# How to integrate Ritekit MCP with LangChain

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

## Introduction

This guide walks you through connecting Ritekit to LangChain 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 LangChain 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

- [OpenAI Agents SDK](https://composio.dev/toolkits/ritekit/framework/open-ai-agents-sdk)
- [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)
- [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
- Connect your Ritekit project to Composio
- Create a Tool Router MCP session for Ritekit
- Initialize an MCP client and retrieve Ritekit tools
- Build a LangChain agent that can interact with Ritekit
- Set up an interactive chat interface for testing

## What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.
Key features include:
- Agent Framework: Build agents that can use tools and make decisions
- MCP Integration: Connect to external services through Model Context Protocol adapters
- Memory Management: Maintain conversation history across interactions
- Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

## 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

No description provided.

### 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).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's happening:
- COMPOSIO_API_KEY authenticates your requests to Composio's API
- COMPOSIO_USER_ID identifies the user for session management
- OPENAI_API_KEY enables access to OpenAI's language models
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

No description provided.
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
```

### 5. Initialize Composio client

What's happening:
- We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
- Creating a Composio instance that will manage our connection to Ritekit tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
```

```typescript
const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Ritekit 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
- This approach allows the agent to dynamically load and use Ritekit tools as needed
```python
# Create Tool Router session for Ritekit
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['ritekit']
)

url = session.mcp.url
```

```typescript
const session = await composio.create(
    userId as string,
    {
        toolkits: ['ritekit']
    }
);

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "ritekit-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
```

```typescript
const client = new MultiServerMCPClient({
    "ritekit-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Ritekit related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Ritekit related question or task to the agent.\n");

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

rl.prompt();

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

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

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

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

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

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

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

## Complete Code

```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['ritekit']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "ritekit-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Ritekit related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

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

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['ritekit']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "ritekit-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Ritekit related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\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

You've successfully built a LangChain agent that can interact with Ritekit through Composio's Tool Router.
Key features of this implementation:
- Dynamic tool loading through Composio's Tool Router
- Conversation history maintenance for context-aware responses
- Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

## How to build Ritekit MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/ritekit/framework/open-ai-agents-sdk)
- [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)
- [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)

## Related Toolkits

- [Reddit](https://composio.dev/toolkits/reddit) - Reddit is a social news platform with thriving user-driven communities (subreddits). It's the go-to place for discussion, content sharing, and viral marketing.
- [Facebook](https://composio.dev/toolkits/facebook) - Facebook is a social media and advertising platform for businesses and creators. It helps you connect, share, and manage content across your public Facebook Pages.
- [Linkedin](https://composio.dev/toolkits/linkedin) - LinkedIn is a professional networking platform for connecting, sharing content, and engaging with business opportunities. It's the go-to place for building your professional brand and unlocking new career connections.
- [Active campaign](https://composio.dev/toolkits/active_campaign) - ActiveCampaign is a marketing automation and CRM platform for managing email campaigns, sales pipelines, and customer segmentation. It helps businesses engage customers and drive growth through smart automation and targeted outreach.
- [ActiveTrail](https://composio.dev/toolkits/active_trail) - ActiveTrail is a user-friendly email marketing and automation platform. It helps you reach subscribers and automate campaigns with ease.
- [Ahrefs](https://composio.dev/toolkits/ahrefs) - Ahrefs is an SEO and marketing platform for site audits, keyword research, and competitor insights. It helps you improve search rankings and drive organic traffic.
- [Amcards](https://composio.dev/toolkits/amcards) - AMCards lets you create and mail personalized greeting cards online. Build stronger customer relationships with easy, automated card campaigns.
- [Beamer](https://composio.dev/toolkits/beamer) - Beamer is a news and changelog platform for in-app announcements and feature updates. It helps companies boost user engagement by sharing news where users are most active.
- [Benchmark email](https://composio.dev/toolkits/benchmark_email) - Benchmark Email is a platform for creating, sending, and tracking email campaigns. It's built to help you engage audiences and analyze results—all in one place.
- [Bigmailer](https://composio.dev/toolkits/bigmailer) - BigMailer is an email marketing platform for managing multiple brands with white-labeling and automation. It helps teams streamline campaigns and simplify integration with Amazon SES.
- [Brandfetch](https://composio.dev/toolkits/brandfetch) - Brandfetch is an API that delivers company logos, colors, and visual branding assets. It helps marketers and developers keep brand visuals consistent everywhere.
- [Brevo](https://composio.dev/toolkits/brevo) - Brevo is an all-in-one email and SMS marketing platform for transactional messaging, automation, and CRM. It helps businesses engage customers and streamline communications through powerful campaign tools.
- [Campayn](https://composio.dev/toolkits/campayn) - Campayn is an email marketing platform for creating, sending, and managing campaigns. It helps businesses engage contacts and grow audiences with easy-to-use tools.
- [Cardly](https://composio.dev/toolkits/cardly) - Cardly is a platform for creating and sending personalized direct mail to customers. It helps businesses break through the digital clutter by getting real engagement via physical mailboxes.
- [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.
- [Crustdata](https://composio.dev/toolkits/crustdata) - CrustData is an AI-powered data intelligence platform for real-time company and people data. It helps B2B sales teams, AI SDRs, and investors react to live business signals.
- [Curated](https://composio.dev/toolkits/curated) - Curated is a platform for collecting, curating, and publishing newsletters. It streamlines content aggregation and distribution for creators and teams.
- [Customerio](https://composio.dev/toolkits/customerio) - Customer.io is a customer engagement platform for targeted messaging across email, SMS, and push. Easily automate, segment, and track communications with your audience.
- [Cutt ly](https://composio.dev/toolkits/cutt_ly) - Cutt.ly is a URL shortening service for managing and analyzing links. Streamline your workflows with quick, trackable, and branded short URLs.
- [Demio](https://composio.dev/toolkits/demio) - Demio is webinar software built for marketers, offering both live and automated sessions with interactive features. It helps teams engage audiences and optimize lead generation through detailed analytics.

## 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 LangChain?

Yes, you can. LangChain 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)