# How to integrate Byteforms MCP with Pydantic AI

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

## Introduction

This guide walks you through connecting Byteforms to Pydantic AI 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 Pydantic AI 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)
- [LangChain](https://composio.dev/toolkits/byteforms/framework/langchain)
- [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:
- How to set up your Composio API key and User ID
- How to create a Composio Tool Router session for Byteforms
- How to attach an MCP Server to a Pydantic AI agent
- How to stream responses and maintain chat history
- How to build a simple REPL-style chat interface to test your Byteforms workflows

## What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.
Key features include:
- Type Safety: Built on Pydantic for automatic data validation
- MCP Support: Native support for Model Context Protocol servers
- Streaming: Built-in support for streaming responses
- Async First: Designed for async/await patterns

## 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

Before starting, make sure you have:
- Python 3.9 or higher
- A Composio account with an active API key
- Basic familiarity with Python and async programming

### 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

Install the required libraries.
What's happening:
- composio connects your agent to external SaaS tools like Byteforms
- pydantic-ai lets you create structured AI agents with tool support
- python-dotenv loads your environment variables securely from a .env file
```bash
pip install composio pydantic-ai python-dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates your agent to Composio's API
- USER_ID associates your session with your account for secure tool access
- OPENAI_API_KEY to access OpenAI LLMs
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key
```

### 4. Import dependencies

What's happening:
- We load environment variables and import required modules
- Composio manages connections to Byteforms
- MCPServerStreamableHTTP connects to the Byteforms MCP server endpoint
- Agent from Pydantic AI lets you define and run the AI assistant
```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
```

### 5. 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
```python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Byteforms
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["byteforms"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
```

### 6. Initialize the Pydantic AI Agent

What's happening:
- The MCP client connects to the Byteforms endpoint
- The agent uses GPT-5 to interpret user commands and perform Byteforms operations
- The instructions field defines the agent's role and behavior
```python
# Attach the MCP server to a Pydantic AI Agent
byteforms_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[byteforms_mcp],
    instructions=(
        "You are a Byteforms assistant. Use Byteforms tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
```

### 7. Build the chat interface

What's happening:
- The agent reads input from the terminal and streams its response
- Byteforms API calls happen automatically under the hood
- The model keeps conversation history to maintain context across turns
```python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Byteforms.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
```

### 8. Run the application

What's happening:
- The asyncio loop launches the agent and keeps it running until you exit
```python
if __name__ == "__main__":
    asyncio.run(main())
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Byteforms
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["byteforms"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    byteforms_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[byteforms_mcp],
        instructions=(
            "You are a Byteforms assistant. Use Byteforms tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Byteforms.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

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

## Conclusion

You've built a Pydantic AI agent that can interact with Byteforms through Composio's Tool Router. With this setup, your agent can perform real Byteforms actions through natural language.
You can extend this further by:
- Adding other toolkits like Gmail, HubSpot, or Salesforce
- Building a web-based chat interface around this agent
- Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Byteforms for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

## 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)
- [LangChain](https://composio.dev/toolkits/byteforms/framework/langchain)
- [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)

## Related Toolkits

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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.
- [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows.
- [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses.
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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 Pydantic AI?

Yes, you can. Pydantic AI 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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