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How to integrate Bannerbear MCP with LangChain

Connect LangChain to Bannerbear MCP. Merge multiple marketing pdfs into one file, list all video assets created this week, and more using natural language, with authentication handled for you.

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Bannerbear is an API-driven platform for generating images and videos automatically at scale. It helps businesses create custom graphics, social visuals, and marketing assets using powerful templates.

33 Tools

Introduction

This guide walks you through connecting Bannerbear to LangChain using the Composio tool router. By the end, you'll have a working Bannerbear agent that can create templates and template sets, list generated images and videos, fetch screenshots and GIFs, and join PDFs through natural language commands.

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

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

Also integrate Bannerbear with

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TL;DR

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

The Bannerbear MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Bannerbear account. It provides structured and secure access to your projects, templates, generated media, fonts, and effects, so your agent can browse creative assets, inspect templates, organize template sets, create video templates, and merge PDFs on your behalf.

  • Project and usage overview: Have your agent list your projects, retrieve project details, and check your current plan, API usage, and quota limits.
  • Generated media retrieval: Let the agent find and inspect your images, videos, animated GIFs, screenshots, and collections.
  • Template discovery and setup: Direct your agent to browse templates, review their layer defaults, import templates, copy templates between projects, or create video templates.
  • Template set management: Instruct your agent to create, inspect, list, and update grouped template sets for coordinated content production.
  • Creative production resources: Have your agent list available fonts and image effects, or combine multiple PDF files into one document.

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 Bannerbear 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 Bannerbear 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: ['bannerbear']
    }
);

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

Configure the agent with the MCP URL

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

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

Create Project

Creates a new Bannerbear project with the specified name and optional settings.

Create Signed Base

Tool to create a signed URL base for a template.

Create Template

Create a new blank template in a Bannerbear project.

Create Template Set

Tool to create a new template set by grouping multiple templates together.

Create Video Template

Tool to create a new video template for video generation in Bannerbear.

Create Webhook

Create a project-level webhook that fires for all events of a specific type.

Delete Template

Permanently delete a template from a Bannerbear project by its unique ID.

Delete Webhook

Delete a webhook by its unique ID.

Get Account Info

Retrieves Bannerbear account information including subscription plan, API usage, and quota limits.

Get Animated GIF

Tool to retrieve a single Animated Gif object by its unique identifier (UID).

Get Auth Status

Verify API authentication and check which project the API key is scoped to.

Get Available Fonts

This tool retrieves a list of all available fonts in Bannerbear.

Get Image

Retrieves a single Image object by its unique identifier (UID).

Get Project

Retrieves a Bannerbear project by UID, including its name, creation date, template count, and feature settings.

Get Screenshot

Retrieve a single Screenshot object referenced by its unique ID.

Get Signed Bases

This tool retrieves a list of signed bases for a specific template.

Get Template

Retrieve a template by its unique ID, including its dimensions and available modifications.

Get Template Set Details

This tool retrieves detailed information about a specific template set using its unique identifier (UID).

Get Webhook

Retrieves a single Webhook object by its unique ID.

Hydrate Project

Hydrates a project by copying all templates from a source project or selected templates by UID.

Import Template

Imports publicly available templates into a project using publication IDs from the Bannerbear library or shared publications.

Join PDFs

Merges multiple PDF files into a single combined PDF document.

List Animated GIFs

Lists all animated GIFs in a Bannerbear project.

List Collections

Lists a page of collections in a Bannerbear project.

List Effects

Tool to list all available image effects in Bannerbear.

List Images

Lists all images in a Bannerbear project.

List Projects

Lists all projects in a Bannerbear account.

List Screenshots

Lists all screenshots in a Bannerbear project.

List Templates

List templates in a Bannerbear project, with pagination and optional tag or partial-name filters.

List Template Sets

Tool to list all template sets inside a project with pagination support.

List Videos

List a page of videos in a Bannerbear project.

List Video Templates

This action retrieves a list of all video templates available in your Bannerbear project.

Update Template Set

Tool to update a template set by modifying its list of templates.

FRAMEWORKS

How to build Bannerbear MCP Agent with another framework

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Claude Code

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Claude Cowork

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OpenAI Agents SDK logo

OpenAI Agents SDK

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Claude Agent SDK logo

Claude Agent SDK

Use Bannerbear MCP with Claude Agent SDK

Codex logo

Codex

Use Bannerbear MCP with Codex

OpenClaw logo

OpenClaw

Use Bannerbear MCP with OpenClaw

Hermes logo

Hermes

Use Bannerbear MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Bannerbear MCP with Atomic Agent

Google ADK logo

Google ADK

Use Bannerbear MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Bannerbear MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Bannerbear MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Bannerbear MCP with LlamaIndex

CrewAI logo

CrewAI

Use Bannerbear MCP with CrewAI

Pydantic AI logo

Pydantic AI

Use Bannerbear MCP with Pydantic AI

AutoGen logo

AutoGen

Use Bannerbear MCP with AutoGen

FAQ

Frequently asked questions

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

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

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