Google ADK vs RAG-Anything

Google Agent Development Kit (ADK) is an open-source Python framework for building, composing, and deploying multi-agent AI systems. It handles the hard parts of agent orchestration — tool use, memory, inter-agent communication, and deployment — with first-class support for Gemini models and Google Cloud, but designed to be model-agnostic. The framework reached 8,200+ GitHub stars within weeks of launch, making it one of the fastest-growing agent infra repos this spring. ADK ships with built-in support for common agent patterns (sequential, parallel, coordinator-worker), a robust tool abstraction layer, and native MCP support. It integrates cleanly with Google's broader AI stack (Vertex AI, Cloud Run) but also works standalone with other model providers. ADK enters a crowded field — LangGraph, CrewAI, and AutoGen all offer overlapping functionality — but Google's official backing, deep Gemini integration, and the framework's quality-of-life improvements (particularly around deployment and state management) have made it an instant reference implementation for many teams.

RAG-Anything is an all-in-one Retrieval-Augmented Generation framework from HKUST's Data Systems Group that handles multimodal documents through a single unified pipeline. Unlike RAG frameworks that only handle plain text, it natively ingests and retrieves across text, tables, images, scientific figures, and mixed-modality documents without requiring separate preprocessing pipelines for each type. The framework covers the full RAG stack: document parsing, chunking strategies adapted to content type, embedding, vector storage, retrieval ranking, and generation. It's built to handle the kinds of documents that real enterprise workloads throw at you — PDFs with embedded tables, research papers with figures, reports that mix structured and unstructured content. With 16,000+ stars and academic backing from HKUDS (the same group behind LightRAG), it carries credibility beyond typical weekend projects. The key insight is that most RAG failures in production happen at the parsing and modality-handling stage, not the retrieval stage. By making multimodal handling a first-class concern rather than a bolt-on, RAG-Anything aims to close the gap between RAG demos and RAG production deployments.