Reasoning

from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.cli.console import console

regular_agent = Agent(model=OpenAIChat(id="gpt-4o"), markdown=True)
reasoning_agent = Agent(
    model=OpenAIChat(id="gpt-4o-2024-08-06"),
    reasoning=True,
    markdown=True,
    structured_outputs=True,
)

task = "How many 'r' are in the word 'supercalifragilisticexpialidocious'?"

console.rule("[bold green]Regular Agent[/bold green]")
regular_agent.print_response(task, stream=True)
console.rule("[bold yellow]Reasoning Agent[/bold yellow]")
reasoning_agent.print_response(task, stream=True, show_full_reasoning=True)

python reasoning_agent.py

reasoning_agent = Agent(
    model=OpenAIChat(id="gpt-4o-2024-08-06"),
    reasoning=True,
    markdown=True,
    structured_outputs=True,
)
reasoning_agent.print_response("How many 'r' are in the word 'supercalifragilisticexpialidocious'?", stream=True, show_full_reasoning=True)

from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.tools.yfinance import YFinanceTools

reasoning_agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)],
    instructions=["Use tables to show data"],
    show_tool_calls=True,
    markdown=True,
    reasoning=True,
)
reasoning_agent.print_response("Write a report comparing NVDA to TSLA", stream=True, show_full_reasoning=True)

from phi.agent import Agent
from phi.model.openai import OpenAIChat

task = (
    "Three missionaries and three cannibals need to cross a river. "
    "They have a boat that can carry up to two people at a time. "
    "If, at any time, the cannibals outnumber the missionaries on either side of the river, the cannibals will eat the missionaries. "
    "How can all six people get across the river safely? Provide a step-by-step solution and show the solutions as an ascii diagram"
)
reasoning_agent = Agent(
    model=OpenAIChat(id="gpt-4o-2024-08-06"), reasoning=True, markdown=True, structured_outputs=True
)
reasoning_agent.print_response(task, stream=True, show_full_reasoning=True)

from phi.agent import Agent
from phi.model.openai import OpenAIChat

task = "Prove that for any positive integer n, the sum of the first n odd numbers is equal to n squared. Provide a detailed proof."
reasoning_agent = Agent(
    model=OpenAIChat(id="gpt-4o-2024-08-06"), reasoning=True, markdown=True, structured_outputs=True
)
reasoning_agent.print_response(task, stream=True, show_full_reasoning=True)

from phi.agent import Agent
from phi.model.openai import OpenAIChat

task = (
    "Read the following abstract of a scientific paper and provide a critical evaluation of its methodology,"
    "results, conclusions, and any potential biases or flaws:\n\n"
    "Abstract: This study examines the effect of a new teaching method on student performance in mathematics. "
    "A sample of 30 students was selected from a single school and taught using the new method over one semester. "
    "The results showed a 15% increase in test scores compared to the previous semester. "
    "The study concludes that the new teaching method is effective in improving mathematical performance among high school students."
)
reasoning_agent = Agent(
    model=OpenAIChat(id="gpt-4o-2024-08-06"), reasoning=True, markdown=True, structured_outputs=True
)
reasoning_agent.print_response(task, stream=True, show_full_reasoning=True)

from phi.agent import Agent
from phi.model.openai import OpenAIChat

task = (
    "You are a train conductor faced with an emergency: the brakes have failed, and the train is heading towards "
    "five people tied on the track. You can divert the train onto another track, but there is one person tied there. "
    "Do you divert the train, sacrificing one to save five? Provide a well-reasoned answer considering utilitarian "
    "and deontological ethical frameworks. "
    "Provide your answer also as an ascii art diagram."
)
reasoning_agent = Agent(
    model=OpenAIChat(id="gpt-4o-2024-08-06"), reasoning=True, markdown=True, structured_outputs=True
)
reasoning_agent.print_response(task, stream=True, show_full_reasoning=True)

from phi.agent import Agent
from phi.model.openai import OpenAIChat

task = "Plan an itinerary from Los Angeles to Las Vegas"
reasoning_agent = Agent(
    model=OpenAIChat(id="gpt-4o-2024-08-06"), reasoning=True, markdown=True, structured_outputs=True
)
reasoning_agent.print_response(task, stream=True, show_full_reasoning=True)

from phi.agent import Agent
from phi.model.openai import OpenAIChat

task = "Write a short story about life in 5000000 years"
reasoning_agent = Agent(
    model=OpenAIChat(id="gpt-4o-2024-08-06"), reasoning=True, markdown=True, structured_outputs=True
)
reasoning_agent.print_response(task, stream=True, show_full_reasoning=True)