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"""
Command-line interface for OpenEvolve
"""
import argparse
import asyncio
import logging
import os
import sys
from typing import Dict, List, Optional
from openevolve import OpenEvolve
from openevolve.config import Config, load_config
logger = logging.getLogger(__name__)
def parse_args() -> argparse.Namespace:
"""Parse command-line arguments"""
parser = argparse.ArgumentParser(description="OpenEvolve - Evolutionary coding agent")
parser.add_argument("initial_program", help="Path to the initial program file")
parser.add_argument(
"evaluation_file", help="Path to the evaluation file containing an 'evaluate' function"
)
parser.add_argument("--config", "-c", help="Path to configuration file (YAML)", default=None)
parser.add_argument("--output", "-o", help="Output directory for results", default=None)
parser.add_argument(
"--iterations", "-i", help="Maximum number of iterations", type=int, default=None
)
parser.add_argument(
"--target-score", "-t", help="Target score to reach", type=float, default=None
)
parser.add_argument(
"--log-level",
"-l",
help="Logging level",
choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
default=None,
)
parser.add_argument(
"--checkpoint",
help="Path to checkpoint directory to resume from (e.g., openevolve_output/checkpoints/checkpoint_50)",
default=None,
)
parser.add_argument("--api-base", help="Base URL for the LLM API", default=None)
parser.add_argument("--primary-model", help="Primary LLM model name", default=None)
parser.add_argument("--secondary-model", help="Secondary LLM model name", default=None)
return parser.parse_args()
async def main_async() -> int:
"""
Main asynchronous entry point
Returns:
Exit code
"""
args = parse_args()
# Check if files exist
if not os.path.exists(args.initial_program):
print(f"Error: Initial program file '{args.initial_program}' not found")
return 1
if not os.path.exists(args.evaluation_file):
print(f"Error: Evaluation file '{args.evaluation_file}' not found")
return 1
# Load base config from file or defaults
config = load_config(args.config)
# Create config object with command-line overrides
if args.api_base or args.primary_model or args.secondary_model:
# Apply command-line overrides
if args.api_base:
config.llm.api_base = args.api_base
print(f"Using API base: {config.llm.api_base}")
if args.primary_model:
config.llm.primary_model = args.primary_model
print(f"Using primary model: {config.llm.primary_model}")
if args.secondary_model:
config.llm.secondary_model = args.secondary_model
print(f"Using secondary model: {config.llm.secondary_model}")
# Rebuild models list to apply CLI overrides
if args.primary_model or args.secondary_model:
config.llm.rebuild_models()
print(f"Applied CLI model overrides - active models:")
for i, model in enumerate(config.llm.models):
print(f" Model {i+1}: {model.name} (weight: {model.weight})")
# Initialize OpenEvolve
try:
openevolve = OpenEvolve(
initial_program_path=args.initial_program,
evaluation_file=args.evaluation_file,
config=config,
output_dir=args.output,
)
# Load from checkpoint if specified
if args.checkpoint:
if not os.path.exists(args.checkpoint):
print(f"Error: Checkpoint directory '{args.checkpoint}' not found")
return 1
print(f"Loading checkpoint from {args.checkpoint}")
openevolve.database.load(args.checkpoint)
print(
f"Checkpoint loaded successfully (iteration {openevolve.database.last_iteration})"
)
# Override log level if specified
if args.log_level:
logging.getLogger().setLevel(getattr(logging, args.log_level))
# Run evolution
best_program = await openevolve.run(
iterations=args.iterations,
target_score=args.target_score,
checkpoint_path=args.checkpoint,
)
# Get the checkpoint path
checkpoint_dir = os.path.join(openevolve.output_dir, "checkpoints")
latest_checkpoint = None
if os.path.exists(checkpoint_dir):
checkpoints = [
os.path.join(checkpoint_dir, d)
for d in os.listdir(checkpoint_dir)
if os.path.isdir(os.path.join(checkpoint_dir, d))
]
if checkpoints:
latest_checkpoint = sorted(
checkpoints, key=lambda x: int(x.split("_")[-1]) if "_" in x else 0
)[-1]
print(f"\nEvolution complete!")
print(f"Best program metrics:")
for name, value in best_program.metrics.items():
# Handle mixed types: format numbers as floats, others as strings
if isinstance(value, (int, float)):
print(f" {name}: {value:.4f}")
else:
print(f" {name}: {value}")
if latest_checkpoint:
print(f"\nLatest checkpoint saved at: {latest_checkpoint}")
print(f"To resume, use: --checkpoint {latest_checkpoint}")
return 0
except Exception as e:
print(f"Error: {str(e)}")
import traceback
traceback.print_exc()
return 1
def main() -> int:
"""
Main entry point
Returns:
Exit code
"""
return asyncio.run(main_async())
if __name__ == "__main__":
sys.exit(main())
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