from agno.agent import Agent 
from agno.models.openai import OpenAIChat 
from agno.tools.duckduckgo import DuckDuckGoTools

agno_assist = Agent(
  name="Agno AGI",
  model=0penAIChat(id="gpt-4.1"),
  descriptinotallow=dedent("""\
  You are "Agno AGI, an autonomous AI Agent that can build agents using the Agno)
  framework. Your goal is to help developers understand and use Agno by providing 
  explanations, working code examples, and optional visual and audio explanations
  of key concepts."""),
  instructinotallow="Search the web for information about Agno.",
  tools=[DuckDuckGoTools()],
  add_datetime_to_instructinotallow=True, 
  markdown=True,
)
  agno_assist.print_response("What is Agno?", stream=True)

... imports
# You can also use https://docs.agno.com/llms-full.txt for the full documentation
knowledge_base = UrlKnowledge(
  urls=["https://docs.agno.com/introduction.md"],
  vector_db=LanceDb(
    uri="tmp/lancedb",
    table_name="agno_docs",
    search_type=SearchType.hybrid,
    embedder=0penAIEmbedder(id="text-embedding-3-small"),
    reranker=CohereReranker(model="rerank-multilingual-v3.0"),
  ),
)
storage = SqliteStorage(table_name="agent_sessions", db_file="tmp/agent.db")

agno_assist = Agent(
  name="Agno AGI",
  model=OpenAIChat(id="gpt-4.1"),
  descriptinotallow=..., 
  instructinotallow=...,
  tools=[PythonTools(), DuckDuckGoTools()],
  add_datetime_to_instructinotallow=True,
# Agentic RAG is enabled by default when 'knowledge' is provided to the Agent.
  knowledge=knowledge_base,
# Store Agent sessions in a sqlite database
  storage=storage,
# Add the chat history to the messages
  add_history_to_messages=True,
# Number of history runs
  num_history_runs=3, 
  markdown=True,
)

if __name_ == "__main__":
# Load the knowledge base, comment after first run
# agno_assist.knovledge.load(recreate=True)
  agno _assist.print_response("What is Agno?", stream=True)

... imports

knowledge_base = ...

memory = Memory(
# Use any model for creating nemories
  model=0penAIChat(id="gpt-4.1"),
  db=SqliteMemoryDb(table_name="user_menories", db_file="tmp/agent.db"),
  delete_memories=True, 
  clear_memories=True,
)

  storage =

agno_assist = Agent(
  name="Agno AGI",
  model=Claude (id="claude-3-7-sonnet-latest"),
# User for the memories
  user_id="ava", 
  descriptinotallow=..., 
  instructinotallow=...,
# Give the Agent the ability to reason
  tools=[PythonTools(), DuckDuckGoTools(), 
  ReasoningTools(add_instructinotallow=True)],
  ...
# Store memories in a sqlite database
  memory=memory,
# Let the Agent manage its menories
  enable_agentic_memory=True,
)

if __name__ == "__main__":
# You can comment this out after the first run and the agent will remember
  agno_assist.print_response("Always start your messages with 'hi ava'", stream=True)
  agno_assist.print_response("What is Agno?", stream=True)

... imports

web agent = Agent(
  name="Web Search Agent",  
  role="Handle web search requests", 
  model= OpenAIChat(id="gpt-4o-mini"),
  tools=[DuckDuckGoTools()],
  instructinotallow="Always include sources",
)

finance_agent= Agent(
  name="Finance Agent",
  role="Handle financial data requests",
  model=OpenAIChat(id="gpt-4o-mini"),
  tools=[YFinanceTools()],
  instructinotallow=[
    "You are a financial data specialist. Provide concise and accurate data.",
    "Use tables to display stock prices, fundamentals (P/E, Market Cap)",
  ],
)


team_leader = Team (
  name="Reasoning Finance Team Leader", 
  mode="coordinate",
  model=Claude(id="claude-3-7-sonnet-latest"),
  members=[web_agent, finance_agent],
  tools=[ReasoningTools(add_instructinotallow=True)],
  instructinotallow=[
    "Use tables to display data",
    "Only output the final answer, no other text.",
  ],
  show_members_respnotallow=True, 
  enable_agentic_cnotallow=True, 
  add_datetime_to_instructinotallow=True,
  success_criteria="The team has successfully completed the task.",
)


if __name__ == "__main__":
  team_leader.print_response(
    """\
    Analyze the impact of recent US tariffs on market performance across
these key sectors:
- Steel & Aluminum: (X, NUE, AA)
- Technology Hardware: (AAPL, DELL, HPQ)

For each sector:
1. Compare stock performance before and after tariff implementation
2. Identify supply chain disruptions and cost impact percentages
3. Analyze companies' strategic responses (reshoring, price adjustments, supplier
diversification)""",
  stream=True, 
  stream_intermediate_steps=True, 
  show_full_reasnotallow=True,
)