# How to integrate Byteforms MCP with LangChain

```json
{
  "title": "How to integrate Byteforms MCP with LangChain",
  "toolkit": "Byteforms",
  "toolkit_slug": "byteforms",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/byteforms/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/byteforms/framework/langchain.md",
  "updated_at": "2026-05-12T10:04:38.686Z"
}
```

## Introduction

This guide walks you through connecting Byteforms to LangChain using the Composio tool router. By the end, you'll have a working Byteforms agent that can create a new customer feedback form, list all forms i made this month, get all responses for your survey through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Byteforms account through Composio's Byteforms MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Byteforms with

- [OpenAI Agents SDK](https://composio.dev/toolkits/byteforms/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/byteforms/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/byteforms/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/byteforms/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/byteforms/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/byteforms/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/byteforms/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/byteforms/framework/cli)
- [Google ADK](https://composio.dev/toolkits/byteforms/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/byteforms/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/byteforms/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/byteforms/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/byteforms/framework/crew-ai)

## TL;DR

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

The Byteforms MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Byteforms account. It provides structured and secure access to your forms and submission data, so your agent can perform actions like creating new forms, retrieving submissions, managing existing forms, and integrating response data on your behalf.
- Automated form creation and setup: Direct your agent to generate new forms tailored to specific workflows, surveys, or data collection needs without manual intervention.
- Efficient form management: List all your existing forms, fetch specific form details, or remove obsolete forms with a simple request to your agent.
- Submission retrieval and analysis: Have your agent pull responses and submissions for any form, including support for pagination and advanced filtering to handle large datasets.
- Seamless integration of form data: Enable your agent to access and process form structures and submission results, making it easy to connect Byteforms data with other tools or workflows.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `BYTEFORMS_CREATE_FORM` | Create form | Creates a new form in ByteForms. Use this to build data collection forms with customizable fields (text, email, phone), styling options (theme, width), and submission rules (limits, deadlines, password protection). Returns the created form with its public_id for sharing. |
| `BYTEFORMS_DELETE_FORM` | Delete Form | Tool to delete a form by its ID. Use when you need to remove an existing form permanently. |
| `BYTEFORMS_GET_ALL_FORMS` | Get All Forms | Tool to fetch all forms created by the authenticated user. Use after authentication to list existing forms. |
| `BYTEFORMS_GET_FORM_BY_ID` | Get Form By ID | Retrieves detailed information about a specific form by its numeric ID. Returns the form's name, fields, configuration options, and metadata. Use the numeric 'id' field (not the 'public_id' string) from form data obtained via BYTEFORMS_GET_ALL_FORMS or BYTEFORMS_CREATE_FORM. |
| `BYTEFORMS_GET_FORM_RESPONSES` | Get Form Responses | Tool to retrieve responses for a specific form with optional pagination and filtering. Use when the form has collected submissions and you need to navigate large result sets. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Byteforms MCP server is an implementation of the Model Context Protocol that connects your AI agent to Byteforms. It provides structured and secure access so your agent can perform Byteforms operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

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

### 3. Set up environment variables

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
```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
```

### 4. Import dependencies

No description provided.
```python
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()
```

```typescript
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();
```

### 5. Initialize Composio client

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 Byteforms tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
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")
```

```typescript
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()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Byteforms 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 Byteforms tools as needed
```python
# Create Tool Router session for Byteforms
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['byteforms']
)

url = session.mcp.url
```

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

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "byteforms-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)
```

```typescript
const client = new MultiServerMCPClient({
    "byteforms-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 });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Byteforms 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")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Byteforms 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);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

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

## Complete Code

```python
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=['byteforms']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "byteforms-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 Byteforms 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())
```

```typescript
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: ['byteforms']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "byteforms-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 Byteforms 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 Byteforms 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 Byteforms MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/byteforms/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/byteforms/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/byteforms/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/byteforms/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/byteforms/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/byteforms/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/byteforms/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/byteforms/framework/cli)
- [Google ADK](https://composio.dev/toolkits/byteforms/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/byteforms/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/byteforms/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/byteforms/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/byteforms/framework/crew-ai)

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- [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots.
- [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. It lets you automate data extraction and workflow tasks at scale—no infrastructure headaches.
- [Autom](https://composio.dev/toolkits/autom) - Autom is a lightning-fast search engine results data platform for Google, Bing, and Brave. Developers use it to access fresh, low-latency SERP data on demand.
- [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance.
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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Byteforms MCP?

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

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

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

---
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