# How to integrate Apilio MCP with Vercel AI SDK v6

```json
{
  "title": "How to integrate Apilio MCP with Vercel AI SDK v6",
  "toolkit": "Apilio",
  "toolkit_slug": "apilio",
  "framework": "Vercel AI SDK",
  "framework_slug": "ai-sdk",
  "url": "https://composio.dev/toolkits/apilio/framework/ai-sdk",
  "markdown_url": "https://composio.dev/toolkits/apilio/framework/ai-sdk.md",
  "updated_at": "2026-05-06T08:01:03.414Z"
}
```

## Introduction

This guide walks you through connecting Apilio to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Apilio agent that can list all boolean variables and their status, show me all your current logicblocks, fetch numeric variables for your thermostat through natural language commands.
This guide will help you understand how to give your Vercel AI SDK agent real control over a Apilio account through Composio's Apilio MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Apilio with

- [OpenAI Agents SDK](https://composio.dev/toolkits/apilio/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/apilio/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/apilio/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/apilio/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/apilio/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/apilio/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/apilio/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/apilio/framework/cli)
- [Google ADK](https://composio.dev/toolkits/apilio/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/apilio/framework/langchain)
- [Mastra AI](https://composio.dev/toolkits/apilio/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/apilio/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/apilio/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- How to set up and configure a Vercel AI SDK agent with Apilio integration
- Using Composio's Tool Router to dynamically load and access Apilio 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 Apilio MCP server, and what's possible with it?

The Apilio MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Apilio account. It provides structured and secure access to your smart home variables, conditions, and automations, so your agent can perform actions like checking device states, evaluating logic, and managing automation variables on your behalf.
- Monitor boolean device states: Instantly fetch and review all your boolean variables, so your agent can check if devices are on or off, active or inactive.
- Track and evaluate automation conditions: Have your agent list and assess all available Apilio conditions, making it easy to troubleshoot or optimize home automation rules.
- Access and analyze logicblocks: Let your agent retrieve your logicblocks to understand, manage, or trigger complex automations across your connected devices and services.
- Review numeric variable values: Enable your agent to pull all numeric variables—like temperature, humidity, or energy usage—for smarter, context-aware automation decisions.
- Manage string variables for messaging and triggers: Allow your agent to fetch and utilize string variables, supporting scenarios like status messages, custom notifications, or dynamic automation triggers.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `APILIO_GET_BOOLEAN_VARIABLES` | Get Boolean Variables | Tool to fetch all boolean variables. use when you need the full list of boolean variables and their current states. |
| `APILIO_GET_CONDITIONS` | Get Conditions | Tool to fetch all evaluatable conditions. use when you need to list available conditions. |
| `APILIO_GET_LOGICBLOCKS` | Get Logicblocks | Tool to fetch all logicblocks of a user. use after authenticating with apilio api when you need to list available logicblocks. |
| `APILIO_GET_NUMERIC_VARIABLES` | Get Numeric Variables | Tool to fetch all numeric variables. use after authenticating the apilio api. |
| `APILIO_GET_STRING_VARIABLES` | Get String Variables | Tool to fetch all string variables. use after authenticating the apilio api. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Apilio MCP server is an implementation of the Model Context Protocol that connects your AI agent to Apilio. It provides structured and secure access so your agent can perform Apilio operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

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

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install required dependencies

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

### 3. Set up environment variables

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
```bash
OPENAI_API_KEY=your_openai_api_key_here
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
```

### 4. Import required modules and validate environment

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
```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,
});
```

### 5. Create Tool Router session and initialize MCP client

What's happening:
- We're creating a Tool Router session that gives your agent access to Apilio 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 Apilio-related tools through the MCP protocol
```typescript
async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["apilio"],
  });

  const mcpUrl = session.mcp.url;
```

### 6. Connect to MCP server and retrieve 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 Apilio tools that the agent can use
```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();
```

### 7. Initialize conversation and CLI interface

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
```typescript
let messages: ModelMessage[] = [];

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

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

rl.prompt();
```

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

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 Apilio 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
```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);
});
```

## Complete Code

```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: ["apilio"],
  });

  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 apilio, 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 Apilio 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.

## How to build Apilio MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/apilio/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/apilio/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/apilio/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/apilio/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/apilio/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/apilio/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/apilio/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/apilio/framework/cli)
- [Google ADK](https://composio.dev/toolkits/apilio/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/apilio/framework/langchain)
- [Mastra AI](https://composio.dev/toolkits/apilio/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/apilio/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/apilio/framework/crew-ai)

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- [Hyperbrowser](https://composio.dev/toolkits/hyperbrowser) - Hyperbrowser is a next-generation platform for scalable browser automation. It empowers AI agents to interact with web apps, automate workflows, and handle browser sessions at scale.
- [La Growth Machine](https://composio.dev/toolkits/lagrowthmachine) - La Growth Machine automates multi-channel sales outreach and routine tasks for sales teams. Streamline your workflow and focus on closing more deals.
- [Leverly](https://composio.dev/toolkits/leverly) - Leverly is a workflow automation platform that connects and coordinates actions across your apps. It streamlines repetitive processes so your business runs smoother, faster, and with fewer manual steps.
- [Maintainx](https://composio.dev/toolkits/maintainx) - Maintainx is a cloud-based CMMS for centralizing maintenance data, communication, and workflows. It helps organizations streamline maintenance operations and improve team coordination.
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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Apilio MCP?

With a standalone Apilio MCP server, the agents and LLMs can only access a fixed set of Apilio tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Apilio 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 Apilio tools.

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

Yes, absolutely. You can configure which Apilio 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 Apilio data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)