# Reasoning
reasoning\_agent.py
```python
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:
```bash
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.
```python
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
```python
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.
```bash
pip install -U bitca openai yfinance
export OPENAI_API_KEY=***
python finance_reasoning.py
```
### More reasoning examples
#### [](https://docs.phidata.com/agents/reasoning#logical-puzzles)Logical puzzles
logical\_puzzle.py
```python
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.
```bash
pip install -U bitca openai
export OPENAI_API_KEY=***
python logical_puzzle.py
```
#### [](https://docs.phidata.com/agents/reasoning#mathematical-proofs)Mathematical proofs
mathematical\_proof.py
```python
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.
```bash
pip install -U bitca openai
export OPENAI_API_KEY=***
python mathematical_proof.py
```
#### [](https://docs.phidata.com/agents/reasoning#scientific-research)Scientific research
scientific\_research.py
```python
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.
```bash
pip install -U bitca openai
export OPENAI_API_KEY=***
python scientific_research.py
```
#### [](https://docs.phidata.com/agents/reasoning#ethical-dilemma)Ethical dilemma
ethical\_dilemma.py
```python
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.
```bash
pip install -U bitca openai
export OPENAI_API_KEY=***
python ethical_dilemma.py
```
#### [](https://docs.phidata.com/agents/reasoning#planning-an-itinerary)Planning an itinerary
planning\_itinerary.py
```python
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.
```bash
pip install -U bitca openai
export OPENAI_API_KEY=***
python planning_itinerary.py
```
#### [](https://docs.phidata.com/agents/reasoning#creative-writing)Creative writing
creative\_writing.py
```python
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.
```bash
pip install -U bitca openai
export OPENAI_API_KEY=***
python creative_writing.py
```
---
# Agent Instructions: Querying This Documentation
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Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://docs.projectbit.ca/documentation/agents/reasoning.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.