How to integrate Raisely MCP with LangChain

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Introduction

This guide walks you through connecting Raisely to LangChain using the Composio tool router. By the end, you'll have a working Raisely agent that can list all active fundraising campaigns, show all fundraising profiles for a campaign, retrieve recent posts from our raisely site, get all user profiles linked to an email through natural language commands.

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

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

TL;DR

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

The Raisely MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Raisely account. It provides structured and secure access to your fundraising campaigns, so your agent can perform actions like listing campaigns, managing profiles, retrieving fundraising posts, and overseeing users or webhook subscriptions on your behalf.

  • Campaign discovery and management: Instantly fetch and list all your Raisely campaigns, making it easy to organize or review ongoing fundraising efforts.
  • Profile and supporter insights: Retrieve detailed fundraising profiles within any campaign, or list all supporter profiles to track progress and engagement.
  • Posts and communications access: Pull all posts published on the Raisely platform, allowing your agent to keep you updated or summarize campaign communications.
  • User administration: Get a comprehensive list of users on your platform or drill into user-specific fundraising profiles, streamlining supporter management.
  • Webhook and event monitoring: View all configured webhook subscriptions and available event types, helping you automate notifications and stay on top of campaign activity.

Supported Tools & Triggers

Tools
Retrieve Raisely API DocumentationTool to retrieve the raisely api documentation.
Get Available EventsTool to retrieve a list of available raisely webhook events.
Get campaignsTool to retrieve a paginated list of campaigns from raisely.
Raisely Get Campaign ProfilesTool to list all fundraising profiles in a campaign.
Retrieve Raisely PostsTool to retrieve all posts available on the raisely platform.
Raisely Get ProfilesTool to retrieve all fundraising profiles in a campaign.
Raisely Get TagsTool to retrieve all tags available in the raisely platform.
Get User ProfilesTool to retrieve all profiles associated with a specific user.
Get usersTool to retrieve a list of all users in the raisely platform.
Raisely Get WebhooksTool to retrieve a list of configured webhooks.
Raisely List SubscriptionsTool to retrieve a list of subscriptions.
Logout from RaiselyTool to invalidate the current user's token and log out.
Create or Update UserTool to create or update a user by email.
Get Raisely Available EventsTool to retrieve a list of available raisely webhook events.
Raisely Get CampaignsTool to retrieve a list of campaigns from raisely.
Raisely Get UsersTool to retrieve a list of all users in the raisely platform.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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

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

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.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-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 is the core agent framework
  • python-dotenv loads environment variables

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

Import dependencies

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

Initialize Composio client

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")
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 Raisely tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Raisely
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['raisely']
)

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

Configure the agent with the MCP URL

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

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Raisely 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")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts 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 ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

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

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=['raisely']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "raisely-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 Raisely 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())

Conclusion

You've successfully built a LangChain agent that can interact with Raisely 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 Raisely MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Raisely MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Raisely MCP with Claude Agent SDK

Google ADK

Google ADK

Use Raisely MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Raisely MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Raisely MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Raisely MCP with LlamaIndex

CrewAI

CrewAI

Use Raisely MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Raisely MCP?

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

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

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

Used by agents from

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Context
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Agent.ai
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