How to integrate Canva MCP with LangChain

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Introduction

This guide walks you through connecting Canva to LangChain using the Composio tool router. By the end, you'll have a working Canva agent that can create a new instagram post design, list my brand templates for social use, start a folder for this project’s assets, reply to comments on a shared design through natural language commands.

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

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

TL;DR

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

The Canva MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Canva account. It provides structured and secure access to your Canva designs, templates, folders, assets, and user details, so your agent can create designs, organize projects, manage assets, and collaborate on feedback for you.

  • Automated design creation and asset integration: Direct your agent to generate new Canva designs using templates or custom dimensions, and add assets from your projects automatically.
  • Seamless folder and project organization: Have the agent create user or subfolders to keep your Canva projects structured and easily accessible.
  • Asset management and cleanup: Let your agent fetch upload statuses, manage, or delete assets by ID, helping you keep your design library up to date.
  • Collaborative design feedback: Empower your agent to add comments or reply within designs, making it easy to facilitate feedback and teamwork directly in Canva.
  • User and team information retrieval: Quickly obtain user or team details, allowing your agent to personalize interactions and automate workflows based on your Canva account info.

Supported Tools & Triggers

Tools
Access user specific brand templates listThis year, brand template ids will change; integrations storing them must update within 6 months.
Create canva design with optional assetCreate a new canva design using a preset or custom dimensions, and add an asset with `asset id` from a user's project using relevant apis.
Create comment reply in designThis preview api allows replying to comments within a design on canva, with a limit of 100 replies per comment.
Create design comment in preview apiThis api is in preview and may change without notice; integrations using it won't pass review.
Create user or sub folderThis api creates a folder in a canva user's projects at the top level or within another folder, returning the new folder's id and additional details upon success.
Delete asset by idYou can delete an asset by specifying its `assetid`.
Exchange oauth 2 0 access or refresh tokenThe oauth 2.
Fetch asset upload job statusSummarize asset upload outcome by repeatedly calling the endpoint until a 'success' or 'failed' status is received after using the create asset upload job api.
Fetch canva connect signing public keysThe api for verifying canva webhooks, 'connect/keys,' is in preview, meaning unstable, not for public integrations, and provides a rotating jwk for signature verification to prevent replay attacks.
Fetch current user detailsReturns the user id, team id, and display name of the user account associated with the provided access token.
Fetch design metadata and access informationGets the metadata for a design.
Get design export job resultGet the outcome of a canva design export job; if done, receive download links for the design’s pages.
Initiate canva design autofill jobUpcoming brand template id updates require migration within 6 months.
Initiates canva design export jobCanva's new job feature exports designs in multiple formats using a design id, with provided download links.
List design pages with paginationPreview api for canva: subject to unannounced changes and not for public integrations.
List folder items by type with sortingLists the items in a folder, including each item's `type`.
List User DesignsProvides a summary of canva user designs, includes search filtering, and allows showing both self-created and shared designs with sorting options.
Move item to specified folderTransfers an item to a different folder by specifying both the destination folder's id and the item's id.
Remove folder and move contents to trashDeletes a folder by moving the user's content to trash and reassigning other users' content to their top-level projects.
Retrieve app public key setReturns the json web key set (public keys) of an app.
Retrieve a specific design commentThis preview api is subject to unannounced changes and can't be used in public integrations.
Retrieve asset metadata by idYou can retrieve the metadata of an asset by specifying its `assetid`.
Retrieve brand template dataset definitionCanva's brand template ids will change later this year, including a 6-month integration migration.
Retrieve canva enterprise brand template metadataUpcoming update will change brand template ids; integrations must migrate within 6 months.
Retrieve design autofill job statusApi users with canva enterprise membership can retrieve design autofill job results, potentially requiring multiple requests until a `success` or `failed` status is received.
Retrieve design import job statusGets the status and results of design import jobs created using the [create design import job api](https://www.
Retrieve folder details by idGets the name and other details of a folder using a folder's `folderid`.
RetrieveuserprofiledataCurrently, this returns the display name of the user account associated with the provided access token.
Revoke oauth tokensRevoke a refresh token to end its lineage and user consent, requiring re-authentication.
Update asset s name and tags by idYou can update the name and tags of an asset by specifying its `assetid`.
Update folder details by idUpdates a folder's details using its `folderid`.
Validate oauth token propertiesCheck an access token's validity and properties via introspection, requiring authentication.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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

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

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.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-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 is the core agent framework
  • python-dotenv loads environment variables

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

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Canva functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
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 Canva tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Canva
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['canva']
)

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

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "canva-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Canva MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Canva tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

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

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

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

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts 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 ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Canva and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['canva']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "canva-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Canva related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

Conclusion

You've successfully built a LangChain agent that can interact with Canva 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.

How to build Canva MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Canva MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Canva MCP with Claude Agent SDK

Google ADK

Google ADK

Use Canva MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Canva MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Canva MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Canva MCP with LlamaIndex

CrewAI

CrewAI

Use Canva MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Canva MCP?

With a standalone Canva MCP server, the agents and LLMs can only access a fixed set of Canva tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Canva and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LangChain?

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 Canva tools.

Can I manage the permissions and scopes for Canva while using Tool Router?

Yes, absolutely. You can configure which Canva 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.

How safe is my data with Composio Tool Router?

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 Canva data and credentials are handled as safely as possible.

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