Upload 5 files

2.finance_agent.py

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+"""Run `pip install yfinance` to install dependencies."""
+
+from phi.agent import Agent
+from phi.model.groq import Groq
+from phi.tools.yfinance import YFinanceTools
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+def get_company_symbol(company: str) -> str:
+    """Use this function to get the symbol for a company.
+
+    Args:
+        company (str): The name of the company.
+
+    Returns:
+        str: The symbol for the company.
+    """
+    symbols = {
+        "Phidata": "MSFT",
+        "Infosys": "INFY",
+        "Tesla": "TSLA",
+        "Apple": "AAPL",
+        "Microsoft": "MSFT",
+        "Amazon": "AMZN",
+        "Google": "GOOGL",
+    }
+    return symbols.get(company, "Unknown")
+
+
+agent = Agent(
+    model=Groq(id="llama-3.3-70b-versatile"),
+    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, stock_fundamentals=True), get_company_symbol],
+    instructions=[
+        "Use tables to display data.",
+        "If you need to find the symbol for a company, use the get_company_symbol tool.",
+    ],
+    show_tool_calls=True,
+    markdown=True,
+    debug_mode=True,
+)
+
+agent.print_response(
+    "Summarize and compare analyst recommendations and fundamentals for TSLA and MSFT. Show in tables.", stream=True
+)

3-agent-team.py

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+from phi.agent import Agent
+from phi.model.openai import OpenAIChat
+from phi.model.groq import Groq
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.tools.yfinance import YFinanceTools
+from dotenv import load_dotenv
+
+load_dotenv()
+
+web_agent = Agent(
+    name="Web Agent",
+    model=Groq(id="llama-3.3-70b-versatile"),
+    # model=OpenAIChat(id="pip install pandas"),
+    tools=[DuckDuckGo()],
+    instructions=["Always include sources"],
+    show_tool_calls=True,
+    markdown=True
+)
+
+finance_agent = Agent(
+    name="Finance Agent",
+    role="Get financial data",
+    model=Groq(id="llama-3.3-70b-versatile"),
+    # model=OpenAIChat(id="gpt-4o"),
+    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True)],
+    instructions=["Use tables to display data"],
+    show_tool_calls=True,
+    markdown=True,
+)
+
+agent_team = Agent(
+    model=Groq(id="llama-3.3-70b-versatile"),
+    team=[web_agent, finance_agent],
+    instructions=["Always include sources", "Use tables to display data"],
+    show_tool_calls=True,
+    markdown=True,
+)
+
+agent_team.print_response("Summarize analyst recommendations and share the latest news for NVDA", stream=True)

Groq-AI-Agent.py

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+from phi.agent import Agent
+from phi.model.groq import Groq
+from dotenv import load_dotenv
+
+load_dotenv()
+
+agent = Agent(
+    model=Groq(id="llama-3.3-70b-versatile")
+)
+
+agent.print_response("Share a 2 sentence love story between dosa and samosa")

playground.py

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+from phi.agent import Agent
+from phi.model.openai import OpenAIChat
+from phi.storage.agent.sqlite import SqlAgentStorage
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.tools.yfinance import YFinanceTools
+from phi.playground import Playground, serve_playground_app
+
+
+web_agent = Agent(
+    name="Web Agent",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[DuckDuckGo()],
+    instructions=["Always include sources"],
+    storage=SqlAgentStorage(table_name="web_agent", db_file="agents.db"),
+    add_history_to_messages=True,
+    markdown=True,
+)
+
+finance_agent = Agent(
+    name="Finance Agent",
+    model=OpenAIChat(id="gpt-4o"),
+    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)],
+    instructions=["Use tables to display data"],
+    storage=SqlAgentStorage(table_name="finance_agent", db_file="agents.db"),
+    add_history_to_messages=True,
+    markdown=True,
+)
+
+app = Playground(agents=[finance_agent, web_agent]).get_app()
+
+
+if __name__ == "__main__":
+    serve_playground_app("playground:app", reload=True)