How to integrate Apify MCP with Vercel AI SDK v6

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Introduction

This guide walks you through connecting Apify to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Apify agent that can create a new dataset for scraped results, fetch items from a specific apify dataset, get details of your latest apify actor through natural language commands.

This guide will help you understand how to give your Vercel AI SDK agent real control over a Apify account through Composio's Apify MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Apify with

TL;DR

Here's what you'll learn:
  • How to set up and configure a Vercel AI SDK agent with Apify integration
  • Using Composio's Tool Router to dynamically load and access Apify tools
  • Creating an MCP client connection using HTTP transport
  • Building an interactive CLI chat interface with conversation history management
  • Handling tool calls and results within the Vercel AI SDK framework

What is Vercel AI SDK?

The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state.

Key features include:

  • streamText: Core function for streaming responses with real-time tool support
  • MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp
  • Step Counting: Control multi-step tool execution with stopWhen: stepCountIs()
  • OpenAI Provider: Native integration with OpenAI models

What is the Apify MCP server, and what's possible with it?

The Apify MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Apify account. It provides structured and secure access to your web scraping and automation workflows, so your agent can create actors, manage datasets, fetch scraped data, schedule tasks, and maintain webhooks on your behalf.

  • Automated Actor Creation and Management: Easily instruct your agent to programmatically create, configure, or delete Apify actors for custom web automation or scraping jobs.
  • Dataset Handling and Data Retrieval: Let your agent spin up new datasets, organize scraped results, and pull items from datasets for downstream analysis or reporting.
  • Task Scheduling and Automation: Have your agent create and manage recurring actor tasks, making it simple to automate data extraction or browser automation at set intervals.
  • Webhook Integration and Event Handling: Direct your agent to set up or remove webhooks for actor tasks, enabling real-time notifications or downstream integrations when a task completes or fails.
  • Actor and Build Metadata Access: Empower your agent to fetch detailed metadata about actors, including build information and configuration details, for monitoring or troubleshooting purposes.

Supported Tools & Triggers

Tools
Build ActorTool to build an Actor with specified configuration.
Abort Actor BuildTool to abort an Actor build that is starting or running.
Delete Actor BuildTool to delete an Actor build permanently.
Get Actor BuildTool to get detailed information about a specific Actor build.
Get Actor Build LogTool to retrieve the log file for a specific Actor build.
Get user builds listTool to get a paginated list of all builds for a user.
Abort Actor RunTool to abort a running or starting Actor run.
Delete Actor RunTool to delete a finished Actor run.
Get Actor RunTool to get details about a specific Actor run.
Update Actor Run Status MessageTool to update the status message of an Actor run.
Delete Actor TaskTool to delete an Actor task permanently.
Get Actor TaskTool to get complete details about an Actor task.
Update Actor TaskTool to update Actor task settings using JSON payload.
Get last actor task runTool to get the most recent run of a specific Actor task.
Run Task Sync (GET)Tool to run a specific task synchronously and return its output.
Run Task Sync & Get Dataset ItemsTool to run an actor task synchronously and retrieve its dataset items.
Run Task Sync with Input Override & Get Dataset ItemsTool to run an actor task synchronously with input overrides and retrieve its dataset items.
Run Task Sync (POST)Tool to run an Actor task synchronously with input override and return its output.
Update ActorTool to update Actor settings using JSON payload.
Get last actor runTool to get the most recent run of a specific Actor.
Run Actor Sync without Input (GET)Tool to run a specific Actor synchronously without input and return its output.
Run Actor Sync & Get Dataset ItemsTool to run Actor synchronously and get dataset items.
Get list of ActorsTool to get the list of all Actors that the user created or used.
Delete Actor VersionTool to delete a specific version of an Actor's source code.
Delete Actor Version Environment VariableTool to delete an environment variable from a specific Actor version.
Get Actor Version Environment VariableTool to get environment variable details for a specific Actor version.
Update Actor Version Environment VariableTool to update environment variable for a specific Actor version using JSON payload.
Get list of Actor version environment variablesTool to get the list of environment variables for a specific Actor version.
Create Actor Version Environment VariableTool to create an environment variable for a specific Actor version.
Get Actor versionTool to get details about a specific version of an Actor.
Update Actor VersionTool to update an Actor version's configuration and source code.
Get list of Actor versionsTool to get the list of versions of a specific Actor.
Create Actor VersionTool to create a new version of an Actor.
Get list of Actor webhooksTool to get a list of webhooks for a specific Actor.
Create ActorTool to create a new Actor with specified configuration.
Create DatasetTool to create a new dataset.
Create Actor TaskTool to create a new Actor task with specified settings.
Create Task WebhookTool to create a webhook for an Actor task.
Delete DatasetTool to delete a dataset permanently.
Get DatasetTool to retrieve dataset metadata by dataset ID.
Update DatasetTool to update a dataset's name via JSON payload.
Get list of datasetsTool to get list of datasets for a user.
Get Dataset StatisticsTool to get dataset field statistics by dataset ID.
Delete ActorTool to delete an Actor permanently.
Delete WebhookTool to delete a webhook by its ID.
Get Actor DetailsTool to get details of a specific Actor.
Get Actor Last Run Dataset ItemsTool to get dataset items from the last run of an Actor.
Get all webhooksTool to get a list of all webhooks created by the user.
Get dataset itemsTool to retrieve items from a dataset.
Get Default BuildTool to get the default build for an Actor.
Get Key-Value RecordTool to retrieve a record from a key-value store.
Get list of buildsTool to get a list of builds for a specific Actor.
Get list of runsTool to get a list of runs for a specific Actor.
Get list of task runsTool to get a list of runs for a specific Actor task.
Get list of tasksTool to fetch a paginated list of tasks belonging to the authenticated user.
Get list of task webhooksTool to get a list of webhooks for a specific Actor task.
Get logTool to retrieve logs for a specific Actor run or build.
Get OpenAPI DefinitionTool to get the OpenAPI definition for a specific Actor build.
Get Run Dataset ItemsTool to get dataset items from a specific Actor run.
Get Task InputTool to retrieve the input configuration of a specific task.
Get Task Last Run Dataset ItemsTool to get dataset items from the last run of an Actor task.
Delete Key-Value StoreTool to delete a key-value store permanently.
Get Key-Value StoreTool to retrieve key-value store metadata by store ID.
Get Key-Value Store KeysTool to retrieve a list of keys from a key-value store.
Delete Key-Value Store RecordTool to delete a record from a key-value store.
Check Key-Value Store Record ExistsTool to check if a record exists in a key-value store.
Get list of key-value storesTool to get the list of key-value stores owned by the user.
Create Key-Value StoreTool to create a new key-value store or retrieve an existing one by name.
List User Actor RunsTool to get a paginated list of all Actor runs for the authenticated user.
Delete Request QueueTool to delete a request queue permanently.
Get Request QueueTool to retrieve request queue metadata by queue ID.
Get Request Queue HeadTool to retrieve first requests from the queue for inspection.
Get Head and Lock Queue RequestsTool to get and lock head requests from the queue.
Update Request QueueTool to update request queue name using JSON payload.
Delete Request from QueueTool to delete a specific request from a request queue.
Get Request from QueueTool to retrieve a specific request from a request queue by its ID.
Delete Request LockTool to delete a request lock from a request queue.
Prolong Request LockTool to prolong request lock in a request queue.
Update Request in QueueTool to update a request in a request queue.
Batch Delete Requests from QueueTool to batch-delete up to 25 requests from a queue.
Batch Add Requests to QueueTool to batch-add up to 25 requests to a request queue.
List Request Queue RequestsTool to list requests in a request queue with pagination support.
Add Request to QueueTool to add a request to the queue.
Unlock Queue RequestsTool to unlock requests in a request queue that are currently locked by the client.
Get list of request queuesTool to get list of request queues for a user.
Create Request QueueTool to create a new request queue or retrieve an existing one by name.
Run Actor AsynchronouslyTool to run a specific Actor asynchronously.
Run Actor SyncTool to run a specific Actor synchronously with input and return its output record.
Run Actor Sync & Get Dataset ItemsTool to run an Actor synchronously and retrieve its dataset items.
Run Task AsynchronouslyTool to run a specific Actor task asynchronously.
Delete ScheduleTool to delete a schedule by its ID.
Get ScheduleTool to get schedule details by ID.
Get Schedule LogTool to get schedule log by ID.
Update ScheduleTool to update an existing schedule with new settings.
Get list of schedulesTool to get list of schedules created by the user.
Create ScheduleTool to create a new schedule with specified settings.
Store Data in DatasetTool to store data items in a dataset.
Store Data in Key-Value StoreTool to create or update a record in a key-value store.
Get list of Actors in StoreTool to get list of public Actors from Apify Store.
Update Key-Value StoreTool to update a key-value store's properties.
Update Task InputTool to update the input configuration of a specific Actor task.
Get Public User DataTool to get public user data.
Get Current User Account DataTool to get private user account information.
Get Account LimitsTool to get a complete summary of account limits and usage.
Update Account LimitsTool to update account limits manageable on the Limits page.
Get Monthly UsageTool to get monthly usage summary with daily breakdown.
Get list of webhook dispatchesTool to get list of webhook dispatches for the user.
Get Webhook DispatchTool to get webhook dispatch object with all details.
Get webhookTool to get webhook object with all details.
Update WebhookTool to update webhook using JSON payload.
Test WebhookTool to test a webhook by creating a test dispatch with a dummy payload.
Get webhook dispatchesTool to get list of webhook dispatches for a specific webhook.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before you begin, make sure you have:
  • Node.js and npm installed
  • A Composio account with API key
  • An OpenAI API key

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install required dependencies

bash
npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv

First, install the necessary packages for your project.

What you're installing:

  • @ai-sdk/openai: Vercel AI SDK's OpenAI provider
  • @ai-sdk/mcp: MCP client for Vercel AI SDK
  • @composio/core: Composio SDK for tool integration
  • ai: Core Vercel AI SDK
  • dotenv: Environment variable management

Set up environment variables

bash
OPENAI_API_KEY=your_openai_api_key_here
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here

Create a .env file in your project root.

What's needed:

  • OPENAI_API_KEY: Your OpenAI API key for GPT model access
  • COMPOSIO_API_KEY: Your Composio API key for tool access
  • COMPOSIO_USER_ID: A unique identifier for the user session

Import required modules and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});
What's happening:
  • We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio
  • The dotenv/config import automatically loads environment variables
  • The MCP client import enables connection to Composio's tool server

Create Tool Router session and initialize MCP client

typescript
async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["apify"],
  });

  const mcpUrl = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Apify tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned mcp object contains the URL and authentication headers needed to connect to the MCP server
  • This session provides access to all Apify-related tools through the MCP protocol

Connect to MCP server and retrieve tools

typescript
const mcpClient = await createMCPClient({
  transport: {
    type: "http",
    url: mcpUrl,
    headers: session.mcp.headers, // Authentication headers for the Composio MCP server
  },
});

const tools = await mcpClient.tools();
What's happening:
  • We're creating an MCP client that connects to our Composio Tool Router session via HTTP
  • The mcp.url provides the endpoint, and mcp.headers contains authentication credentials
  • The type: "http" is important - Composio requires HTTP transport
  • tools() retrieves all available Apify tools that the agent can use

Initialize conversation and CLI interface

typescript
let messages: ModelMessage[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log(
  "Ask any questions related to apify, like summarize my last 5 emails, send an email, etc... :)))\n",
);

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();
What's happening:
  • We initialize an empty messages array to maintain conversation history
  • A readline interface is created to accept user input from the command line
  • Instructions are displayed to guide the user on how to interact with the agent

Handle user input and stream responses with real-time tool feedback

typescript
rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({ role: "user", content: trimmedInput });
  console.log("\nAgent is thinking...\n");

  try {
    const stream = streamText({
      model: openai("gpt-5"),
      messages,
      tools,
      toolChoice: "auto",
      stopWhen: stepCountIs(10),
      onStepFinish: (step) => {
        for (const toolCall of step.toolCalls) {
          console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • We use streamText instead of generateText to stream responses in real-time
  • toolChoice: "auto" allows the model to decide when to use Apify tools
  • stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations
  • onStepFinish callback displays which tools are being used in real-time
  • We iterate through the text stream to create a typewriter effect as the agent responds
  • The complete response is added to conversation history to maintain context
  • Errors are caught and displayed with helpful retry suggestions

Complete Code

Here's the complete code to get you started with Apify and Vercel AI SDK:

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});

async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["apify"],
  });

  const mcpUrl = session.mcp.url;

  const mcpClient = await createMCPClient({
    transport: {
      type: "http",
      url: mcpUrl,
      headers: session.mcp.headers, // Authentication headers for the Composio MCP server
    },
  });

  const tools = await mcpClient.tools();

  let messages: ModelMessage[] = [];

  console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
  console.log(
    "Ask any questions related to apify, like summarize my last 5 emails, send an email, etc... :)))\n",
  );

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
      console.log("\nGoodbye!");
      rl.close();
      process.exit(0);
    }

    if (!trimmedInput) {
      rl.prompt();
      return;
    }

    messages.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    try {
      const stream = streamText({
        model: openai("gpt-5"),
        messages,
        tools,
        toolChoice: "auto",
        stopWhen: stepCountIs(10),
        onStepFinish: (step) => {
          for (const toolCall of step.toolCalls) {
            console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});

Conclusion

You've successfully built a Apify agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback.

Key features of this implementation:

  • Real-time streaming responses for a better user experience with typewriter effect
  • Live tool execution feedback showing which tools are being used as the agent works
  • Dynamic tool loading through Composio's Tool Router with secure authentication
  • Multi-step tool execution with configurable step limits (up to 10 steps)
  • Comprehensive error handling for robust agent execution
  • Conversation history maintenance for context-aware responses

You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

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FAQ

What are the differences in Tool Router MCP and Apify MCP?

With a standalone Apify MCP server, the agents and LLMs can only access a fixed set of Apify tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Apify and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with Vercel AI SDK v6?

Yes, you can. Vercel AI SDK v6 fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Apify tools.

Can I manage the permissions and scopes for Apify while using Tool Router?

Yes, absolutely. You can configure which Apify scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Apify data and credentials are handled as safely as possible.

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