How to integrate Webflow MCP with LangChain

BOOK A CALLGET STARTED FOR FREE
Framework Integration Gradient
Webflow Logo
LangChain Logo
divider

Introduction

This guide walks you through connecting Webflow to LangChain using the Composio tool router. By the end, you'll have a working Webflow agent that can add a new blog post to my site, list all products in my store collection, get details for order #12345, delete a collection item by its id through natural language commands.

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

The Webflow MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Webflow account. It provides structured and secure access to your Webflow sites, collections, and e-commerce data, so your agent can perform actions like managing content, updating inventory, handling orders, and retrieving detailed site information on your behalf.

  • Effortless content management: Ask your agent to create, update, or delete collection items—perfect for adding new blog posts, products, or dynamic content without manual entry.
  • Comprehensive site and collection insights: Retrieve up-to-date details about your Webflow sites and collections, including schema, settings, and structure, to power content-aware automations.
  • Inventory and order automation: Have your agent check inventory levels, update stock, and mark orders as fulfilled, streamlining your Webflow e-commerce operations.
  • Bulk data handling: Let your agent list all items in a collection or all collections on a site, enabling smart reporting, audits, or content migrations with a simple prompt.
  • Seamless integration with creative workflows: Enable real-time, AI-driven updates to your site content, inventory, or orders in response to team or customer needs—no coding required.

Supported Tools & Triggers

Tools
Create Webflow Collection ItemThis tool creates a new item in a specified webflow collection.
Delete Webflow Collection ItemThis tool allows you to delete a specific item from a collection in webflow.
Fulfill OrderThis tool allows you to mark an order as fulfilled in webflow's e-commerce system.
Get Collection DetailsRetrieves a specific collection by its id from a webflow site.
Get Collection ItemThis tool retrieves a specific item from a webflow collection.
Get Item InventoryThis tool retrieves the current inventory levels for a specific item in a webflow collection.
Get Order DetailsThis tool retrieves detailed information about a specific order in webflow.
Get Webflow Site InformationThis tool retrieves detailed information about a specific webflow site.
List Collection ItemsThis tool retrieves a list of items from a specified collection in webflow.
List Webflow CollectionsThis tool retrieves a list of all collections for a given webflow site.
List Form SubmissionsThis tool retrieves a list of form submissions for a specific webflow site.
List Webflow OrdersThis tool retrieves a list of all orders for a specified webflow site using the get /sites/{site id}/orders endpoint.
List PagesThis tool retrieves a list of all pages for a specified webflow site.
List Webflow SitesThis tool retrieves a list of all webflow sites accessible to the authenticated user.
Publish Webflow SiteThis tool publishes a webflow site, making all staged changes live.
Refund OrderThis tool allows you to refund a webflow e-commerce order.
Unfulfill OrderThis tool allows you to mark a previously fulfilled order as unfulfilled in webflow.
Update Webflow Collection ItemThis tool allows updating an existing item in a webflow collection using the patch /collections/{collection id}/items/{item id} endpoint.
Update Item InventoryThis tool allows you to update the inventory levels of a specific sku item in your webflow e-commerce site by either setting the inventory quantity directly or updating it incrementally.
Update OrderThis tool allows updating specific fields of an existing order in webflow.
Upload Asset to WebflowThis tool allows users to upload assets (files, images, etc.

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 Webflow 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 Webflow tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

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

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "webflow-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 Webflow MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Webflow 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 Webflow 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 Webflow 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=['webflow']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "webflow-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 Webflow 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 Webflow 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 Webflow MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Webflow MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Webflow MCP with Claude Agent SDK

Google ADK

Google ADK

Use Webflow MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Webflow MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Webflow MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Webflow MCP with LlamaIndex

CrewAI

CrewAI

Use Webflow MCP with CrewAI

FAQ

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

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

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

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

Used by agents from

Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai
Context
ASU
Letta
glean
HubSpot
Agent.ai
Altera
DataStax
Entelligence
Rolai

Never worry about agent reliability

We handle tool reliability, observability, and security so you never have to second-guess an agent action.

Book a call↗Get started for free