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This is a team reasoning example with knowledge tools.
Enabling the knowledge option on the team leader helps optimize delegation and enhances multi-agent collaboration by selectively invoking deeper knowledge when required.
1

Create a Python file

touch knowledge_tool_team.py
2

Add the following code to your Python file

knowledge_tool_team.py
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.team.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.knowledge import KnowledgeTools
from agno.vectordb.lancedb import LanceDb, SearchType

agno_docs = Knowledge(
    # Use LanceDB as the vector database and store embeddings in the `agno_docs` table
    vector_db=LanceDb(
        uri="tmp/lancedb",
        table_name="agno_docs",
        search_type=SearchType.hybrid,
    ),
)
# Add content to the knowledge
agno_docs.add_content(url="https://www.paulgraham.com/read.html")

knowledge_tools = KnowledgeTools(
    knowledge=agno_docs,
    think=True,
    search=True,
    analyze=True,
    add_few_shot=True,
)

web_agent = Agent(
    name="Web Search Agent",
    role="Handle web search requests",
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[DuckDuckGoTools()],
    instructions="Always include sources",
    add_datetime_to_context=True,
)

finance_agent = Agent(
    name="Finance Agent",
    role="Handle financial data requests",
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[DuckDuckGoTools(search=True)],
    add_datetime_to_context=True,
)

team_leader = Team(
    name="Reasoning Finance Team",
    model=OpenAIChat(id="gpt-5-mini"),
    members=[
        web_agent,
        finance_agent,
    ],
    tools=[knowledge_tools],
    instructions=[
        "Only output the final answer, no other text.",
        "Use tables to display data",
    ],
    markdown=True,
    show_members_responses=True,
    add_datetime_to_context=True,
)


def run_team(task: str):
    team_leader.print_response(
        task,
        stream=True,
        show_full_reasoning=True,
    )


if __name__ == "__main__":
    run_team("What does Paul Graham talk about the need to read in this essay?")
3

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
4

Install libraries

pip install -U openai agno ddgs
5

Export your OpenAI API key

  export OPENAI_API_KEY="your_openai_api_key_here"
6

Run Agent

python knowledge_tool_team.py