# How to integrate Ritekit MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Ritekit MCP with OpenAI Agents SDK",
  "toolkit": "Ritekit",
  "toolkit_slug": "ritekit",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/ritekit/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/ritekit/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-12T10:24:14.621Z"
}
```

## Introduction

This guide walks you through connecting Ritekit to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Ritekit agent that can suggest hashtags for your blog post draft, check if these instagram hashtags are banned, analyze hashtag stats for marketing campaign through natural language commands.
This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Ritekit account through Composio's Ritekit MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Ritekit with

- [Claude Agent SDK](https://composio.dev/toolkits/ritekit/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/ritekit/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/ritekit/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/ritekit/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/ritekit/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/ritekit/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/ritekit/framework/cli)
- [Google ADK](https://composio.dev/toolkits/ritekit/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/ritekit/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/ritekit/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/ritekit/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/ritekit/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/ritekit/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 Ritekit
- Configure an AI agent that can use Ritekit as a tool
- Run a live chat session where you can ask the agent to perform Ritekit 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 Ritekit MCP server, and what's possible with it?

The Ritekit MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Ritekit account. It provides structured and secure access to Ritekit’s social media optimization tools, so your agent can generate hashtags, analyze links, validate email addresses, and boost content engagement automatically on your behalf.
- Smart hashtag generation and suggestions: Instantly get relevant and trending hashtags for any post or campaign to maximize visibility and reach.
- Banned hashtag detection for Instagram: Automatically filter out banned or unsafe hashtags before publishing to keep your posts compliant and effective.
- Comprehensive hashtag analytics: Retrieve real-time engagement stats on up to 100 hashtags, including metrics like tweets, retweets, exposure, and popularity grades.
- Email address validation: Have your agent detect disposable or free email addresses to improve lead quality and reduce spam signups.
- Link ad management: Enable deletion of link ads directly through your agent to keep your promotional content up to date and relevant.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `RITEKIT_AUTO_HASHTAG` | Auto Hashtag | Tool to automatically add relevant hashtags to a given post. Use when you have plain text and need suggested hashtags appended or inserted in context. |
| `RITEKIT_BANNED_INSTAGRAM_HASHTAGS` | Check Banned Instagram Hashtags | Tool to identify which hashtags are banned on Instagram. Use when preparing content and need to filter out banned hashtags before posting. |
| `RITEKIT_DETECT_DISPOSABLE_EMAIL` | Detect Disposable Email | Tool to detect if an email address is disposable. Use when validating email addresses to filter out temporary or fake email services. |
| `RITEKIT_DETECT_EMAIL_TYPO` | Detect Email Typo | Tool to detect common typos in email addresses and suggest corrections. Use when validating email input to help users correct mistakes like gml.com -> gmail.com. |
| `RITEKIT_FREEMAIL_DETECTION` | Free Email Detection | Tool to detect whether an email address belongs to a free email provider. Use when validating lead quality before ingestion. |
| `RITEKIT_GET_ACCESS_TOKEN` | Get Access Token | Tool to obtain a RiteKit access token. Prefer using a stored token from connection metadata or request. Falls back to OAuth2 client credentials if both client_id and client_secret are provided and no token is otherwise available. |
| `RITEKIT_GET_CLIENT_ID` | RiteKit Get Client ID | Tool to retrieve stored RiteKit client_id. Use when child actions require the client_id query parameter. |
| `RITEKIT_GET_CLIENT_SECRET` | RiteKit Get Client Secret | Tool to retrieve stored RiteKit client_secret. Use when child actions require the client_secret parameter. |
| `RITEKIT_GET_FULL_EMAIL_INSIGHTS` | Get Full Email Insights | Tool to retrieve comprehensive email address insights including full name, free mail detection, business email detection, and typo suggestions. Use when you need detailed analysis of an email address for lead qualification or email validation. |
| `RITEKIT_HASHTAG_SUGGESTIONS` | RiteKit Hashtag Suggestions | Tool to get hashtag suggestions for a given text. Use when you need relevant hashtags for social media posts. |
| `RITEKIT_LINK_AD_DELETE` | Delete Link Ad | Tool to delete a link ad. Use when you need to permanently remove a link ad by its ID. |
| `RITEKIT_LIST_LINK_ADS` | List Link Ads | Tool to retrieve a list of link ads. Use after authenticating to fetch all link ads for the user. |
| `RITEKIT_SHORTEN_LINK` | Shorten Link | Tool to shorten a URL with a specified CTA. Use when you need to generate a call-to-action-enabled short link. |
| `RITEKIT_TEXT_TO_IMAGE` | Convert Text to Image | Tool to convert a quote into a styled image. Use after preparing quote text and style options. |

## Supported Triggers

None listed.

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

The Ritekit MCP server is an implementation of the Model Context Protocol that connects your AI agent to Ritekit. It provides structured and secure access so your agent can perform Ritekit 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 Ritekit 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 Ritekit.
```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 ritekit.
- The router checks the user's Ritekit connection and prepares the MCP endpoint.
- The returned session.mcp.url is the MCP URL that your agent will use to access Ritekit.
- This approach keeps things lightweight and lets the agent request Ritekit tools only when needed during the conversation.
```python
# Create a Ritekit Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["ritekit"]
)

mcp_url = session.mcp.url
```

```typescript
// Create Tool Router session for Ritekit
const session = await composio.create(userId as string, {
  toolkits: ['ritekit'],
});
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 Ritekit. "
        "Help users perform Ritekit 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 Ritekit. Help users perform Ritekit 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=["ritekit"]
)
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 Ritekit. "
        "Help users perform Ritekit 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: ['ritekit'],
  });
  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 Ritekit. Help users perform Ritekit 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 Ritekit MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Ritekit.
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 Ritekit MCP Agent with another framework

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

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- [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management.
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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Ritekit MCP?

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

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

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

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)