# How to integrate Scrapfly MCP with LangChain

```json
{
  "title": "How to integrate Scrapfly MCP with LangChain",
  "toolkit": "Scrapfly",
  "toolkit_slug": "scrapfly",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/scrapfly/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/scrapfly/framework/langchain.md",
  "updated_at": "2026-05-12T10:24:48.991Z"
}
```

## Introduction

This guide walks you through connecting Scrapfly to LangChain using the Composio tool router. By the end, you'll have a working Scrapfly agent that can extract product prices from amazon listings, scrape job postings from linkedin search, get latest news headlines from bbc homepage through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Scrapfly account through Composio's Scrapfly MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Scrapfly with

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

## TL;DR

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

The Scrapfly MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Scrapfly account. It provides structured and secure access to powerful web scraping capabilities, so your agent can perform actions like extracting website data, bypassing anti-bot protection, rendering JavaScript content, and rotating proxies—all on your behalf.
- Dynamic web data extraction: Instruct your agent to fetch and extract content from almost any website, even those with heavy client-side rendering or complex structures.
- JavaScript rendering support: Enable your agent to scrape websites that require full JavaScript execution for content loading, making dynamic sites accessible for data extraction.
- Anti-bot protection bypass: Allow your agent to automatically navigate sites with CAPTCHAs, bot detection, or rate limiting using Scrapfly's built-in countermeasures.
- Automatic proxy rotation: Let your agent leverage Scrapfly's proxy network to rotate requests, reduce blocks, and ensure more reliable scraping at scale.
- Custom request handling: Have your agent specify advanced options like custom headers or cookies for targeted and flexible scraping sessions.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SCRAPFLY_CAPTURE_SCREENSHOT` | Capture Website Screenshot | Tool to capture a full-page or viewport screenshot of a website. Use when you need to take a screenshot with options like JS rendering, custom resolution, or accessibility testing. Returns the screenshot image directly. Supports vision deficiency simulations and dark mode. |
| `SCRAPFLY_CAPTURE_SCREENSHOT_HEAD` | Capture Screenshot Metadata (HEAD) | Tool to capture screenshot metadata without downloading the image body. Use this for async screenshot workflows where you need the URL to retrieve the image later. Returns the screenshot URL in response, saving bandwidth compared to full screenshot retrieval. |
| `SCRAPFLY_CREATE_CRAWLER` | Create Scrapfly Crawler | Tool to create a new web crawler to recursively crawl an entire website. Returns a crawler UUID for tracking progress. Use when you need to crawl multiple pages from a website with configurable limits and extraction rules. |
| `SCRAPFLY_EXTRACT_DATA` | Extract Structured Data | Tool to extract structured data from HTML or other content using AI models, LLM prompts, or custom templates. Use when you need to parse web pages or documents into structured JSON data. Supports predefined extraction models for common types (articles, products, events) or custom extraction via prompts/templates. |
| `SCRAPFLY_GET_ACCOUNT_INFO` | Get Scrapfly Account Information | Tool to retrieve Scrapfly account information. Use after authenticating to get API credit balance and usage stats. Returns comprehensive account data including subscription plan, usage statistics, billing info, and project settings. |
| `SCRAPFLY_GET_CRAWLER_ARTIFACT` | Get Crawler Artifact | Tool to download crawler artifact files in WARC or HAR format. Use when you need to retrieve the complete crawl results as an archive file. WARC format is recommended for large crawls as it includes gzip compression. |
| `SCRAPFLY_GET_CRAWLER_CONTENTS` | Get Crawler Contents | Tool to retrieve extracted content from crawled pages. Supports multiple output formats including markdown, text, HTML, and JSON. Use when you need to access the actual content extracted during a crawl, with optional filtering by URL and format selection. |
| `SCRAPFLY_GET_CRAWLER_STATUS` | Get Crawler Status | Tool to get the current status of a crawler including progress, pages crawled, and completion state. Use for polling workflow to monitor crawl progress. |
| `SCRAPFLY_GET_CRAWLER_URLS` | Get Crawler URLs | Tool to retrieve the list of discovered and crawled URLs from a crawler. Use when you need to get all URLs found during a crawl or filter by status to analyze failed URLs with error codes. Supports pagination for large result sets. |
| `SCRAPFLY_SCRAPE` | Scrapfly Scrape | Tool to perform a web scraping request. Use when you need to fetch a page with custom configuration like JS rendering, proxies, and extraction. |
| `SCRAPFLY_SCRAPE_POST` | Scrapfly Scrape POST | Tool to scrape web pages using POST method to send data in the request body. Use when you need to scrape endpoints that require POST requests, such as form submissions or APIs that expect data payload. |
| `SCRAPFLY_SCRAPE_WITH_PUT` | Scrape With PUT | Tool to scrape web pages using PUT method with body payload. Use when the target API requires PUT requests with data in the request body. Forwards PUT request with custom body to the target URL. If not specified, content-type defaults to application/x-www-form-urlencoded. |

## Supported Triggers

None listed.

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

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

url = session.mcp.url
```

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

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

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

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

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

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

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

## Related Toolkits

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- [Ambee](https://composio.dev/toolkits/ambee) - Ambee is an environmental data platform providing real-time, hyperlocal APIs for air quality, weather, and pollen. Get precise environmental insights to power smarter decisions in your apps and workflows.
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- [Anonyflow](https://composio.dev/toolkits/anonyflow) - Anonyflow is a service for encryption-based data anonymization and secure data sharing. It helps organizations meet GDPR, CCPA, and HIPAA data privacy compliance requirements.
- [Api ninjas](https://composio.dev/toolkits/api_ninjas) - Api ninjas offers 120+ public APIs spanning categories like weather, finance, sports, and more. Developers use it to supercharge apps with real-time data and actionable endpoints.
- [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots.
- [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. It lets you automate data extraction and workflow tasks at scale—no infrastructure headaches.
- [Autom](https://composio.dev/toolkits/autom) - Autom is a lightning-fast search engine results data platform for Google, Bing, and Brave. Developers use it to access fresh, low-latency SERP data on demand.
- [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance.
- [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows.
- [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses.
- [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research.
- [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction.
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- [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation.
- [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses.

## Frequently Asked Questions

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

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

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

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

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