We can combine multiple Agents to form a team and tackle tasks as a cohesive unit. Here’s a simple example that uses a team of agents to write an article about the top stories on hackernews.
from phi.agent import Agent
from phi.tools.hackernews import HackerNews
from phi.tools.duckduckgo import DuckDuckGo
from phi.tools.newspaper4k import Newspaper4k
hn_researcher = Agent(
name="HackerNews Researcher",
role="Gets top stories from hackernews.",
tools=[HackerNews()],
)
web_searcher = Agent(
name="Web Searcher",
role="Searches the web for information on a topic",
tools=[DuckDuckGo()],
add_datetime_to_instructions=True,
)
article_reader = Agent(
name="Article Reader",
role="Reads articles from URLs.",
tools=[Newspaper4k()],
)
hn_team = Agent(
name="Hackernews Team",
team=[hn_researcher, web_searcher, article_reader],
instructions=[
"First, search hackernews for what the user is asking about.",
"Then, ask the article reader to read the links for the stories to get more information.",
"Important: you must provide the article reader with the links to read.",
"Then, ask the web searcher to search for each story to get more information.",
"Finally, provide a thoughtful and engaging summary.",
],
show_tool_calls=True,
markdown=True,
)
hn_team.print_response("Write an article about the top 2 stories on hackernews", stream=True)
Run the script to see the output.
pip install -U openai duckduckgo-search newspaper4k lxml_html_clean phidata
python hn_team.py
Open-ended Agentic teams are great to play with, but are not reliable for real-world problems that require high reliability.
They need constant oversight and can get confused on very complex tasks. This drawback should improve as models get better (eagerly waiting for gpt-5o).
In our experience, Agent teams work best for simple tasks that require a small number of steps. We highly recommend using Workflows for production applications.
