AI tool comparison
Google ADK 2.0 vs Rocky
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
Google ADK 2.0
Open-source agent framework: Python 2.0 beta + TypeScript 1.0 drop
75%
Panel ship
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Community
Paid
Entry
Google's Agent Development Kit (ADK) just hit two major milestones simultaneously: ADK Python 2.0 Beta with workflows and agent teams, and ADK TypeScript 1.0 reaching stable release. This open-source framework is Google's answer to LangChain and CrewAI — a code-first toolkit for building production-grade AI agents that are testable, versionable, and deployable anywhere. What separates ADK from the competition is its context management philosophy: it treats sessions, memory, tool outputs, and artifacts like source code, assembling structured context where "every token earns its place." The 2.0 beta introduces graph-based workflows and collaborative multi-agent systems, letting developers compose teams of specialized agents into complex hierarchies. It's model-agnostic despite being optimized for Gemini, and supports MCP natively. Deployment is a first-class citizen — native integrations with Cloud Run, GKE, and Vertex AI Agent Engine, plus Google's new Agents CLI for scaffolding, eval, and deploy in one command. With Apache 2.0 licensing and a bi-weekly release cadence, this is shaping up as the enterprise-grade foundation serious agent builders have been waiting for.
Developer Tools
Rocky
Rust-compiled SQL for data pipelines: branches, lineage, AI intent layer
50%
Panel ship
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Community
Paid
Entry
Rocky is a Rust-based SQL transformation engine that brings software engineering discipline to data pipelines. Where tools like dbt gave data teams a version-controlled workflow, Rocky goes further: type-safe compile-time SQL, column-level lineage visualization, git-style branches for isolated testing, and a built-in AI intent layer that stores your purpose as metadata alongside the code. The branching feature is the standout — you can create a branch, run it against an isolated schema, inspect the results, then drop or promote. The column-level lineage shows the full downstream blast radius before you ship a change, tracing any single column back through every aggregation and join to its source. This is the kind of visibility that prevents the "who broke the revenue dashboard" post-mortems that happen in every data team. The AI intent layer is genuinely novel: it stores what a model is supposed to do as metadata, so AI can later explain models, auto-update them when upstream schemas change, and generate tests based on the original intent. Rocky integrates with Dagster via an official plugin and supports DuckDB for local development with no credentials required. With Hacker News coverage and a Rust-native architecture, it's positioned as the data pipeline tool for engineering-forward teams who are tired of YAML-based transformations.
Reviewer scorecard
“Graph-based workflows in 2.0 Beta finally make multi-agent orchestration feel sane. The Agents CLI scaffolding saves an hour of boilerplate every new project. Apache 2.0 means no licensing headaches at scale.”
“Compile-time type safety for SQL is the feature I've wanted for years — catching type mismatches before the pipeline runs instead of finding out when a dashboard breaks at 9am. The column-level lineage alone justifies the migration cost for any team managing complex pipelines.”
“It's 'model-agnostic' but the Cloud Run and Vertex AI integrations make it a Google Cloud lock-in play dressed in open-source clothing. LangGraph and CrewAI have a 2-year head start and larger ecosystems — ADK needs to prove itself outside Google's walls.”
“dbt has a massive ecosystem, hundreds of integrations, and years of community knowledge — migrating to Rocky means giving all that up for a Rust tool with a small user base. The AI intent layer sounds cool but 'stores intent as metadata' is vague; in practice this is probably just comments with extra steps.”
“ADK being 'designed to be written by both humans and AI' is the key insight here — we're entering an era where agents build agents, and ADK is building the scaffolding for that recursion. TypeScript 1.0 stable means the frontend ecosystem is now fully in play.”
“Data pipelines are the next frontier for AI-assisted maintenance, and Rocky's intent metadata approach is ahead of the curve. When AI can auto-reconcile pipelines after schema changes because it knows what each model was meant to do, that's a qualitative shift in how data infrastructure gets maintained.”
“Visual debugging and evaluation frameworks finally make agent behavior legible — no more blind faith in what your agent actually did. This lowers the floor for non-ML engineers to build reliable agent pipelines.”
“Rocky is clearly built for engineering-heavy data teams — the VS Code extension, compile-time guarantees, and Dagster integration signal a developer-first product. For data analysts and business intelligence folks who just need their transforms to work, the learning curve is steep.”
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