AutoGen

Use Firecrawl as a tool inside Microsoft AutoGen multi-agent conversations.

Integrate Firecrawl with Microsoft AutoGen to give multi-agent conversations live web search, scrape, and crawl tools.

Setup

pip install -U "autogen-agentchat" "autogen-ext[openai]" firecrawl-py

Set your keys:

export FIRECRAWL_API_KEY=fc-YOUR-API-KEY
export OPENAI_API_KEY=sk-YOUR-OPENAI-KEY

Firecrawl as an AutoGen Tool

This example wraps Firecrawl's scrape and search as AutoGen function tools, then lets a single AssistantAgent use them to answer a question.

import asyncio
import os
from firecrawl import FirecrawlApp
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient

firecrawl = FirecrawlApp(api_key=os.environ["FIRECRAWL_API_KEY"])

def scrape_url(url: str) -> str:
  """Scrape a URL and return clean markdown."""
  result = firecrawl.scrape(url, formats=["markdown"])
  return result.markdown or ""

def web_search(query: str, limit: int = 5) -> list[dict]:
  """Search the web and return the top results."""
  result = firecrawl.search(query, limit=limit)
  return [
      {"title": r.title, "url": r.url, "snippet": r.description}
      for r in result.web or []
  ]

async def main() -> None:
  model = OpenAIChatCompletionClient(model="gpt-4o-mini")

  researcher = AssistantAgent(
      name="researcher",
      model_client=model,
      tools=[scrape_url, web_search],
      system_message=(
          "You are a web researcher. Use web_search to find candidate sources, "
          "then scrape_url to read the most relevant ones. Cite URLs in your answer."
      ),
  )

  await Console(
      researcher.run_stream(
          task="What does Firecrawl's /agent endpoint do? Cite the docs."
      )
  )

if __name__ == "__main__":
  asyncio.run(main())

Run it:

python researcher.py

Multi-Agent: Researcher + Writer

Hand Firecrawl output from a researcher agent to a writer agent in a round-robin team.

import asyncio
import os
from firecrawl import FirecrawlApp
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import MaxMessageTermination
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient

firecrawl = FirecrawlApp(api_key=os.environ["FIRECRAWL_API_KEY"])

def scrape_url(url: str) -> str:
  result = firecrawl.scrape(url, formats=["markdown"])
  return result.markdown or ""

def web_search(query: str, limit: int = 5) -> list[dict]:
  result = firecrawl.search(query, limit=limit)
  return [
      {"title": r.title, "url": r.url, "snippet": r.description}
      for r in result.web or []
  ]

async def main() -> None:
  model = OpenAIChatCompletionClient(model="gpt-4o-mini")

  researcher = AssistantAgent(
      name="researcher",
      model_client=model,
      tools=[scrape_url, web_search],
      system_message="Gather sources with web_search + scrape_url. Reply with bullet-point findings and URLs.",
  )

  writer = AssistantAgent(
      name="writer",
      model_client=model,
      system_message="Turn the researcher's findings into a 200-word briefing with inline citations.",
  )

  team = RoundRobinGroupChat(
      [researcher, writer],
      termination_condition=MaxMessageTermination(max_messages=6),
  )

  await Console(team.run_stream(task="Write a briefing on Firecrawl's crawl endpoint."))

if __name__ == "__main__":
  asyncio.run(main())

Notes

• Firecrawl's Python SDK is synchronous; AutoGen will call your wrappers inside its event loop without issues for small workloads. For heavy concurrent scraping, move calls off the main thread or use batch scrape.
• Replace OpenAIChatCompletionClient with any AutoGen-supported model client (Azure OpenAI, Anthropic via autogen-ext, Ollama, etc.). Firecrawl is model-agnostic.
• See the AutoGen docs for agent patterns beyond round-robin (selector, swarm, nested teams).