How to integrate CaptureKit MCP with Autogen

This guide walks you through connecting CaptureKit to AutoGen using the Composio tool router. By the end, you'll have a working CaptureKit agent that can capture webpages, analyze pages, extract page content, fetch html, and check your usage through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a CaptureKit account through Composio's CaptureKit MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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CaptureKit is a web capture API for screenshots, structured content extraction, crawling, and AI website analysis. Use it to turn live webpages into clean visual and data outputs without building scrapers.

5 Tools

Introduction

This guide walks you through connecting CaptureKit to AutoGen using the Composio tool router. By the end, you'll have a working CaptureKit agent that can capture webpages, analyze pages, extract page content, fetch html, and check your usage through natural language commands.

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

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

Also integrate CaptureKit 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 CaptureKit
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call CaptureKit tools
  • Run a live chat loop where you ask the agent to perform CaptureKit 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 CaptureKit MCP server, and what's possible with it?

The CaptureKit MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your CaptureKit account. It provides structured and secure access to webpage capture, content extraction, website analysis, raw HTML, and account usage, so your agent can capture pages, extract web content, analyze websites, fetch HTML, and monitor usage on your behalf.

  • Webpage screenshots and PDFs: Have your agent capture any webpage as an image or PDF for visual reviews, records, or reports.
  • Structured content extraction: Let the agent extract page metadata, links, rendered HTML, Markdown, sitemap data, or cleaned main content.
  • AI website analysis: Direct your agent to summarize a webpage and identify its value proposition, features, categories, use cases, audience, or custom details.
  • Fast HTML retrieval: Instruct your agent to fetch raw webpage HTML for scraping, audits, or change monitoring without browser rendering.
  • Usage and quota checks: Have your agent check your current allowance, usage, API key status, quotas, and effective rate limits.

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 CaptureKit 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 CaptureKit 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 CaptureKit 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 CaptureKit session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["capturekit"]
    )
    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 CaptureKit 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 CaptureKit assistant agent with MCP tools
    agent = AssistantAgent(
        name="capturekit_assistant",
        description="An AI assistant that helps with CaptureKit 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 CaptureKit 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 CaptureKit 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 CaptureKit 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 CaptureKit 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 CaptureKit session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["capturekit"]
    )
    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 CaptureKit assistant agent with MCP tools
        agent = AssistantAgent(
            name="capturekit_assistant",
            description="An AI assistant that helps with CaptureKit 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 CaptureKit 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 CaptureKit 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 CaptureKit, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every CaptureKit action and event your agent gets out of the box.

Analyze Webpage

Analyze a webpage with AI to identify its summary, value proposition, features, categories, use cases, audience, and optional custom fields.

Capture Webpage

Capture a webpage as an image or PDF and return a downloadable file reference.

Extract Webpage Content

Extract webpage metadata and links, optionally including rendered HTML, Markdown, sitemap data, or cleaned main content.

Fetch Page HTML

Fetch a webpage's raw HTML without browser rendering for fast scraping, audits, or monitoring.

Get Usage

Return the connected workspace's current CaptureKit allowance, usage, API-key status, quotas, and effective rate limits.

FRAMEWORKS

How to build CaptureKit MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use CaptureKit MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use CaptureKit MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use CaptureKit MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use CaptureKit MCP with Claude Cowork

Codex logo

Codex

Use CaptureKit MCP with Codex

OpenClaw logo

OpenClaw

Use CaptureKit MCP with OpenClaw

Hermes logo

Hermes

Use CaptureKit MCP with Hermes

CLI logo

CLI

Use CaptureKit MCP with CLI

Google ADK logo

Google ADK

Use CaptureKit MCP with Google ADK

LangChain logo

LangChain

Use CaptureKit MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use CaptureKit MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use CaptureKit MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use CaptureKit MCP with LlamaIndex

CrewAI logo

CrewAI

Use CaptureKit MCP with CrewAI

FAQ

Frequently asked questions

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

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

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