AutoGenLib is a Python library designed to automatically generate code on-the-fly using OpenAI's API. It targets developers who want to rapidly prototype or explore functionality by describing desired modules and functions, which are then created dynamically based on context and existing code. The primary benefit is accelerating development by generating boilerplate or novel code snippets without manual implementation.

How It Works

AutoGenLib leverages OpenAI's API to generate code. When a non-existent module or function is imported under the autogenlib namespace, the library analyzes the calling code and any previously generated modules. This context, along with a user-provided description, is sent to the OpenAI API. The generated code is then validated, executed, and made available for use, with options for caching to improve consistency and reduce API calls.

Quick Start & Requirements

• Install via pip: pip install autogenlib or from source.
• Requires Python 3.12+ and an OpenAI API key set as the OPENAI_API_KEY environment variable.
• Optional configuration for OPENAI_API_BASE_URL and OPENAI_MODEL.
• Quick start example: from autogenlib.tokens import generate_token; token = generate_token(length=32); print(token)
• Official documentation and examples are available in the README.

Highlighted Details

• Dynamic code generation for non-existent modules/functions.
• Context-aware generation, analyzing calling code and existing modules.
• Progressive enhancement of existing modules.
• Automatic exception handling and explanation via LLM.
• No default caching, promoting varied results; caching can be enabled.

Maintenance & Community

• The project explicitly states "Contributions are not welcome! This is just a fun PoC project."
• No community links (Discord/Slack) or roadmap are provided.

Licensing & Compatibility

• Licensed under the MIT License.
• Suitable for experimentation and prototyping; review generated code for production use.

Limitations & Caveats

This library requires an internet connection and depends on OpenAI API availability. The quality of generated code is contingent on the clarity of user descriptions. The project is explicitly marked as a Proof of Concept (PoC) and not recommended for production-critical code without thorough review.