RAGApp is designed to simplify the entire RAG pipeline by providing out-of-the-box integrations with popular vector databases (FAISS, Pinecone, Chroma, Qdrant) and large language models (OpenAI, Anthropic, Hugging Face). It includes data ingestion tools to convert documents into embeddings, context-aware retrieval mechanisms for precise knowledge selection, and a built-in chat UI or REST API server for deployment. Developers can easily extend or replace any component—add custom preprocessors, integrate external APIs as tools, or swap LLM providers—while leveraging Docker and CLI tooling for rapid prototyping and production deployment.

MAGI (Modular AI Generative Intelligence) is an open-source framework designed to simplify the creation and management of AI agents. It offers a plugin architecture for custom tool integration, persistent memory modules, chain-of-thought planning, and real-time orchestration of multi-step workflows. Developers can register external APIs or local scripts as agent tools, configure memory backends, and define task policies. MAGI's extensible design supports both synchronous and asynchronous tasks, making it ideal for chatbots, automation pipelines, and research prototypes.