Cerebras Llama Reasoning Tools

"""
Cerebras Llama Reasoning Tools
==============================

Demonstrates this reasoning cookbook example.
"""

from textwrap import dedent

from agno.agent import Agent
from agno.models.cerebras import Cerebras
from agno.tools.reasoning import ReasoningTools


# ---------------------------------------------------------------------------
# Create Example
# ---------------------------------------------------------------------------
def run_example() -> None:
    reasoning_agent = Agent(
        model=Cerebras(id="llama-3.3-70b"),
        tools=[ReasoningTools(add_instructions=True)],
        instructions=dedent("""\
            You are an expert problem-solving assistant with strong analytical skills! 

            Your approach to problems:
            1. First, break down complex questions into component parts
            2. Clearly state your assumptions
            3. Develop a structured reasoning path
            4. Consider multiple perspectives
            5. Evaluate evidence and counter-arguments
            6. Draw well-justified conclusions

            When solving problems:
            - Use explicit step-by-step reasoning
            - Identify key variables and constraints
            - Explore alternative scenarios
            - Highlight areas of uncertainty
            - Explain your thought process clearly
            - Consider both short and long-term implications
            - Evaluate trade-offs explicitly

            For quantitative problems:
            - Show your calculations
            - Explain the significance of numbers
            - Consider confidence intervals when appropriate
            - Identify source data reliability

            For qualitative reasoning:
            - Assess how different factors interact
            - Consider psychological and social dynamics
            - Evaluate practical constraints
            - Address value considerations
            \
        """),
        add_datetime_to_context=True,
        stream_events=True,
        markdown=True,
    )

    # Example usage with a complex reasoning problem
    reasoning_agent.print_response(
        "Solve this logic puzzle: A man has to take a fox, a chicken, and a sack of grain across a river. "
        "The boat is only big enough for the man and one item. If left unattended together, the fox will "
        "eat the chicken, and the chicken will eat the grain. How can the man get everything across safely?",
        stream=True,
    )

    # # Economic analysis example
    # reasoning_agent.print_response(
    #     "Is it better to rent or buy a home given current interest rates, inflation, and market trends? "
    #     "Consider both financial and lifestyle factors in your analysis.",
    #     stream=True
    # )

    # # Strategic decision-making example
    # reasoning_agent.print_response(
    #     "A startup has $500,000 in funding and needs to decide between spending it on marketing or "
    #     "product development. They want to maximize growth and user acquisition within 12 months. "
    #     "What factors should they consider and how should they analyze this decision?",
    #     stream=True
    # )


# ---------------------------------------------------------------------------
# Run Example
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    run_example()