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How to integrate Ably MCP with LangChain

Connect LangChain to Ably MCP. List all active channels and their details, get message history from 'support-chat' channel, and more using natural language, with authentication handled for you.

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Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences.

25 Tools

Introduction

This guide walks you through connecting Ably to LangChain using the Composio tool router. By the end, you'll have a working Ably agent that can publish messages to channels, read channel history and presence, send push notifications, manage push devices, and check app stats through natural language commands.

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

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

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TL;DR

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

The Ably MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Ably account. It provides structured and secure access to your channels, messages, presence data, application statistics, and push notifications, so your agent can publish messages, inspect active channels, review channel history, monitor presence, and manage push notifications on your behalf.

  • Channel messaging: Have your agent publish a message to one channel or send batches of messages across multiple channels.
  • Channel activity and history: Let the agent list active channels, retrieve channel details, review message history, and inspect earlier versions of a message.
  • Presence monitoring: Direct your agent to check who is currently present in one or more channels and review past presence activity.
  • Push notification management: Instruct your agent to send individual or batch push notifications, manage channel subscriptions, and register, update, inspect, or unregister receiving devices.
  • Application insights: Have your agent retrieve Ably service time and review application usage statistics.

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 Ably 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 Ably 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: ['ably']
    }
);

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

Configure the agent with the MCP URL

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

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

Query Batch Presence

Queries the presence states of multiple Ably channels in a single request, retrieving member presence details for the specified channels.

Query Batch Presence History

Query presence history for multiple Ably channels, with optional start and end times and forwards or backwards ordering.

Delete Channel Subscription

Unsubscribes a device or client from push notifications for a specified channel, or across all channels if no channel is provided.

Get Channel Details

Retrieves status and occupancy metadata for a specific Ably channel, including connection, presence, publisher, and subscriber metrics.

Get Channel History

Retrieves a page of message history for a specified Ably channel, with time range, limit, and direction filters.

Get Channel Presence

Tool to obtain the set of members currently present for a channel.

Get Message Versions

Retrieves a page of historical versions of a specific Ably channel message, including the original and subsequent updates or deletes.

Get Channel Presence History

This tool retrieves the history of presence messages for a specified channel in Ably.

Get Push Device Registration

Retrieves a device's push registration by device ID, including available platform, state, recipient, and metadata fields.

Get Ably Service Time

This tool retrieves the current server time from Ably's service in milliseconds since the epoch.

Get Application Stats

Retrieve a page of Ably application usage statistics by minute, hour, day or month, with optional time range, direction and limit.

List Channels

Tool to enumerate all active channels in the Ably application.

List Push Channels

Lists a page of Ably channels with at least one subscribed device for push notifications.

List Push Channel Subscriptions

Lists a page of push channel subscriptions filtered by channel, client ID, or device ID, with pagination links; at least one filter is required.

List Registered Push Devices

List a page of Ably push device registrations, optionally filtered by device ID or client ID.

Patch Push Device Registration

Partially update an existing Ably push device registration.

Batch Publish Messages

Tool to batch publish messages to multiple channels in parallel.

Publish Message to Channel

Publishes a message to a specified Ably channel with a string or JSON object payload and an optional event name and client ID.

Publish Push Notification

Tool to publish a push notification directly to device(s) via Ably's Push Notifications API.

Batch Publish Push Notifications

Tool to batch publish push notifications directly to specific recipients.

Register Push Device

Register an iOS, Android, or browser device with Ably to receive push notifications.

Request Access Token

Request an Ably access token with optional capabilities, client ID and lifetime.

Unregister All Push Devices

Unregister matching Ably push devices, optionally filtering by device ID or client ID, but not both.

Unregister Push Device

Unregister a single Ably push device by device ID.

Update Push Device Registration

Upsert an Ably push device registration, creating it or replacing an existing registration.

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FAQ

Frequently asked questions

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

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

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