AI tool comparison
Google ADK Python 1.0 vs Hopper
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 Python 1.0
Google's production-ready framework for building AI agents
75%
Panel ship
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Community
Free
Entry
Google's Agent Development Kit (ADK) Python hit v1.0.0 stable on April 17, marking it production-ready for teams building and deploying AI agents at scale. ADK is a modular, code-first framework that applies standard software engineering principles to agent development — graph-based workflow execution, structured agent-to-agent delegation via a Task API, native MCP support for tool integration, and built-in evaluation tooling. Unlike LangChain's general-purpose orchestration or CrewAI's role-based crews, ADK leans into composable determinism: you define explicit graphs of agent behavior that are auditable, testable, and deployable directly to Google Cloud's Vertex AI Agent Engine. It supports Python, TypeScript, Go, and Java, making it one of the few multi-language agent frameworks in production. The 1.0 stable label matters. Google has been iterating ADK roughly every two weeks, and teams that held off on building with it due to API instability now have a stable target. With Vertex AI providing the deployment layer and Agent Engine handling orchestration at scale, this is Google's full-stack answer to the agent infrastructure question.
Developer Tools
Hopper
The first AI agent dev environment built for COBOL and mainframes
75%
Panel ship
—
Community
Free
Entry
Hopper, from YC S24 startup Hypercubic, is the first agentic development environment purpose-built for mainframe systems. It lets AI agents navigate TN3270 terminals autonomously, write and submit JCL jobs, monitor JES output, debug failed jobs by analyzing spool data, query VSAM datasets, compile and run COBOL code, and manage CICS transactions—all via natural language prompts. Tasks that traditionally took mainframe specialists hours of manual TN3270 navigation can now be expressed as a single instruction. The technical challenge here is real: mainframes don't have nice REST APIs or modern dev tooling. They run on green-screen terminal protocols from the 1970s, and the humans who know how to operate them are retiring faster than they can be replaced. Hopper essentially wraps the entire mainframe interaction surface in an agent-friendly interface, translating intent into the arcane sequences of keystrokes and JCL that mainframes actually require. The product is free for individual developers (all core features, macOS/Windows/Linux) with Enterprise pricing for SSO, on-prem deployment, and SOC 2 reports. Hypercubic's team includes alumni from Cognition, Apple, and Windsurf. Given that mainframes still process an estimated $3 trillion in daily commerce and the COBOL developer shortage is acute, Hopper is targeting a genuinely underserved market with unusual urgency.
Reviewer scorecard
“The 1.0 stable tag finally gives us something to build on. The graph-based execution engine is exactly what I want for deterministic multi-step pipelines where I can't afford unpredictable LLM routing. Native MCP support means my existing tool ecosystem plugs straight in without adapter layers.”
“This solves a real crisis. I've watched financial institutions pay six-figure consultant fees for tasks that Hopper demos suggest could be automated in minutes. If it's reliable on diverse JCL and CICS environments, this is immediately commercial.”
“ADK's tight coupling to Vertex AI is a genuine lock-in concern. The 'production-ready' badge comes with an implicit 'on Google Cloud' qualifier. For teams running on AWS or Azure, the deployment story is clunky. LangGraph and CrewAI are more cloud-agnostic and have larger community ecosystems right now.”
“Mainframe environments at major banks are extraordinarily heterogeneous—custom RACF configurations, vendor-specific CICS extensions, and decades of undocumented JCL conventions. An agent that confidently submits the wrong job in a production batch environment could be catastrophic.”
“Google going stable on a multi-language agent framework signals they're treating this as core infrastructure, not a demo. The Agent-to-Agent (A2A) protocol work alongside ADK hints at Google's real play: defining how agents communicate at internet scale, the same way HTTP defined how documents communicate.”
“The $3 trillion in daily mainframe commerce has been a black box to AI modernization. Hopper is the Rosetta Stone moment—once there's an agent-friendly interface to legacy systems, every other AI tool in the stack becomes accessible to that infrastructure.”
“For no-code and low-code builders who want to graduate to real agent workflows, ADK's structured graph model is more approachable than writing raw LangChain chains. The TypeScript version in particular opens this to a much wider pool of front-end developers who want to add agentic features to their apps.”
“There's something poetic about AI agents handling COBOL—the language written by Grace Hopper, now managed by a tool named after her. For teams modernizing legacy fintech systems, this is the missing piece.”
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