How to integrate Screenshot API MCP with LangChain

This guide walks you through connecting Screenshot API to LangChain using the Composio tool router. By the end, you'll have a working Screenshot API agent that can capture webpages, convert pages to pdfs, extract page text, run bulk jobs, and download results through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Screenshot API account through Composio's Screenshot API MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Screenshot API is a web rendering service that turns webpages and custom HTML into images, PDFs, videos, or extracted content. It saves you from running browser infrastructure while supporting async bulk capture jobs at scale.

5 Tools

Introduction

This guide walks you through connecting Screenshot API to LangChain using the Composio tool router. By the end, you'll have a working Screenshot API agent that can capture webpages, convert pages to pdfs, extract page text, run bulk jobs, and download results through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Screenshot API account through Composio's Screenshot API MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Screenshot API with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

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

The Screenshot API MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Screenshot API account. It provides structured and secure access to webpage and custom HTML rendering, so your agent can create screenshots, generate PDFs or videos, extract content, and manage bulk capture jobs on your behalf.

  • Webpage and HTML capture: Have your agent render webpages or custom HTML as images for previews, reports, and visual records.
  • PDF and video generation: Direct your agent to turn webpages or custom HTML into PDFs or videos and return links to the stored results.
  • Content extraction: Let the agent capture a page and request links to extracted content when needed.
  • Bulk capture jobs: Instruct your agent to submit batches of capture requests from JSON or an uploaded CSV file.
  • Bulk job tracking and results: Have your agent check capture progress and success or failure counts, then download the completed result CSV.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step10 STEPS
1

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

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

Install the required packages for LangChain with MCP support.

What's happening:

  • @composio/langchain provides Composio integration for LangChain
  • @langchain/mcp-adapters enables MCP client connections
  • @langchain/core is the core agent framework
  • dotenv/config loads environment variables
4

Set up environment variables

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

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
5

Import dependencies

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();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Screenshot API functionality through MCP
6

Initialize Composio client

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()
    });
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 Screenshot API tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

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

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Screenshot API 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 Screenshot API tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "screenshot_api-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 });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Screenshot API MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Screenshot API tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Screenshot API 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);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

Here's the complete code to get you started with Screenshot API and LangChain:

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: ['screenshot_api']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "screenshot_api-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 Screenshot API 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 Screenshot API 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.
TOOLS

Supported Tools

Every Screenshot API action and event your agent gets out of the box.

Create Bulk CSV Job

Upload a CSV file to start a paid asynchronous bulk screenshot job and return the provider's complete job acceptance payload containing its UUID.

Create Bulk JSON Job

Start a paid asynchronous bulk screenshot job from up to 50 JSON request items and return the provider's complete job acceptance payload containing its UUID.

Download Bulk Results

Download the processed result CSV for a completed paid bulk screenshot job UUID.

Get Bulk Job Status

Get progress and success/failure counts for a paid asynchronous bulk screenshot job UUID.

Render Screenshot

Render a webpage or custom HTML and return a stored image, PDF, or video URL plus optional extracted content links.

FRAMEWORKS

How to build Screenshot API MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Screenshot API MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Screenshot API MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Screenshot API MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Screenshot API MCP with Claude Cowork

Codex logo

Codex

Use Screenshot API MCP with Codex

OpenClaw logo

OpenClaw

Use Screenshot API MCP with OpenClaw

Hermes logo

Hermes

Use Screenshot API MCP with Hermes

CLI logo

CLI

Use Screenshot API MCP with CLI

Google ADK logo

Google ADK

Use Screenshot API MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Screenshot API MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Screenshot API MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Screenshot API MCP with LlamaIndex

CrewAI logo

CrewAI

Use Screenshot API MCP with CrewAI

FAQ

Frequently asked questions

With a standalone Screenshot API MCP server, the agents and LLMs can only access a fixed set of Screenshot API tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Screenshot API and many other apps based on the task at hand, all through a single MCP endpoint.

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 Screenshot API tools.

Yes, absolutely. You can configure which Screenshot API 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.

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 Screenshot API data and credentials are handled as safely as possible.

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