TreeInstruct provides a framework to build hierarchical, decision-tree based prompting pipelines for large language models. Users can define nodes representing prompts or function calls, set conditional branches based on model output, and execute the tree to guide complex workflows. It supports integration with OpenAI and other LLM providers, offering logging, error handling, and customizable node parameters to ensure transparency and flexibility in multi-turn interactions.

LAWLIA provides a structured interface to define agent behaviors, plugin tools, and memory management for conversational or autonomous workflows. Developers can integrate with major LLM APIs, configure prompt templates, and register custom tools like search, calculators, or database connectors. Through its Agent class, LAWLIA handles planning, action execution, and response interpretation, allowing multi-turn interactions and dynamic tool invocation. Its modular design supports extending capabilities via plugins, enabling agents for customer support, data analysis, code assistance, or content generation. The framework streamlines agent development by managing context, memory, and error handling under a unified API.