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How to integrate Reply MCP with Pydantic AI

Connect Pydantic AI to Reply MCP. List all active email campaigns this week, get contacts in your 'leads q3' list, and more using natural language, with authentication handled for you.

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Api Key

Reply.io is a sales engagement platform for automating multichannel outreach and managing email campaigns. It helps sales teams boost productivity by tracking responses and centralizing campaign performance.

21 Tools

Introduction

This guide walks you through connecting Reply to Pydantic AI using the Composio tool router. By the end, you'll have a working Reply agent that can list campaigns, search contacts by email, organize contacts into lists, mark contacts as replied or finished, and manage your blacklist through natural language commands.

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

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

Also integrate Reply with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKGrok BotCodexOpenClawHermesAtomic AgentGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAIAutoGen

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

The Reply MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Reply.io account. It provides structured and secure access to your sales engagement platform, so your agent can create and manage lists, fetch campaign schedules, organize contacts, and monitor outreach performance for you.

  • Personal list creation and management: Easily instruct your agent to create, organize, or delete personal contact lists to streamline your outreach workflow.
  • Contact organization and retrieval: Have your agent fetch contacts within specific lists, helping you target and personalize campaigns more effectively.
  • Campaign and schedule insights: Ask your agent to retrieve all campaign schedules or see which campaigns a specific contact belongs to, giving you up-to-date visibility into your outreach pipeline.
  • Email account and template discovery: Let your agent pull a list of available email accounts or fetch your personal, team, and community templates for quick access while composing new campaigns.
  • Blacklist monitoring and management: Direct your agent to retrieve the full list of blacklisted domains and emails, so you can ensure compliance and maintain deliverability standards with ease.

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

Create Personal List

Tool to add a new personal list on the People page.

Delete Email Account

Requests deletion of a Reply email account by ID.

Delete List By ID

Tool to delete a list by its ID.

Delete Sequence

Tool to delete a sequence.

Delete User

Requests deletion of a Reply user by ID.

Generate ULID

Generates a ULID-like identifier using a hexadecimal millisecond timestamp and a random alphanumeric suffix.

Get All Lists

Tool to retrieve all available people lists.

Get full blacklist of domains and emails

Tool to retrieve the full list of blacklisted domains and emails.

Get all campaign schedules

Retrieves a page of Reply.

Get Campaigns For Contact

Retrieves a page of campaigns associated with a contact by contact ID.

Get Contacts in List by ID

Tool to retrieve contacts in a specific personal list.

Get List by ID

Tool to return a specific people list by its ID.

Get templates list

Retrieves email templates from Reply.

List Campaigns

Tool to list all campaigns (sequences).

List Contacts Basic

Lists contacts and their IDs.

Reply.io List Email Accounts

Lists email accounts with requested pagination and an optional status filter.

Mark Contact As Finished

Marks a contact (by email) or all contacts under a domain as finished in all Reply.

Mark Contact as Replied

Tool to mark a contact as replied in all campaigns by email or domain.

Move Contacts to Lists

Requests moving contacts to specified lists by their IDs.

Remove Domain from Blacklist

Tool to remove the specified domain from the blacklist.

Search Contacts by Email

Searches Reply contacts by email address and returns normalized contact records, including IDs when available.

FRAMEWORKS

How to build Reply MCP Agent with another framework

Claude Code logo

Claude Code

Use Reply MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Reply MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Reply MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Reply MCP with Claude Agent SDK

Grok Bot logo

Grok Bot

Use Reply MCP with Grok Bot

Codex logo

Codex

Use Reply MCP with Codex

OpenClaw logo

OpenClaw

Use Reply MCP with OpenClaw

Hermes logo

Hermes

Use Reply MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Reply MCP with Atomic Agent

Google ADK logo

Google ADK

Use Reply MCP with Google ADK

LangChain logo

LangChain

Use Reply MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Reply MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Reply MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Reply MCP with LlamaIndex

CrewAI logo

CrewAI

Use Reply MCP with CrewAI

AutoGen logo

AutoGen

Use Reply MCP with AutoGen

FAQ

Frequently asked questions

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

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

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