", "edit", "reject"]},})],)

</python>
<typescript>
Set up an agent with HITL that pauses before sending emails for human approval.

import { createAgent, humanInTheLoopMiddleware } from "langchain"; import { MemorySaver } from "@langchain/langgraph"; import { tool } from "@langchain/core/tools"; import { z } from "zod";

const sendEmail = tool( async ({ to, subject, body }) => Email sent to ${to}, { name: "send_email", description: "Send an email", schema: z.object({ to: z.string(), subject: z.string(), body: z.string() }), } );

const agent = createAgent({ model: "anthropic:claude-sonnet-4-5", tools: [sendEmail], checkpointer: new MemorySaver(), middleware: [ humanInTheLoopMiddleware({ interruptOn: { send_email: { allowedDecisions: ["approve", "edit", "reject"] } }, }), ], });

config = {"configurable": {"thread_id": "session-1"}}

# Step 1: Agent runs until it needs to call tool

result1 = agent.invoke({"messages": [{"role": "user", "content": "Send email to [john@example.com](https://github.com/langchain-ai/langchain-skills/blob/HEAD/config/skills/langchain-middleware/mailto:john@example.com)"}]}, config=config)

# Check for interrupt

if "**interrupt**" in result1: print(f"Waiting for approval: {result1['**interrupt**']}")

# Step 2: Human approves

result2 = agent.invoke(Command(resume={"decisions": [{"type": "approve"}]}), config=config)

</python> <typescript> Run the agent, detect an interrupt, then resume execution after human approval.

import { Command } from "@langchain/langgraph";

const config = { configurable: { thread_id: "session-1" } };

// Step 1: Agent runs until it needs to call tool
const result1 = await agent.invoke({
  messages: [{ role: "user", content: "Send email to john@example.com" }]
}, config);

// Check for interrupt
if (result1.__interrupt__) {
  console.log(`Waiting for approval: ${result1.__interrupt__}`);
}

// Step 2: Human approves
const result2 = await agent.invoke(
  new Command({ resume: { decisions: [{ type: "approve" }] } }),
  config
);

• Which tools require approval (per-tool policies)
• Allowed decisions per tool (approve, edit, reject)
• Custom middleware hooks: before_model, after_model, wrap_tool_call, before_agent, after_agent
• Tool-specific middleware (apply only to certain tools)

Custom Middleware Hooks

Six decorator hooks are available. Two patterns:

• Wrap hooks (wrap_tool_call, wrap_model_call): (request, handler) — call handler(request) to proceed, or return early to short-circuit.
• Before/after hooks (before_model, after_model, before_agent, after_agent): (state, runtime) — inspect or modify state. Return None or a dict of state updates.

from langchain.agents.middleware import wrap_tool_call

@wrap_tool_call
def retry_middleware(request, handler):
    for attempt in range(3):
        try:
            return handler(request)
        except Exception:
            if attempt == 2:
                raise

@wrap_tool_call
def guard_middleware(request, handler):
    if request.tool_call["name"] == "dangerous_tool":
        return "This tool is disabled"  # short-circuit
    return handler(request)

import { createMiddleware } from "langchain";

const retryMiddleware = createMiddleware({
  wrapToolCall: async (request, handler) => {
    for (let attempt = 0; attempt < 3; attempt++) {
      try { return await handler(request); }
      catch (e) { if (attempt === 2) throw e; }
    }
  },
});

from langchain.agents.middleware import before_model, after_model

@before_model
def log_calls(state, runtime):
    print(f"Calling model with {len(state['messages'])} messages")

@after_model
def check_output(state, runtime):
    print(f"Model responded")

import { createMiddleware } from "langchain";

const loggingMiddleware = createMiddleware({
  beforeModel: (state, runtime) => {
    console.log(`Calling model with ${state.messages.length} messages`);
  },
  afterModel: (state, runtime) => {
    console.log("Model responded");
  },
});

• Interrupt after tool execution (must be before)
• Skip checkpointer requirement for HITL

CORRECT

agent = create_agent(model="gpt-4.1", tools=[send_email], checkpointer=MemorySaver(), # Required middleware=[HumanInTheLoopMiddleware({...})])

</python>
<typescript>
HITL requires a checkpointer to persist state.

// WRONG: No checkpointer const agent = createAgent({ model: "anthropic:claude-sonnet-4-5", tools: [sendEmail], middleware: [humanInTheLoopMiddleware({ interruptOn: { send_email: true } })], });

// CORRECT: Add checkpointer const agent = createAgent({ model: "anthropic:claude-sonnet-4-5", tools: [sendEmail], checkpointer: new MemorySaver(), middleware: [humanInTheLoopMiddleware({ interruptOn: { send_email: true } })], });

# CORRECT

agent.invoke(input, config={"configurable": {"thread_id": "user-123"}})

</python> </fix-no-thread-id>

<fix-wrong-resume-syntax> <python> Use Command class to resume execution after an interrupt.

# WRONG
agent.invoke({"resume": {"decisions": [...]}})

# CORRECT
from langgraph.types import Command
agent.invoke(Command(resume={"decisions": [{"type": "approve"}]}), config=config)

// CORRECT import {Command} from "@langchain/langgraph"; await agent.invoke(new Command({resume: {decisions: [{type: "approve"}]}}), config);

</typescript>
</fix-wrong-resume-syntax>