How to integrate Skyvern MCP with Pydantic AI

This guide walks you through connecting Skyvern to Pydantic AI using the Composio tool router. By the end, you'll have a working Skyvern agent that can run browser tasks, extract website data, complete online forms, and manage your folders through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Skyvern account through Composio's Skyvern MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Skyvern is an AI browser automation platform for running tasks on websites. It helps teams automate messy web workflows without brittle scripts.

10 Tools

Introduction

This guide walks you through connecting Skyvern to Pydantic AI using the Composio tool router. By the end, you'll have a working Skyvern agent that can run browser tasks, extract website data, complete online forms, and manage your folders through natural language commands.

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

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

Also integrate Skyvern with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

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 Skyvern
  • 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 Skyvern 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 Skyvern MCP server, and what's possible with it?

The Skyvern MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Skyvern account. It provides structured and secure access to your browser tasks, agents, runs, and folders, so your agent can start browser tasks, monitor runs, cancel active work, find agents, and organize folders on your behalf.

  • Autonomous browser tasks: Have your agent start browser tasks that navigate websites, click elements, complete forms, and submit information.
  • Run tracking and results: Let the agent check task and workflow progress, review outputs and errors, and find related files or observation links.
  • Run search and cancellation: Direct your agent to find runs by status, type, workflow, or text, and cancel active work when needed.
  • Agent discovery: Instruct your agent to search and list the latest Skyvern agents available to your organization.
  • Folder organization: Have your agent create, find, review, rename, describe, or delete folders used to organize Skyvern agents.

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 step09 STEPS
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
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

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Skyvern
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

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
5

Import dependencies

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()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Skyvern
  • MCPServerStreamableHTTP connects to the Skyvern MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

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 Skyvern
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["skyvern"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Skyvern 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
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
skyvern_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[skyvern_mcp],
    instructions=(
        "You are a Skyvern assistant. Use Skyvern tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Skyvern endpoint
  • The agent uses GPT-5 to interpret user commands and perform Skyvern operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

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 Skyvern.\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()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Skyvern API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

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

Complete Code

Here's the complete code to get you started with Skyvern and Pydantic AI:

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 Skyvern
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["skyvern"],
    )
    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
    skyvern_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[skyvern_mcp],
        instructions=(
            "You are a Skyvern assistant. Use Skyvern 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 Skyvern.\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 Skyvern through Composio's Tool Router. With this setup, your agent can perform real Skyvern 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 + Skyvern for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every Skyvern action and event your agent gets out of the box.

Cancel Runs

Cancel one or more active task or workflow runs in one request and report which IDs succeeded or failed.

Create Folder

Create a folder for organizing Skyvern agents.

Delete Folder

Delete a Skyvern folder without deleting the agents it contains.

Get Folder

Get one Skyvern folder by ID, including its current agent count.

Get Run

Get current status, output, errors, files, and observation links for a task or workflow run.

List Agents

Search and list the latest versions of Skyvern agents available to the connected organization.

List Folders

Search and list folders used to organize Skyvern agents.

List Runs

Find task and workflow runs for the connected organization, with status, type, workflow, and text filters.

Start Browser Task

Start an autonomous browser task that can navigate and interact with websites, including clicking and submitting forms.

Update Folder

Change the title or description of an existing Skyvern folder.

FRAMEWORKS

How to build Skyvern MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Skyvern MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Skyvern MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Skyvern MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Skyvern MCP with Claude Cowork

Codex logo

Codex

Use Skyvern MCP with Codex

OpenClaw logo

OpenClaw

Use Skyvern MCP with OpenClaw

Hermes logo

Hermes

Use Skyvern MCP with Hermes

CLI logo

CLI

Use Skyvern MCP with CLI

Google ADK logo

Google ADK

Use Skyvern MCP with Google ADK

LangChain logo

LangChain

Use Skyvern MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Skyvern MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Skyvern MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Skyvern MCP with LlamaIndex

CrewAI logo

CrewAI

Use Skyvern MCP with CrewAI

FAQ

Frequently asked questions

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

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 Skyvern tools.

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

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