How to integrate Webflow MCP with CrewAI

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Introduction

This guide walks you through connecting Webflow to CrewAI 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 CrewAI 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 a Composio API key and configure your Webflow connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Webflow
  • Build a conversational loop where your agent can execute Webflow operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Webflow connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

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

bash
pip install composio crewai crewai-tools python-dotenv
What's happening:
  • composio connects your agent to Webflow via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools includes MCP helpers
  • python-dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_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 with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Webflow MCP URL

Create a Composio Tool Router session for Webflow

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["webflow"],
)
url = session.mcp.url
What's happening:
  • You create a Webflow only session through Composio
  • Composio returns an MCP HTTP URL that exposes Webflow tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Webflow Assistant",
    goal="Help users interact with Webflow through natural language commands",
    backstory=(
        "You are an expert assistant with access to Webflow tools. "
        "You can perform various Webflow operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Webflow MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Webflow operations.\n")

conversation_context = ""

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

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

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Webflow related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_agent],
        tasks=[task],
        verbose=False,
    )

    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's happening:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_webflow_agent.py

Complete Code

Here's the complete code to get you started with Webflow and CrewAI:

python
# file: crewai_webflow_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

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

    # Configure LLM
    llm = LLM(
        model="gpt-5-mini",
        api_key=os.getenv("OPENAI_API_KEY"),
    )

    # Create Webflow assistant agent
    toolkit_agent = Agent(
        role="Webflow Assistant",
        goal="Help users interact with Webflow through natural language commands",
        backstory=(
            "You are an expert assistant with access to Webflow tools. "
            "You can perform various Webflow operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Webflow operations.\n")

    conversation_context = ""

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

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

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Webflow related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Webflow through Composio's Tool Router. The agent can perform Webflow operations through natural language commands. Next steps:
  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations

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

LangChain

LangChain

Use Webflow MCP with LangChain

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

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 CrewAI?

Yes, you can. CrewAI 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.

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