Build AIs of AI Using AgNo-Alent Frame agent at the total marketing agent and accident reported

Today's fastest financial instance, resistance to special AIs of AI to manage different analysis features is the key to bringing a timely, accurate. Agno's Lightweight, Modelic Framework Developers Enpowers Empowers Empowerment Empowerment Directors Printing Information Office, as Our Organized Market Information and Coilerplate Code or Code of Boilerplate. By explaining clear instructions and designing a multi-agent agent “,” Agno manages coordinates, urgent the management of the scenes, making each agent focused on the seamstress.

!pip install -U agno google-genai duckduckgo-search yfinance

It includes and improves the framework of the Agno Agno, Genai Sdk of Geniai Geniai Geniai, the Search Live Library Library Live information, the YFinance of Seague access to the stock market details. By conducting it at the beginning of our session of Colob, we ensure that all the necessary buttons are available and at-date construction and use your risk assessment agents.

from getpass import getpass
import os


os.environ["GOOGLE_API_KEY"] = getpass("Enter your Google API key: ")

The above code stimulates you to enter your Google ACI Centob key without installing it on the screen, and then is kept in the natural environment of Google_ap_. Agno's Gemino Model Wrapper and Google Genai SDK can automatically confirm the following API calls by setting this variable.

from agno.agent import Agent
from agno.models.google import Gemini
from agno.tools.reasoning import ReasoningTools
from agno.tools.yfinance import YFinanceTools


agent = Agent(
    model=Gemini(id="gemini-1.5-flash"),  
    tools=[
        ReasoningTools(add_instructions=True),
        YFinanceTools(
            stock_price=True,
            analyst_recommendations=True,
            company_info=True,
            company_news=True
        ),
    ],
    instructions=[
        "Use tables to display data",
        "Only output the report, no other text",
    ],
    markdown=True,
)


agent.print_response(
    "Write a report on AAPL",
    stream=True,
    show_full_reasoning=True,
    stream_intermediate_steps=True
)

We start an enabled AgNO agent (1.5 flash), equipped with consulting tools of YFININ to download AAPL, and fill in the medical, directly of Colob tools.

finance_agent = Agent(
    name="Finance Agent",
    model=Gemini(id="gemini-1.5-flash"),
    tools=[
        YFinanceTools(
            stock_price=True,
            analyst_recommendations=True,
            company_info=True,
            company_news=True
        )
    ],
    instructions=[
        "Use tables to display stock price, analyst recommendations, and company info.",
        "Only output the financial report without additional commentary."
    ],
    markdown=True
)


risk_agent = Agent(
    name="Risk Assessment Agent",
    model=Gemini(id="gemini-1.5-flash"),
    tools=[
        YFinanceTools(
            stock_price=True,
            company_news=True
        ),
        ReasoningTools(add_instructions=True)
    ],
    instructions=[
        "Analyze recent price volatility and news sentiment to provide a risk assessment.",
        "Use tables where appropriate and only output the risk assessment section."
    ],
    markdown=True
)

These definitions make up two special Agino's agents using the Gemino's Gemini (1.5 Flash) model: Financial agent recommendations, and corporate stories, risk assessment issues where a section-based risk assessment.

from agno.team.team import Team
from textwrap import dedent


team = Team(
    name="Finance-Risk Team",
    mode="coordinate",
    model=Gemini(id="gemini-1.5-flash"),
    members=[finance_agent, risk_agent],
    tools=[ReasoningTools(add_instructions=True)],
    instructions=[
        "Delegate financial analysis requests to the Finance Agent.",
        "Delegate risk assessment requests to the Risk Assessment Agent.",
        "Combine their outputs into one comprehensive report."
    ],
    markdown=True,
    show_members_responses=True,
    enable_agentic_context=True
)


task = dedent("""
1. Provide a financial overview of AAPL.
2. Provide a risk assessment for AAPL based on volatility and recent news.
""")


response = team.run(task)
print(response.content)

We combine the combined team “risk of financial risk” using Agno and Google. It moves financial analysis to the financial agent and news matters / newsletters in the risk assessment agent, and includes their effects in one, complete text. By beating Team.run in a two-part AAPL work, it is clearly organizing each scholar agent and is printed in united results.

team.print_response(
    task,
    stream=True,
    stream_intermediate_steps=True,
    show_full_reasoning=True
)

We teach a financial accident team to do AAPL's work in real time, spreading internal thinking, tools, and partiality as it happens. By enabling the distribution of_interitemiate_steps and showe_ful_ful_Eful_Eft how Agno directs financial agents and the action agents on the last day, combined.

In conclusion, arresting AgNo's AgNo-agents traditionally convincing skills that may be monolithic AI to work through the workshop. Each agent in a group can be especially effective in complying metrics in the financial legs, emotions of analysis, or exploring risk factors. At the same time, APNO AGNO API Orchestrates Hope Offers, Boxi Sharing, and Last Strength. The result is a solid formulation, advanced construction from simulating two agents in complex problems with a minimum code change and higher clarity.

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