How to integrate Retellai MCP with Vercel AI SDK v6

This guide walks you through connecting Retellai to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Retellai agent that can list all phone numbers linked to your account, retrieve call details for a specific agent this week, buy a new phone number with area code 415 through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Retellai account through Composio's Retellai MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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RetellAI is a platform for capturing calls and transcripts, centralizing business conversations. It helps teams analyze interactions and extract insights to improve customer experience.

67 Tools

Introduction

This guide walks you through connecting Retellai to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Retellai agent that can list all phone numbers linked to your account, retrieve call details for a specific agent this week, buy a new phone number with area code 415 through natural language commands.

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

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

Also integrate Retellai with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainMastra AILlamaIndexCrewAI

TL;DR

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

The Retellai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Retellai account. It provides structured and secure access to your call records, phone numbers, and conversation transcripts, so your agent can perform actions like retrieving call details, managing phone numbers, initiating outbound calls, and analyzing voice data on your behalf.

  • Retrieve and analyze call records: Your agent can fetch detailed call logs, filter by agent or time, and surface insights from past conversations.
  • Initiate outbound and web-based calls: Easily direct your agent to start new phone or web calls between specific numbers or agents, supporting various business workflows.
  • Manage phone numbers and assignments: Buy, update, or delete phone numbers, and bind them to agents for streamlined inbound and outbound call handling.
  • Access and review call transcripts and details: Let your agent drill down into specific calls, pulling transcripts and metadata for compliance, training, or analytics.
  • Explore and configure voice settings: Fetch detailed information about available voice options, including provider, accent, gender, and preview audio for customization of call experiences.

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

Step by step09 STEPS
1

Prerequisites

Before you begin, make sure you have:
  • Node.js and npm installed
  • A Composio account with API key
  • An OpenAI API key
2

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.
3

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
4

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
5

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
6

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: ["retellai"],
  });

  const mcpUrl = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Retellai 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 Retellai-related tools through the MCP protocol
7

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 Retellai tools that the agent can use
8

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 retellai, 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
9

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 Retellai 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 Retellai 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: ["retellai"],
  });

  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 retellai, 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 Retellai 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.
TOOLS

Supported Tools

Every Retellai action and event your agent gets out of the box.

Add community voice

Add a community voice from ElevenLabs to your Retell voice library.

Add sources to knowledge base

Tool to add sources (documents, URLs, text) to an existing knowledge base in Retell AI.

Buy a new phone number bind agents

This endpoint allows purchasing a new phone number with a specified area code and binding it to designated agents for inbound and outbound calls.

Create Voice AI Agent

Create a new voice AI agent with specified configuration.

Create a new outbound phone call

Initiate an outbound call by POST to '/v2/create-phone-call'.

Create a new web call

The /v2/create-web-call endpoint creates a web call with a unique agent ID, returning call details like type, token, call ID, and status in JSON format, with a 201 response.

Create Batch Test

Tool to create a batch test job that runs multiple test cases against an agent.

Create a new chat session

Tool to create a new chat session with a chat agent.

Create a new chat agent

Create a new chat agent with specified configuration.

Create chat completion

Tool to create a chat completion for an existing chat session, generating the agent's response to a user message.

Create conversation flow

Create a new Conversation Flow that can be attached to an agent for response generation.

Create conversation flow component

Creates a new shared conversation flow component at POST '/create-conversation-flow-component'.

Create a new knowledge base

Tool to create a new knowledge base in Retell AI with texts, files, and URLs.

Create Retell LLM Response Engine

Create a new Retell LLM Response Engine that can be attached to an agent.

Create Test Case Definition

Tool to create a test case definition for agent QA testing in Retell AI.

Delete agent

Deletes an existing agent by its unique identifier.

Delete call

Delete a specific call and its associated data by call ID.

Delete chat agent

Delete an existing chat agent by its unique identifier.

Delete conversation flow

Delete a conversation flow and all its versions.

Delete conversation flow component

Delete a shared conversation flow component.

Delete knowledge base

Delete an existing knowledge base by its unique identifier.

Delete knowledge base source

Delete an existing source from a knowledge base.

Delete phone number

Tool to delete an existing phone number from Retell AI.

Delete Retell LLM

Delete an existing Retell LLM Response Engine by its unique identifier.

Delete test case definition

Delete a test case definition by its unique identifier.

End chat

Tool to end an active chat session.

Retrieve details of a specific agent

Retrieve details of a specific agent by its unique identifier.

Get agent versions

Tool to retrieve all versions of a specific agent.

Get batch test

Retrieve details and results of a specific batch test job.

Get chat details

Tool to retrieve details of a specific chat session by chat ID.

Retrieve details of a specific chat agent

Retrieve details of a specific chat agent by its unique identifier.

Get all versions of a chat agent

Retrieve all versions of a specific chat agent by its unique identifier.

Get concurrency

Retrieves the current concurrency and concurrency limits for the organization.

Get Conversation Flow

Retrieve details of a specific Conversation Flow by its ID.

Get conversation flow component

Retrieves a shared conversation flow component by its unique identifier.

Get knowledge base

Retrieve details of a specific knowledge base by its unique identifier.

Retrieve details of a specific Retell LLM

Retrieve details of a specific Retell LLM Response Engine by its unique identifier.

Import phone number

Tool to import a phone number from custom telephony and bind agents to it.

List agents

Retrieves a list of all agents associated with the account.

List all chats

Tool to retrieve a list of all chats associated with the account.

List all phone numbers

Retrieves a list of all phone numbers associated with the account.

List batch tests

Tool to list batch test jobs for a response engine.

List chat agents

Tool to retrieve a list of all chat agents associated with the account.

List conversation flow components

Retrieves a list of all shared conversation flow components.

List conversation flows

Tool to list all conversation flows that can be attached to an agent.

List knowledge bases

Tool to retrieve all knowledge bases associated with the account.

List Retell LLMs

Tool to list all Retell LLM Response Engines that can be attached to an agent.

List test case definitions

Tool to list test case definitions for a response engine (Retell LLM or Conversation Flow).

List test runs

Tool to list all test case jobs (test runs) for a batch test job.

List voices

List all voices available to the user.

Publish agent

Publishes the latest version of the agent and creates a new draft agent with a newer version.

Publish chat agent

Publishes the latest version of the chat agent and creates a new draft chat agent with a newer version.

Register phone call

Register a phone call for custom telephony integration with Retell AI.

Retrieve call details

Tool to retrieve call details with filtering options.

Retrieve call details by id

Retrieve call details by ID for web/phone calls, including type, agent ID, status, timestamps, and web access token; covering responses from success to server errors.

Retrieve phone number details

Tool to retrieve details of a specific phone number from Retell AI.

Retrieve details of a specific voice

Tool to retrieve details of a specific voice by its voice_id.

Search community voice

Search for community voices from voice providers.

Update agent

Update an existing agent's latest draft version.

Update call

Update an active call's parameters such as metadata, dynamic variables, or data storage settings.

Update chat agent

Update an existing chat agent configuration.

Update chat metadata

Tool to update metadata and sensitive data storage settings for an existing chat.

Update conversation flow

Update an existing conversation flow configuration.

Update conversation flow component

Update an existing shared conversation flow component by its ID.

Update phone number configuration

Update agent bound to a purchased phone number.

Update Retell LLM Response Engine

Update an existing Retell LLM Response Engine by its unique identifier.

Update test case definition

Update a test case definition for agent testing.

FRAMEWORKS

How to build Retellai MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Retellai MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Retellai MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Retellai MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Retellai MCP with Claude Cowork

Codex logo

Codex

Use Retellai MCP with Codex

OpenClaw logo

OpenClaw

Use Retellai MCP with OpenClaw

Hermes logo

Hermes

Use Retellai MCP with Hermes

CLI logo

CLI

Use Retellai MCP with CLI

Google ADK logo

Google ADK

Use Retellai MCP with Google ADK

LangChain logo

LangChain

Use Retellai MCP with LangChain

Mastra AI logo

Mastra AI

Use Retellai MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Retellai MCP with LlamaIndex

CrewAI logo

CrewAI

Use Retellai MCP with CrewAI

FAQ

Frequently asked questions

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

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 Retellai tools.

Yes, absolutely. You can configure which Retellai 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.

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 Retellai data and credentials are handled as safely as possible.

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A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

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As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

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Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

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