How to integrate Formbricks MCP with LangChain

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Introduction

This guide walks you through connecting Formbricks to LangChain using the Composio tool router. By the end, you'll have a working Formbricks agent that can create a new customer feedback survey, add a contact to our user list, record survey responses from yesterday's event through natural language commands.

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

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

Also integrate Formbricks with

TL;DR

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

The Formbricks MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Formbricks account. It provides structured and secure access to your survey management tools, so your agent can perform actions like creating surveys, collecting responses, managing contacts, and handling webhooks automatically on your behalf.

  • Survey creation and management: Easily instruct your agent to create new surveys, define questions, and set up feedback forms tailored to your needs.
  • Automated response collection: Have your agent log responses to surveys, link displays to responses, and streamline data gathering effortlessly.
  • Contact and attribute management: Direct your agent to add or remove contacts, create or delete attribute classes, and segment audiences for more targeted feedback analysis.
  • Webhook configuration for real-time events: Let your agent register new webhooks to automatically send survey response data to external systems or endpoints.
  • Cleanup and maintenance tools: Authorize your agent to delete surveys, survey responses, persons, or unused attributes, keeping your Formbricks workspace organized and up to date.

Supported Tools & Triggers

Tools
Check HealthTool to check the health status of the Formbricks API.
Create Action ClassTool to create a new action class.
Create Attribute ClassCreates a new attribute class (custom contact attribute) in Formbricks.
Create Client UserTool to create or identify a user within a specified environment.
Create ContactCreates a new contact in a Formbricks environment.
Create DisplayCreate a display record to track when a survey is shown to users.
Create Survey ResponseTool to create a response for a survey.
Create SurveyTool to create a new survey.
Create WebhookTool to create a new webhook.
Delete Attribute ClassTool to delete an attribute class.
Delete PersonTool to delete a person.
Delete Survey ResponseTool to delete a survey response by its ID.
Delete SurveyDeletes a survey from Formbricks by its unique identifier.
Delete TeamTool to delete an organization team by its ID.
Delete WebhookTool to delete a webhook by ID.
Get Account InfoRetrieves environment information for the authenticated API key.
Get All ContactsTool to retrieve all contacts within the organization.
Get Attribute ClassTool to get a specific attribute class by ID.
Get Client Contacts StateTool to get the current state of a contact including surveys and segment information.
Get Contact Attribute KeyTool to retrieve detailed information about a specific contact attribute key by ID (v2 API).
Get Contact by IDTool to retrieve a specific contact by its ID.
Get MeTool to retrieve current authenticated organization's and environment details.
Get Person by IDTool to retrieve a person by their internal ID in Formbricks.
Get ResponsesRetrieve survey responses with flexible filtering, sorting, and pagination.
Get RolesTool to retrieve all available roles in the system.
Get WebhookTool to retrieve details of a specific webhook.
List Action ClassesList all action classes in your Formbricks environment.
List Attribute ClassesTool to list all attribute classes.
List Client EnvironmentTool to retrieve environment state for Formbricks SDKs.
List Contact Attribute KeysTool to retrieve contact attribute keys from Formbricks.
List HealthTool to check the health status of critical application dependencies including database and cache.
List Management Contact AttributesTool to retrieve all contact attributes in the environment.
List Management MeTool to retrieve authenticated user's environment and project information.
List Management PeopleTool to retrieve all people (legacy term for contacts) in the environment.
List Organizations Project TeamsTool to list all project-team assignments for an organization (v2 API only).
List Organization TeamsTool to retrieve all teams in an organization (v2 API).
List SurveysList all surveys in the environment.
List WebhooksList all webhooks configured for the current environment.
Update Contact AttributesTool to update a contact's attributes in Formbricks.
Update Survey ResponseTool to update an existing survey response.
Update SurveyUpdates an existing Formbricks survey with new properties.
Update WebhookTool to update an existing webhook.
Upload Bulk ContactsUpload multiple contacts to a Formbricks environment in bulk (up to 250 per request).
Upload Private FileTool to obtain S3 presigned upload data for a private survey file.
Upload Public FileRetrieves S3 presigned upload URLs and form fields for uploading a public file to Formbricks storage.

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 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 Formbricks 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 Formbricks tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

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

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "formbricks-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 Formbricks MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Formbricks 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 Formbricks 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 Formbricks 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=['formbricks']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "formbricks-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 Formbricks 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 Formbricks 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.

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FAQ

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

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

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

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

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