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A Team internally has a team-leader agent that delegates tasks to the members. When you call run or arun on a team, the team leader agent uses a model to determine which member to delegate the task to. The basic flow is:
  1. The team receives user input
  2. A Team Leader analyzes the input and decides how to break it down into subtasks
  3. The Team Leader delegates specific tasks to appropriate team members
  4. Team members complete their assigned tasks and return their results
  5. The Team Leader synthesizes all outputs into a final, cohesive response
Below are some examples of how to change the behaviour of how tasks are delegated to the members.

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Determine input for members

When a team is run, by default the team leader will determine the “task” to give a specific member. This then becomes the input when that member is run. If you set determine_input_for_members to False, the team leader will send the user-provided input directly to the member agent(s). The team leader still determines the appropriate member to delegate the task to.
This feature is particularly useful when you have specialized agents with distinct expertise areas and want to automatically direct queries to the right specialist.
In the example below, we want to send stuctured pydantic input directly to the member agent. We don’t want the team leader to ingest this input and determine a task to give to the member agent.
1

Create Team

Create a file determine_input_for_members.py
determine_input_for_members.py
from typing import List

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.team.team import Team
from agno.tools.hackernews import HackerNewsTools
from pydantic import BaseModel, Field


class ResearchTopic(BaseModel):
    """Structured research topic with specific requirements."""

    topic: str = Field(description="The main research topic")
    focus_areas: List[str] = Field(description="Specific areas to focus on")
    target_audience: str = Field(description="Who this research is for")
    sources_required: int = Field(description="Number of sources needed", default=5)


# Create specialized Hacker News research agent
hackernews_agent = Agent(
    name="Hackernews Agent",
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[HackerNewsTools()],
    role="Extract key insights and content from Hackernews posts",
    instructions=[
        "Search Hacker News for relevant articles and discussions",
        "Extract key insights and summarize findings",
        "Focus on high-quality, well-discussed posts",
    ],
)

# Create collaborative research team
team = Team(
    name="Hackernews Research Team",
    model=OpenAIChat(id="gpt-5-mini"),
    members=[hackernews_agent],
    determine_input_for_members=False,  # The member gets the input directly, without the team leader synthesizing it
    instructions=[
        "Conduct thorough research based on the structured input",
        "Address all focus areas mentioned in the research topic",
        "Tailor the research to the specified target audience",
        "Provide the requested number of sources",
    ],
    show_members_responses=True,
)

# Use Pydantic model as structured input
research_request = ResearchTopic(
    topic="AI Agent Frameworks",
    focus_areas=["AI Agents", "Framework Design", "Developer Tools", "Open Source"],
    target_audience="Software Developers and AI Engineers",
    sources_required=7,
)
# Execute research with structured input
team.print_response(input=research_request)
2

Run the team

Install libraries
pip install openai agno
Run the team
python determine_input_for_members.py

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Respond directly

During normal team execution, the team leader will process the responses from the members and return a single response to the user. If instead you want to return the response of members directly, you can set respond_directly to True.
It can make sense to use this feature in combination with determine_input_for_members=False.
1

Create Multi Language Team

Create a file multi_language_team.py
multi_language_team.py
from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.models.deepseek import DeepSeek
from agno.models.openai import OpenAIChat
from agno.team.team import Team

english_agent = Agent(
    name="English Agent",
    role="You can only answer in English",
    model=OpenAIChat(id="gpt-4.5-preview"),
    instructions=[
        "You must only respond in English",
    ],
)

japanese_agent = Agent(
    name="Japanese Agent",
    role="You can only answer in Japanese",
    model=DeepSeek(id="deepseek-chat"),
    instructions=[
        "You must only respond in Japanese",
    ],
)
chinese_agent = Agent(
    name="Chinese Agent",
    role="You can only answer in Chinese",
    model=DeepSeek(id="deepseek-chat"),
    instructions=[
        "You must only respond in Chinese",
    ],
)
spanish_agent = Agent(
    name="Spanish Agent",
    role="You can only answer in Spanish",
    model=OpenAIChat(id="gpt-4.5-preview"),
    instructions=[
        "You must only respond in Spanish",
    ],
)

german_agent = Agent(
    name="German Agent",
    role="You can only answer in German",
    model=Claude("claude-3-5-sonnet-20241022"),
    instructions=[
        "You must only respond in German",
    ],
)
multi_language_team = Team(
    name="Multi Language Team",
    model=OpenAIChat("gpt-4.5-preview"),
    respond_directly=True,
    members=[
        english_agent,
        spanish_agent,
        japanese_agent,
        german_agent,
        chinese_agent,
    ],
    markdown=True,
    instructions=[
        "You are a language router that directs questions to the appropriate language agent.",
        "If the user asks in a language whose agent is not a team member, respond in English with:",
        "'I can only answer in the following languages: English, Spanish, Japanese, and German. Please ask your question in one of these languages.'",
        "Always check the language of the user's input before routing to an agent.",
        "For unsupported languages like Italian, respond in English with the above message.",
    ],
    show_members_responses=True,
)


# Ask "How are you?" in all supported languages
multi_language_team.print_response(
    "How are you?", stream=True  # English
)

multi_language_team.print_response(
    "你好吗？", stream=True  # Chinese
)

multi_language_team.print_response(
    "お元気ですか?", stream=True  # Japanese
)
2

Run the team

Install libraries
pip install openai agno
Run the team
python multi_language_team.py
This is not compatible with delegate_task_to_all_members.

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Delegate task to all members

When you set delegate_task_to_all_members to True, the team leader will delegate the task to all members simultaneously, instead of one by one. When running async (using arun) members will run concurrently.
1

Create a collaborative team

Create a file discussion_team.py
discussion_team.py
import asyncio
from textwrap import dedent

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.team.team import Team
from agno.tools.arxiv import ArxivTools
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.googlesearch import GoogleSearchTools
from agno.tools.hackernews import HackerNewsTools

reddit_researcher = Agent(
    name="Reddit Researcher",
    role="Research a topic on Reddit",
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[DuckDuckGoTools()],
    add_name_to_context=True,
    instructions=dedent("""
    You are a Reddit researcher.
    You will be given a topic to research on Reddit.
    You will need to find the most relevant posts on Reddit.
    """),
)

hackernews_researcher = Agent(
    name="HackerNews Researcher",
    model=OpenAIChat("gpt-5-mini"),
    role="Research a topic on HackerNews.",
    tools=[HackerNewsTools()],
    add_name_to_context=True,
    instructions=dedent("""
    You are a HackerNews researcher.
    You will be given a topic to research on HackerNews.
    You will need to find the most relevant posts on HackerNews.
    """),
)

academic_paper_researcher = Agent(
    name="Academic Paper Researcher",
    model=OpenAIChat("gpt-5-mini"),
    role="Research academic papers and scholarly content",
    tools=[GoogleSearchTools(), ArxivTools()],
    add_name_to_context=True,
    instructions=dedent("""
    You are a academic paper researcher.
    You will be given a topic to research in academic literature.
    You will need to find relevant scholarly articles, papers, and academic discussions.
    Focus on peer-reviewed content and citations from reputable sources.
    Provide brief summaries of key findings and methodologies.
    """),
)

twitter_researcher = Agent(
    name="Twitter Researcher",
    model=OpenAIChat("gpt-5-mini"),
    role="Research trending discussions and real-time updates",
    tools=[DuckDuckGoTools()],
    add_name_to_context=True,
    instructions=dedent("""
    You are a Twitter/X researcher.
    You will be given a topic to research on Twitter/X.
    You will need to find trending discussions, influential voices, and real-time updates.
    Focus on verified accounts and credible sources when possible.
    Track relevant hashtags and ongoing conversations.
    """),
)


agent_team = Team(
    name="Discussion Team",
    model=OpenAIChat("gpt-5-mini"),
    members=[
        reddit_researcher,
        hackernews_researcher,
        academic_paper_researcher,
        twitter_researcher,
    ],
    instructions=[
        "You are a discussion master.",
        "You have to stop the discussion when you think the team has reached a consensus.",
    ],
    delegate_task_to_all_members=True,
    markdown=True,
    show_members_responses=True,
)

if __name__ == "__main__":
    asyncio.run(
        agent_team.aprint_response(
            message="Start the discussion on the topic: 'What is the best way to learn to code?'",
            stream=True,
            stream_intermediate_steps=True,
        )
    )
2

Run the team

Install libraries
pip install openai ddgs arxiv pypdf googlesearch-python pycountry
Run the team
python discussion_team.py

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