The Kapso Evolve system: how experiment campaigns run

from kapso import Kapso, DeployStrategy

kapso = Kapso()

# Build a solution - agent builds evaluation dynamically
# Include success criteria in the goal
solution = kapso.evolve(
    goal="Build a random forest classifier for Iris with accuracy > 0.9",
    max_iterations=10,
)

# With user-provided evaluation and data
solution = kapso.evolve(
    goal="Optimize model inference speed to < 10ms",
    eval_dir="./my_evaluation/",   # Copied to kapso_evaluation/
    data_dir="./my_data/",         # Copied to kapso_datasets/
    initial_repo="https://github.com/org/starter",  # Or local path
)

# Deploy and run
deployed_program = kapso.deploy(solution, strategy=DeployStrategy.LOCAL)
result = deployed_program.run({"data_path": "./test.csv"})

# Use a specific mode
solution = kapso.evolve(
    goal="...",
    mode="MINIMAL",
)

workspace/
├── kapso_evaluation/     # Agent builds evaluation here
│   └── evaluate.py       # Evaluation script
├── kapso_datasets/       # Data files (from data_dir)
├── .kapso/
│   └── repo_memory.json  # Codebase understanding
└── (solution code)       # From initial_repo or agent-built

# Get cumulative cost
cost = orchestrator.get_cumulative_cost()
print(f"Total cost: ${cost:.2f}")