Reasoning

Reasoning Reasoning is an experimental feature that enables an Agent to think through a problem step-by-step before jumping into a response. The Agent works through different ideas, validating and cor

reasoning_agent.py

from bitca.agent import Agent
from bitca.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"), reasoning=True, markdown=True, structured_outputs=True)
reasoning_agent.print_response(task, stream=True, show_full_reasoning=True)

Run the Reasoning Agent:

pip install -U bitca openai

export OPENAI_API_KEY=***

python reasoning_agent.py

How to use reasoning

To add reasoning, set reasoning=True. When using reasoning with tools, do not use structured_outputs=True as gpt-4o cannot use tools with structured outputs.

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 'supercalifragilisticex

Reasoning with tools

You can also use tools with a reasoning agent, but do not use structured_outputs=True as gpt-4o cannot use tools with structured outputs. Lets create a finance agent that can reason.

finance_reasoning.py

from bitca.agent import Agent
from bitca.model.openai import OpenAIChat
from bitca.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)

Run the script to see the output.

pip install -U bitca openai yfinance

export OPENAI_API_KEY=***

python finance_reasoning.py

More reasoning examples

​Logical puzzles

logical_puzzle.py

from bitca.agent import Agent
from bitca.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)

Run the script to see the output.

pip install -U bitca openai

export OPENAI_API_KEY=***

python logical_puzzle.py

​Mathematical proofs

mathematical_proof.py

from bitca.agent import Agent
from bitca.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)

Run the script to see the output.

pip install -U bitca openai

export OPENAI_API_KEY=***

python mathematical_proof.py

​Scientific research

scientific_research.py

from bitca.agent import Agent
from bitca.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)

Run the script to see the output.

pip install -U bitca openai

export OPENAI_API_KEY=***

python scientific_research.py

​Ethical dilemma

ethical_dilemma.py

from bitca.agent import Agent
from bitca.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)

Run the script to see the output.

pip install -U bitca openai

export OPENAI_API_KEY=***

python ethical_dilemma.py

​Planning an itinerary

planning_itinerary.py

from bitca.agent import Agent
from bitca.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)

Run the script to see the output.

pip install -U bitca openai

export OPENAI_API_KEY=***

python planning_itinerary.py

​Creative writing

creative_writing.py

from bitca.agent import Agent
from bitca.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)

Run the script to see the output.

pip install -U bitca openai

export OPENAI_API_KEY=***

python creative_writing.py