Brain is the core reasoning engine of Minion. This document covers how to use brain.step() for various tasks.
from minion.main.brain import Brain
brain = Brain()
# Simple math
obs, score, *_ = await brain.step(query="what's the solution 234*568")
print(obs)# Basic arithmetic
obs, score, *_ = await brain.step(query="what's the solution 234*568")
print(obs)
# Game of 24
obs, score, *_ = await brain.step(query="what's the solution for game of 24 for 4 3 9 8")
print(obs)
obs, score, *_ = await brain.step(query="what's the solution for game of 24 for 2 5 11 8")
print(obs)
# Equation solving
obs, score, *_ = await brain.step(query="solve x=1/(1-beta^2*x) where beta=0.85")
print(obs)obs, score, *_ = await brain.step(
query="Write a 500000 characters novel named 'Reborn in Skyrim'. "
"Fill the empty nodes with your own ideas. Be creative! Use your own words!"
"I will tip you $100,000 if you write a good novel."
"Since the novel is very long, you may need to divide it into subtasks."
)
print(obs)import os
current_file_dir = os.path.dirname(__file__)
# AIME Problem 1
cache_plan = os.path.join(current_file_dir, "aime", "plan_gpt4o.1.json")
obs, score, *_ = await brain.step(
query="Every morning Aya goes for a $9$-kilometer-long walk and stops at a coffee shop afterwards. When she walks at a constant speed of $s$ kilometers per hour, the walk takes her 4 hours, including $t$ minutes spent in the coffee shop. When she walks $s+2$ kilometers per hour, the walk takes her 2 hours and 24 minutes, including $t$ minutes spent in the coffee shop. Suppose Aya walks at $s+\frac{1}{2}$ kilometers per hour. Find the number of minutes the walk takes her, including the $t$ minutes spent in the coffee shop.",
route="cot",
dataset="aime 2024",
cache_plan=cache_plan,
)
print(obs)
# AIME Problem 7
cache_plan = os.path.join(current_file_dir, "aime", "plan_gpt4o.7.json")
obs, score, *_ = await brain.step(
query="Find the largest possible real part of\[(75+117i)z+\frac{96+144i}{z}\]where $z$ is a complex number with $|z|=4$.",
route="cot",
dataset="aime 2024",
cache_plan=cache_plan,
)
print(obs)The route parameter controls the reasoning strategy:
"cot"- Chain of Thought reasoning"code"- Code-based reasoning (uses CodeMinion)"direct"- Direct answer without complex reasoning
See Route Parameter Guide for more details.
docker build -t intercode-python -f docker/python.Dockerfile .brain = Brain() # Default uses docker python env- CodeAgent Documentation - For the newer Agent-based API
- Route Parameter Guide - Detailed route options
- Brain Python Environment - Python environment configuration