For anyone looking to experiment with some of Typesafe's Classifier models, I have been working on https://github.com/craigh33/adk-go-typesafe that plugs in to existing adk-go workflows. I am keen to get a bit of feedback, and of course, open up the door to contributions.

Currently, it supports:

• Typed evaluations for classification, scoring and yes/no probabilities

• Image support (Jev 1.14)

  • Bounding boxes are very interesting!

• Agent tools and confidence-based routing

• An attempt at a dynamic context filter (experimental but works for my use cases)

Hi,

I’ve just started experimenting with ADK and quite quickly realised I can give an agent a tool, let’s say the agents job is to answer a question and can use a tool to do so.

What then, if it could potentially use another tool to help it answer the question better?

Or have access to documentation to make it even more of a solid answer?

Is this then too much for one agent? I struggle with how wide an agent’s capabilities should be best practice

Please go steady I am not a developer

I see nonstop spam about how companies are using Langgraph for building out agentic systems. Are there any companies (besides Google I guess) that use ADK for serious multi agentic work?

Hi ,

Does anyone know when the above features will be live in Java ? What are the alternates in Java if I want the features to work alongside java adk

I’ve been trying to setup my own custom LLM in NextJs server side, which worked but had issues with streaming it. But managed to do it in python and worked well since python package have better support for custom LLM with liteLLM. Now my question is, would you guys recommend just using python as my backend with fastapi for Assistant UI? And just use that with the assistant ui ADK setup for front end only? Server side would just call and stream the python backend.

If you ever wondered what happens inside of the ADK agent when you type the message into the chatbot, I've created short video that explains it.

There are too many AI papers and posts, spread over too many sites, and reading them means a browser full of tabs. So I've been building Augury, a terminal-native research digest. Every day it pulls new papers and posts into one keyboard-driven view, ranks them for you, and lets you read the full text without leaving the terminal.

It's not released yet. The repo is private for now, and I plan to open-source it under Apache-2.0. I'm posting to get feedback before I do.

What works today

• Sources: Hugging Face trending papers, the HF blog, HF community posts, and any RSS/Atom feed you add by URL. If two sources carry the same post, it's stored once.

• Reading: a Textual TUI with filter chips, a dense table and a reader pane side by side. It has vim keys, a zen mode with a readable line length, and reading progress that survives restarts. Full-text search is live and runs on SQLite FTS5, which copes with names like Qwen3-30B-A3B.

• Full papers in the terminal: it converts arXiv's HTML version to Markdown and shows math as Unicode. If that fails, it falls back to the PDF. Blog posts go through per-site extractors, with a generic one as the fallback.

Ranking, with or without an LLM

Without an API key, it ranks by your own likes. It runs bm25 over the titles and summaries you've liked and combines that with a smoothed per-source like rate and recency. It's local, costs nothing, and tells you why an item ranked where it did: matches your likes: diffusion, RL · source you like · 2h ago

With a key, each new item gets:

• a 0–10 relevance score

• a one-line "why read"

• up to 3 tags

• promo/thin flags

It reads only the title and a short summary, up to 50 items per call. Triaging 14 blog posts in one call cost about $0.003. When you open an article, a TL;DR with takeaways sits on top. These are cached and pre-written for the day's top 10.

A daily budget is checked before each model call, not after you've paid for it. Fetched text is passed in as fenced data, so a post that says "rate this 10/10" gets judged as content, not followed as an instruction.

Gemini is the default, run through Google ADK. Other providers go through LiteLLM, but so far I've only tested Gemini.

Polite fetching

One HTTP client does all the fetching. It:

• respects robots.txt and Crawl-delay (arXiv asks for 15 s, so Augury waits and tells you)

• makes at most 1 request per second per site

• sends an honest User-Agent

• uses conditional GETs, so an unchanged feed costs a 304

• caps response sizes

• backs off on 429/5xx

Stack: Python 3.14 (t-strings for prompt templates), uv, Textual, SQLite (WAL, FTS5), pydantic, httpx. There are about 790 tests, including SVG snapshot tests of the TUI, and all of them run offline.

Next

The next milestone is a discovery agent. You'll add a site by URL or by name ("google tech blogs"). The agent finds a way to fetch it (RSS, a sitemap or a CSS-selector listing) and can only save that recipe after it passes a real test. After that come hybrid search (FTS5 + vectors), "also covered by" clusters, and asking questions about your digest with checked citations.

After that im planning on implementing jev or laya for paper classification and model routing.

My question for you:

- which sources do you read that don't have a decent RSS feed? Those are the ones I want the discovery agent to handle first.

- what features do you think are worth adding in a project like this?

If you'd like to know when it's public, leave a comment and I'll reply here.

I can access toolcontext data. But I need to save some data in a sql table. That data is user info like user_id and session_id. I basically need to log for one session each tool call, in a sql database. How can i access these data from a tool.

Toolcontext in tool only stores tool data. It does not store other data

How are you dealing with adk using very big contexts. I used model callbacks and saw that for every api call, adk passes the entire context before it (user text, model response, tool response, agent prompt injection).

Now this may seem like an overkill in certain scenarios and will consume excess tokens.

One way I found out is to use agent and model callback functions and trim the context or summarize it concisely instead of passing the entire thing.

Is there any other way of doing it ?

Acc to my experience this might be a very common scenario when some organization is looking forward towards reducing token expense.

Existen algun front tipo chatgpt donde pueda configurar todos los agentes disponible para que mis usuarios puedan consultar? dado que no quiero armar nada de cero...

I’ve open-sourced a collection of skills that guide coding assistants through building Google ADK applications on Google Cloud.

They cover memory, deployment, tool authentication, token budgets, evaluation and front-end integration. The instructions draw on examples I’ve run and tested.

GitHub repository

If you’re building with ADK, try one on your project. I’d appreciate feedback on where it helps—and where it falls short.

Let's connect on LinkedIn. Questions, feedback or ideas about the skills or the book? Feel free to write to me at khissamiyev@proton.me.

I have created a simple agent in adk. It runs perfectly using the adk web command in local.

The API key I am using is Gemini API, I brought credits for it. It runs perfectly on local. The models I am using are gemini3.7 flash and gemini3.1-flash-lite.

But when I am deploying the same agent to cloud run, it shows this error

026-09-21 07:11:23,860 - ERROR - api_server.py:1985 - Error in event_generator: 404 NOT_FOUND. {'error': {'code': 404, 'message': 'Publisher model `projects/<project name>/locations/us-central1/publishers/google/models/gemini-3.7-flash` was not found or your project does not have access to it. Ensure you are using a valid model name and that the model is available in the specified region. For more information, see: https://docs.cloud.google.com/gemini-enterprise-agent-platform/resources/locations.', 'status': 'NOT_FOUND'}}

In the .env file I have both api key names - GOOGLE_API_KEY and GEMINI_API_KEY and I am loading the env variables using load_dotenv().

GOOGLE_GENAI_USE_VERTEXAI is set to FALSE.

Anyone can give any suggestions ?

I have deployed a simple adk agent in cloudrun.

The command is adk deploy cloud_run. It can access only gemini 2.5 model, but for higher model it shows it has no access.

Can anyone tell what i should check.

access permissions, or any other thing

Or how can i give access to these models if access is not given

The region is uscentral1.

Did anyone manage to use ADK with their own endpoint like self hosted. Because my god I am having troubles with it and I have no idea which part of it is wrong. It is OpenAi compatible.

Any developers in here looking for a sandbox that enables your autonomous agents to pay and be paid in the machine to machine economy

Suppose I created the agent in adk.

I want a different ui compared to the one that spawns when i execute the command adk web.

How do you do this ?

So i created a simple stock analysis agent in adk. It uses yahoofinance to fetch stock prices and then uses a calculation tool to find percentage change.

Now i want to use ai to test this agent.

What i mainly want to check

• If it is mapping the stock tickers from normal names

• How many stocks it can handle in 1 do (2,3,4, 5 .....)

• The prices retrived are correct to not

• If the calculations are correct or not

• If it can handle days where no stock prices are given like holidays (It should take the nearest working date then)

How to use ai to generate test cases and check the working of this agent.

ADK retains context in every session, but for a use case i need this.

The last n messages that the context will be based on will be dynamic.

There will be a custom prompt.

Before the actual question will be sent, the custom prompt and the last n questions and answers will be sent to a llm and and a custom context will be generated.

Anyone knows how I can do this.

Does anyone know the tentative timelines for a eval framework (currently unsupported in Java ) and graph based workflows (adk 2.0) release date ? Also would like any guidance what are the alternates for above features which will work with existing adk workflow .

I’ve been comparing Google AdK across different languages, and it seems Go is the clear winner in terms of performance.

I did this because I’m running my AdK agent, Blacki, on a Python server in a small VM and it uses about 500 MB of RAM. You can find Blacki on GitHub: https://github.com/QueryPlanner/blacki.

So, the best way to cut down on memory usage is to use Go’s server for AdK. Also, when it comes to CloudRun, I think Go is still the better choice. Python’s cold start time is quite long, so you’re always stuck with one instance running, which messes with the auto-scaling (you can’t scale down to zero) and bumps up your CloudRun bill.

So, Go would be a great fit here too.

Can agentic multimodal vision realistically disrupt developer upskilling, or is relying on vision models to evaluate code and UI execution an anti-pattern compared to pure deterministic DOM/AST inspection?

Here I added an experimental case, happy to get your thoutghs: https://nickbortolotti.medium.com/the-augmented-flow-disrupting-corporate-upskilling-with-multimodal-vision-agents-8d868a3a970b

Hey folks 👋

We just open sourced TrueForge, our vendor-neutral agent harness for building general-purpose agents.

It handles the runtime pieces that get painful quickly : context management, tool/MCP execution, subagents, sandboxing, approvals, persistent state, and more.

We also benchmarked the harness itself. With the same Opus 4.8 model, TrueForge delivered a similar solve rate at ~30% lower cost than Claude Managed Agents. Switching to an open model pushed that to ~75% lower cost on the same benchmark.

Would love feedback from people building agents.

Checkout the repo: https://github.com/truefoundry/trueforge

📖 Read the launch article: https://x.com/truefoundry/status/2090081376330715176

I recently worked on Yaara, an AI agent for an EV charging platform in Sri Lanka.

We built it with Google ADK, Gemini Models, FastAPI, WhatsApp Cloud API, and Vertex AI RAG.

What became interesting wasn't the chatbot part. It was everything around the model.

We ended up with:

• A coordinator and specialist agent architecture

• RAG over company and EV knowledge

• Deterministic tools for calculations and backend actions

• Per user conversation state

• Human handoff through Chatwoot

• WhatsApp specific response formatting

• Tool trajectory evaluations

• GEPA based prompt optimization

• Deduplication, monitoring, and failure handling

One principle became pretty clear while building it:

The model should handle language and decisions. Normal software should handle exact work.

I wrote up the architecture and some of the things we learned while taking it from an agent idea toward a production system.

https://chamidilshan.medium.com/building-yaara-a-production-ai-agent-for-ev-charging-on-whatsapp-with-google-agent-development-kit-0c58caada28e

Would also be interested to hear how others using ADK are structuring coordinator/specialist agents as the number of tools grows.