featured · github
Google's ADK: A Python Agent Toolkit That Treats Evaluation as a First-Class Problem
Google open-sourced a code-first framework for building and shipping AI agents. The real move: built-in evaluation and deployment patterns that cut through the usual orchestration mess.
google/adk-python ↗Most agent frameworks let you chain LLM calls together, then cross your fingers in production. ADK is Google's answer: a Python toolkit where you write agent logic directly, but evaluation is baked in from the start, not bolted on later.
You define your agent's tools and behavior in code, then run it against test cases before deploying. Think of it as the difference between "hope my agent works" and "prove my agent works on these scenarios." It's production-grade because Google runs it internally. For builders shipping agent products, this means less guessing and more confidence that your agent won't hallucinate or miss edge cases in the wild.
Share kit
Google Open-Sources ADK: Agent Toolkit with Evaluation Built In
ADK is a Python framework for building AI agents with eval-first design. Tools, logic, and deployment all in code. Less DIY orchestration, more proof that your agent actually works before shipping.
Google just open-sourced ADK: a Python toolkit for building AI agents where evaluation isn't an afterthought. Write your agent logic in code, test it against real scenarios, deploy with confidence. No more orchestration roulette.
ADK is Google's agent framework for builders who want eval-first architecture. Define your agent's tools and behavior in Python, run it against test cases, then ship to production knowing it works. Built by Google, open-sourced for the ecosystem.
Just found Google's answer to the agent framework sprawl. ADK-python landed in my feed and it's the kind of thing that makes you realize how much friction we've been tolerating. They've baked evaluation and deployment into the core instead of bolting it on as an afterthought. That matters because right now, most agent toolkits treat you like you're building a prototype, then ghost you when it's time to actually ship. Three things that jumped out: → Evaluation is first-class. You're not reverse-engineering evals into your pipeline at 2am. → Python-native, no framework gauntlet. If you know Python, you can reason about what's happening. → Built by Google, which means it's been stress-tested on real agent workloads, not just a GitHub showcase. The agent space is noisy. Most entries are "we have orchestration too." This one actually solves the build-eval-deploy loop that kills half the agent projects I see. Worth a look if you're past the YAML-config phase and need something that scales without a rewrite.
google/adk-python just landed and it's a material shift for agent devs. Evaluation + deployment baked into the toolkit, not bolted on later. Python-first, production-hardened by Google. Most agent frameworks treat you like you're building a prototype. This one actually scales to shipping. Worth the 10min read if you're past the YAML phase.