Build customer support with handoffs

The state machine pattern describes workflows where an agent’s behavior changes as it moves through different states of a task. This tutorial shows how to implement a state machine by using tool calls to dynamically change a single agent’s configuration—updating its available tools and instructions based on the current state. The state can be determined from multiple sources: the agent’s past actions (tool calls), external state (such as API call results), or even initial user input (for example, by running a classifier to determine user intent). In this tutorial, you’ll build a customer support agent that does the following:

Collects warranty information before proceeding.
Classifies issues as hardware or software.
Provides solutions or escalates to human support.
Maintains conversation state across multiple turns.

Unlike the subagents pattern where sub-agents are called as tools, the state machine pattern uses a single agent whose configuration changes based on workflow progress. Each “step” is just a different configuration (system prompt + tools) of the same underlying agent, selected dynamically based on state. Here’s the workflow we’ll build:

Setup

Installation

This tutorial requires the langchain package:

npm install langchain

yarn add langchain

pnpm add langchain

For more details, see our Installation guide.

LangSmith

Set up LangSmith to inspect what is happening inside your agent. Then set the following environment variables:

export LANGSMITH_TRACING="true"
export LANGSMITH_API_KEY="..."

process.env.LANGSMITH_TRACING = "true";
process.env.LANGSMITH_API_KEY = "...";

Select an LLM

Select a chat model from LangChain’s suite of integrations:

OpenAI
Anthropic
Azure
Google Gemini
Bedrock Converse

👉 Read the OpenAI chat model integration docs

npm install @langchain/openai

pnpm install @langchain/openai

yarn add @langchain/openai

bun add @langchain/openai

import { initChatModel } from "langchain";

process.env.OPENAI_API_KEY = "your-api-key";

const model = await initChatModel("gpt-5.5");

import { ChatOpenAI } from "@langchain/openai";

const model = new ChatOpenAI({
  model: "gpt-5.5",
  apiKey: "your-api-key"
});

👉 Read the Anthropic chat model integration docs

npm install @langchain/anthropic

pnpm install @langchain/anthropic

yarn add @langchain/anthropic

pnpm add @langchain/anthropic

import { initChatModel } from "langchain";

process.env.ANTHROPIC_API_KEY = "your-api-key";

const model = await initChatModel("claude-sonnet-4-6");

import { ChatAnthropic } from "@langchain/anthropic";

const model = new ChatAnthropic({
  model: "claude-sonnet-4-6",
  apiKey: "your-api-key"
});

👉 Read the Azure chat model integration docs

npm install @langchain/azure

pnpm install @langchain/azure

yarn add @langchain/azure

bun add @langchain/azure

import { initChatModel } from "langchain";

process.env.AZURE_OPENAI_API_KEY = "your-api-key";
process.env.AZURE_OPENAI_ENDPOINT = "your-endpoint";
process.env.OPENAI_API_VERSION = "your-api-version";

const model = await initChatModel("azure_openai:gpt-5.5");

import { AzureChatOpenAI } from "@langchain/openai";

const model = new AzureChatOpenAI({
  model: "gpt-5.5",
  azureOpenAIApiKey: "your-api-key",
  azureOpenAIApiEndpoint: "your-endpoint",
  azureOpenAIApiVersion: "your-api-version"
});

👉 Read the Google GenAI chat model integration docs

npm install @langchain/google-genai

pnpm install @langchain/google-genai

yarn add @langchain/google-genai

bun add @langchain/google-genai

import { initChatModel } from "langchain";

process.env.GOOGLE_API_KEY = "your-api-key";

const model = await initChatModel("google-genai:gemini-2.5-flash-lite");

import { ChatGoogleGenerativeAI } from "@langchain/google-genai";

const model = new ChatGoogleGenerativeAI({
  model: "gemini-2.5-flash-lite",
  apiKey: "your-api-key"
});

👉 Read the AWS Bedrock chat model integration docs

npm install @langchain/aws

pnpm install @langchain/aws

yarn add @langchain/aws

bun add @langchain/aws

import { initChatModel } from "langchain";

// Follow the steps here to configure your credentials:
// https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html

const model = await initChatModel("bedrock:gpt-5.5");

import { ChatBedrockConverse } from "@langchain/aws";

// Follow the steps here to configure your credentials:
// https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html

const model = new ChatBedrockConverse({
  model: "gpt-5.5",
  region: "us-east-2"
});

1. Define custom state

First, define a custom state schema that tracks which step is currently active:

import { StateSchema } from "@langchain/langgraph";
import { z } from "zod";

// Define the possible workflow steps
const SupportStepSchema = z.enum(["warranty_collector", "issue_classifier", "resolution_specialist"]);
const WarrantyStatusSchema = z.enum(["in_warranty", "out_of_warranty"]);
const IssueTypeSchema = z.enum(["hardware", "software"]);

// State for customer support workflow
const SupportState = new StateSchema({
  currentStep: SupportStepSchema.optional(),
  warrantyStatus: WarrantyStatusSchema.optional(),
  issueType: IssueTypeSchema.optional(),
});

The current_step field is the core of the state machine pattern - it determines which configuration (prompt + tools) is loaded on each turn.

2. Create tools that manage workflow state

Create tools that update the workflow state. These tools allow the agent to record information and transition to the next step. The key is using Command to update state, including the current_step field:

import { z } from "zod";
import { tool, ToolMessage, type ToolRuntime } from "langchain";
import { Command } from "@langchain/langgraph";

const recordWarrantyStatus = tool(
  async (input, config: ToolRuntime<typeof SupportState.State>) => {
    return new Command({
      update: {
        messages: [
          new ToolMessage({
            content: `Warranty status recorded as: ${input.status}`,
            tool_call_id: config.toolCallId,
          }),
        ],
        warrantyStatus: input.status,
        currentStep: "issue_classifier",
      },
    });
  },
  {
    name: "record_warranty_status",
    description:
      "Record the customer's warranty status and transition to issue classification.",
    schema: z.object({
      status: WarrantyStatusSchema,
    }),
  }
);

const recordIssueType = tool(
  async (input, config: ToolRuntime<typeof SupportState.State>) => {
    return new Command({
      update: {
        messages: [
          new ToolMessage({
            content: `Issue type recorded as: ${input.issueType}`,
            tool_call_id: config.toolCallId,
          }),
        ],
        issueType: input.issueType,
        currentStep: "resolution_specialist",
      },
    });
  },
  {
    name: "record_issue_type",
    description:
      "Record the type of issue and transition to resolution specialist.",
    schema: z.object({
      issueType: IssueTypeSchema,
    }),
  }
);

const escalateToHuman = tool(
  async (input) => {
    // In a real system, this would create a ticket, notify staff, etc.
    return `Escalating to human support. Reason: ${input.reason}`;
  },
  {
    name: "escalate_to_human",
    description: "Escalate the case to a human support specialist.",
    schema: z.object({
      reason: z.string(),
    }),
  }
);

const provideSolution = tool(
  async (input) => {
    return `Solution provided: ${input.solution}`;
  },
  {
    name: "provide_solution",
    description: "Provide a solution to the customer's issue.",
    schema: z.object({
      solution: z.string(),
    }),
  }
);

Notice how record_warranty_status and record_issue_type return Command objects that update both the data (warranty_status, issue_type) AND the current_step. This is how the state machine works - tools control workflow progression.

3. Define step configurations

Define prompts and tools for each step. First, define the prompts for each step:

View complete prompt definitions

// Define prompts as constants for easy reference
const WARRANTY_COLLECTOR_PROMPT = `You are a customer support agent helping with device issues.

CURRENT STAGE: Warranty verification

At this step, you need to:
1. Greet the customer warmly
2. Ask if their device is under warranty
3. Use record_warranty_status to record their response and move to the next step

Be conversational and friendly. Don't ask multiple questions at once.`;

const ISSUE_CLASSIFIER_PROMPT = `You are a customer support agent helping with device issues.

CURRENT STAGE: Issue classification
CUSTOMER INFO: Warranty status is {warranty_status}

At this step, you need to:
1. Ask the customer to describe their issue
2. Determine if it's a hardware issue (physical damage, broken parts) or software issue (app crashes, performance)
3. Use record_issue_type to record the classification and move to the next step

If unclear, ask clarifying questions before classifying.`;

const RESOLUTION_SPECIALIST_PROMPT = `You are a customer support agent helping with device issues.

CURRENT STAGE: Resolution
CUSTOMER INFO: Warranty status is {warranty_status}, issue type is {issue_type}

At this step, you need to:
1. For SOFTWARE issues: provide troubleshooting steps using provide_solution
2. For HARDWARE issues:
   - If IN WARRANTY: explain warranty repair process using provide_solution
   - If OUT OF WARRANTY: escalate_to_human for paid repair options

Be specific and helpful in your solutions.`;

Then map step names to their configurations using a dictionary:

// Step configuration: maps step name to (prompt, tools, required_state)
const STEP_CONFIG = {
  warranty_collector: {
    prompt: WARRANTY_COLLECTOR_PROMPT,
    tools: [recordWarrantyStatus],
    requires: [],
  },
  issue_classifier: {
    prompt: ISSUE_CLASSIFIER_PROMPT,
    tools: [recordIssueType],
    requires: ["warrantyStatus"],
  },
  resolution_specialist: {
    prompt: RESOLUTION_SPECIALIST_PROMPT,
    tools: [provideSolution, escalateToHuman],
    requires: ["warrantyStatus", "issueType"],
  },
} as const;

This dictionary-based configuration makes it easy to:

See all steps at a glance
Add new steps (just add another entry)
Understand the workflow dependencies (requires field)
Use prompt templates with state variables (e.g., {warranty_status})

4. Create step-based middleware

Create middleware that reads current_step from state and applies the appropriate configuration. We’ll use the @wrap_model_call decorator for a clean implementation:

import { createMiddleware } from "langchain";

const applyStepMiddleware = createMiddleware({
  name: "applyStep",
  stateSchema: SupportState,
  wrapModelCall: async (request, handler) => {
    // Get current step (defaults to warranty_collector for first interaction)
    const currentStep = request.state.currentStep ?? "warranty_collector";

    // Look up step configuration
    const stepConfig = STEP_CONFIG[currentStep];

    // Validate required state exists
    for (const key of stepConfig.requires) {
      if (request.state[key] === undefined) {
        throw new Error(`${key} must be set before reaching ${currentStep}`);
      }
    }

    // Format prompt with state values (supports {warrantyStatus}, {issueType}, etc.)
    let systemPrompt: string = stepConfig.prompt;
    for (const [key, value] of Object.entries(request.state)) {
      systemPrompt = systemPrompt.replace(`{${key}}`, String(value ?? ""));
    }

    // Inject system prompt and step-specific tools
    return handler({
      ...request,
      systemPrompt,
      tools: [...stepConfig.tools],
    });
  },
});

This middleware:

Reads current step: Gets current_step from state (defaults to “warranty_collector”).
Looks up configuration: Finds the matching entry in STEP_CONFIG.
Validates dependencies: Ensures required state fields exist.
Formats prompt: Injects state values into the prompt template.
Applies configuration: Overrides the system prompt and available tools.

The request.override() method is key - it allows us to dynamically change the agent’s behavior based on state without creating separate agent instances.

5. Create the agent

Now create the agent with the step-based middleware and a checkpointer for state persistence:

import { createAgent } from "langchain";
import { MemorySaver } from "@langchain/langgraph";
import { ChatOpenAI } from "@langchain/openai";

// Collect all tools from all step configurations
const allTools = [
  recordWarrantyStatus,
  recordIssueType,
  provideSolution,
  escalateToHuman,
];

// Initialize the model
const model = new ChatOpenAI({
  model: "gpt-5.4-mini",
  temperature: 0.7,
});

// Create the agent with step-based configuration
const agent = createAgent({
  model,
  tools: allTools,
  stateSchema: SupportState,
  middleware: [applyStepMiddleware],
  checkpointer: new MemorySaver(),
});

Why a checkpointer? The checkpointer maintains state across conversation turns. Without it, the current_step state would be lost between user messages, breaking the workflow.

6. Test the workflow

Test the complete workflow:

import { HumanMessage } from "@langchain/core/messages";
import { v7 as uuid7 } from "uuid";

// Configuration for this conversation thread
const threadId = uuid7();
const config = { configurable: { thread_id: threadId } };

// Turn 1: Initial message - starts with warranty_collector step
console.log("=== Turn 1: Warranty Collection ===");
let result = await agent.invoke(
  { messages: [new HumanMessage("Hi, my phone screen is cracked")] },
  config
);
for (const msg of result.messages) {
  console.log(msg.content);
}

// Turn 2: User responds about warranty
console.log("\n=== Turn 2: Warranty Response ===");
result = await agent.invoke(
  { messages: [new HumanMessage("Yes, it's still under warranty")] },
  config
);
for (const msg of result.messages) {
  console.log(msg.content);
}
console.log(`Current step: ${result.currentStep}`);

// Turn 3: User describes the issue
console.log("\n=== Turn 3: Issue Description ===");
result = await agent.invoke(
  { messages: [new HumanMessage("The screen is physically cracked from dropping it")] },
  config
);
for (const msg of result.messages) {
  console.log(msg.content);
}
console.log(`Current step: ${result.currentStep}`);

// Turn 4: Resolution
console.log("\n=== Turn 4: Resolution ===");
result = await agent.invoke(
  { messages: [new HumanMessage("What should I do?")] },
  config
);
for (const msg of result.messages) {
  console.log(msg.content);
}

Expected flow:

Warranty verification step: Asks about warranty status
Issue classification step: Asks about the problem, determines it’s hardware
Resolution step: Provides warranty repair instructions

7. Understanding state transitions

Let’s trace what happens at each turn:

Turn 1: Initial message

{
  messages: [new HumanMessage("Hi, my phone screen is cracked")],
  currentStep: "warranty_collector"  // Default value
}

Middleware applies:

System prompt: WARRANTY_COLLECTOR_PROMPT
Tools: [record_warranty_status]

Turn 2: After warranty recorded

Tool call: recordWarrantyStatus("in_warranty") returns:

new Command({
  update: {
    warrantyStatus: "in_warranty",
    currentStep: "issue_classifier"  // State transition!
  }
})

Next turn, middleware applies:

System prompt: ISSUE_CLASSIFIER_PROMPT (formatted with warranty_status="in_warranty")
Tools: [record_issue_type]

Turn 3: After issue classified

Tool call: recordIssueType("hardware") returns:

new Command({
  update: {
    issueType: "hardware",
    currentStep: "resolution_specialist"  // State transition!
  }
})

Next turn, middleware applies:

System prompt: RESOLUTION_SPECIALIST_PROMPT (formatted with warranty_status and issue_type)
Tools: [provide_solution, escalate_to_human]

The key insight: Tools drive the workflow by updating current_step, and middleware responds by applying the appropriate configuration on the next turn.

8. Manage message history

As the agent progresses through steps, message history grows. Use summarization middleware to compress earlier messages while preserving conversational context:

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

const agent = createAgent({
  model,
  tools: allTools,
  stateSchema: SupportState,
  middleware: [
    applyStepMiddleware,
    new SummarizationMiddleware({
      model: "gpt-5.4-mini",
      trigger: { tokens: 4000 },
      keep: { messages: 10 },
    }),
  ],
  checkpointer: new MemorySaver(),
});

See the short-term memory guide for other memory management techniques.

9. Add flexibility: Go back

Some workflows need to allow users to return to previous steps to correct information (e.g., changing warranty status or issue classification). However, not all transitions make sense—for example, you typically can’t go back once a refund has been processed. For this support workflow, we’ll add tools to return to the warranty verification and issue classification steps.

If your workflow requires arbitrary transitions between most steps, consider whether you need a structured workflow at all. This pattern works best when steps follow a clear sequential progression with occasional backwards transitions for corrections.

Add “go back” tools to the resolution step:

import { tool } from "langchain";
import { Command } from "@langchain/langgraph";
import { z } from "zod";

const goBackToWarranty = tool(  
  async () => {
    return new Command({ update: { currentStep: "warranty_collector" } });
  },
  {
    name: "go_back_to_warranty",
    description: "Go back to warranty verification step.",
    schema: z.object({}),
  }
);

const goBackToClassification = tool(  
  async () => {
    return new Command({ update: { currentStep: "issue_classifier" } });
  },
  {
    name: "go_back_to_classification",
    description: "Go back to issue classification step.",
    schema: z.object({}),
  }
);

// Update the resolution_specialist configuration to include these tools
STEP_CONFIG.resolution_specialist.tools.push(
  goBackToWarranty,
  goBackToClassification
);

Update the resolution specialist’s prompt to mention these tools:

const RESOLUTION_SPECIALIST_PROMPT = `You are a customer support agent helping with device issues.

CURRENT STAGE: Resolution
CUSTOMER INFO: Warranty status is {warrantyStatus}, issue type is {issueType}

At this step, you need to:
1. For SOFTWARE issues: provide troubleshooting steps using provide_solution
2. For HARDWARE issues:
   - If IN WARRANTY: explain warranty repair process using provide_solution
   - If OUT OF WARRANTY: escalate_to_human for paid repair options

If the customer indicates any information was wrong, use:
- go_back_to_warranty to correct warranty status
- go_back_to_classification to correct issue type

Be specific and helpful in your solutions.`;

Now the agent can handle corrections:

const result = await agent.invoke(
  { messages: [new HumanMessage("Actually, I made a mistake - my device is out of warranty")] },
  config
);
// Agent will call go_back_to_warranty and restart the warranty verification step

Complete example

Here’s everything together in a runnable script:

Show Complete code

import { createMiddleware, createAgent } from "langchain";

import { z } from "zod";
import { tool, ToolMessage, type ToolRuntime, HumanMessage } from "langchain";
import { Command, MemorySaver, StateSchema } from "@langchain/langgraph";
import { ChatOpenAI } from "@langchain/openai";

// Define the possible workflow steps
const SupportStepSchema = z.enum([
  "warranty_collector",
  "issue_classifier",
  "resolution_specialist",
]);
const WarrantyStatusSchema = z.enum(["in_warranty", "out_of_warranty"]);
const IssueTypeSchema = z.enum(["hardware", "software"]);

// State for customer support workflow
const SupportState = new StateSchema({
  currentStep: SupportStepSchema.optional(),
  warrantyStatus: WarrantyStatusSchema.optional(),
  issueType: IssueTypeSchema.optional(),
});

const recordWarrantyStatus = tool(
  async (input, config: ToolRuntime<typeof SupportState.State>) => {
    return new Command({

      update: {

        messages: [
          new ToolMessage({
            content: `Warranty status recorded as: ${input.status}`,
            tool_call_id: config.toolCallId,
          }),
        ],
        warrantyStatus: input.status,
        currentStep: "issue_classifier",
      },
    });
  },
  {
    name: "record_warranty_status",
    description:
      "Record the customer's warranty status and transition to issue classification.",
    schema: z.object({
      status: WarrantyStatusSchema,
    }),
  }
);

const recordIssueType = tool(
  async (input, config: ToolRuntime<typeof SupportState.State>) => {
    return new Command({

      update: {

        messages: [
          new ToolMessage({
            content: `Issue type recorded as: ${input.issueType}`,
            tool_call_id: config.toolCallId,
          }),
        ],
        issueType: input.issueType,
        currentStep: "resolution_specialist",
      },
    });
  },
  {
    name: "record_issue_type",
    description:
      "Record the type of issue and transition to resolution specialist.",
    schema: z.object({
      issueType: IssueTypeSchema,
    }),
  }
);

const escalateToHuman = tool(
  async (input) => {
    // In a real system, this would create a ticket, notify staff, etc.
    return `Escalating to human support. Reason: ${input.reason}`;
  },
  {
    name: "escalate_to_human",
    description: "Escalate the case to a human support specialist.",
    schema: z.object({
      reason: z.string(),
    }),
  }
);

const provideSolution = tool(
  async (input) => {
    return `Solution provided: ${input.solution}`;
  },
  {
    name: "provide_solution",
    description: "Provide a solution to the customer's issue.",
    schema: z.object({
      solution: z.string(),
    }),
  }
);

// Define prompts as constants for easy reference
const WARRANTY_COLLECTOR_PROMPT = `You are a customer support agent helping with device issues.

CURRENT STAGE: Warranty verification

At this step, you need to:
1. Greet the customer warmly
2. Ask if their device is under warranty
3. Use record_warranty_status to record their response and move to the next step

Be conversational and friendly. Don't ask multiple questions at once.`;

const ISSUE_CLASSIFIER_PROMPT = `You are a customer support agent helping with device issues.

CURRENT STAGE: Issue classification
CUSTOMER INFO: Warranty status is {warranty_status}

At this step, you need to:
1. Ask the customer to describe their issue
2. Determine if it's a hardware issue (physical damage, broken parts) or software issue (app crashes, performance)
3. Use record_issue_type to record the classification and move to the next step

If unclear, ask clarifying questions before classifying.`;

const RESOLUTION_SPECIALIST_PROMPT = `You are a customer support agent helping with device issues.

CURRENT STAGE: Resolution
CUSTOMER INFO: Warranty status is {warranty_status}, issue type is {issue_type}

At this step, you need to:
1. For SOFTWARE issues: provide troubleshooting steps using provide_solution
2. For HARDWARE issues:
   - If IN WARRANTY: explain warranty repair process using provide_solution
   - If OUT OF WARRANTY: escalate_to_human for paid repair options

Be specific and helpful in your solutions.`;

// Step configuration: maps step name to (prompt, tools, required_state)
const STEP_CONFIG = {
  warranty_collector: {
    prompt: WARRANTY_COLLECTOR_PROMPT,
    tools: [recordWarrantyStatus],
    requires: [],
  },
  issue_classifier: {
    prompt: ISSUE_CLASSIFIER_PROMPT,
    tools: [recordIssueType],
    requires: ["warrantyStatus"],
  },
  resolution_specialist: {
    prompt: RESOLUTION_SPECIALIST_PROMPT,
    tools: [provideSolution, escalateToHuman],
    requires: ["warrantyStatus", "issueType"],
  },
} as const;

const applyStepMiddleware = createMiddleware({
  name: "applyStep",
  stateSchema: SupportState,
  wrapModelCall: async (request, handler) => {
    // Get current step (defaults to warranty_collector for first interaction)
    const currentStep = request.state.currentStep ?? "warranty_collector";

    // Look up step configuration
    const stepConfig = STEP_CONFIG[currentStep];

    // Validate required state exists
    for (const key of stepConfig.requires) {
      if (request.state[key] === undefined) {
        throw new Error(`${key} must be set before reaching ${currentStep}`);
      }
    }

    // Format prompt with state values (supports {warrantyStatus}, {issueType}, etc.)
    let systemPrompt: string = stepConfig.prompt;
    for (const [key, value] of Object.entries(request.state)) {
      systemPrompt = systemPrompt.replace(`{${key}}`, String(value ?? ""));
    }

    // Inject system prompt and step-specific tools
    return handler({
      ...request,
      systemPrompt,
      tools: [...stepConfig.tools],
    });
  },
});

// Collect all tools from all step configurations
const allTools = [
  recordWarrantyStatus,
  recordIssueType,
  provideSolution,
  escalateToHuman,
];

const model = new ChatOpenAI({
  model: "gpt-5.4-mini",
});

// Create the agent with step-based configuration
const agent = createAgent({
  model,
  tools: allTools,
  middleware: [applyStepMiddleware],
  checkpointer: new MemorySaver(),
});

// Configuration for this conversation thread
const threadId = crypto.randomUUID();
const config = { configurable: { thread_id: threadId } };

// Turn 1: Initial message - starts with warranty_collector step
console.log("=== Turn 1: Warranty Collection ===");
let result = await agent.invoke(
  { messages: [new HumanMessage("Hi, my phone screen is cracked")] },
  config
);
for (const msg of result.messages) {
  console.log(msg.content);
}

// Turn 2: User responds about warranty
console.log("\n=== Turn 2: Warranty Response ===");
result = await agent.invoke(
  { messages: [new HumanMessage("Yes, it's still under warranty")] },
  config
);
for (const msg of result.messages) {
  console.log(msg.content);
}
console.log(`Current step: ${result.currentStep}`);

// Turn 3: User describes the issue
console.log("\n=== Turn 3: Issue Description ===");
result = await agent.invoke(
  {
    messages: [
      new HumanMessage("The screen is physically cracked from dropping it"),
    ],
  },
  config
);
for (const msg of result.messages) {
  console.log(msg.content);
}
console.log(`Current step: ${result.currentStep}`);

// Turn 4: Resolution
console.log("\n=== Turn 4: Resolution ===");
result = await agent.invoke(
  { messages: [new HumanMessage("What should I do?")] },
  config
);
for (const msg of result.messages) {
  console.log(msg.content);
}

Next steps

Learn about the subagents pattern for centralized orchestration
Explore middleware for more dynamic behaviors
Read the multi-agent overview to compare patterns
Use LangSmith to debug and monitor your multi-agent system

Connect these docs to Claude, VSCode, and more via MCP for real-time answers.

Edit this page on GitHub or file an issue.

​Setup

​Installation

​LangSmith

​Select an LLM

​1. Define custom state

​2. Create tools that manage workflow state

​3. Define step configurations

​4. Create step-based middleware

​5. Create the agent

​6. Test the workflow

​7. Understanding state transitions

​Turn 1: Initial message

​Turn 2: After warranty recorded

​Turn 3: After issue classified

​8. Manage message history

​9. Add flexibility: Go back

​Complete example

​Next steps

Setup

Installation

LangSmith

Select an LLM

1. Define custom state

2. Create tools that manage workflow state

3. Define step configurations

4. Create step-based middleware

5. Create the agent

6. Test the workflow

7. Understanding state transitions

Turn 1: Initial message

Turn 2: After warranty recorded

Turn 3: After issue classified

8. Manage message history

9. Add flexibility: Go back

Complete example

Next steps