• 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"}]
}, 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.
```typescript
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.
```typescript
// 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.
```python
# 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>