How to integrate Agenty MCP with LangChain

This guide walks you through connecting Agenty to LangChain using the Composio tool router. By the end, you'll have a working Agenty agent that can clone your top-performing agent for news sites, list all your running web scraping agents, create a new agent to monitor product prices through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Agenty account through Composio's Agenty MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Agenty is a web scraping and automation platform for extracting data and automating browser tasks—no coding needed. It streamlines data collection, monitoring, and repetitive online actions.

79 Tools

Introduction

This guide walks you through connecting Agenty to LangChain using the Composio tool router. By the end, you'll have a working Agenty agent that can clone your top-performing agent for news sites, list all your running web scraping agents, create a new agent to monitor product prices through natural language commands.

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

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

Also integrate Agenty with

ChatGPTOpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenCodeCursorVS CodeOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

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

The Agenty MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Agenty account. It provides structured and secure access to your web scraping agents and automation tools, so your agent can perform actions like creating, managing, cloning, and monitoring scraping agents, as well as handling API keys and templates—all on your behalf.

  • Agent creation and configuration: Instantly create new scraping or automation agents, set up their configurations, and optionally auto-start them—all without manual coding.
  • Clone and update agents: Duplicate existing agents to streamline workflows or update agent settings to refine your data extraction processes.
  • Fetch and manage agents: List all active agents in your account, retrieve details for any agent, and organize your entire automation fleet from a single place.
  • Template selection and management: Browse public agent templates or sample agents, making it easy to kickstart new projects or standardize scraping tasks.
  • API key management: Create, download, or delete API keys for secure programmatic access and efficient credential management, keeping your automation environment safe and organized.

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 Agenty 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 Agenty 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: ['agenty']
    }
);

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

Configure the agent with the MCP URL

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

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

Add List Rows

Tool to add new rows to a list.

Create Agent

Creates a new Agenty agent for web scraping, change detection, crawling, map monitoring, or brand monitoring.

Get Agent Templates

Tool to fetch all public agent templates and sample agents.

Delete Agent by ID

Tool to delete a single agent by its ID.

Fetch all agents

Tool to fetch all active agents under an account.

Get Agent by ID

Retrieves complete details of a specific agent including its configuration, input settings, scheduler, and metadata.

Update Agent by ID

Updates an existing agent's configuration, settings, and metadata.

Create API Key

Creates a new API key for programmatic access to the Agenty API.

Delete API key by ID

Delete an API key by its unique identifier.

Download API keys

Tool to download all API keys under an account in CSV format.

Get all API keys

Tool to retrieve all API keys under an account.

Get API key by ID

Retrieves detailed information about a specific API key by its ID.

Reset API key by ID

Resets (regenerates) the secret value of an existing API key.

Update API key by ID

Updates an existing API key's name and role by its unique identifier.

Capture Screenshot

Tool to capture a full-page or visible screenshot of any webpage URL.

Capture Screenshot with Options

Tool to capture webpage screenshots with extensive customization options including full-page capture, image format, quality settings, viewport configuration, and post-processing.

Change API key status by ID

Toggles the enabled/disabled status of an API key.

Get all connections

Retrieves all connections from your Agenty account.

Convert URL to PDF

Tool to convert a webpage URL to a PDF document.

Convert URL to PDF with Options

Tool to convert a URL or raw HTML to PDF with customizable options.

Copy Agent

Tool to copy an existing agent by its ID, creating a duplicate with optionally a new name.

Create Workflow

Creates a new workflow in Agenty to automate actions based on agent events.

Get dashboard reports and usage

Tool to fetch account reports like pages used by agent, date, and product.

Delete List Row by ID

Tool to delete a specific row from a list by its unique identifier.

Delete List Rows by IDs

Tool to delete specific rows from a list by their IDs.

Delete Project

Tool to delete a project by its ID.

Delete Schedule

Tool to delete a schedule for an agent by its agent ID.

Delete Workflow by ID

Tool to delete a workflow by its ID.

Download Agent Result

Tool to download agent results by agent ID in CSV, TSV or JSON format.

Download List Rows

Tool to download list rows as CSV file.

Download users

Tool to download users list in CSV format.

Download workflows

Tool to download all workflows in CSV format.

Extract Structured Data

Tool to auto-extract structured data from a webpage including schema.

Extract Structured Data from URL

Tool to auto-extract structured data from a webpage URL.

Get Agent Result

Tool to get the most recent result data for an agent.

Get all team members

Tool to retrieve all team members (users) under an account.

Get URL Redirects

Tool to get the complete redirect chain for a URL.

Get Job Result

Tool to get the result data from a completed job.

Get list by ID

Retrieves detailed information about a specific list by its ID.

Get List Row by ID

Tool to fetch a specific row by its ID from a list.

Get Page Content

Tool to fetch the complete HTML content of any webpage URL.

Get Page Content with Options

Tool to fetch HTML content of a webpage with custom options including ad blocking.

Get Project by ID

Retrieves complete details of a specific project by its ID, including name, description, creator information, and timestamps.

Get Redirects with Options

Tool to get the complete redirect chain of a URL with custom navigation options.

Get Agent Schedule

Tool to retrieve the schedule configuration for a specific agent.

Get User by ID

Tool to retrieve detailed information about a user by their ID.

Get Workflow by ID

Retrieves complete details of a specific workflow by its ID.

Get agent input by ID

Retrieves the input configuration for a specific agent by its ID.

Update Input by Agent ID

Updates the input configuration for a specific agent in Agenty.

Download jobs

Tool to download all jobs in CSV format.

Download job file by ID

Tool to download output files by job ID.

Download Job Result by ID

Tool to download the agent output result by job ID.

Fetch all jobs

Tool to fetch all jobs under an account.

Get Job by ID

Retrieves comprehensive details about a specific job including its status, progress metrics (pages processed/succeeded/failed), timing information (created/started/completed times), resource consumption (page credits), and any error messages.

Get Job Logs by ID

Tool to fetch logs for a given job by its ID.

List job output files

Lists all output files generated by a specific job.

Start Agent Job

Tool to start a new agent job.

Stop Job by ID

Tool to stop a running job by job ID.

Clear List Rows

Tool to clear all rows in a list by its ID.

Create List

Tool to create a new list.

Delete List by ID

Tool to delete a specific list by its ID.

Download lists

Tool to download all lists in CSV format.

Get all lists

Tool to retrieve all lists under an account.

Fetch List Rows by ID

Tool to fetch all rows in a specified list.

Update List by ID

Tool to update a list's name and optionally description by list ID.

Upload CSV file to List

Tool to upload a CSV file to an Agenty list for bulk import of data rows.

Patch Workflow

Tool to partially update a workflow by ID.

Add Agents to Project

Add one or more agents to an Agenty project to organize and group related agents together.

Create Project

Creates a new project in Agenty.

Get all projects

Retrieve all projects in the authenticated user's account.

Remove Agent from Project

Remove an agent from an Agenty project.

Scrape Webpage Data

Tool to scrape data from any webpage using jQuery/CSS selectors.

Toggle Agent Schedule

Tool to toggle schedule on/off for an agent.

Transfer Agent Ownership

Tool to transfer agent ownership to another Agenty account.

Update List Row

Tool to update a specific row in a list by list ID and row ID.

Update Project

Update an existing project's name and description in Agenty.

Update Agent Schedule

Updates the schedule configuration for a specific agent.

Update User by ID

Tool to update a user's information by user ID.

Update Workflow

Tool to update an existing workflow's configuration by workflow ID.

FRAMEWORKS

How to build Agenty MCP Agent with another framework

ChatGPT logo

ChatGPT

Use Agenty MCP with ChatGPT

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Agenty MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Agenty MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Agenty MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Agenty MCP with Claude Cowork

Codex logo

Codex

Use Agenty MCP with Codex

Cursor logo

Cursor

Use Agenty MCP with Cursor

OpenClaw logo

OpenClaw

Use Agenty MCP with OpenClaw

Hermes logo

Hermes

Use Agenty MCP with Hermes

CLI logo

CLI

Use Agenty MCP with CLI

Google ADK logo

Google ADK

Use Agenty MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Agenty MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Agenty MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Agenty MCP with LlamaIndex

CrewAI logo

CrewAI

Use Agenty MCP with CrewAI

OpenCode logo

OpenCode

Use Agenty MCP with OpenCode

VS Code logo

VS Code

Use Agenty MCP with VS Code

FAQ

Frequently asked questions

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

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

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agent.aiZoomLettaGleanHubSpotAWSWabi

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati
Harsha Gaddipati
Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya
Abhi Arya
Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave
Nirman Dave
CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu
Ryan Yu
Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola
Tomisin Jenrola
Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche
Jerome Leclanche
Co-Founder, Ingram Technologies