AI tool comparison
Google ADK vs Plain
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
Google's open-source Python framework for production AI agent systems
75%
Panel ship
—
Community
Paid
Entry
Google's Agent Development Kit (ADK) is an open-source Python framework that brings software engineering discipline to AI agent development. It takes a code-first approach — developers define agent logic directly in Python, making agents testable, composable, and deployable across different environments without lock-in. ADK supports pre-built tools, custom functions, OpenAPI specs, and MCP integrations. It's designed for multi-agent architectures where specialized sub-agents are orchestrated into scalable hierarchies. A built-in development UI makes local testing and debugging far easier than most competing frameworks, and Cloud Run and Vertex AI deployments are first-class deployment targets. With 19,300+ stars and an Apache 2.0 license, ADK is gaining real traction. While optimized for Google's Gemini models, it's designed to be model-agnostic — an important choice that signals Google understands developers want flexibility, not a guided tour of their cloud bill.
Developer Tools
Plain
A Django fork rebuilt for AI agents — typed, predictable, agent-readable
75%
Panel ship
—
Community
Free
Entry
Plain is a full-stack Python web framework that forks Django with one overriding goal: make the codebase maximally readable and understandable by AI coding agents. Built by Dropseed (Adam Engebretson), it started in 2023 and has quietly matured into a production-ready framework — today's Show HN submission (93 points) brought it to wider attention. The design philosophy is radical clarity over magic. Plain eliminates Django's more implicit behaviors, adds strict typing throughout, and includes built-in AI integration hooks: a `.claude/rules/` directory for Claude Code context, a CLI command for on-demand documentation retrieval, and OpenTelemetry instrumentation out of the box. The idea is that when a coding agent touches your codebase, it should be able to understand what's happening without fighting through Django's layers of metaclass magic. This represents a genuine philosophical bet: as AI agents write more of our code, the framework's readability to machines matters as much as its readability to humans. Plain is ahead of the curve on this — most frameworks were designed for human ergonomics first. The Show HN traction suggests senior engineers are taking the concept seriously, even if migration from Django remains a real cost.
Reviewer scorecard
“ADK hits the sweet spot between the simplicity of a prompt wrapper and the complexity of LangChain. The MCP integration and built-in dev UI make it the most productive framework I've tried for real multi-agent systems. The Python-native design means you can test agents like real software.”
“The `.claude/rules/` integration and typed APIs are exactly what you want when you're letting agents modify your codebase. OTel built-in is a legitimate win — no more strapping on tracing as an afterthought. If you're starting a new Python project in 2026, Plain is worth serious consideration.”
“It's a Google project, which means 'optimized for Gemini' in practice regardless of what the docs promise. The Apache license is great, but you're betting on Google's continued commitment — and Google has an impressive graveyard of abandoned developer tools.”
“Django's 'magic' is also its ecosystem — 20 years of packages, tutorials, and institutional knowledge. Plain's ecosystem is tiny. For any non-trivial project, you'll hit the ecosystem wall fast. 'Designed for agents' is a compelling narrative but the migration cost from Django is real and steep.”
“ADK represents Google's serious entry into the agent framework wars. The code-first philosophy and MCP-native design suggest they studied what developers actually want. If Gemini and Vertex AI keep improving, this stack will be formidable.”
“The question 'is this codebase understandable to an AI agent?' is going to be central to framework design by 2027. Plain is three years ahead of that conversation. Frameworks that don't add agent-readability features will be retrofitting them later at significant cost.”
“The dev UI for testing agents demystifies what your AI is actually doing — which matters enormously when you're building creative automation. Steep learning curve for non-engineers, but if you have a technical partner, ADK is worth exploring.”
“As someone who ships products, not just writes code, I care about the full stack being coherent. Plain's opinionated structure means less time arbitrating between packages and more time building. The built-in OTel means I can debug AI-assisted changes without adding another tool.”
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