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"""
Integration tests for OpenEvolve library API with real LLM inference
Tests the end-to-end flow of using OpenEvolve as a library
"""
import pytest
import tempfile
import shutil
from pathlib import Path
from openevolve import run_evolution, evolve_function, evolve_code, evolve_algorithm
from openevolve.config import Config, LLMModelConfig
def _get_library_test_config(port: int = 8000) -> Config:
"""Get config for library API tests with optillm server"""
config = Config()
config.max_iterations = 100
config.checkpoint_interval = 1
config.database.in_memory = True
config.evaluator.cascade_evaluation = False
config.evaluator.parallel_evaluations = 1
config.evaluator.timeout = 60
# Configure to use optillm server
base_url = f"http://localhost:{port}/v1"
config.llm.api_base = base_url
config.llm.timeout = 120
config.llm.retries = 0
config.llm.models = [
LLMModelConfig(
name="google/gemma-3-270m-it",
api_key="optillm",
api_base=base_url,
weight=1.0,
timeout=120,
retries=0
)
]
return config
class TestLibraryAPIIntegration:
"""Test OpenEvolve library API with real LLM integration"""
@pytest.mark.slow
def test_evolve_function_real_integration(
self,
optillm_server,
temp_workspace
):
"""Test evolve_function with real optillm server - simple optimization task"""
def simple_multiply(x, y):
"""A simple function that can be optimized"""
# Inefficient implementation that can be improved
result = 0
for i in range(x):
result += y
return result
# Test cases - the function should return x * y
test_cases = [
((2, 3), 6),
((4, 5), 20),
((1, 7), 7),
((0, 10), 0)
]
print("Testing evolve_function with real LLM...")
# Run evolution with minimal iterations for testing
result = evolve_function(
simple_multiply,
test_cases,
iterations=2, # Very small number for CI speed
output_dir=str(temp_workspace / "evolve_function_output"),
cleanup=False, # Keep files for inspection
config=_get_library_test_config(optillm_server['port'])
)
# Verify the result structure
assert result is not None
assert hasattr(result, 'best_score')
assert hasattr(result, 'best_code')
assert hasattr(result, 'metrics')
assert hasattr(result, 'output_dir')
# Basic checks
assert result.best_score >= 0.0
assert "def simple_multiply" in result.best_code
assert result.output_dir == str(temp_workspace / "evolve_function_output")
# Check that output directory was created
output_path = Path(result.output_dir)
assert output_path.exists()
assert (output_path / "best").exists()
print(f"✅ evolve_function completed successfully!")
print(f" Best score: {result.best_score}")
print(f" Output dir: {result.output_dir}")
print(f" Code length: {len(result.best_code)} chars")
@pytest.mark.slow
def test_evolve_code_real_integration(
self,
optillm_server,
temp_workspace
):
"""Test evolve_code with real optillm server - code string optimization"""
# Initial code that can be optimized
initial_code = """
# EVOLVE-BLOCK-START
def fibonacci(n):
# Inefficient recursive implementation
if n <= 1:
return n
return fibonacci(n-1) + fibonacci(n-2)
# EVOLVE-BLOCK-END
"""
def fibonacci_evaluator(program_path):
"""Simple evaluator for fibonacci function"""
try:
# Import the evolved program
import importlib.util
spec = importlib.util.spec_from_file_location("evolved", program_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
# Test the function
if hasattr(module, 'fibonacci'):
fib = module.fibonacci
# Test cases
test_cases = [
(0, 0), (1, 1), (2, 1), (3, 2), (4, 3), (5, 5)
]
correct = 0
for input_val, expected in test_cases:
try:
result = fib(input_val)
if result == expected:
correct += 1
except:
pass
accuracy = correct / len(test_cases)
return {
"score": accuracy,
"correctness": accuracy,
"test_cases_passed": correct,
"combined_score": accuracy # Use accuracy as combined score
}
else:
return {"score": 0.0, "error": "fibonacci function not found"}
except Exception as e:
return {"score": 0.0, "error": str(e)}
print("Testing evolve_code with real LLM...")
# Run evolution
result = evolve_code(
initial_code,
fibonacci_evaluator,
iterations=1, # Minimal for CI speed
output_dir=str(temp_workspace / "evolve_code_output"),
cleanup=False, # Keep output directory
config=_get_library_test_config(optillm_server['port'])
)
# Verify result structure
assert result is not None
assert result.best_score >= 0.0
assert "fibonacci" in result.best_code.lower()
assert "# EVOLVE-BLOCK-START" in result.best_code
assert "# EVOLVE-BLOCK-END" in result.best_code
# Check output directory
output_path = Path(result.output_dir)
assert output_path.exists()
print(f"✅ evolve_code completed successfully!")
print(f" Best score: {result.best_score}")
print(f" Output dir: {result.output_dir}")
@pytest.mark.slow
def test_run_evolution_real_integration(
self,
optillm_server,
temp_workspace
):
"""Test run_evolution with real optillm server - basic program evolution"""
# Create initial program file
initial_program = temp_workspace / "initial_program.py"
initial_program.write_text("""
# Simple sorting program to evolve
# EVOLVE-BLOCK-START
def sort_numbers(numbers):
# Basic bubble sort implementation
n = len(numbers)
for i in range(n):
for j in range(0, n - i - 1):
if numbers[j] > numbers[j + 1]:
numbers[j], numbers[j + 1] = numbers[j + 1], numbers[j]
return numbers
# EVOLVE-BLOCK-END
""")
# Create evaluator file
evaluator_file = temp_workspace / "evaluator.py"
evaluator_file.write_text("""
def evaluate(program_path):
\"\"\"Evaluate sorting function performance\"\"\"
try:
import importlib.util
spec = importlib.util.spec_from_file_location("program", program_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
if hasattr(module, 'sort_numbers'):
sort_func = module.sort_numbers
# Test cases
test_cases = [
[3, 1, 4, 1, 5],
[9, 2, 6, 5, 3],
[1],
[],
[2, 1]
]
correct = 0
for test_case in test_cases:
try:
input_copy = test_case.copy()
result = sort_func(input_copy)
expected = sorted(test_case)
if result == expected:
correct += 1
except:
pass
accuracy = correct / len(test_cases) if test_cases else 0
return {
"score": accuracy,
"correctness": accuracy,
"complexity": 10, # Fixed complexity for simplicity
"combined_score": accuracy # Use accuracy as combined score
}
else:
return {"score": 0.0, "error": "sort_numbers function not found"}
except Exception as e:
return {"score": 0.0, "error": str(e)}
""")
print("Testing run_evolution with real LLM...")
# Run evolution using file paths (most common usage)
result = run_evolution(
initial_program=str(initial_program),
evaluator=str(evaluator_file),
iterations=1, # Minimal for CI speed
output_dir=str(temp_workspace / "run_evolution_output"),
cleanup=False, # Keep output directory
config=_get_library_test_config(optillm_server['port'])
)
# Verify result
assert result is not None
assert result.best_score >= 0.0
assert "sort_numbers" in result.best_code
# Check that files were created
output_path = Path(result.output_dir)
assert output_path.exists()
assert (output_path / "best").exists()
assert (output_path / "checkpoints").exists()
print(f"✅ run_evolution completed successfully!")
print(f" Best score: {result.best_score}")
print(f" Output dir: {result.output_dir}")
# Test string input as well
print("Testing run_evolution with string inputs...")
result2 = run_evolution(
initial_program=initial_program.read_text(),
evaluator=lambda path: {"score": 0.8, "test": "passed"}, # Simple callable evaluator
iterations=1,
output_dir=str(temp_workspace / "run_evolution_string_output"),
cleanup=False, # Keep output directory
config=_get_library_test_config(optillm_server['port'])
)
assert result2 is not None
assert result2.best_score >= 0.0
print(f"✅ run_evolution with string inputs completed!")
@pytest.fixture
def temp_workspace():
"""Create a temporary workspace for integration tests"""
temp_dir = tempfile.mkdtemp()
workspace = Path(temp_dir)
yield workspace
shutil.rmtree(temp_dir, ignore_errors=True) |