# How to integrate Fraudlabs pro MCP with LangChain

```json
{
  "title": "How to integrate Fraudlabs pro MCP with LangChain",
  "toolkit": "Fraudlabs pro",
  "toolkit_slug": "fraudlabs_pro",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/fraudlabs_pro/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/fraudlabs_pro/framework/langchain.md",
  "updated_at": "2026-05-12T10:12:16.845Z"
}
```

## Introduction

This guide walks you through connecting Fraudlabs pro to LangChain using the Composio tool router. By the end, you'll have a working Fraudlabs pro agent that can screen this new order for fraud risk, send sms otp to verify customer phone, get analysis result for order 123456 through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Fraudlabs pro account through Composio's Fraudlabs pro MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Fraudlabs pro with

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

## TL;DR

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

The Fraudlabs pro MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Fraudlabs Pro account. It provides structured and secure access to your fraud detection and order screening tools, so your agent can perform actions like screening transactions, sending SMS OTPs, retrieving fraud analysis results, and managing reseller subscriptions on your behalf.
- Real-time fraud screening for orders: Instantly have your agent assess the risk of new transactions before fulfillment to minimize chargebacks and detect suspicious activity.
- Automated SMS OTP verification: Directly send one-time passwords to customers via SMS and verify phone numbers for added transaction security.
- Retrieve and analyze fraud results: Quickly pull fraud analysis reports on previously screened orders for smarter decision-making and audit trails.
- Feedback and continuous learning: Submit order feedback—approved or rejected—to improve future fraud detection accuracy and adapt to evolving threats.
- Reseller account management: Automate account creation and manage subscription plans for resellers directly from your agent, streamlining onboarding and upgrades.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `FRAUDLABS_PRO_CREATE_ACCOUNT` | Create FraudLabs Pro Reseller Sub-Account | Creates a new FraudLabs Pro user account under a reseller account. Requires a Reseller API key (not a regular API key). Use this action to provision new sub-accounts for customers when operating as a FraudLabs Pro reseller. Returns account credentials (username and password) upon successful creation. Prerequisites: - Must have a FraudLabs Pro Reseller account (apply at https://www.fraudlabspro.com/reseller/program) - Requires valid Reseller API key (different from standard FraudLabs Pro API key) - All required fields must contain valid data matching FraudLabs Pro validation rules Important: - Phone numbers should contain only digits (no +, -, spaces, or parentheses) - Country must be valid ISO 3166-1 alpha-2 code (e.g., US, GB, CA) - Industry must be a valid ID from 1-14 |
| `FRAUDLABS_PRO_FLP_FEEDBACK_ORDER` | FLP Feedback Order | Submit fraud screening feedback to improve FraudLabs Pro's machine learning model. Use this after reviewing a screened order to mark it as approved, rejected, or blacklisted. This feedback trains the algorithm to better detect fraud patterns in future transactions. |
| `FRAUDLABS_PRO_FLP_SCREEN_ORDER` | FraudLabs Pro Screen Order | Tool to screen orders for fraud. Use when you need to assess transaction risk before fulfillment. |
| `FRAUDLABS_PRO_GET_ORDER_RESULT2` | Get FraudLabs Pro Order Result (v2) | Tool to retrieve an existing transaction from FraudLabs Pro fraud detection system using the v2 API. This API is only available for paid plans. |
| `FRAUDLABS_PRO_GET_SMS_VERIFICATION_RESULT` | Get SMS Verification Result | Verify an OTP (one-time password) received via SMS. Returns 'Y' if the OTP is valid, 'N' if invalid or expired. Must be called after using Send SMS Verification to obtain a transaction ID. |
| `FRAUDLABS_PRO_SEND_SMS_VERIFICATION` | Send SMS OTP Verification | Sends an SMS message containing a one-time password (OTP) to verify a user's phone number. The API generates a random 6-digit OTP, replaces the placeholder in your message template, and sends the SMS to the recipient. Returns a transaction ID that you must use with the Get SMS Verification Result action to verify the OTP the user enters. Testing: Use phone number '+11' for sandbox testing (no credits deducted). For verification testing with sandbox number, use OTP '123456'. |
| `FRAUDLABS_PRO_SUBSCRIBE_PLAN` | Subscribe Reseller Plan | Tool to subscribe a reseller account to a specific plan. Use after obtaining a reseller API key and selecting a plan. Example: Subscribe user "john_doe" to the MICRO plan. |
| `FRAUDLABS_PRO_WEBHOOK_ORDER_STATUS_CHANGED` | Test Order Status Changed Webhook Delivery | Tool to send a test webhook payload to the provided callback URL for FraudLabs Pro order status change notifications. This helps validate your endpoint behavior. Important: FraudLabs Pro webhook registration must be configured manually via dashboard. |

## Supported Triggers

None listed.

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

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

url = session.mcp.url
```

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

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

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

No description provided.
```python
client = MultiServerMCPClient({
    "fraudlabs_pro-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({
    "fraudlabs_pro-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 Fraudlabs pro 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 Fraudlabs pro 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=['fraudlabs_pro']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "fraudlabs_pro-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 Fraudlabs pro 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: ['fraudlabs_pro']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "fraudlabs_pro-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 Fraudlabs pro 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 Fraudlabs pro 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 Fraudlabs pro MCP Agent with another framework

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

## Related Toolkits

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- [Baselinker](https://composio.dev/toolkits/baselinker) - BaseLinker is an all-in-one e-commerce management platform connecting stores, marketplaces, carriers, and more. It streamlines order processing, inventory control, and automates your sales operations.
- [Bestbuy](https://composio.dev/toolkits/bestbuy) - Best Buy is a leading retailer offering APIs for product, store, and recommendation data. Instantly access up-to-date retail insights for smarter shopping and decision-making.
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- [Cloudcart](https://composio.dev/toolkits/cloudcart) - CloudCart is an e-commerce platform for building and managing online stores. It helps businesses streamline product listings, orders, and customer engagement.
- [Countdown api](https://composio.dev/toolkits/countdown_api) - Countdown API gives you real-time, structured eBay product data, reviews, and seller feedback. Perfect for powering price monitoring, product research, or marketplace analytics workflows.
- [Dpd2](https://composio.dev/toolkits/dpd2) - Dpd2 is a robust email management platform for handling, sorting, and automating email workflows. Streamline your communications and boost productivity with advanced sorting, labeling, and response tools.
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- [Fingertip](https://composio.dev/toolkits/fingertip) - Fingertip is a business management platform for selling, booking, and customer engagement—all from a single link. It helps businesses streamline operations and connect with customers across social channels.
- [Gift up](https://composio.dev/toolkits/gift_up) - Gift Up! is a digital platform for selling, managing, and redeeming gift cards online. It streamlines promotions and gift card transactions for businesses and their customers.
- [Goody](https://composio.dev/toolkits/goody) - Goody is a gifting platform that lets users send gifts and physical products without handling logistics. It streamlines gifting by managing delivery, fulfillment, and recipient experience.
- [Gumroad](https://composio.dev/toolkits/gumroad) - Gumroad is a platform for selling digital products, physical goods, and memberships with a simple checkout and marketing tools. It streamlines creator payouts and helps you grow your audience effortlessly.
- [Instacart](https://composio.dev/toolkits/instacart) - Instacart is an online grocery delivery and pickup service platform. It lets you discover local retailers and create shoppable lists and recipes with ease.
- [Junglescout](https://composio.dev/toolkits/junglescout) - Junglescout is an Amazon product research and analytics platform for sellers. It delivers sales estimates, competitive insights, and optimization tools to boost your Amazon business.
- [Ko fi](https://composio.dev/toolkits/ko_fi) - Ko-fi is a platform that lets creators receive donations, memberships, and sales from fans. It helps creators monetize their work and grow their audience with minimal friction.
- [Lemon squeezy](https://composio.dev/toolkits/lemon_squeezy) - Lemon Squeezy is a payments and subscription platform built for software companies. It makes managing payments, taxes, and customer subscriptions effortless.
- [Loyverse](https://composio.dev/toolkits/loyverse) - Loyverse is a point-of-sale (POS) platform for small businesses, offering tools for sales, inventory, and customer loyalty. It helps streamline retail operations and boost customer engagement.
- [Memberstack](https://composio.dev/toolkits/memberstack) - Memberstack lets you add user authentication, payments, and member management to your website—no backend code required. Easily manage your site's members and subscriptions from a single platform.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Fraudlabs pro MCP?

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

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

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

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