# How to integrate Supportivekoala MCP with LangChain

```json
{
  "title": "How to integrate Supportivekoala MCP with LangChain",
  "toolkit": "Supportivekoala",
  "toolkit_slug": "supportivekoala",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/supportivekoala/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/supportivekoala/framework/langchain.md",
  "updated_at": "2026-05-12T10:27:35.620Z"
}
```

## Introduction

This guide walks you through connecting Supportivekoala to LangChain using the Composio tool router. By the end, you'll have a working Supportivekoala agent that can create a personalized welcome image for new users, generate event flyers using your saved template, list all image templates i’ve set up through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Supportivekoala account through Composio's Supportivekoala MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Supportivekoala with

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

## TL;DR

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

The Supportivekoala MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Supportivekoala account. It provides structured and secure access to your image generation workflows, so your agent can create images, manage templates, retrieve image lists, and automate visual content creation entirely on your behalf.
- Automated image creation using templates: Instantly generate custom images by selecting and modifying templates with dynamic data, all through your agent.
- Template management and customization: Let your agent create new templates for recurring graphic needs or update existing ones to fit fresh branding and messaging.
- Image and asset retrieval: Effortlessly list all images generated by your account, allowing your agent to fetch, manage, or reference previous visual assets as needed.
- Template discovery and details: Retrieve and review all available templates, or fetch full details for any template using its ID—perfect for scaling up campaigns or personalizing content.
- User registration automation: Seamlessly create new user accounts so your workflows can onboard new teammates or clients without manual intervention.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SUPPORTIVEKOALA_CREATE_IMAGE` | Create Image | Tool to create a new image based on a template. Use when you have selected your template and prepared modifications. |
| `SUPPORTIVEKOALA_CREATE_TEMPLATE` | Create Template | Tool to create a new template for image generation. Use after gathering template details. |
| `SUPPORTIVEKOALA_LIST_IMAGES` | List Images | Tool to list images associated with the authenticated user. Use after confirming authentication. |
| `SUPPORTIVEKOALA_LIST_TEMPLATES` | List Templates | Tool to retrieve all templates. Use when you need to list all templates for the authenticated user. |
| `SUPPORTIVEKOALA_REGISTER_USER` | Register User | Tool to register a new user account. Use after collecting valid user credentials. |
| `SUPPORTIVEKOALA_RETRIEVE_TEMPLATE` | Retrieve Template by ID | Tool to retrieve a template by ID. Use when you have a valid template ID and need full template details. Use after confirming this detail. |

## Supported Triggers

None listed.

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

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

url = session.mcp.url
```

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

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

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

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

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

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

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

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## Frequently Asked Questions

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

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

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

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

---
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