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How to integrate Re amaze MCP with Pydantic AI

Connect Pydantic AI to Re amaze MCP. List all tags used in recent reports, show most popular response templates for support, and more using natural language, with authentication handled for you.

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Api Key

Re:amaze is a multi-channel customer support platform for live chat, email, and social messaging. Streamline customer conversations and automate workflows across all your support channels.

9 Tools

Introduction

This guide walks you through connecting Re amaze to Pydantic AI using the Composio tool router. By the end, you'll have a working Re amaze agent that can list conversations and messages, look up contacts by email, browse help articles, get response templates, and view staff performance reports through natural language commands.

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

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

Also integrate Re amaze with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKCodexOpenClawHermesAtomic 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 Re amaze
  • 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 Re amaze 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 Re amaze MCP server, and what's possible with it?

The Re amaze MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Re:amaze account. It provides structured and secure access to your customer support environment, so your agent can retrieve report tags, access canned response templates, and streamline support workflows for your team.

  • Tag usage analytics: Instantly pull all tags used in reports so your agent can analyze conversation trends, monitor support topics, or generate insights about ticket categorization.
  • Effortless response template retrieval: Fetch your brand's pre-built response templates, making it easy for the agent to suggest or automate consistent replies across channels.
  • Streamlined reply suggestions: Quickly surface relevant canned responses for agents or bots to use, speeding up customer replies and ensuring brand consistency.
  • Support workflow automation: Leverage tags and templates to help your agent automate repetitive customer support tasks and standardize communication processes.

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

Get Contact by Email

Retrieve a Re:amaze contact by an exact email-address match.

Get Conversation

Retrieve one Re:amaze conversation by its supported identifier.

Get report tags

Tool to retrieve a list of tags used in reports.

Get Response Templates

Tool to retrieve response templates for the brand.

Get Staff Performance Report

Retrieve staff performance metrics for a Re:amaze report period.

List Articles

List and search Re:amaze Help Center articles.

List Contact Identities

Retrieve the identities linked to a Re:amaze contact email.

List Conversations

Lists Re:amaze conversations with optional status, order, and date filters.

List Messages

List messages across a Re:amaze brand or within one conversation.

FRAMEWORKS

How to build Re amaze MCP Agent with another framework

Claude Code logo

Claude Code

Use Re amaze MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Re amaze MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Re amaze MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Re amaze MCP with Claude Agent SDK

Codex logo

Codex

Use Re amaze MCP with Codex

OpenClaw logo

OpenClaw

Use Re amaze MCP with OpenClaw

Hermes logo

Hermes

Use Re amaze MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Re amaze MCP with Atomic Agent

Google ADK logo

Google ADK

Use Re amaze MCP with Google ADK

LangChain logo

LangChain

Use Re amaze MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Re amaze MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Re amaze MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Re amaze MCP with LlamaIndex

CrewAI logo

CrewAI

Use Re amaze MCP with CrewAI

AutoGen logo

AutoGen

Use Re amaze MCP with AutoGen

FAQ

Frequently asked questions

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

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

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