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.
hn_team.py
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from phi.agent import Agentfrom phi.tools.hackernews import HackerNewsfrom phi.tools.duckduckgo import DuckDuckGofrom phi.tools.newspaper4k import Newspaper4khn_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)
Add a name and role parameter to the member Agents.
Create a Team Leader that can delegate tasks to team-members.
Use your Agent team just like you would use a regular Agent.
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.