AlgoSmith takes your Python function, runs it against a range of input sizes, and matches the timing data to a Big O class using curve fitting. It also does a static read of your code before running anything, so you get a prediction instantly and a measured result after.
The two results are compared. If they disagree, the AI tells you why.
Full Python editor with syntax highlighting. Pick a built-in algorithm from the dropdown or write your own from scratch.
Detected complexity shown with a confidence score and a ranked list of all candidate classes. A runtime chart plots the actual measured times against input size.
A plain English breakdown of which parts of your code drive the complexity, and a concrete suggestion to improve it if one exists.
Rewrites your function with better time complexity if possible. Runs only after the Bug Detector clears the code.
Scans your code for syntax errors, logic errors, infinite loops, and other common problems before any analysis runs.
Run your function with a specific input and see the raw output. Good for checking your code works before running the full analysis.
A built-in AI assistant for any questions about algorithms, complexity, or code.
git clone https://github.com/your-repo/AlgoSmith.git
cd AlgoSmith
python -m venv venv
venv\Scripts\activate
pip install -r requirements.txtCreate a .env file in the root folder:
OPENROUTER_API_KEY=your_key_here
MODEL_NAME=openai/gpt-oss-120b:free
MAX_TOKENS=2000
python main.pygit clone https://github.com/your-repo/AlgoSmith.git
cd AlgoSmith
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txtCreate a .env file in the root folder:
OPENROUTER_API_KEY=your_key_here
MODEL_NAME=openai/gpt-oss-120b:free
MAX_TOKENS=2000
python3 main.pyIf the window does not open on Linux, make sure Chromium is installed:
sudo apt install chromium-browser
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