How to integrate Typecast MCP with LangChain

This guide walks you through connecting Typecast to LangChain using the Composio tool router. By the end, you'll have a working Typecast agent that can compose voiceovers, generate speech, add timestamps, browse voices, and check your plan through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Typecast account through Composio's Typecast MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Typecast is an AI voice platform for generating expressive speech and discovering voices. Use it to create natural voiceovers and manage custom voices at scale.

7 Tools

Introduction

This guide walks you through connecting Typecast to LangChain using the Composio tool router. By the end, you'll have a working Typecast agent that can compose voiceovers, generate speech, add timestamps, browse voices, and check your plan through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Typecast account through Composio's Typecast MCP server.

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

Also integrate Typecast with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Typecast project to Composio
  • Create a Tool Router MCP session for Typecast
  • Initialize an MCP client and retrieve Typecast tools
  • Build a LangChain agent that can interact with Typecast
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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

The Typecast MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Typecast account. It provides structured and secure access to speech generation, voice discovery, voice details, and subscription usage, so your agent can create expressive audio, compose speech sequences, generate timestamps, recommend voices, and check account usage on your behalf.

  • Text to speech generation: Have your agent turn text into complete WAV or MP3 audio using an available Typecast voice.
  • Speech composition: Direct your agent to create one audio file from an ordered mix of spoken passages and pauses.
  • Timed speech alignment: Instruct your agent to generate speech with word timestamps, character timestamps, or both for captions and synchronized experiences.
  • Voice discovery and recommendations: Let the agent list available built-in and custom voices, filter them by traits and use cases, recommend matches from a natural-language description, and review voice details, models, and emotions.
  • Plan and usage checks: Have your agent check your Typecast plan, credit usage, concurrency limit, and custom voice allowance.

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 step10 STEPS
1

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming
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 dependencies

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • @composio/langchain provides Composio integration for LangChain
  • @langchain/mcp-adapters enables MCP client connections
  • @langchain/core is the core agent framework
  • dotenv/config loads environment variables
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models
5

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Typecast functionality through MCP
6

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Typecast tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['typecast']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Typecast tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Typecast tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "typecast-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Typecast MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Typecast tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Typecast related question or task to the agent.\n");

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

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;
    }

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

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

Here's the complete code to get you started with Typecast and LangChain:

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['typecast']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "typecast-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Typecast related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    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;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

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

Conclusion

You've successfully built a LangChain agent that can interact with Typecast through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.
TOOLS

Supported Tools

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

Compose Speech

Generate one audio file from an ordered sequence of speech and pause segments.

Generate Speech

Generate a complete WAV or MP3 audio file from text using a Typecast voice.

Generate Speech With Timestamps

Generate speech plus word, character, or both alignment timestamp arrays.

Get Subscription

Get the connected Typecast API plan, credit usage, concurrency limit, and custom-voice allowance.

Get Voice

Get one voice's metadata, supported models, and available emotions by voice ID.

List Voices

List available built-in and owned custom Typecast voices, optionally filtered by speech model, gender, age, use case, and voice type.

Recommend Voices

Find and rank Typecast voices that match a natural-language description.

FRAMEWORKS

How to build Typecast MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Typecast MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Typecast MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Typecast MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Typecast MCP with Claude Cowork

Codex logo

Codex

Use Typecast MCP with Codex

OpenClaw logo

OpenClaw

Use Typecast MCP with OpenClaw

Hermes logo

Hermes

Use Typecast MCP with Hermes

CLI logo

CLI

Use Typecast MCP with CLI

Google ADK logo

Google ADK

Use Typecast MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Typecast MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Typecast MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Typecast MCP with LlamaIndex

CrewAI logo

CrewAI

Use Typecast MCP with CrewAI

FAQ

Frequently asked questions

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

Yes, you can. LangChain 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 Typecast tools.

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

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