### Run OpenAlpha_Evolve Example Task

Source: https://github.com/shyamsaktawat/openalpha_evolve/blob/main/README.md

This command executes an example task, Dijkstra's algorithm, using the `main` module and a specified YAML configuration file. It initiates the evolutionary process, with logs displayed in the terminal and saved to a file.

```bash
python -m main examples/shortest_path.yaml
```

--------------------------------

### Install Python Dependencies

Source: https://github.com/shyamsaktawat/openalpha_evolve/blob/main/README.md

This command installs all required Python packages listed in the `requirements.txt` file into the active virtual environment. It ensures all necessary libraries for the project are available.

```bash
pip install -r requirements.txt
```

--------------------------------

### Launch OpenAlpha_Evolve Gradio Web Interface

Source: https://github.com/shyamsaktawat/openalpha_evolve/blob/main/README.md

This command starts the Gradio web application, providing a user interface to interact with the OpenAlpha_Evolve system. It displays a local URL for browser access, allowing users to define custom tasks interactively.

```bash
python app.py
```

--------------------------------

### Copy Environment Variable Example File

Source: https://github.com/shyamsaktawat/openalpha_evolve/blob/main/README.md

This command copies the `.env_example` file to `.env`, creating a personal environment configuration file. The `.env` file is crucial for storing sensitive API keys and overriding default settings.

```bash
cp .env_example .env
```

--------------------------------

### Clone OpenAlpha_Evolve Repository

Source: https://github.com/shyamsaktawat/openalpha_evolve/blob/main/README.md

This command clones the OpenAlpha_Evolve Git repository from GitHub and navigates into the project directory. It is the first step after ensuring Git is installed.

```bash
git clone https://github.com/shyamsaktawat/OpenAlpha_Evolve.git
cd OpenAlpha_Evolve
```

--------------------------------

### OpenAlpha Evolve Project Directory Structure

Source: https://github.com/shyamsaktawat/openalpha_evolve/blob/main/README.md

This snippet illustrates the hierarchical organization of the OpenAlpha Evolve project, detailing the purpose of each main directory and file. It provides an overview of the modular agent architecture and configuration setup.

```text
./
├── code_generator/      # Agent responsible for generating code using LLMs.
├── database_agent/      # Agent for managing the storage and retrieval of programs and their metadata.
├── evaluator_agent/     # Agent that evaluates the generated code for syntax, execution, and fitness.
├── prompt_designer/     # Agent that crafts prompts for the LLM for initial generation, mutation, and bug fixing.
├── selection_controller/  # Agent that implements the selection strategy for parent and survivor programs.
├── task_manager/        # Agent that orchestrates the overall evolutionary loop and coordinates other agents.
├── config/                  # Holds configuration files, primarily `settings.py` for system parameters and API keys.
├── core/                    # Defines core data structures and interfaces, like `Program` and `TaskDefinition`.
├── tests/                   # Includes unit and integration tests to ensure code quality and correctness.
├── main.py                  # The main entry point to run the OpenAlpha_Evolve system and start an evolutionary run.
├── requirements.txt         # Lists all Python package dependencies required to run the project.
├── .env.example             # An example file showing the environment variables needed, such as API keys. Copy this to `.env` and fill in your values.
├── .gitignore               # Specifies intentionally untracked files that Git should ignore (e.g., `.env`, `__pycache__/`).
├── LICENSE.md               # Contains the full text of the MIT License under which the project is distributed.
└── README.md                # This file! Provides an overview of the project, setup instructions, and documentation.
```

--------------------------------

### Define Tasks Programmatically with Python (Legacy)

Source: https://github.com/shyamsaktawat/openalpha_evolve/blob/main/README.md

This snippet demonstrates how to define a task using the `TaskDefinition` class in Python. It allows specifying a unique ID, a detailed problem description, the function name to be evolved, input/output examples for testing, and allowed imports for the task's environment. This is the legacy method for task definition.

```python
from core.task_definition import TaskDefinition

task = TaskDefinition(
    id="your_task_id",
    description="Your detailed problem description",
    function_name_to_evolve="your_function_name",
    input_output_examples=[
        {"input": [arg1, arg2], "output": expected_output},
        # More examples...
    ],
    allowed_imports=["module1", "module2"]
)
```

--------------------------------

### Set Up Python Virtual Environment

Source: https://github.com/shyamsaktawat/openalpha_evolve/blob/main/README.md

These commands create an isolated Python virtual environment named 'venv' and then activate it. Using a virtual environment is recommended to manage project dependencies without conflicts with system-wide Python packages.

```bash
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
```

--------------------------------

### Configure Other LLM API Keys (OpenAI, Anthropic, Cohere)

Source: https://github.com/shyamsaktawat/openalpha_evolve/blob/main/README.md

This snippet shows common environment variables for integrating with various LLM providers via LiteLLM, such as OpenAI, Anthropic, and Cohere. Users should add the relevant API keys to their `.env` file based on their chosen provider.

```bash
# OPENAI_API_KEY="your_openai_api_key"
# ANTHROPIC_API_KEY="your_anthropic_api_key"
# COHERE_API_KEY="your_cohere_api_key"
```

--------------------------------

### Configure Google Cloud LLM API Keys

Source: https://github.com/shyamsaktawat/openalpha_evolve/blob/main/README.md

This snippet provides options for configuring Google Cloud LLM access, either through Application Default Credentials (ADC) or by directly setting a GEMINI_API_KEY. These variables should be added to the `.env` file for Google Cloud services like Vertex AI or AI Studio.

```bash
# For Google Cloud (Vertex AI / AI Studio)
# Option 1: Using Application Default Credentials (ADC)
# Ensure you have authenticated via gcloud CLI:
# gcloud auth application-default login
# Or set the GOOGLE_APPLICATION_CREDENTIALS environment variable:
# GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/service-account-key.json"

# Option 2: Directly using an API Key for specific Google services (e.g., Gemini API)
# GEMINI_API_KEY="your_gemini_api_key"
```

--------------------------------

### Define Custom Algorithmic Quests with YAML

Source: https://github.com/shyamsaktawat/openalpha_evolve/blob/main/README.md

This YAML structure defines a custom algorithmic task for OpenAlpha_Evolve, specifying the task ID, description, function name, allowed imports, and a series of test cases. Test cases can use direct output comparison or a custom Python validation function.

```yaml
task_id: "your_task_id"
task_description: |
  Your detailed problem description here.
  Be specific about function names, expected behavior, and constraints.
function_name: "your_function_name"
allowed_imports: ["module1", "module2"]

tests:
  - description: "Test group description" # Describes a group of related tests
    name: "Test group name" # A name for this test group
    test_cases: # This should be a list of individual test cases
      - input: [arg1, arg2]  # First test case
        output: expected_output # Expected result for this input
        # Each test case uses either 'output' for direct comparison
        # or 'validation_func' for more complex validation.
      - input: [arg_for_validation_func_1, arg_for_validation_func_2] # Second test case
        validation_func: |
          def validate(output_from_function):
              # Custom validation logic for this specific test case's output
              # For example, check if output is within a certain range,
              # or if it has specific properties.
              return isinstance(output_from_function, bool) and output_from_function is True
```

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