How to integrate Agenty MCP with Pydantic AI

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Introduction

This guide walks you through connecting Agenty to Pydantic AI using the Composio tool router. By the end, you'll have a working Agenty agent that can clone my top-performing agent for news sites, list all my running web scraping agents, create a new agent to monitor product prices, delete an outdated agent by its id through natural language commands.

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

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

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

The Agenty MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Agenty account. It provides structured and secure access to your web scraping agents and automation tools, so your agent can perform actions like creating, managing, cloning, and monitoring scraping agents, as well as handling API keys and templates—all on your behalf.

  • Agent creation and configuration: Instantly create new scraping or automation agents, set up their configurations, and optionally auto-start them—all without manual coding.
  • Clone and update agents: Duplicate existing agents to streamline workflows or update agent settings to refine your data extraction processes.
  • Fetch and manage agents: List all active agents in your account, retrieve details for any agent, and organize your entire automation fleet from a single place.
  • Template selection and management: Browse public agent templates or sample agents, making it easy to kickstart new projects or standardize scraping tasks.
  • API key management: Create, download, or delete API keys for secure programmatic access and efficient credential management, keeping your automation environment safe and organized.

Supported Tools & Triggers

Tools
Clone Agent by IDTool to clone an existing agent by its id.
Create AgentTool to create a new agent.
Get Agent TemplatesTool to fetch all public agent templates and sample agents.
Delete Agent by IDTool to delete a single agent by its id.
Fetch all agentsTool to fetch all active agents under an account.
Get Agent by IDTool to fetch details of a specific agent by its id.
Update Agent by IDTool to update an agent's configuration and settings by agent id.
Create API KeyTool to create a new api key.
Delete API key by IDTool to delete an api key by its key id.
Download API keysTool to download all api keys under an account in csv format.
Get all API keysTool to retrieve all api keys under an account.
Get API key by IDTool to get an api key by key id.
Reset API key by IDTool to reset an api key by key id.
Update API key by IDTool to update an api key by its id.
Change API key status by IDTool to enable or disable an api key by its id.
Get all connectionsTool to get all connections.
Create API KeyTool to create a new api key.
Get dashboard reports and usageTool to fetch account reports like pages used by agent, date, and product.
Get agent input by IDTool to get agent input by agent id.
Update Input by Agent IDTool to update agent input by agent id.
Download jobsTool to download all jobs in csv format.
Download job file by IDTool to download output files by job id.
Download Job Result by IDTool to download the agent output result by job id.
Fetch all jobsTool to fetch all jobs under an account.
Get Job by IDTool to fetch details of a specific job by its id.
Get Job Logs by IDTool to fetch logs for a given job by its id.
List job files by IDTool to list output files by job id.
Start Agent JobTool to start a new agent job.
Stop Job by IDTool to stop a running job by job id.
Clear List RowsTool to clear all rows in a list by its id.
Create ListTool to create a new list.
Delete List by IDTool to delete a specific list by its id.
Download listsTool to download all lists in csv format.
Get all listsTool to retrieve all lists under an account.
Fetch List Rows by IDTool to fetch all rows in a specified list.
Update List by IDTool to update a list's name or description by list id.
Upload CSV file to ListTool to upload a csv file to a list.
Add Agents to ProjectTool to add agent(s) to a project.
Create ProjectTool to create a new project.
Get all projectsTool to retrieve all projects under an account.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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

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

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.

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 Agenty
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file

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

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 Agenty
  • MCPServerStreamableHTTP connects to the Agenty MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant

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 Agenty
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["agenty"],
    )
    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 Agenty 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

Initialize the Pydantic AI Agent

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

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 Agenty.\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
  • Agenty API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns

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 Agenty and Pydantic AI:

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 Agenty
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["agenty"],
    )
    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
    agenty_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[agenty_mcp],
        instructions=(
            "You are a Agenty assistant. Use Agenty 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 Agenty.\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 Agenty through Composio's Tool Router. With this setup, your agent can perform real Agenty 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 + Agenty 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 Agenty MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Agenty MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Agenty MCP with Claude Agent SDK

Google ADK

Google ADK

Use Agenty MCP with Google ADK

LangChain

LangChain

Use Agenty MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Agenty MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Agenty MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Agenty MCP with LlamaIndex

CrewAI

CrewAI

Use Agenty MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Agenty MCP?

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

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

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

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