How to integrate Adyntel MCP with LangChain

This guide walks you through connecting Adyntel to LangChain using the Composio tool router. By the end, you'll have a working Adyntel agent that can find linkedin ads for nike.com, show all google ads for amazon.com, search meta ads for fitness trackers through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Adyntel account through Composio's Adyntel MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Adyntel is an API that retrieves LinkedIn ads for any company using a domain or LinkedIn Page ID. Easily access competitive ad intelligence to power your marketing workflows.

8 Tools

Introduction

This guide walks you through connecting Adyntel to LangChain using the Composio tool router. By the end, you'll have a working Adyntel agent that can find linkedin ads for nike.com, show all google ads for amazon.com, search meta ads for fitness trackers through natural language commands.

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

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

Also integrate Adyntel with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

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

The Adyntel MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Adyntel account. It provides structured and secure access to company ad intelligence, so your agent can retrieve LinkedIn, Google, Meta, and TikTok ads, analyze campaign performance, and surface competitive ad insights on your behalf.

  • Company-specific ad retrieval: Instantly fetch all Google ads associated with any company's domain for in-depth competitive analysis.
  • Meta ad library search: Direct your agent to explore Meta's ad archive, surfacing ads by keyword, brand, or campaign theme.
  • TikTok ad discovery: Search and analyze TikTok ads using targeted keywords to identify creative trends and campaign strategies.
  • Cross-platform ad intelligence: Aggregate and compare ad content and strategies across platforms for a unified marketing view.
  • Competitive campaign benchmarking: Let your agent compile and summarize ad performance data to benchmark competitors and inform your own ad strategy.

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 Adyntel 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 Adyntel 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: ['adyntel']
    }
);

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

Configure the agent with the MCP URL

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

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

Get Google Ads By Company

Retrieve Google ads for a company domain.

Get Google Shopping Search Status

Check the status and retrieve results of a Google Shopping ads search.

Get LinkedIn Ads By Company

Retrieve LinkedIn ads for a company by domain or LinkedIn page ID.

Get Meta Ads By Company Or Page

Retrieve Meta/Facebook ads for a company domain or Facebook page URL.

Get Paid Vs Organic Keywords

Analyze paid vs organic keyword performance for a company domain.

Search Meta Ads

Search the Meta ad library.

Search TikTok Ads By Keyword

Search the TikTok ad library using keywords.

Start Google Shopping Search

Start an asynchronous Google Shopping ads search for a company domain.

FRAMEWORKS

How to build Adyntel MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Adyntel MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Adyntel MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Adyntel MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Adyntel MCP with Claude Cowork

Codex logo

Codex

Use Adyntel MCP with Codex

OpenClaw logo

OpenClaw

Use Adyntel MCP with OpenClaw

Hermes logo

Hermes

Use Adyntel MCP with Hermes

CLI logo

CLI

Use Adyntel MCP with CLI

Google ADK logo

Google ADK

Use Adyntel MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Adyntel MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Adyntel MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Adyntel MCP with LlamaIndex

CrewAI logo

CrewAI

Use Adyntel MCP with CrewAI

FAQ

Frequently asked questions

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

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

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