SmartRAG is a modular Python library designed for retrieval-augmented generation (RAG) workflows with large language models. It combines document ingestion, vector indexing, and state-of-the-art LLM APIs to deliver accurate, context-rich responses. Users can import PDFs, text files, or web pages, index them using popular vector stores like FAISS or Chroma, and define custom prompt templates. SmartRAG orchestrates the retrieval, prompt assembly, and LLM inference, returning coherent answers grounded in source documents. By abstracting the complexity of RAG pipelines, it accelerates development of knowledge base Q&A systems, chatbots, and research assistants. Developers can extend connectors, swap LLM providers, and fine-tune retrieval strategies to fit specific knowledge domains.

ThreeAgents is built in Python, leveraging OpenAI's chat completions API to instantiate multiple AI agents with distinct roles (system, assistant, user). It provides abstractions for agent prompting, role-based message handling, and context memory management. Developers can define custom prompt templates, configure agent personalities, and chain interactions to simulate realistic dialogues or task-oriented workflows. The framework handles message passing, context window management, and logging, enabling experiments in collaborative decision-making or hierarchical task decomposition. With support for environment variables and modular agents, ThreeAgents allows seamless swapping between OpenAI and local LLM backends, facilitating rapid prototyping of multi-agent AI systems. It ships with example scripts and Docker support for quick setup.