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How to integrate Replicate MCP with Pydantic AI

Connect Pydantic AI to Replicate MCP. Run stable diffusion to generate an image, list all your uploaded files on replicate, and more using natural language, with authentication handled for you.

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Introduction

This guide walks you through connecting Replicate to Pydantic AI using the Composio tool router. By the end, you'll have a working Replicate agent that can run AI models with predictions, search public models, manage deployments, start training jobs, and upload files through natural language commands.

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

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

Also integrate Replicate with

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

The Replicate MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Replicate account. It provides structured and secure access to your Replicate resources, so your agent can perform actions like running AI model predictions, managing files, browsing model collections, and retrieving model documentation on your behalf.

  • Run and manage AI model predictions: Easily instruct your agent to create, monitor, and manage predictions on any deployed Replicate model using custom input parameters.
  • Browse and discover model collections: Ask your agent to fetch and list available model collections or retrieve example predictions to explore what’s possible on Replicate.
  • Upload and organize files: Let your agent upload new files, list all stored files, or inspect file details to streamline your model workflows.
  • Access model metadata and documentation: Retrieve full model details, schemas, and markdown README docs for any model to help you choose and utilize the right model for your tasks.

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

Get Account Information

Tool to get authenticated account information.

Cancel Prediction

Tool to cancel a prediction that is still running.

Get model collection

Tool to get a specific collection of models by its slug.

List model collections

Tool to list all collections of models.

Create Model

Tool to create a new Replicate model with specified owner, name, visibility, and hardware.

Create Prediction

Create a prediction for a Replicate Deployment using model inputs.

Create Deployment

Tool to create a new deployment with specified model, version, hardware, and scaling parameters.

Delete Deployment

Tool to delete a deployment from your account.

Get Deployment Details

Tool to get deployment details by owner and name.

List deployments

List deployments associated with the account, including their latest release configurations.

Create File

Tool to create or upload a file to Replicate.

Delete File

Delete a file by its ID.

Download File

Download a stored file using Replicate's signed download fields.

Get File Details

Tool to get details of a file by its ID.

List Files

Tool to retrieve a paginated list of uploaded files.

Get Prediction

Tool to get the status and output of a prediction by its ID.

List Available Hardware

Tool to list available hardware SKUs for models and deployments.

List model examples

List example predictions for a specific model, with cursor-based pagination.

Get Model Details

Get details of a specific model by owner and name.

List Public Models

Tool to list public models with pagination and sorting.

Create Model Prediction

Tool to create a prediction using an official Replicate model.

Get Model README

Tool to get the README content for a model in Markdown format.

Get Model Version

Tool to get a specific version of a model.

List Model Versions

List versions of a specific model, newest first, with cursor-based pagination.

Create Prediction

Tool to create a prediction to run a model by version ID.

List All Predictions

Tool to list all predictions for the authenticated user or organization with pagination.

Search Models and Collections

Tool to search for models, collections, and docs using text queries (beta).

Cancel Training

Tool to cancel an ongoing training operation in Replicate.

Create Training Job

Tool to create a training job for a specific model version.

Get Training

Get a training's current state, output, logs, and runtime metrics by ID.

List Training Jobs

Tool to list all training jobs for the authenticated user or organization with pagination.

Update Model Metadata

Update a model's metadata, including its description, README, and associated URLs.

Get Webhook Signing Secret

Tool to get the signing secret for the default webhook.

FRAMEWORKS

How to build Replicate MCP Agent with another framework

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Claude Code

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Cursor

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ChatGPT logo

ChatGPT

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Claude Cowork logo

Claude Cowork

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Antigravity logo

Antigravity

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OpenAI Agents SDK logo

OpenAI Agents SDK

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Claude Agent SDK logo

Claude Agent SDK

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Codex logo

Codex

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Grok Build logo

Grok Build

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VS Code logo

VS Code

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OpenCode logo

OpenCode

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OpenClaw logo

OpenClaw

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Hermes logo

Hermes

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Atomic Agent logo

Atomic Agent

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Google ADK logo

Google ADK

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LangChain logo

LangChain

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Vercel AI SDK logo

Vercel AI SDK

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Mastra AI logo

Mastra AI

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LlamaIndex logo

LlamaIndex

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CrewAI logo

CrewAI

Use Replicate MCP with CrewAI

AutoGen logo

AutoGen

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FAQ

Frequently asked questions

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

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

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