# How to integrate Re amaze MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Re amaze MCP with OpenAI Agents SDK",
  "toolkit": "Re amaze",
  "toolkit_slug": "re_amaze",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/re_amaze/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/re_amaze/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-12T10:23:25.810Z"
}
```

## Introduction

This guide walks you through connecting Re amaze to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Re amaze agent that can list all tags used in recent reports, show most popular response templates for support, analyze tag trends from last week's conversations through natural language commands.
This guide will help you understand how to give your OpenAI Agents SDK 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 Agent SDK](https://composio.dev/toolkits/re_amaze/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/re_amaze/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/re_amaze/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/re_amaze/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/re_amaze/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/re_amaze/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/re_amaze/framework/cli)
- [Google ADK](https://composio.dev/toolkits/re_amaze/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/re_amaze/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/re_amaze/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/re_amaze/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/re_amaze/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/re_amaze/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Install the necessary dependencies
- Initialize Composio and create a Tool Router session for Re amaze
- Configure an AI agent that can use Re amaze as a tool
- Run a live chat session where you can ask the agent to perform Re amaze operations

## What is OpenAI Agents SDK?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.
Key features include:
- Hosted MCP Tools: Connect to external services through hosted MCP endpoints
- SQLite Sessions: Persist conversation history across interactions
- Simple API: Clean interface with Agent, Runner, and tool configuration
- Streaming Support: Real-time response streaming for interactive applications

## 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.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `RE_AMAZE_GET_REPORTS_TAGS` | Get report tags | Tool to retrieve a list of tags used in reports. Use when analyzing tag usage metrics across conversations. |
| `RE_AMAZE_GET_RESPONSE_TEMPLATES` | Get Response Templates | Tool to retrieve response templates for the brand. Use when you need canned responses to streamline replies. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Re amaze MCP server is an implementation of the Model Context Protocol that connects your AI agent to Re amaze. It provides structured and secure access so your agent can perform Re amaze operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

Before starting, make sure you have:
- Composio API Key and OpenAI API Key
- Primary know-how of OpenAI Agents SDK
- A live Re amaze project
- Some knowledge of Python or Typescript

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Go to Settings and copy your API key.

### 2. Install dependencies

Install the Composio SDK and the OpenAI Agents SDK.
```python
pip install composio_openai_agents openai-agents python-dotenv
```

```typescript
npm install @composio/openai-agents @openai/agents dotenv
```

### 3. Set up environment variables

Create a .env file and add your OpenAI and Composio API keys.
```bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com
```

### 4. Import dependencies

What's happening:
- You're importing all necessary libraries.
- The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Re amaze.
```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';
```

### 5. Set up the Composio instance

No description provided.
```python
load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
```

```typescript
dotenv.config();

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});
```

### 6. Create a Tool Router session

What is happening:
- You give the Tool Router the user id and the toolkits you want available. Here, it is only re_amaze.
- The router checks the user's Re amaze connection and prepares the MCP endpoint.
- The returned session.mcp.url is the MCP URL that your agent will use to access Re amaze.
- This approach keeps things lightweight and lets the agent request Re amaze tools only when needed during the conversation.
```python
# Create a Re amaze Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["re_amaze"]
)

mcp_url = session.mcp.url
```

```typescript
// Create Tool Router session for Re amaze
const session = await composio.create(userId as string, {
  toolkits: ['re_amaze'],
});
const mcpUrl = session.mcp.url;
```

### 7. Configure the agent

No description provided.
```python
# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Re amaze. "
        "Help users perform Re amaze operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
```

```typescript
// Configure agent with MCP tool
const agent = new Agent({
  name: 'Assistant',
  model: 'gpt-5',
  instructions:
    'You are a helpful assistant that can access Re amaze. Help users perform Re amaze operations through natural language.',
  tools: [
    hostedMcpTool({
      serverLabel: 'tool_router',
      serverUrl: mcpUrl,
      headers: { 'x-api-key': composioApiKey },
      requireApproval: 'never',
    }),
  ],
});
```

### 8. Start chat loop and handle conversation

No description provided.
```python
print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
// Keep conversation state across turns
const conversationSession = new OpenAIConversationsSession();

// Simple CLI
const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: 'You: ',
});

console.log('\nComposio Tool Router session created.');
console.log('\nChat started. Type your requests below.');
console.log("Commands: 'exit', 'quit', or 'q' to end\n");

try {
  const first = await run(agent, 'What can you help me with?', { session: conversationSession });
  console.log(`Assistant: ${first.finalOutput}\n`);
} catch (e) {
  console.error('Error:', e instanceof Error ? e.message : e, '\n');
}

rl.prompt();

rl.on('line', async (userInput) => {
  const text = userInput.trim();

  if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
    console.log('Goodbye!');
    rl.close();
    process.exit(0);
  }

  if (!text) {
    rl.prompt();
    return;
  }

  try {
    const result = await run(agent, text, { session: conversationSession });
    console.log(`\nAssistant: ${result.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();
});

rl.on('close', () => {
  console.log('\n👋 Session ended.');
  process.exit(0);
});
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["re_amaze"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Re amaze. "
        "Help users perform Re amaze operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});

async function main() {
  // Create Tool Router session
  const session = await composio.create(userId as string, {
    toolkits: ['re_amaze'],
  });
  const mcpUrl = session.mcp.url;

  // Configure agent with MCP tool
  const agent = new Agent({
    name: 'Assistant',
    model: 'gpt-5',
    instructions:
      'You are a helpful assistant that can access Re amaze. Help users perform Re amaze operations through natural language.',
    tools: [
      hostedMcpTool({
        serverLabel: 'tool_router',
        serverUrl: mcpUrl,
        headers: { 'x-api-key': composioApiKey },
        requireApproval: 'never',
      }),
    ],
  });

  // Keep conversation state across turns
  const conversationSession = new OpenAIConversationsSession();

  // Simple CLI
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: ',
  });

  console.log('\nComposio Tool Router session created.');
  console.log('\nChat started. Type your requests below.');
  console.log("Commands: 'exit', 'quit', or 'q' to end\n");

  try {
    const first = await run(agent, 'What can you help me with?', { session: conversationSession });
    console.log(`Assistant: ${first.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();

  rl.on('line', async (userInput) => {
    const text = userInput.trim();

    if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
      console.log('Goodbye!');
      rl.close();
      process.exit(0);
    }

    if (!text) {
      rl.prompt();
      return;
    }

    try {
      const result = await run(agent, text, { session: conversationSession });
      console.log(`\nAssistant: ${result.finalOutput}\n`);
    } catch (e) {
      console.error('Error:', e instanceof Error ? e.message : e, '\n');
    }

    rl.prompt();
  });

  rl.on('close', () => {
    console.log('\nSession ended.');
    process.exit(0);
  });
}

main().catch((err) => {
  console.error('Fatal error:', err);
  process.exit(1);
});
```

## Conclusion

This was a starter code for integrating Re amaze MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Re amaze.
Key features:
- Hosted MCP tool integration through Composio's Tool Router
- SQLite session persistence for conversation history
- Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

## How to build Re amaze MCP Agent with another framework

- [Claude Agent SDK](https://composio.dev/toolkits/re_amaze/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/re_amaze/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/re_amaze/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/re_amaze/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/re_amaze/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/re_amaze/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/re_amaze/framework/cli)
- [Google ADK](https://composio.dev/toolkits/re_amaze/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/re_amaze/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/re_amaze/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/re_amaze/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/re_amaze/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/re_amaze/framework/crew-ai)

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- [Emelia](https://composio.dev/toolkits/emelia) - Emelia is an all-in-one B2B prospecting platform for cold-email, LinkedIn outreach, and prospect research. It streamlines outbound campaigns so you can find, engage, and warm up leads faster.
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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Re amaze MCP?

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.

### Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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.

### Can I manage the permissions and scopes for Re amaze while using Tool Router?

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.

### 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 Re amaze data and credentials are handled as safely as possible.

---
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