Weco: The Platform for Self-Improving Code

pip install weco

Weco systematically optimizes your code, guided directly by your evaluation metrics.

Example applications include:

• GPU Kernel Optimization: Reimplement PyTorch functions using CUDA or Triton, optimizing for latency, throughput, or memory_bandwidth.
• Model Development: Tune feature transformations, architectures or the whole training pipeline, optimizing for validation_accuracy, AUC, or Sharpe Ratio.
• Prompt Engineering: Refine prompts for LLMs (e.g., for math problems), optimizing for win_rate, relevance, or format_adherence

Overview

The weco CLI leverages a tree search approach guided by LLMs to iteratively explore and refine your code. It automatically applies changes, runs your evaluation script, parses the results, and proposes further improvements based on the specified goal.

Setup

1. Install the Package:

   pip install weco
   

2. Set Up LLM API Keys (Required):

   weco requires API keys for the LLMs it uses internally. You must provide these keys via environment variables:

   • OpenAI: export OPENAI_API_KEY="your_key_here" (Create your API key here)
   • Anthropic: export ANTHROPIC_API_KEY="your_key_here" (Create your API key here)
   • Google: export GEMINI_API_KEY="your_key_here" (Google AI Studio has a free API usage quota. Create your API key here to use weco for free.)

Get Started

Quick Start (Recommended for New Users)

The easiest way to get started with Weco is to use the interactive copilot. Simply navigate to your project directory and run:

weco

Or specify a project path:

weco /path/to/your/project

This launches Weco’s interactive copilot that will:

1. Analyze your codebase using AI to understand your project structure and identify optimization opportunities
2. Suggest specific optimizations tailored to your code (e.g., GPU kernel optimization, model improvements, prompt engineering)
3. Generate evaluation scripts automatically or help you configure existing ones
4. Set up the complete optimization pipeline with appropriate metrics and commands
5. Run the optimization or provide you with the exact command to execute

Manual Setup

Configure optimization parameters yourself - If you need precise control over the optimization parameters, you can use the direct weco run command:

Example: Optimizing Simple PyTorch Operations

# Navigate to the example directory
cd examples/hello-kernel-world

# Install dependencies
pip install torch

# Run Weco with manual configuration
weco run --source optimize.py \
     --eval-command "python evaluate.py --solution-path optimize.py --device cpu" \
     --metric speedup \
     --goal maximize \
     --steps 15 \
     --additional-instructions "Fuse operations in the forward method while ensuring the max float deviation remains small. Maintain the same format of the code."

Note: If you have an NVIDIA GPU, change the device in the --eval-command to cuda. If you are running this on Apple Silicon, set it to mps.

For more advanced examples, including Triton, CUDA kernel optimization, ML model optimization, and prompt engineering for math problems, please see the README.md files within the corresponding subdirectories under the examples/ folder.

Arguments for weco run

Required:

Optional:

Authentication & Dashboard

Weco offers both anonymous and authenticated usage:

Anonymous Usage

You can use Weco without creating an account by providing LLM API keys via environment variables. This is perfect for trying out Weco or for users who prefer not to create accounts.

Authenticated Usage (Recommended)

To save your optimization runs and view them on the Weco dashboard, you can log in using Weco’s secure device authentication flow:

1. During onboarding: When you run weco for the first time, you’ll be prompted to log in or skip
2. Manual login: Use weco logout to clear credentials, then run weco again to re-authenticate
3. Device flow: Weco will open your browser automatically and guide you through a secure OAuth-style authentication

Benefits of authenticated usage:

• Run history: View all your optimization runs on the Weco dashboard
• Progress tracking: Monitor long-running optimizations remotely
• Enhanced support: Get better assistance with your optimization challenges

Command Reference

Basic Usage Patterns

Model Selection

You can specify which LLM model to use with the -M or --model flag:

# Use with onboarding
weco --model gpt-4o

# Use with direct execution
weco run --model claude-3.5-sonnet --source optimize.py [other options...]

Available models:

• gpt-4o, o4-mini (requires OPENAI_API_KEY)
• claude-3.5-sonnet, claude-sonnet-4-20250514 (requires ANTHROPIC_API_KEY)
• gemini-2.5-pro (requires GEMINI_API_KEY)

If no model is specified, Weco automatically selects the best available model based on your API keys.

Performance & Expectations

Weco, powered by the AIDE algorithm, optimizes code iteratively based on your evaluation results. Achieving significant improvements, especially on complex research-level tasks, often requires substantial exploration time.

The following plot from the independent Research Engineering Benchmark (RE-Bench) report shows the performance of AIDE (the algorithm behind Weco) on challenging ML research engineering tasks over different time budgets.

As shown, AIDE demonstrates strong performance gains over time, surpassing lower human expert percentiles within hours and continuing to improve. This highlights the potential of evaluation-driven optimization but also indicates that reaching high levels of performance comparable to human experts on difficult benchmarks can take considerable time (tens of hours in this specific benchmark, corresponding to many --steps in the Weco CLI). Factor this into your planning when setting the number of --steps for your optimization runs.

Important Note on Evaluation

The command specified by --eval-command is crucial. It’s responsible for executing the potentially modified code from --source and assessing its performance. This command MUST print the metric you specified with --metric along with its numerical value to the terminal (standard output or standard error). Weco reads this output to understand how well each code version performs and guide the optimization process.

For example, if you set --metric speedup, your evaluation script (eval.py in the examples) should output a line like:

speedup: 1.5

or

Final speedup value = 1.5

Weco will parse this output to extract the numerical value (1.5 in this case) associated with the metric name (‘speedup’).

Contributing

We welcome your contributions! To get started:

1. Fork & Clone the Repository:

   git clone https://github.com/WecoAI/weco-cli.git
   cd weco-cli
   

2. Install Dependencies:

   pip install -e ".[dev]"
   

3. Create a Feature Branch:

   git checkout -b feature/your-feature-name
   

4. Make Changes: Ensure your code adheres to our style guidelines and includes relevant tests.

5. Commit, Push & Open a PR: Commit your changes, and open a pull request with a clear description of your enhancements.