Self-Improving Agents

A Python system for LLM prompt optimization.

Project Overview

This project provides tools for evaluating and optimizing prompts for large language models (LLMs). It includes components for prompt evaluation, optimization, and testing.

Installation

# Clone the repository
git clone https://github.com/yourusername/self_improving_agents.git
cd self_improving_agents

# Create and activate virtual environment using UV
uv venv
source .venv/bin/activate  # On Windows use: .venv\Scripts\activate

# Install the package with development dependencies
uv pip install -e ".[dev]"

Project Structure

self_improving_agents/
├── config/                # Configuration templates
├── examples/              # Usage examples
├── src/                   # Source code
│   └── self_improving_agents/
│       ├── evaluators/    # Components for evaluation
│       ├── instrumentation/ # Wrappers for tracking eval function usage
│       ├── models/        # Data structures and schemas
│       ├── optimizers/    # Components for improving prompts
│       ├── runners/       # Orchestration of evaluation/optimization loops
│       └── utils/         # Shared utilities
└── tests/                 # Test files

Usage

# Basic usage example will be provided here

Development

This project uses:

• UV for dependency management
• Ruff for linting and formatting
• Black for code formatting
• MyPy for static type checking
• Pre-commit for git hooks

To set up the development environment:

# Install pre-commit hooks
pre-commit install

License

MIT