How to integrate Typeform MCP with LangChain

GET STARTED FOR FREE
Framework Integration Gradient
Typeform Logo
LangChain Logo
divider

Introduction

This guide walks you through connecting Typeform to LangChain using the Composio tool router. By the end, you'll have a working Typeform agent that can list all recent responses for a form, create a new form for event signup, export submissions from my survey to csv through natural language commands.

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

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

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Typeform project to Composio
  • Create a Tool Router MCP session for Typeform
  • Initialize an MCP client and retrieve Typeform tools
  • Build a LangChain agent that can interact with Typeform
  • 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 Typeform MCP server, and what's possible with it?

The Typeform MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Typeform account. It provides structured and secure access so your agent can perform Typeform operations on your behalf.

Supported Tools & Triggers

Tools
Create Account WorkspaceTool to create a new workspace in a specific Typeform account.
Create FormTool to create a new Typeform form with customizable fields, logic, and settings.
Create ImageTool to upload a new image to your Typeform account via base64 encoding or URL.
Create or Update WebhookTool to create a new webhook or update an existing one for a specified Typeform.
Create ThemeTool to create a new custom theme in Typeform with colors, fonts, background, and layout settings.
Create WorkspaceTool to create a new workspace in Typeform.
Delete FormTool to permanently delete a Typeform and all of its responses.
Delete ImageTool to delete an image from your Typeform account.
Delete ResponsesTool to delete specific responses from a Typeform by response IDs.
Delete ThemeTool to delete a theme from your Typeform account.
Delete WebhookTool to delete a webhook configuration from a Typeform form.
Delete WorkspaceTool to delete a workspace from your Typeform account.
Get About MeGet information about the owner account in Typeform.
Get All Response FilesTool to retrieve a compressed archive containing all files that respondents uploaded for a specified form.
Get Background By SizeTool to retrieve a background image by size from Typeform.
Get Choice Image By SizeTool to retrieve a choice image by size from Typeform.
Get FormTool to retrieve a specific form's complete configuration including fields, logic, settings, and theme.
Get Form MessagesTool to retrieve custom messages for a Typeform including button labels, error messages, and UI text.
Get Form ResponsesTool to retrieve form responses from Typeform with filtering by date, pagination, search, and response type.
Get Image By SizeTool to retrieve an image in a specific size from Typeform.
Get ThemeTool to retrieve a specific theme's configuration including colors, fonts, and layout settings.
Get WebhookTool to retrieve a single webhook by specifying both the form ID and webhook tag.
Get WorkspaceTool to retrieve detailed information about a specific workspace including its name, forms, and team members.
List FormsTool to retrieve a list of all forms in your Typeform account with filtering, pagination, and sorting options.
List ImagesTool to retrieve all images in your Typeform account in reverse-chronological order.
List Typeform ThemesTool to retrieve a paginated list of themes in your Typeform account.
List Form WebhooksTool to retrieve all webhooks associated with a specified typeform.
List WorkspacesTool to retrieve all workspaces in a Typeform account with their IDs, names, form counts, and members.
Patch FormTool to partially update a Typeform using JSON Patch operations.
Update Theme (Partial)Tool to partially update a Typeform theme by ID.
Update FormTool to update an existing Typeform by completely replacing its configuration.
Update Form MessagesTool to update custom messages for form UI elements like buttons, errors, and placeholders in Typeform.
Update ThemeTool to update a theme's complete definition in Typeform.
Update WorkspaceTool to update a workspace's name or manage team member access (add/remove members) in Typeform.
Upload VideoInitiate a video upload to Typeform by obtaining a signed upload URL.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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 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

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 dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-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 is the core agent framework
  • python-dotenv loads environment variables

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

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

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

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
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 Typeform tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Typeform
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['typeform']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Typeform 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 Typeform tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "typeform-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

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

Set up interactive chat interface

conversation_history = []

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

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts 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 ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

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

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['typeform']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "typeform-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Typeform related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've successfully built a LangChain agent that can interact with Typeform 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.

How to build Typeform MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Typeform MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Typeform MCP with Claude Agent SDK

Google ADK

Google ADK

Use Typeform MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Typeform MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Typeform MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Typeform MCP with LlamaIndex

CrewAI

CrewAI

Use Typeform MCP with CrewAI

Claude Code

Claude Code

Use Typeform MCP with Claude Code

Codex

Codex

Use Typeform MCP with Codex

FAQ

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

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

Can I use Tool Router MCP with LangChain?

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 Typeform tools.

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

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

Used by agents from

Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

Never worry about agent reliability

We handle tool reliability, observability, and security so you never have to second-guess an agent action.

Book a call↗Get started for free