# How to integrate Screenshotone MCP with LangChain

```json
{
  "title": "How to integrate Screenshotone MCP with LangChain",
  "toolkit": "Screenshotone",
  "toolkit_slug": "screenshotone",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/screenshotone/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/screenshotone/framework/langchain.md",
  "updated_at": "2026-05-12T10:24:56.639Z"
}
```

## Introduction

This guide walks you through connecting Screenshotone to LangChain using the Composio tool router. By the end, you'll have a working Screenshotone agent that can capture animated scroll of home page, generate gif of website login process, create video walkthrough of landing page through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Screenshotone account through Composio's Screenshotone MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Screenshotone with

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

## TL;DR

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

The Screenshotone MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Screenshotone account. It provides structured and secure access to high-quality website screenshot capabilities, so your agent can capture animated screenshots, customize output formats, control viewport settings, and automate site capture workflows on your behalf.
- Instant animated website capture: Direct your agent to take animated screenshots (videos or GIFs) of any public website with a single prompt.
- Custom animation scenarios: Have your agent simulate scrolling or user interactions to create more dynamic, realistic captures.
- Flexible format and duration: Specify if you want the output as a video or GIF and set the animation duration to suit your needs.
- Viewport and device emulation: Let your agent adjust viewport size, resolution, and device type for pixel-perfect screenshots across platforms.
- Automated visual documentation: Use your agent to generate and manage visual records of web pages for reporting, monitoring, or archiving workflows.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SCREENSHOTONE_GET_USAGE` | Get Usage | Tool to retrieve current API plan usage information. Returns total requests allowed, available requests remaining, used requests count, and concurrency limits for the current billing period. |
| `SCREENSHOTONE_LIST_DEVICES` | List Devices | Tool to retrieve the list of supported devices for viewport emulation. Use when you need to get available device IDs and their viewport configurations for device emulation in screenshot operations. |
| `SCREENSHOTONE_TAKE_ANIMATED_SCREENSHOT` | Take Animated Screenshot | This tool captures an animated screenshot (video or GIF) of a given website URL. It allows customization of the animation format, duration, viewport dimensions, and animation scenario (e.g., scrolling). |
| `SCREENSHOTONE_TAKE_BULK_SCREENSHOTS` | Take Bulk Screenshots | Tool to take multiple screenshots in a single request with shared defaults and individual overrides. Use when you need to capture screenshots of multiple URLs or the same URL with different parameters. Supports lazy loading (default) where screenshots are taken on download, or immediate execution with execute=true. |
| `SCREENSHOTONE_TAKE_SCREENSHOT` | Take Screenshot | Tool to generate a screenshot or PDF of a website, render HTML, or Markdown using POST request. Use when you need to capture webpage content in various formats (PNG, JPEG, WebP, PDF, HTML). Supports both returning binary content directly or getting a cached URL via JSON response. |

## Supported Triggers

None listed.

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

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

url = session.mcp.url
```

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

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

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

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

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

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

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

## Related Toolkits

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- [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place.
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- [Anchor browser](https://composio.dev/toolkits/anchor_browser) - Anchor browser is a developer platform for AI-powered web automation. It transforms complex browser actions into easy API endpoints for streamlined web interaction.
- [Apiflash](https://composio.dev/toolkits/apiflash) - Apiflash is a website screenshot API for programmatically capturing web pages. It delivers high-quality screenshots on demand for automation, monitoring, or reporting.
- [Apiverve](https://composio.dev/toolkits/apiverve) - Apiverve delivers a suite of powerful APIs that simplify integration for developers. It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache.
- [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence.
- [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place.
- [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines.
- [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure.
- [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code.
- [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows.
- [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights.
- [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews.
- [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease.
- [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data.

## Frequently Asked Questions

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

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

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

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

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