How to integrate Fly MCP with Pydantic AI

GET STARTED FOR FREE
Framework Integration Gradient
Fly Logo
Pydantic AI Logo
divider

Introduction

This guide walks you through connecting Fly to Pydantic AI using the Composio tool router. By the end, you'll have a working Fly agent that can deploy latest image to fly in tokyo, list all running fly apps by region, scale up my fly app to 3 instances through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Fly account through Composio's Fly 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 Fly
  • 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 Fly 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 Fly MCP server, and what's possible with it?

The Fly MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Fly account. It provides structured and secure access so your agent can perform Fly operations on your behalf.

Supported Tools & Triggers

Tools
Add WireGuard PeerTool to add a WireGuard peer connection to a Fly.
Check App Name AvailabilityTool to validate an app name for Fly.
Check JobsExecute GraphQL queries against the Fly.
Check User Only TokenCheck whether the authentication token only allows user access.
Create Health Check JobTool to create a health check job for monitoring application endpoints in Fly.
Create Check Job RunTriggers a run of an existing health check job on Fly.
Create Delegated WireGuard TokenTool to create a delegated WireGuard token for peer management in a Fly.
Create Third-Party ConfigurationTool to create a third-party service configuration for discharging macaroon caveats.
Delete Delegated WireGuard TokenTool to delete a delegated WireGuard token from a Fly.
Delete OrganizationTool to delete a Fly.
Delete Remote BuilderTool to delete a remote builder configuration for a Fly.
Delete Third Party ConfigurationTool to delete a third-party service configuration from Fly.
Detach Postgres ClusterTool to detach a Postgres cluster from a Fly.
Establish SSH KeyTool to establish an SSH key for a Fly.
Fetch Nodes by IDsFetches a list of node objects from Fly.
Get Add-OnTool to find a Fly.
Get Add-On ProviderTool to query information about a specific Fly.
Get app detailsTool to retrieve detailed information about a specific Fly.
Get CertificateTool to retrieve a certificate by its ID from Fly.
Get Current Token InfoTool to get information about the current authentication token.
Get Latest Image DetailsTool to retrieve the latest available tag details for a given image repository from Fly.
Get Latest Image TagTool to retrieve the latest available image tag for a Fly.
Get MachineTool to get a single machine by ID from Fly.
Get Nearest RegionTool to retrieve the nearest Fly.
Get Node by IDTool to fetch an object by its globally unique ID using Fly.
Get OrganizationTool to find a Fly.
Get Personal OrganizationTool to retrieve the user's personal organization details from Fly.
Get PlacementsTool to get placement recommendations for Machines in Fly.
Get Platform InformationTool to retrieve Fly.
Get Products and PricingTool to retrieve Fly.
Get RegionsTool to get the list of available Fly.
Get Viewer InfoTool to retrieve the authenticated user's account information from Fly.
Issue CertificateTool to issue an SSH certificate for accessing Fly.
List Add-On PlansTool to list available add-on service plans from Fly.
List Add-OnsTool to list add-ons associated with an organization in Fly.
List AppsTool to list all Fly Apps in an organization.
List Apps via GraphQLList all Fly.
Check LocationsRetrieve all available Fly.
List MachinesTool to list Fly.
List Organization MachinesTool to list all Machines across all apps in a Fly organization.
Remove WireGuard PeerTool to remove a WireGuard peer connection from a Fly.
Set Apps V2 DefaultTool to configure whether new apps in an organization use Apps V2 by default on Fly.
Update Third-Party ConfigurationTool to update an existing third-party service configuration for discharging macaroon caveats.
Validate ConfigTool to validate a Fly.
Validate WireGuard PeersTool to validate WireGuard peer IP addresses in a Fly.

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 Fly
  • 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 Fly
  • MCPServerStreamableHTTP connects to the Fly 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 Fly
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["fly"],
    )
    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 Fly 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
fly_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[fly_mcp],
    instructions=(
        "You are a Fly assistant. Use Fly tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Fly endpoint
  • The agent uses GPT-5 to interpret user commands and perform Fly 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 Fly.\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
  • Fly 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 Fly 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 Fly
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["fly"],
    )
    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
    fly_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[fly_mcp],
        instructions=(
            "You are a Fly assistant. Use Fly 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 Fly.\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 Fly through Composio's Tool Router. With this setup, your agent can perform real Fly 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 + Fly 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 Fly MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Fly MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Fly MCP with Claude Agent SDK

Google ADK

Google ADK

Use Fly MCP with Google ADK

LangChain

LangChain

Use Fly MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Fly MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Fly MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Fly MCP with LlamaIndex

CrewAI

CrewAI

Use Fly MCP with CrewAI

Claude Code

Claude Code

Use Fly MCP with Claude Code

Codex

Codex

Use Fly MCP with Codex

FAQ

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

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

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

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

Used by agents from

Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

Never worry about agent reliability

We handle tool reliability, observability, and security so you never have to second-guess an agent action.

Book a call↗Get started for free