LLM Strategy
Freeimplementing the Strategy Pattern using LLMs 
About LLM Strategy
llm-strategy is an open-source Python library that implements the Strategy Pattern using Large Language Models (LLMs), such as OpenAI's GPT-3. It provides a decorator @llm_strategy that connects Python code to an LLM, enabling the LLM to 'implement' abstract methods in interface classes. The library automatically converts LLM responses back into Python data structures using @dataclasses, leveraging docstrings, type annotations, and function/method names as prompts. It includes support for hyperparameter tracking and trace collection, facilitating meta-optimization and experimentation. The project aims to reduce boilerplate code for LLM integration and explores future possibilities of using cheaper LLMs for automated parsing of structured data.
Key Features
Pros & Cons
- Provides a strongly-typed interface to LLMs, reducing runtime errors
- Minimizes boilerplate code for integrating LLM responses into Python applications
- Enables systematic experimentation and optimization of LLM behavior
- Open source with active development and documentation
- Currently designed for OpenAI's GPT-3 (requires an API key and incurs costs)
- Limited to Python `@dataclasses` for structured output (no support for other serialization formats out of the box)
- Experimental project with potential limitations in handling complex or nested type schemas
- Documentation and examples may be limited for advanced use cases