# How to integrate Zylvie MCP with Pydantic AI ```json { "title": "How to integrate Zylvie MCP with Pydantic AI", "toolkit": "Zylvie", "toolkit_slug": "zylvie", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/zylvie/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/zylvie/framework/pydantic-ai.md", "updated_at": "2026-05-06T08:35:15.299Z" } ``` ## Introduction This guide walks you through connecting Zylvie to Pydantic AI using the Composio tool router. By the end, you'll have a working Zylvie agent that can add a new digital course product, fetch your current zylvie account profile, remove webhook for subscription cancellations through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Zylvie account through Composio's Zylvie MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Zylvie with - [OpenAI Agents SDK](https://composio.dev/toolkits/zylvie/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/zylvie/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/zylvie/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/zylvie/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/zylvie/framework/codex) - [OpenClaw](https://composio.dev/toolkits/zylvie/framework/openclaw) - [Hermes](https://composio.dev/toolkits/zylvie/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/zylvie/framework/cli) - [Google ADK](https://composio.dev/toolkits/zylvie/framework/google-adk) - [LangChain](https://composio.dev/toolkits/zylvie/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/zylvie/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/zylvie/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/zylvie/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/zylvie/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 Zylvie - 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 Zylvie 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 Zylvie MCP server, and what's possible with it? The Zylvie MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zylvie account. It provides structured and secure access to your Zylvie workspace, so your agent can perform actions like creating products, managing subscriptions, and retrieving user information on your behalf. - Automated product creation: Instantly add new digital products to your Zylvie store with customizable options, all through natural language prompts to your agent. - User profile retrieval: Quickly fetch details about the currently authenticated user, making it easy to personalize workflows and manage account settings. - Webhook management: Seamlessly unsubscribe from webhooks by deleting workflow objects associated with specific webhook URLs, ensuring your integrations stay clean and up to date. - Subscription and workflow automation: Let your agent help manage ongoing workflows by removing unneeded subscriptions, keeping your sales automations streamlined. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `ZYLVIE_CREATE_PRODUCT` | Create Product | Tool to create a new product. use when you need to add a product with detailed custom options in zylvie platform. | | `ZYLVIE_GET_AUTHENTICATING_USER` | Get Authenticating User | Tool to retrieve information about the currently authenticated user. use when you have a valid bearer token and need to fetch the authenticated user's profile. | | `ZYLVIE_UNSUBSCRIBE_WEBHOOK` | Unsubscribe Webhook | Tool to unsubscribe from a webhook by deleting the workflow object associated with the specified webhook url. use after confirming the webhook url to remove. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Zylvie MCP server is an implementation of the Model Context Protocol that connects your AI agent to Zylvie. It provides structured and secure access so your agent can perform Zylvie 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 Zylvie - 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 Zylvie - MCPServerStreamableHTTP connects to the Zylvie 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 Zylvie 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 Zylvie composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["zylvie"], ) 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 Zylvie endpoint - The agent uses GPT-5 to interpret user commands and perform Zylvie operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent zylvie_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[zylvie_mcp], instructions=( "You are a Zylvie assistant. Use Zylvie 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 - Zylvie 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 Zylvie.\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 Zylvie composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["zylvie"], ) 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 zylvie_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[zylvie_mcp], instructions=( "You are a Zylvie assistant. Use Zylvie 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 Zylvie.\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 Zylvie through Composio's Tool Router. With this setup, your agent can perform real Zylvie 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 + Zylvie 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 Zylvie MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/zylvie/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/zylvie/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/zylvie/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/zylvie/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/zylvie/framework/codex) - [OpenClaw](https://composio.dev/toolkits/zylvie/framework/openclaw) - [Hermes](https://composio.dev/toolkits/zylvie/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/zylvie/framework/cli) - [Google ADK](https://composio.dev/toolkits/zylvie/framework/google-adk) - [LangChain](https://composio.dev/toolkits/zylvie/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/zylvie/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/zylvie/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/zylvie/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/zylvie/framework/crew-ai) ## Related Toolkits - [Addresszen](https://composio.dev/toolkits/addresszen) - Addresszen is a real-time address autocomplete and verification service. 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It lets merchants accept Bitcoin payments directly, cutting out middlemen and boosting privacy. - [Cdr platform](https://composio.dev/toolkits/cdr_platform) - Cdr platform is an API for purchasing carbon dioxide removal services. It enables businesses to offset emissions by accessing verified carbon removal projects. - [Cloudcart](https://composio.dev/toolkits/cloudcart) - CloudCart is an e-commerce platform for building and managing online stores. It helps businesses streamline product listings, orders, and customer engagement. - [Countdown api](https://composio.dev/toolkits/countdown_api) - Countdown API gives you real-time, structured eBay product data, reviews, and seller feedback. Perfect for powering price monitoring, product research, or marketplace analytics workflows. - [Dpd2](https://composio.dev/toolkits/dpd2) - Dpd2 is a robust email management platform for handling, sorting, and automating email workflows. 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It streamlines promotions and gift card transactions for businesses and their customers. - [Goody](https://composio.dev/toolkits/goody) - Goody is a gifting platform that lets users send gifts and physical products without handling logistics. It streamlines gifting by managing delivery, fulfillment, and recipient experience. - [Gumroad](https://composio.dev/toolkits/gumroad) - Gumroad is a platform for selling digital products, physical goods, and memberships with a simple checkout and marketing tools. It streamlines creator payouts and helps you grow your audience effortlessly. - [Instacart](https://composio.dev/toolkits/instacart) - Instacart is an online grocery delivery and pickup service platform. It lets you discover local retailers and create shoppable lists and recipes with ease. - [Junglescout](https://composio.dev/toolkits/junglescout) - Junglescout is an Amazon product research and analytics platform for sellers. It delivers sales estimates, competitive insights, and optimization tools to boost your Amazon business. - [Ko fi](https://composio.dev/toolkits/ko_fi) - Ko-fi is a platform that lets creators receive donations, memberships, and sales from fans. It helps creators monetize their work and grow their audience with minimal friction. - [Lemon squeezy](https://composio.dev/toolkits/lemon_squeezy) - Lemon Squeezy is a payments and subscription platform built for software companies. It makes managing payments, taxes, and customer subscriptions effortless. - [Loyverse](https://composio.dev/toolkits/loyverse) - Loyverse is a point-of-sale (POS) platform for small businesses, offering tools for sales, inventory, and customer loyalty. It helps streamline retail operations and boost customer engagement. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Zylvie MCP? With a standalone Zylvie MCP server, the agents and LLMs can only access a fixed set of Zylvie tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Zylvie 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 Zylvie tools. ### Can I manage the permissions and scopes for Zylvie while using Tool Router? Yes, absolutely. You can configure which Zylvie 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 Zylvie data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)