llm-lab provides a flexible toolkit for creating intelligent agents using large language models. It includes an agent orchestration engine, support for custom prompt templates, memory and state tracking, and seamless integration with external APIs and plugins. Users can write scenarios, define toolchains, simulate interactions, and collect performance logs. The framework also offers a built-in testing suite to validate agent behavior against expected outcomes. Extensible by design, llm-lab enables developers to swap LLM providers, add new tools, and evolve agent logic through iterative experimentation.

Advanced_RAG provides a modular pipeline for retrieval-augmented generation tasks, including document loaders, vector index builders, and chain managers. Users can configure different vector databases (FAISS, Pinecone), customize retriever strategies (similarity search, hybrid search), and plug in any LLM to generate contextual answers. It also supports evaluation metrics and logging for performance tuning and is designed for scalability and extensibility in production environments.