How to integrate Firecrawl MCP with Autogen

Trusted by
AWS
Glean
Zoom
Airtable
GET STARTED FOR FREEGET A DEMO

30 min · no commitment · see it on your stack

Firecrawl logo
AutoGen logo
divider

Introduction

This guide walks you through connecting Firecrawl to AutoGen using the Composio tool router. By the end, you'll have a working Firecrawl agent that can extract all product prices from this e-commerce site, crawl competitor blogs for latest article summaries, map all subpages linked from homepage url through natural language commands.

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

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

Also integrate Firecrawl with

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

The Firecrawl MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Firecrawl account. It provides structured and secure access to automated web crawling, scraping, and data extraction, so your agent can perform actions like indexing sites, extracting structured content, mapping URLs, and searching the web on your behalf.

  • Automated web crawling and indexing: Let your agent launch and manage web crawl jobs to gather content or index entire websites efficiently.
  • Structured data extraction: Instruct your agent to extract targeted data from web pages using custom prompts or schemas, turning unstructured sites into actionable information.
  • URL mapping and discovery: Have the agent explore and map all URLs within a website, including options for subdomain inclusion, sitemap processing, or search-based discovery.
  • On-demand scraping and content retrieval: Enable your agent to scrape specific URLs, retrieve page content, and even extract structured JSON using LLM-powered methods.
  • Integrated web search and data collection: Task your agent with running web searches, scraping top result pages, and returning relevant details—all in one workflow.

Supported Tools & Triggers

Tools
Cancel an agent jobTool to cancel an in-progress agent job by its ID.
Batch scrape multiple URLsTool to scrape multiple URLs in batch with concurrent processing.
Cancel a batch scrape jobTool to cancel a running batch scrape job using its unique identifier.
Get batch scrape statusRetrieves the current status and results of a batch scrape job using the job ID.
Get errors from batch scrape jobTool to retrieve error details from a batch scrape job, including failed URLs and URLs blocked by robots.
Start a web crawlInitiates a Firecrawl web crawl from a given URL, applying various filtering and content extraction rules, and polls until the job is complete; ensure the URL is accessible and any regex patterns for paths are valid.
Cancel a crawl jobCancels an active or queued web crawl job using its ID; attempting to cancel completed, failed, or previously canceled jobs will not change their state.
Cancel a crawl jobTool to cancel a running crawl job by its ID.
Get crawl job statusTool to retrieve the status and results of a Firecrawl crawl job.
Get errors from a crawl jobTool to retrieve errors from a Firecrawl crawl job.
Get all active crawl jobsTool to retrieve all active crawl jobs for the authenticated team.
Preview crawl parametersPreview crawl parameters before starting a crawl by generating optimal configuration from natural language instructions.
Start a web crawl (v2) [NEW][NEW v2 API] Initiates a Firecrawl v2 web crawl with enhanced features over v1: natural language prompts for automatic crawler configuration, crawlEntireDomain for sibling/parent page discovery, better depth control with maxDiscoveryDepth, subdomain support, and full webhook configuration.
Get team credit usageTool to get current team credit usage information.
Get historical team credit usageTool to retrieve historical team credit usage on a monthly basis.
Extract structured dataExtracts structured data from web pages by initiating an extraction job and polling for completion; requires a natural language `prompt` or a JSON `schema` (one must be provided).
Get extract job statusTool to retrieve the status and results of a previously submitted extract job.
Get agent job statusTool to get the status and results of an agent job.
Get deep research statusRetrieves the status and results of a deep research job by its ID.
Get the status of a crawl jobRetrieves the current status, progress, and details of a web crawl job, using the job ID obtained when the crawl was initiated.
Generate LLMs.txt for a websiteInitiates an async job to generate an LLMs.
Get LLMs.txt generation job statusTool to get the status and results of an LLMs.
Map multiple URLsMaps a website by discovering URLs from a starting base URL, with options to customize the crawl via search query, subdomain inclusion, sitemap handling, and result limits; search effectiveness is site-dependent.
Get team queue statusTool to retrieve metrics about the team's scrape queue.
Scrape URLScrapes a publicly accessible URL, optionally performing pre-scrape browser actions or extracting structured JSON using an LLM, to retrieve content in specified formats.
SearchPerforms a web search for a query, scrapes content from the top search results using Firecrawl, and returns details in specified formats.
Start an agent jobTool to start an agent job for agentic web extraction with multi-page navigation and interaction capabilities.
Get team token usageTool to retrieve the current team's token usage and balance information for Firecrawl's Extract feature.
Get historical team token usageTool to retrieve historical team token usage on a monthly basis.

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

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Firecrawl account you can connect to Composio
  • Some basic familiarity with Autogen and Python async

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 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 Firecrawl via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

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 Firecrawl connections to use

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 Firecrawl session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["firecrawl"]
    )
    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 Firecrawl tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to

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

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 Firecrawl assistant agent with MCP tools
    agent = AssistantAgent(
        name="firecrawl_assistant",
        description="An AI assistant that helps with Firecrawl 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 Firecrawl tools from the workbench

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Firecrawl 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 Firecrawl 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 Firecrawl 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 Firecrawl session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["firecrawl"]
    )
    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 Firecrawl assistant agent with MCP tools
        agent = AssistantAgent(
            name="firecrawl_assistant",
            description="An AI assistant that helps with Firecrawl 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 Firecrawl 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 Firecrawl 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 Firecrawl, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Firecrawl MCP Agent with another framework

ChatGPT logo

ChatGPT

Use Firecrawl MCP with ChatGPT

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Firecrawl MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Firecrawl MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Firecrawl MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Firecrawl MCP with Claude Cowork

Codex logo

Codex

Use Firecrawl MCP with Codex

Cursor logo

Cursor

Use Firecrawl MCP with Cursor

OpenClaw logo

OpenClaw

Use Firecrawl MCP with OpenClaw

Hermes logo

Hermes

Use Firecrawl MCP with Hermes

CLI logo

CLI

Use Firecrawl MCP with CLI

Google ADK logo

Google ADK

Use Firecrawl MCP with Google ADK

LangChain logo

LangChain

Use Firecrawl MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Firecrawl MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Firecrawl MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Firecrawl MCP with LlamaIndex

CrewAI logo

CrewAI

Use Firecrawl MCP with CrewAI

OpenCode logo

OpenCode

Use Firecrawl MCP with OpenCode

VS Code logo

VS Code

Use Firecrawl MCP with VS Code

FAQ

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

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

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

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

Used by agents from

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

Get started for freeGet a demo↗