- 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/jackjin1997/clawforge/blob/HEAD/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)
What You CANNOT Configure
- 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>