Team with Knowledge Tools

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. 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 OpenAIResponses
   from agno.team.team import Team
   from agno.tools.hackernews import HackerNewsTools
   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.insert(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=OpenAIResponses(id="gpt-5.2"),
       tools=[HackerNewsTools()],
       instructions="Always include sources",
       add_datetime_to_context=True,
   )
   
   finance_agent = Agent(
       name="Finance Agent",
       role="Handle financial data requests",
       model=OpenAIResponses(id="gpt-5.2"),
       tools=[HackerNewsTools()],
       add_datetime_to_context=True,
   )
   
   team_leader = Team(
       name="Reasoning Finance Team",
       model=OpenAIResponses(id="gpt-5.2"),
       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?")
   
2. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   

   uv venv --python 3.12
   .venv\Scripts\activate
   
3. Install dependencies

   uv pip install -U openai agno ddgs
   
4. Export your OpenAI API key

   Mac/Linux

     export OPENAI_API_KEY="your_openai_api_key_here"
   

     $Env:OPENAI_API_KEY="your_openai_api_key_here"
   
5. Run Agent

   python knowledge_tool_team.py