LLPhant is an open-source Python framework enabling developers to create versatile LLM-driven agents. It offers built-in abstractions for tool integration (APIs, search, databases), memory management for multi-turn conversations, and customizable decision loops. With support for multiple LLM backends (OpenAI, Hugging Face, others), plugin-style components, and configuration-driven workflows, LLPhant accelerates agent development. Use it to prototype chatbots, automate tasks, or build digital assistants that leverage external tools and contextual memory without boilerplate code.

Rags provides a modular pipeline to build retrieval-augmented generative applications. It integrates with popular vector stores (e.g., FAISS, Pinecone), offers configurable prompt templates, and includes memory modules to maintain conversational context. Developers can switch between LLM providers like Llama-2, GPT-4, and Claude2 through a unified API. Rags supports streaming responses, custom preprocessing, and evaluation hooks. Its extensible design enables seamless integration into production services, allowing automated document ingestion, semantic search, and generation tasks for chatbots, knowledge assistants, and document summarization at scale.