# How to integrate Piggy MCP with LangChain

```json
{
  "title": "How to integrate Piggy MCP with LangChain",
  "toolkit": "Piggy",
  "toolkit_slug": "piggy",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/piggy/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/piggy/framework/langchain.md",
  "updated_at": "2026-05-12T10:21:56.571Z"
}
```

## Introduction

This guide walks you through connecting Piggy to LangChain using the Composio tool router. By the end, you'll have a working Piggy agent that can check your current piggy loyalty points, redeem points for a store discount, list recent cashback transactions through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Piggy account through Composio's Piggy MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Piggy with

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

## TL;DR

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

The Piggy MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Piggy account. It provides structured and secure access to your loyalty and rewards programs, so your agent can perform actions like managing customer points, handling cashback offers, updating loyalty rewards, and analyzing user engagement on your behalf.
- Customer points management: Let your agent track, update, and redeem customer loyalty points across your online store.
- Automated cashback processing: Enable your agent to issue, adjust, or report cashback rewards to users as part of promotional campaigns.
- Loyalty reward configuration: Allow your agent to set up, modify, or deactivate loyalty program rewards to keep your offers fresh and competitive.
- User engagement analytics: Have your agent analyze program participation to help identify top customers and optimize retention strategies.
- Discount and offer management: Let your agent create or update custom discounts and promotional offers linked to your loyalty program.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `PIGGY_CLAIM_ANONYMOUS_CONTACT` | Claim Anonymous Contact | Tool to claim an anonymous contact by associating it with a real email address. Use when converting an anonymous contact (with a fictional email) into a verified contact with a real email address. |
| `PIGGY_CREATE_CONTACT_ATTRIBUTE` | Create Contact Attribute | Tool to create a custom Contact Attribute. Use when you need to define new fields for contacts after initial setup. |
| `PIGGY_CREATE_CREDIT_RECEPTION` | Create Credit Reception | Tool to create a credit reception for a contact. Use when awarding credits to customers based on purchases or fixed amounts. |
| `PIGGY_CREATE_VOUCHERS_BATCH` | Create Vouchers Batch | Tool to create a batch of vouchers for a promotion. Use when you need to generate multiple vouchers at once for a specific promotion. Batch processing is asynchronous and returns a PENDING status initially. |
| `PIGGY_FIND_OR_CREATE_PRODUCTS` | Find or Create Products | Tool to find an existing product by external_identifier or create a new one if it doesn't exist. Use when you need to ensure a product exists in Piggy's system for loyalty programs or rewards. |
| `PIGGY_FIND_VOUCHER_BY_CODE` | Find Voucher By Code | Tool to find a voucher by its unique code. Use when you need to retrieve voucher details, check redemption status, or validate a voucher code. |
| `PIGGY_GET_CONTACT_AUTH_TOKEN` | Get Contact Auth Token | Tool to get an auth token for a Contact. Use after obtaining a Contact UUID and needing to verify identity for secure operations. |
| `PIGGY_GET_CONTACTS_CREDIT_BALANCE` | Get Contact's Credit Balance | Tool to get a Contact's credit balance. Use when you need to check a contact's current credit balance before processing rewards or promotions. |
| `PIGGY_LIST_FORMS` | List Forms | Tool to list all forms in the Piggy account. Use when you need to retrieve available forms for customer interactions. |
| `PIGGY_LIST_PERKS` | List Perks | Tool to list all available perks in Piggy. Use when you need to retrieve the catalog of perks that can be associated with contacts or transactions. |
| `PIGGY_MERGE_CONTACTS` | Merge Contacts | Merges a source contact into a destination contact in Piggy's CRM. The source contact's data (attributes, balances, transactions) is transferred to the destination contact, and the source contact is removed. This operation is irreversible and processed asynchronously via a job queue. Use this when consolidating duplicate customer records. |
| `PIGGY_SEND_CONTACT_VERIFICATION_EMAIL` | Send Contact Verification Email | Send a verification email to a Piggy contact for identity verification. The contact must exist in the system with a configured Contacts Portal. Returns success message with email sent confirmation. Use this when implementing email-based authentication workflows or when contacts need to verify their email address to access the Contacts Portal. |
| `PIGGY_UPDATE_BOOKINGS` | Update Bookings | Tool to update an existing booking in Piggy. Use when you need to modify booking details such as party size, status, or company name. Note: Shop and contact cannot be updated after creation. |
| `PIGGY_UPDATE_CONTACT_IDENTIFIERS` | Update Contact Identifiers | Tool to update a contact identifier in Piggy. Use when you need to modify the display name or active state of an existing contact identifier. Only the name and active properties can be updated; the identifier value itself cannot be changed. |

## Supported Triggers

None listed.

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

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

url = session.mcp.url
```

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

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

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

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

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

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

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

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## Frequently Asked Questions

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

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

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

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

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