Google ADK vs qmd

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.

qmd is a lightweight local search engine built by Tobi Luetke, CEO of Shopify, for indexing and querying personal knowledge bases, documentation, and meeting notes — entirely offline. It combines three retrieval approaches in a single pipeline: BM25 full-text search for exact keyword matches, vector semantic search via ONNX-based embeddings, and LLM re-ranking using GGUF models through node-llama-cpp. All three stages run locally with no cloud dependency. The tool ships in multiple deployment modes: a CLI for ad-hoc queries, a Node.js library for programmatic use, an HTTP service for local API access, and — most useful for AI workflows — a native MCP server that lets Claude Code, Cursor, and similar editors query your local knowledge base directly during coding sessions. The hybrid retrieval approach means it handles both "find the exact error message from last week's standup notes" and "what was our decision about the auth architecture" equally well. What makes this notable beyond its technical approach is provenance: Luetke shipped it as a personal tool he actually uses, not a startup product. The GitHub history shows active iteration and he's been talking about it on X. It's a credible signal of where pragmatic AI-augmented knowledge management is heading for technical users who prefer local-first tools.