pydeltacode

Optimise your code for runtime performance, loc or peak memory on demand inside your script and compare LLM suggestions against your code. Solutions are tested end-to-end against the function calls executed and results are stored locally for manual verification.

📖 Full documentation: www.deltacode.org

Install

pip install pydeltacode

For development (editable install from a checkout of this repo):

pip install -e .

Usage

from pydeltacode import refactor, llm

# One of eight providers: openai, anthropic, gemini, xai, deepseek,
# mistral, kimi, openrouter. ${VAR} is resolved from the environment
# at runtime, so the key never lives in your source.
llm.set_credentials("openai", "${PROVIDER_API_KEY}")

optimise = refactor(objective="speed")


@optimise.track
def sum_of_squares(values):
    squares = []
    for v in values:
        squares.append(v * v)
    total = 0
    for s in squares:
        total = total + s
    return total


if __name__ == "__main__":
    sum_of_squares(list(range(5_000)))     # record a real call
    result = optimise.optimise(            # refactor, verify, keep the best
        sum_of_squares, tries=5, use_captured_inputs=True
    )
    print(result["winner_version"], result["report_path"])

optimise() sends the tracked function to your provider, runs each suggestion against the calls you actually recorded, and only keeps a candidate whose outputs match the original and which improves the objective. The winner is written back into your source file; every attempt, the code sent and received, and a PDF report are saved locally.

Tracked/refactored data is written to .pydeltacode/<hash>/ (gitignored) in the current working directory. Save location can also be changed.