How to integrate OneUp MCP with LangChain

This guide walks you through connecting OneUp to LangChain using the Composio tool router. By the end, you'll have a working OneUp agent that can create social posts, upload media, edit scheduled posts, comment on posts, and analyze engagement through natural language commands. This guide will help you understand how to give your LangChain agent real control over a OneUp account through Composio's OneUp MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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OneUp is a social media management platform for scheduling posts, comments, accounts, media, and analytics. Use it to manage multiple social networks from one place and keep publishing consistent.

27 Tools

Introduction

This guide walks you through connecting OneUp to LangChain using the Composio tool router. By the end, you'll have a working OneUp agent that can create social posts, upload media, edit scheduled posts, comment on posts, and analyze engagement through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a OneUp account through Composio's OneUp 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 OneUp project to Composio
  • Create a Tool Router MCP session for OneUp
  • Initialize an MCP client and retrieve OneUp tools
  • Build a LangChain agent that can interact with OneUp
  • 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 OneUp MCP server, and what's possible with it?

The OneUp MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your OneUp account. It provides structured and secure access to your social accounts, posts, comments, media, and analytics, so your agent can schedule content, manage conversations, review performance, organize accounts, and discover trending sounds on your behalf.

  • Social post creation and scheduling: Have your agent create text, image, or video posts as drafts, scheduled content, or timeslot posts for selected social accounts.
  • Post management and recovery: Direct your agent to list posts, edit or delete scheduled content, and reschedule failed posts for a future date and time.
  • Comment and review management: Let the agent refresh and review comment threads, publish comments and replies, delete comments, and check Google Business Profile reviews.
  • Social and advertising analytics: Instruct your agent to review account performance, audience demographics, individual content results, and Meta Ads analytics, or request updated analytics across connected accounts.
  • Account and content discovery: Have your agent check connected account health, browse account categories, find Instagram or TikTok trending sounds, and list Pinterest boards or YouTube playlists.

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 OneUp 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 OneUp 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: ['oneup']
    }
);

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

Configure the agent with the MCP URL

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

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

Comment On Post

Publish a new top-level comment on a social post through a connected OneUp social account.

Create Image Post

Create a scheduled, timeslot, or draft image post for selected OneUp social accounts.

Create Media Upload URL

Create a temporary OneUp media upload URL and return instructions for a separate direct upload.

Create Text Post

Create a scheduled, timeslot, or draft text post for selected OneUp social accounts.

Create Video Post

Create a scheduled, timeslot, or draft video post for selected OneUp social accounts.

Delete Comment

Permanently and irreversibly delete a social-platform comment and its local OneUp record.

Delete Scheduled Post

Permanently and irreversibly delete an unpublished scheduled OneUp post.

Edit Scheduled Post

Replace the content and optionally the title of a scheduled OneUp post.

Get Analytics Overview

Get account-level overview metrics and ranked performance for a connected account on a supported social platform.

Get Audience Demographics

Get audience demographics for a connected Facebook, Instagram, TikTok, or YouTube account for a preset or custom reporting period.

Get Content Analytics

Get per-post, Reel, or Story analytics for a connected account on a supported social platform.

Get Google Business Reviews

Get Google Business Profile reviews and aggregate rating information for a connected account or location.

Get Meta Ads Analytics

Get campaign, ad-set, or individual-ad analytics for a connected Meta Ads account.

List Categories

List OneUp categories used to group connected social accounts.

List Category Accounts

List social accounts assigned to a OneUp category.

List Comment Replies

Fetch child replies for a parent social-platform comment.

List Comment Threads

List social posts with top-level comments and child replies in the requested order, one page at a time.

List Instagram Trending Sounds

Search Instagram trending sounds for a Facebook-connected Instagram Business or Creator account.

List Pinterest Boards

List boards available to a connected Pinterest username for post targeting.

List Posts

List scheduled, published, or failed OneUp posts, one page at a time.

List Social Accounts

List all connected OneUp social accounts and their connection health.

List TikTok Trending Sounds

Get TikTok trending commercial sounds for a documented country, period, genre, and connected TikTok account.

List YouTube Playlists

List playlists for a connected YouTube channel.

Refresh Analytics

Start a background refresh of analytics for all connected social accounts.

Refresh Comments

Start a background refresh of comments from connected social platforms.

Reply To Comment

Publish a direct reply to an existing social-platform comment.

Reschedule Failed Post

Move a failed post back into the schedule at a future date and time.

FAQ

Frequently asked questions

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

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

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