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How to integrate Daffy MCP with LangChain

Connect LangChain to Daffy MCP. Show your current daffy fund balance, list your donations from the past year, and more using natural language, with authentication handled for you.

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Daffy is a modern charitable giving platform with a donor-advised fund. Easily set aside funds, grow them tax-free, and donate to over 1.7 million U.S. charities.

12 Tools

Introduction

This guide walks you through connecting Daffy to LangChain using the Composio tool router. By the end, you'll have a working Daffy agent that can search nonprofits, check your balance, review donations and contributions, create gifts, and explore user causes through natural language commands.

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

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

Also integrate Daffy with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKCodexOpenClawHermesAtomic AgentGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAIPydantic AIAutoGen

TL;DR

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

The Daffy MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Daffy account. It provides structured and secure access to your donor-advised fund, so your agent can check balances, make donations, search for nonprofits, and manage your giving history on your behalf.

  • Effortless charitable gifting and donations: Instruct your agent to create new charitable gifts, make donations, or send gifts to specific nonprofits right from your account.
  • Donation and contribution tracking: Have your agent pull detailed lists of your past donations and contributions, giving you a clear view of your giving activity and history.
  • Fund balance and account management: Let your agent check your current fund balance or retrieve your profile details, keeping you updated on your financial standing in Daffy.
  • Non-profit discovery and research: Ask your agent to search for nonprofits by name, cause, or EIN, and fetch detailed information about organizations you might want to support.
  • User causes and personalized insights: Enable your agent to list your preferred causes or explore your giving patterns to suggest new ways to maximize your charitable impact.

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 Daffy 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 Daffy 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: ['daffy']
    }
);

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

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "daffy-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 Daffy MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Daffy 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 Daffy 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 Daffy 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: ['daffy']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "daffy-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 Daffy 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 Daffy 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 Daffy action and event your agent gets out of the box.

Create Gift

Tool to create a new charitable gift.

Get Balance

Retrieve the authenticated user's Daffy fund balance information.

Get Contributions

Retrieves paginated list of contributions made to the authenticated user's Daffy donor-advised fund (DAF).

Get Donations

Retrieve the authenticated Daffy user's donation history, with limit and offset pagination and creation-date filter parameters.

Get Gift by Code

Tool to retrieve details of a specific gift by its unique code.

Get Gifts

Retrieve a paginated list of gifts associated with the user's Daffy account.

Get Non-Profit by EIN (v2)

Tool to retrieve detailed information about a non-profit organization by its EIN.

Get User by Username

Tool to retrieve a user's public profile information by username.

Get User Causes

Retrieves the list of charitable causes associated with a user's Daffy fund.

Get User Donations

Retrieve a user's donations by user ID, with pagination.

Get User Profile

Tool to retrieve the authenticated user's profile.

Search Non-Profits

Tool to search non-profit organizations by cause ID and query text.

FRAMEWORKS

How to build Daffy MCP Agent with another framework

Claude Code logo

Claude Code

Use Daffy MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Daffy MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Daffy MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Daffy MCP with Claude Agent SDK

Codex logo

Codex

Use Daffy MCP with Codex

OpenClaw logo

OpenClaw

Use Daffy MCP with OpenClaw

Hermes logo

Hermes

Use Daffy MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Daffy MCP with Atomic Agent

Google ADK logo

Google ADK

Use Daffy MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Daffy MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Daffy MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Daffy MCP with LlamaIndex

CrewAI logo

CrewAI

Use Daffy MCP with CrewAI

Pydantic AI logo

Pydantic AI

Use Daffy MCP with Pydantic AI

AutoGen logo

AutoGen

Use Daffy MCP with AutoGen

FAQ

Frequently asked questions

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

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

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