How to integrate Bannerbite MCP with LangChain

This guide walks you through connecting Bannerbite to LangChain using the Composio tool router. By the end, you'll have a working Bannerbite agent that can generate campaign images from saved templates, create product videos for new launches, render social banners for holiday sale through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Bannerbite account through Composio's Bannerbite MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Bannerbite is a creative automation platform for generating images and videos from reusable projects and templates. It helps teams produce on-brand visual assets faster without rebuilding designs from scratch.

6 Tools

Introduction

This guide walks you through connecting Bannerbite to LangChain using the Composio tool router. By the end, you'll have a working Bannerbite agent that can generate campaign images from saved templates, create product videos for new launches, render social banners for holiday sale through natural language commands.

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

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

Also integrate Bannerbite 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 Bannerbite project to Composio
  • Create a Tool Router MCP session for Bannerbite
  • Initialize an MCP client and retrieve Bannerbite tools
  • Build a LangChain agent that can interact with Bannerbite
  • 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 Bannerbite MCP server, and what's possible with it?

The Bannerbite MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Bannerbite account. It provides structured and secure access to your creative projects and reusable templates, so your agent can browse projects, inspect templates, review editable scene fields, and generate images, videos, or overlays on your behalf.

  • Project discovery: Have your agent list your Bannerbite projects and retrieve details about a specific creative project.
  • Template browsing: Let the agent find the reusable creative templates within a project and review their available output sizes.
  • Scene field review: Direct your agent to inspect the editable text, images, and other scene fields in a template before creating media.
  • Image and video generation: Instruct your agent to generate an image or video from a selected template using your chosen scene content.
  • Overlay creation: Have your agent create an overlay render from a reusable Bannerbite template for use in your creative workflow.

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 Bannerbite 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 Bannerbite 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: ['bannerbite']
    }
);

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

Configure the agent with the MCP URL

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

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

Generate Media

Queue one asynchronous image, video, or overlay render from a Bannerbite bite.

Get Bite

Return one Bannerbite bite by its numeric bite ID, including template and output-size details; fail explicitly when the bite does not exist.

Get Project

Return one Bannerbite project by its numeric project ID; fail explicitly when the project does not exist.

Get Scene Data

Return the editable scene fields for a Bannerbite bite so their names and current values can be used in a render request.

List Bites

Return one provider page of Bannerbite bites in a project, up to 100 bites; the API exposes no verified way to request another page.

List Projects

Return one provider page of projects owned by the connected Bannerbite account, up to 100 projects; the API exposes no verified way to request another page.

FRAMEWORKS

How to build Bannerbite MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Bannerbite MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Bannerbite MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Bannerbite MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Bannerbite MCP with Claude Cowork

Codex logo

Codex

Use Bannerbite MCP with Codex

OpenClaw logo

OpenClaw

Use Bannerbite MCP with OpenClaw

Hermes logo

Hermes

Use Bannerbite MCP with Hermes

CLI logo

CLI

Use Bannerbite MCP with CLI

Google ADK logo

Google ADK

Use Bannerbite MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Bannerbite MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Bannerbite MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Bannerbite MCP with LlamaIndex

CrewAI logo

CrewAI

Use Bannerbite MCP with CrewAI

FAQ

Frequently asked questions

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

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

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