> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Team with Knowledge Tools

This is a team reasoning example with knowledge tools.

<Tip>
  Enabling the reasoning option on the team leader helps optimize delegation and enhances multi-agent collaboration by selectively invoking deeper reasoning when required.
</Tip>

## Code

```python cookbook/reasoning/teams/knowledge_tool_team.py theme={null}
from textwrap import dedent

from agno.agent import Agent
from agno.knowledge.url import UrlKnowledge
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 = UrlKnowledge(
    urls=["https://www.paulgraham.com/read.html"],
    # 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,
    ),
)

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-4o-mini"),
    tools=[DuckDuckGoTools()],
    instructions="Always include sources",
    add_datetime_to_instructions=True,
)

research_agent = Agent(
    name="Research Agent",
    role="Handle research and news requests",
    model=OpenAIChat(id="gpt-4o-mini"),
    tools=[
        DuckDuckGoTools(search=True, news=True)
    ],
    add_datetime_to_instructions=True,
)

team_leader = Team(
    name="Reasoning Research Team",
    mode="coordinate",
    model=OpenAIChat(id="gpt-4o"),
    members=[
        web_agent,
        research_agent,
    ],
    tools=[knowledge_tools],
    instructions=[
        "Only output the final answer, no other text.",
        "Use tables to display data",
    ],
    markdown=True,
    show_members_responses=True,
    enable_agentic_context=True,
    add_datetime_to_instructions=True,
    success_criteria="The team has successfully completed the task.",
    debug_mode=True,
)


def run_team(task: str):
    # Comment out after first run
    agno_docs.load(recreate=True)
    team_leader.print_response(
        task,
        stream=True,
        stream_intermediate_steps=True,
        show_full_reasoning=True,
    )


if __name__ == "__main__":
    run_team("What insights can you provide about reading and learning based on the knowledge and recent educational trends?")
```

## Usage

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Set your API key">
    ```bash theme={null}
    export OPENAI_API_KEY=xxx
    ```
  </Step>

  <Step title="Install libraries">
    ```bash theme={null}
    pip install -U openai agno
    ```
  </Step>

  <Step title="Run Example">
    <CodeGroup>
      ```bash Mac theme={null}
      python cookbook/reasoning/teams/knowledge_tool_team.py
      ```

      ```bash Windows theme={null}
      python cookbook/reasoning/teams/knowledge_tool_team.py
      ```
    </CodeGroup>
  </Step>
</Steps>