This is one of those cases where it's really helpful to code, at least once, at one layer of abstraction below the one that seems most natural to you.

- It’s conceptually simple. An agent is just an object, you assign it tools that are just functions, and agents can call other agents.

- It’s "batteries included". You get a built-in code execution environment for doing math, session management, and web-server mode for debugging with a front-end.

- Optional callbacks provide clean hooks into the magic (for example, anonymizing or de-anonymizing data before and after LLM calls).

- It integrates with any model, supports MCP servers, and easy enough to hack in your existing session management system.

I'm working on a course in agent development and it's the framework I plan to teach with.

I would absolutely take this for a spin if I didn't hate Go so much :)

[1] https://github.com/google/adk-go/tree/main/examples/web

Python is way more ergonomic when dealing with text than go. Go's performance advantages are basically irrelevant in an AI agent, as execution time is dominated by inference time.

Gonna point Claude at our repo and see if I can do an easy conversion, makes the amount of reviews I have to do a bit more bearable.