# How to integrate Hyperise MCP with LangChain

```json
{
  "title": "How to integrate Hyperise MCP with LangChain",
  "toolkit": "Hyperise",
  "toolkit_slug": "hyperise",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/hyperise/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/hyperise/framework/langchain.md",
  "updated_at": "2026-05-12T10:15:31.398Z"
}
```

## Introduction

This guide walks you through connecting Hyperise to LangChain using the Composio tool router. By the end, you'll have a working Hyperise agent that can generate a personalized short link for this campaign image, list all active image templates for your team, create a new client sub-account for agency onboarding through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Hyperise account through Composio's Hyperise MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Hyperise with

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

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Connect your Hyperise project to Composio
- Create a Tool Router MCP session for Hyperise
- Initialize an MCP client and retrieve Hyperise tools
- Build a LangChain agent that can interact with Hyperise
- 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 Hyperise MCP server, and what's possible with it?

The Hyperise MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Hyperise account. It provides structured and secure access to your personalization toolkit, so your agent can perform actions like managing templates, generating personalized links, handling prospect data, and automating account creation on your behalf.
- Personalized short link generation: Instantly create dynamic short URLs with embedded personalization and OGP metadata for each recipient.
- Template management and discovery: Retrieve and list all your active image templates, making it easy for your agent to use or recommend the right creative assets.
- Client account provisioning: Effortlessly create new client sub-accounts under your agency plan, streamlining onboarding and management tasks.
- Prospect business data automation: Enable your agent to perform CRUD operations on Hyperise business prospect records, keeping your marketing and sales data fresh and actionable.
- User authentication and account info: Allow your agent to authenticate API tokens and fetch user details securely, ensuring safe and compliant automation.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `HYPERISE_CREATE_CLIENT_ACCOUNT` | Create Client Account | Creates a new Hyperise business/client account for personalized image campaigns. Use this to provision prospect records that can be linked to Hyperise image templates. The website field is required in the extras parameter. Returns the created account's ID, which can be used with other Hyperise actions like personalized short links. |
| `HYPERISE_LIST_IMAGE_TEMPLATES` | List Image Templates | Retrieves all active personalized image templates for the authenticated user. Use this tool when you need to: - Get a list of available Hyperise image templates - Find template IDs for use with other Hyperise actions - Check template dimensions, preview URLs, or metadata Returns an empty list if no templates exist. |
| `HYPERISE_PERSONALIZED_SHORT_LINKS` | Generate Personalized Short Link | Generate a personalized short link with Open Graph metadata for rich link previews. This tool creates a shareable URL that: - Redirects to your destination page - Shows personalized image previews when shared on social media - Passes personalization data to your landing page Prerequisites: 1. At least one image template must exist in your Hyperise account 2. A custom link domain must be configured in Settings > Custom Domain Use HYPERISE_LIST_IMAGE_TEMPLATES first to get the image_hash parameter. |
| `HYPERISE_PROSPECT_BUSINESS_DATA` | Hyperise Prospect Business Data (CRUD) | Tool to perform CRUD operations on Hyperise business prospect records. Use when managing business data via the Hyperise API. |
| `HYPERISE_USER_AUTHENTICATION` | User Authentication | Tool to authenticate an API token and retrieve user details. Use after obtaining a valid API token from Hyperise settings. |

## Supported Triggers

None listed.

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

The Hyperise MCP server is an implementation of the Model Context Protocol that connects your AI agent to Hyperise. It provides structured and secure access so your agent can perform Hyperise 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 Hyperise 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 Hyperise 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 Hyperise tools as needed
```python
# Create Tool Router session for Hyperise
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['hyperise']
)

url = session.mcp.url
```

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

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

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

No description provided.
```python
client = MultiServerMCPClient({
    "hyperise-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({
    "hyperise-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 Hyperise 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 Hyperise 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=['hyperise']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "hyperise-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 Hyperise 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: ['hyperise']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "hyperise-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 Hyperise 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 Hyperise 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 Hyperise MCP Agent with another framework

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

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- [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.
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- [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.
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- [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 Hyperise MCP?

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

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

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

---
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