Team with Knowledge Tools

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,
        stream_intermediate_steps=True,
        show_full_reasoning=True,
    )


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