How to integrate Fillout forms MCP with LangChain

This guide walks you through connecting Fillout forms to LangChain using the Composio tool router. By the end, you'll have a working Fillout forms agent that can list all your active fillout forms, show details for your latest created form, invalidate api token for fillout account through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Fillout forms account through Composio's Fillout forms MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Fillout forms is an online platform for building and managing forms with a flexible API. It lets you create, distribute, and collect responses from forms with ease.

22 Tools

Introduction

This guide walks you through connecting Fillout forms to LangChain using the Composio tool router. By the end, you'll have a working Fillout forms agent that can list all your active fillout forms, show details for your latest created form, invalidate api token for fillout account through natural language commands.

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

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

Also integrate Fillout forms 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 Fillout forms project to Composio
  • Create a Tool Router MCP session for Fillout forms
  • Initialize an MCP client and retrieve Fillout forms tools
  • Build a LangChain agent that can interact with Fillout forms
  • 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 Fillout forms MCP server, and what's possible with it?

The Fillout forms MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Fillout account. It provides structured and secure access to your forms and form management tools, so your agent can fetch form data, list all your forms, manage authorization, and help automate form workflows on your behalf.

  • Comprehensive form listing: Instantly retrieve and display a list of all forms in your Fillout account, making it easy to review and manage your surveys and data collection tools.
  • Seamless authorization management: Let your agent handle the OAuth authorization flow for securely connecting third-party applications to your Fillout account—no manual steps required.
  • Token revocation and security: Instruct your agent to programmatically invalidate or revoke access tokens, ensuring that only trusted applications and users have access to your Fillout data.
  • Automated workflow integration: Use your agent to connect Fillout forms with other apps or workflows, streamlining data collection and processing without manual intervention.

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 Fillout forms 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 Fillout forms 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: ['fillout_forms']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Fillout forms 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 Fillout forms tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "fillout_forms-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 Fillout forms MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Fillout forms 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 Fillout forms 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 Fillout forms 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: ['fillout_forms']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "fillout_forms-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 Fillout forms 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 Fillout forms 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 Fillout forms action and event your agent gets out of the box.

Authorize OAuth

Tool to initiate the OAuth authorization process for third-party applications.

Create Database

Tool to create a new Zite database instance with tables and fields.

Create Database Webhook

Tool to create a webhook subscription for a Fillout database.

Create field

Tool to add a new field to an existing table with specified type, name, and configuration.

Create record

Tool to create a new record in a Fillout table with the provided field data.

Create table

Tool to add a new table with custom schema to an existing database.

Delete database

Tool to permanently delete a database and all its data including tables, fields, views, and records.

Delete database webhook

Tool to remove a webhook subscription from a Fillout database.

Delete field

Tool to permanently delete a field from a table.

Delete record

Tool to permanently delete a record from a table in Fillout Database.

Delete table

Tool to permanently delete a table and all its data including fields, views, and records from a Fillout database.

Get database by ID

Tool to retrieve detailed information about a specific database including all tables, fields, and views.

Get databases

Tool to retrieve a list of all databases for your organization.

Get forms

Tool to retrieve a list of all forms in your account.

Get record by ID

Tool to retrieve a single record by its UUID with all field data.

Invalidate Access Token

Revokes an OAuth access token obtained from Fillout's OAuth authorization flow.

List database webhooks

Tool to retrieve all webhook subscriptions configured for a specific database.

List Records

Tool to retrieve records from a Fillout table with filtering, sorting, and pagination.

Remove form webhook

Tool to remove a webhook by its ID.

Update field

Tool to modify field properties and configuration for an existing field in a Fillout database table.

Update record

Tool to update specific fields of an existing record in a Fillout table.

Update table

Tool to update table properties like name.

FRAMEWORKS

How to build Fillout forms MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Fillout forms MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Fillout forms MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Fillout forms MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Fillout forms MCP with Claude Cowork

Codex logo

Codex

Use Fillout forms MCP with Codex

OpenClaw logo

OpenClaw

Use Fillout forms MCP with OpenClaw

Hermes logo

Hermes

Use Fillout forms MCP with Hermes

CLI logo

CLI

Use Fillout forms MCP with CLI

Google ADK logo

Google ADK

Use Fillout forms MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Fillout forms MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Fillout forms MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Fillout forms MCP with LlamaIndex

CrewAI logo

CrewAI

Use Fillout forms MCP with CrewAI

FAQ

Frequently asked questions

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

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

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