How to integrate Bannerbear MCP with Autogen

This guide walks you through connecting Bannerbear to AutoGen using the Composio tool router. By the end, you'll have a working Bannerbear agent that can merge multiple marketing pdfs into one file, list all video assets created this week, get available fonts for instagram templates through natural language commands. This guide will help you understand how to give your AutoGen 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.

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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 AutoGen using the Composio tool router. By the end, you'll have a working Bannerbear agent that can merge multiple marketing pdfs into one file, list all video assets created this week, get available fonts for instagram templates through natural language commands.

This guide will help you understand how to give your AutoGen 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

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Bannerbear
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Bannerbear tools
  • Run a live chat loop where you ask the agent to perform Bannerbear operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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 Bannerbear workspace, so your agent can generate images, create videos, manage templates, merge PDFs, and retrieve creative assets on your behalf.

  • Automated image and video generation: Enable your agent to create customized graphics or videos at scale using your Bannerbear templates and project assets.
  • Template browsing and management: Let your agent list, inspect, and select templates or template sets for creative projects, making it easy to automate content workflows.
  • Font and asset discovery: Have your agent retrieve available fonts and signed bases, ensuring the right design elements are used for every creative output.
  • PDF merging automation: Direct your agent to combine multiple PDFs into a single document, streamlining report or collateral creation.
  • Account and usage monitoring: Allow your agent to fetch current account status, API usage, and quota information to keep your creative operations running smoothly.

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 step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Bannerbear account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
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

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Bannerbear via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Bannerbear connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Bannerbear session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["bannerbear"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Bannerbear tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Bannerbear assistant agent with MCP tools
    agent = AssistantAgent(
        name="bannerbear_assistant",
        description="An AI assistant that helps with Bannerbear operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Bannerbear tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Bannerbear related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Bannerbear tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Bannerbear and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Bannerbear session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["bannerbear"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Bannerbear assistant agent with MCP tools
        agent = AssistantAgent(
            name="bannerbear_assistant",
            description="An AI assistant that helps with Bannerbear operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Bannerbear related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Bannerbear through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Bannerbear, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
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

Tool to delete a template referenced by its unique ID.

Delete Webhook

Tool to delete a webhook referenced 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 detailed information about a specific Bannerbear project by its unique identifier (UID).

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

Tool to retrieve a single template by its unique ID with layer defaults.

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

Hydrate a project by copying templates from another project.

Import Template

Tool to import templates from the Bannerbear template library or from other projects.

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 all 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

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

List Template Sets

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

List Videos

This action retrieves a list of all videos created in your Bannerbear account.

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

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Bannerbear MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Bannerbear MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Bannerbear MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Bannerbear MCP with Claude Cowork

Codex logo

Codex

Use Bannerbear MCP with Codex

OpenClaw logo

OpenClaw

Use Bannerbear MCP with OpenClaw

Hermes logo

Hermes

Use Bannerbear MCP with Hermes

CLI logo

CLI

Use Bannerbear MCP with CLI

Google ADK logo

Google ADK

Use Bannerbear MCP with Google ADK

LangChain logo

LangChain

Use Bannerbear MCP with LangChain

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

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. Autogen 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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