Human-in-the-loop

import { createAgent, humanInTheLoopMiddleware } from "langchain";
import { MemorySaver } from "@langchain/langgraph";

const agent = createAgent({
    model: "gpt-5.4",
    tools: [writeFileTool, executeSQLTool, readDataTool],
    middleware: [
        humanInTheLoopMiddleware({
            interruptOn: {
                write_file: true, // All decisions (approve, edit, reject, respond) allowed
                execute_sql: {
                    allowedDecisions: ["approve", "reject"],
                    // No editing allowed
                    description: "🚨 SQL execution requires DBA approval",
                },
                // Safe operation, no approval needed
                read_data: false,
            },
            // Prefix for interrupt messages - combined with tool name and args to form the full message
            // e.g., "Tool execution pending approval: execute_sql with query='DELETE FROM...'"
            // Individual tools can override this by specifying a "description" in their interrupt config
            descriptionPrefix: "Tool execution pending approval",
        }),
    ],
    // Human-in-the-loop requires checkpointing to handle interrupts.
    // In production, use a persistent checkpointer like AsyncPostgresSaver.
    checkpointer: new MemorySaver(),
});

import { HumanMessage } from "@langchain/core/messages";
import { Command } from "@langchain/langgraph";

// You must provide a thread ID to associate the execution with a conversation thread,
// so the conversation can be paused and resumed (as is needed for human review).
const config = { configurable: { thread_id: "some_id" } };

// Run the graph until the interrupt is hit.
const result = await agent.invoke(
    {
        messages: [new HumanMessage("Delete old records from the database")],
    },
    config 
);


// The interrupt contains the full HITL request with action_requests and review_configs
console.log(result.__interrupt__);
// > [
// >    Interrupt(
// >       value: {
// >          actionRequests: [
// >             {
// >                name: 'execute_sql',
// >                arguments: { query: 'DELETE FROM records WHERE created_at < NOW() - INTERVAL \'30 days\';' },
// >                description: 'Tool execution pending approval\n\nTool: execute_sql\nArgs: {...}'
// >             }
// >          ],
// >          reviewConfigs: [
// >             {
// >                actionName: 'execute_sql',
// >                allowedDecisions: ['approve', 'reject']
// >             }
// >          ]
// >       }
// >    )
// > ]

// Resume with approval decision
await agent.invoke(
    new Command({
        resume: { decisions: [{ type: "approve" }] }, // or "reject"
    }),
    config // Same thread ID to resume the paused conversation
);

{
    decisions: [
        { type: "approve" },
        {
            type: "edit",
            editedAction: {
                name: "tool_name",
                args: { param: "new_value" }
            }
        },
        {
            type: "reject",
            message: "This action is not allowed"
        }
    ]
}

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

const config = { configurable: { thread_id: "some_id" } };

// Stream agent progress and LLM tokens until interrupt
for await (const [mode, chunk] of await agent.stream(
    { messages: [{ role: "user", content: "Delete old records from the database" }] },
    { ...config, streamMode: ["updates", "messages"] }
)) {
    if (mode === "messages") {
        // LLM token
        const [token, metadata] = chunk;
        if (token.content) {
            process.stdout.write(token.content);
        }
    } else if (mode === "updates") {
        // Check for interrupt
        if ("__interrupt__" in chunk) {
            console.log(`\n\nInterrupt: ${JSON.stringify(chunk.__interrupt__)}`);
        }
    }
}

// Resume with streaming after human decision
for await (const [mode, chunk] of await agent.stream(
    new Command({ resume: { decisions: [{ type: "approve" }] } }),
    { ...config, streamMode: ["updates", "messages"] }
)) {
    if (mode === "messages") {
        const [token, metadata] = chunk;
        if (token.content) {
            process.stdout.write(token.content);
        }
    }
}