# How to integrate Klazify MCP with Autogen

```json
{
  "title": "How to integrate Klazify MCP with Autogen",
  "toolkit": "Klazify",
  "toolkit_slug": "klazify",
  "framework": "AutoGen",
  "framework_slug": "autogen",
  "url": "https://composio.dev/toolkits/klazify/framework/autogen",
  "markdown_url": "https://composio.dev/toolkits/klazify/framework/autogen.md",
  "updated_at": "2026-03-29T06:39:37.234Z"
}
```

## Introduction

This guide walks you through connecting Klazify to AutoGen using the Composio tool router. By the end, you'll have a working Klazify agent that can categorize https://example.com and get its logo, extract social media links for a company website, identify technology stack of a given domain through natural language commands.
This guide will help you understand how to give your AutoGen agent real control over a Klazify account through Composio's Klazify MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Klazify with

- [OpenAI Agents SDK](https://composio.dev/toolkits/klazify/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/klazify/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/klazify/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/klazify/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/klazify/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/klazify/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/klazify/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/klazify/framework/cli)
- [Google ADK](https://composio.dev/toolkits/klazify/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/klazify/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/klazify/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/klazify/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/klazify/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/klazify/framework/crew-ai)

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

The Klazify MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Klazify account. It provides structured and secure access so your agent can perform Klazify operations on your behalf.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `KLAZIFY_CATEGORIZE_URL` | Categorize URL | Tool to retrieve IAB and Klazify category classifications for a specified domain or URL with confidence scores. Use when you need to categorize websites into 620+ IAB V3 categories using machine learning, or obtain detailed domain information including company data and social media links. |
| `KLAZIFY_GET_COMPANY_DATA` | Get Company Data | Tool to retrieve comprehensive company information for a specified domain including business data, location, employee count, revenue, funding, and industry classifications. Use when you need detailed company profile information from a domain URL. |
| `KLAZIFY_GET_DOMAIN_EXPIRATION` | Get Domain Expiration | Tool to retrieve domain registration and expiration information for a specified domain. Use when you need to check domain age, registration date, expiration date, or days until expiration. |
| `KLAZIFY_GET_DOMAIN_LOGO` | Get Domain Logo | Tool to obtain the logo URL associated with a given domain. Use when you need to retrieve a company's logo from their website URL. |
| `KLAZIFY_GET_SIMILAR_COMPANIES` | Get Similar Companies | Tool to identify companies similar to the specified domain by analyzing category and target market. Use when you need to find competitors or similar businesses to a given domain. |
| `KLAZIFY_GET_SOCIAL_MEDIA_LINKS` | Get Social Media Links | Tool to retrieve the list of social media links for a given domain. Use when you need to extract social media profile URLs across multiple platforms (Facebook, Twitter, Instagram, LinkedIn, YouTube, Medium, GitHub, Pinterest). |
| `KLAZIFY_GET_TECH_STACK` | Get Tech Stack | Tool to retrieve the technological stack utilized by a website including frameworks, platforms, and services. Use when you need to identify what technologies a domain uses for competitive analysis, lead generation, or market research. |
| `KLAZIFY_REAL_TIME_CATEGORIZATION` | Real Time Categorization | Tool to perform real-time website categorization with immediate AI-powered analysis and classification. Use when you need instant domain categorization into 621+ IAB V3 categories with confidence scores, along with company data, social media profiles, and technology stack information. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Klazify MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Klazify. Instead of manually wiring Klazify APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

You will need:
- A Composio API key
- An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
- A Klazify account you can connect to Composio
- Some basic familiarity with Autogen and Python async

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

Install Composio, Autogen extensions, and dotenv.
What's happening:
- composio connects your agent to Klazify via MCP
- autogen-agentchat provides the AssistantAgent class
- autogen-ext-openai provides the OpenAI model client
- autogen-ext-tools provides MCP workbench support
```bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools
```

### 3. Set up environment variables

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 Klazify connections to use
```bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com
```

### 4. Import dependencies and create Tool Router session

What's happening:
- load_dotenv() reads your .env file
- Composio(api_key=...) initializes the SDK
- create(...) creates a Tool Router session that exposes Klazify tools
- session.mcp.url is the MCP endpoint that Autogen will connect to
```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 Klazify session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["klazify"]
    )
    url = session.mcp.url
```

### 5. Configure MCP parameters for Autogen

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
```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")}
)
```

### 6. Create the model client and agent

What's happening:
- OpenAIChatCompletionClient wraps the OpenAI model for Autogen
- McpWorkbench connects the agent to the MCP tools
- AssistantAgent is configured with the Klazify tools from the workbench
```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 Klazify assistant agent with MCP tools
    agent = AssistantAgent(
        name="klazify_assistant",
        description="An AI assistant that helps with Klazify operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )
```

### 7. Run the interactive chat loop

What's happening:
- The script prompts you in a loop with You:
- Autogen passes your input to the model, which decides which Klazify 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
```python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Klazify 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")
```

## Complete Code

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

## How to build Klazify MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/klazify/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/klazify/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/klazify/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/klazify/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/klazify/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/klazify/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/klazify/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/klazify/framework/cli)
- [Google ADK](https://composio.dev/toolkits/klazify/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/klazify/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/klazify/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/klazify/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/klazify/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/klazify/framework/crew-ai)

## Related Toolkits

- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources.
- [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.
- [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research.
- [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows.
- [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research.
- [Reddit](https://composio.dev/toolkits/reddit) - Reddit is a social news platform with thriving user-driven communities (subreddits). It's the go-to place for discussion, content sharing, and viral marketing.
- [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.
- [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data.
- [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds.
- [Facebook](https://composio.dev/toolkits/facebook) - Facebook is a social media and advertising platform for businesses and creators. It helps you connect, share, and manage content across your public Facebook Pages.
- [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.
- [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights.
- [Linkedin](https://composio.dev/toolkits/linkedin) - LinkedIn is a professional networking platform for connecting, sharing content, and engaging with business opportunities. It's the go-to place for building your professional brand and unlocking new career connections.
- [Active campaign](https://composio.dev/toolkits/active_campaign) - ActiveCampaign is a marketing automation and CRM platform for managing email campaigns, sales pipelines, and customer segmentation. It helps businesses engage customers and drive growth through smart automation and targeted outreach.
- [ActiveTrail](https://composio.dev/toolkits/active_trail) - ActiveTrail is a user-friendly email marketing and automation platform. It helps you reach subscribers and automate campaigns with ease.
- [Ahrefs](https://composio.dev/toolkits/ahrefs) - Ahrefs is an SEO and marketing platform for site audits, keyword research, and competitor insights. It helps you improve search rankings and drive organic traffic.
- [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows.
- [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application.
- [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Klazify MCP?

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

### Can I use Tool Router MCP with Autogen?

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 Klazify tools.

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

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

---
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