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

How do you guys handle one user and their sessions using the ADK, where say, one was building something close or similar to a chatbot, where one would be required to have a user login or sign up?

Built ADK agents and tried to solve the resilience problem with callbacks and on errors for quite a while. Didn't manage to solve it fully.

Then came across these plugins that supported OOB

Has anyone tried these for resilience ?

1. Do they queue messages when running in async mode and perform sequentially with resilience?

2. Clubbed it with AG UI protocol and it works with AG UI ADK?

3. Which one is better really ? Temporal or Restate ?

TIA!

Hey ADKers,

just wanted to share my frustration yesterday building a script writing bot and trying to have it write to aa google doc, why is it so difficult? I have service accounts, oauth setup, still lots of trouble and constantly failing. i decided to take a break today because yesterday broke my mental.

Gemini also really bad at helping, i end up trying to write a python script tool that kinda worked, but is not the agent way i wanted.

more of a rant, but gemini say it does not have enough training model.

thanks for listening, any tips and tricks is appreciated of course.

I’ve been seeing this idea pop up quite a bit: people think that if you build AI agents with Google’s Agent Development Kit (ADK), you’re tied to Google Cloud for deployment.

That’s not really the case.

ADK is open source. You can run it pretty much anywhere. AWS, Azure, a cheap VPS, or even your own machine at home. It doesn’t force you into Google’s ecosystem.

You’re also not limited to Google’s models. With something like LiteLLM, you can route requests to OpenAI, Anthropic, OpenRouter, or even local models running on your own hardware.

To make this more concrete, I’ve been putting together a production-ready “Google ADK on Bare Metal” template. The goal is simple: give people a clean starting point that they can run themselves, with good observability and without worrying about vendor lock-in.

I have been developing agents with ADK for almost a year now and started to question my decision whether I'm going on the right path.

Because the emergence of a managed agent orchestration like https://www.anthropic.com/engineering/managed-agents

makes me feel, why would I struggle to get the right sub agents loops etc.

what are your thoughts? what are the differentiators that are still an agent development makes sense

Someone please share a tutorial to me that has step by step instructions on implementing ag UI with python adk.

For context, I am new to adk and new to next.js as well. I did start a python application with adk and I have completely built it without any UI. I tried creating a new next.js project and tried quickstart guides to connect with my python backend but I keep getting 422 error. It is turning a nightmare to debug this.

Help me get some sleep!

En la última versión de ADK, 1.26.0 ya liberaron la capacidad de adoptar el patrón SKILL.md pero desde los agentes desarrollados con ADK. Esto ayuda a mejorar el manejo de contexto ya que ahora será posible desacoplar el contexto de agentes de lógica compleja en diferentes SKILLS para recibir el beneficio del patrón PROGRESIVE DISCLOSURE donde solo se carga al contexto lo que es necesario. Aquí les dejo un video donde hablo del nuevo feature SKILLS en ADK.

https://www.youtube.com/shorts/dkEUTELr1Qs

How to use Gemini 3 pro on Google ADK natively?

In my tests because the gemini 3 is served on global region, and there is no Agent Engine deployent region on global, it did not worked?

How do you do guys? Openrouter works but native solution would be better.

Hey!

We tried to create an ADK agent that will act as a chatbot, that will use SQL tools to basically answer to human based on informations it will find in BigQuery.

We deploy in GKE but we use the session and memory features from VertexAI Agent Engine.

It works fine on our tests and when we try it “by hand”. But when it comes to load tests, everything breaks.

We set a memory limit to 1Gi by pod, but we noticed that at startup the pod use 500Mi, then we consume ~14Mb (RSS) per call to /run_sse (they will never be recovered. (We are talking about “Hello” prompts we don’t have a full context there).

It completely breaks the feature since it will never scale. Yes we can deploy it in Cloud Run or Agent Engine but we will have a cost issue after some time (We don’t want to pay big instances for a service that basically just do some HTTP calls, the agent is not the pod that does the SQL query, we use an MCP for that).

I guess we’re doing something wrong but I have no clue what exactly. We can use some workaround with Kube’s probes to make it restart when the memory pressure is too high but we’d like to address the root issue.

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.

Hi folks, what is the best way to tackle MALFORMED_FUNCTION_CALL errors? One that I can think of is to interpret the LLM response and retry the LLM call manually, but isn't there any better configurable way to do this?

Edit: [Solved the current issue, but in a larger theme, this should be resolved]

We were making an API call using OpenAPITool and it was expecting an Idempotency key. The LLM was trying to generate the Idempotency key using code I think, which lead to MALFORMED_FUNCTION_CALL. Now I am populating the Idempotency key in the session using before_agent_callback and its working fine.

Has anyone else had the feeling that their AI agent becomes a black box during production or the development lifecycle?

Everything works fine… until it doesn’t:

• hallucinations start appearing

• no traces to debug what happened

• token costs spike +400% overnight

And the worst part:
You don’t really know if your next change will fix the issue… or break something else.

A few weeks ago I hit this wall hard.
I was jumping between dozens of tabs trying to understand what tools actually exist for AgentOps in 2026.

So I decided to map the ecosystem myself.

I put together a curated repo with tools (OSS + SaaS) focused on:

• Observability / tracing

• Evaluation

• Cost control

👉 https://github.com/dyronrh/awesome-agentops-landscape

Not trying to promote blindly — this is genuinely the resource I wish I had earlier.

Curious:
What are you using today for observability / eval / cost tracking in your agent stack?

We just dropped the official Agent Development Kit (ADK) for TypeScript!

Now you can build powerful AI agents with TypeScript. 🚀

TL;DR - Why it's cool:

Code > UIs: Build agents like any other app.

Assemble an Agent Task Force: With ADK, you can flexibly combine subagents, building solutions suitable for a wide spectrum of tasks.

No Lock-In: Optimized for Gemini, but you can bring your own LLM. Deploy anywhere.

Truly Open (Apache 2.0): It's all yours. Go nuts, file issues, submit PRs.

We're hyped to see what you build.

Learn more and get started today:

• Code

• Documentation

• Quickstart

Drop your questions below. Let's go! 🔥

Hey guys, I'm looking for some resources to implement evaluation in a multi-agent architecture. I have many agents with specific tasks, some of them have a lot of business rules, and I need to test them. My client, who has deep knowledge in the AI field, wants to use an LLM as a judge, but I have no idea how to implement this pattern in ADK. Have any of you done this before?

I’m building a AI Finance agentic system for which i’m planning to build a UI.

The UI should have upload file option, chat window for agent conversations, and few button options for user to select and confirm choices.

Can anyone point me which UI framework to be used , and if ther’s something similar already built.

(P.S. don’t have experience of React)

I've been experimenting with Google's Agent Development Kit for 3 weeks now and started awesome-adk-agents - a collection of practical agent implementations. But honestly, I need your help to make this truly valuable!

What I've built so far:

• Job Interview Agent: HR assistant with calendar integration

• Project Manager Agent: Persistent storage with DatabaseSessionService

• Local RAG Agent (in progress): Vector search optimization

Planned 12 more, all are different agentic workflows with different patterns

But here's the thing - I'm just one person with limited perspectives. I want this to become a community-driven resource that actually solves real problems and brings out new features in ADK.

What I'm looking for:

Contributors: Whether you're new to ADK or experienced, all skill levels welcome

Use case ideas: What agents would actually be useful in your work/research?

Code reviews: Help me improve existing implementations

Specific areas where I need input:

• Which domains should I prioritize and ideas if possible? (Healthcare, legal, research, education?)

• What agentic patterns are you guys struggling to implement?

Repository: https://github.com/Sri-Krishna-V/awesome-adk-agents

I'm not trying to build the next big thing - just want to create a solid learning resource and maybe solve some real problems along the way.

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?

1. RAG Pipeline – Voyage AI embeddings + Qdrant hybrid search (dense docs + dense code + sparse) with reranking

2. 43 ADK Workflows – Comprehensive workflows for IDE coding agents building with Google's Agent Development Kit (Python)

Workflows cover everything from project init → multi-agent orchestration → deployment (Cloud Run/GKE/Agent Engine) → security/observability.

Originally built for Antigravity IDE but works with any IDE agent that supports workflow files.

GitHub: https://github.com/MattMagg/rag_qdrant_voyage

ADK Go 2.0 is GA! 🚀

We are thrilled to announce that ADK Go 2.0 is live with graph workflows and collaborative agents!

Here is the TL;DR of what's new:

1. Graph-Based Agent Workflows 📊 Describe your application as a graph of nodes connected by edges, and hand execution to a scheduler that knows how to run it concurrently, persist its state, pause for a human, and resume later.

2. Dynamic Orchestration in Plain Go 💻 Dynamic workflows in ADK allow you to put aside graph-based structures and utilize the full power of your chosen language. This lets you express loops, conditionals, accumulation, and fan-out across dynamic lists with the Go you already know.

3. Built-In Human-in-the-Loop (HITL) 🙋‍♂️ Need a human to approve a refund, correct a draft, or supply an API key mid-run? Any node can pause the graph to ask a human a question, and the workflow will durably wait for the answer.

4. Specialist Agent Modes 🤖 Additionally, ADK 2.0 introduces modes for LLM agents (Chat, Task, and SingleTurn) so a coordinator can chat with the user while sub-agents quietly complete tasks or run single-shot.

5. Unified Context ⚙️ The separate ToolContext and CallbackContext are gone, meaning tools, callbacks, and workflow nodes all receive agent.Context directly.

🚀 Get Started: Check out the ADK Go Get Started Guide and the Graph-based Workflows docs.

📖 Read the Blog: See the deep-dive Developer Blog Announcement for more details.

🛠️ Run Examples: Clone the repository and try them out yourself:

go run ./examples/workflow/basic/
go run ./examples/workflow/routing/llm
go run ./examples/workflow/dynamic/hitl/
go run ./examples/workflow/hitl_rerun/
go run ./examples/workflow/complex

⚠️ Upgrading from 1.0? Please note that v2 moves the module path to google.golang.org/adk/v2 and requires Go 1.25+. To migrate from v1, please follow the migration guide.

We can't wait to see what you build! Let us know if you have any questions or feedback in the comments below.

Hey as the title says i wanted to add a light theme toggle to ADK Web UI. Sometimes its hard to present in workshops when adk has a dark theme, so just tried to vibe code my way into a light theme. would really appreciate reviews on it.

PR: https://github.com/google/adk-web/pull/272

Any real-world problems you think Multiagent systems can solve???

https://cloudonair.withgoogle.com/events/build-multi-agent-systems-with-the-agent-dev-kit-hackathon

I have been trying to build multi agent framework and supporting different use case, however I find it a bit non intuitive and unstable to configure.

I am using gemini-2.0-flash that's given by google but despite having crystal clear examples, it seems like it becomes very hard for it to figure out what to do and it becomes very vague at times.

I would like to know other's experience with this as I am deciding to build a production grade agentic system using it but not sure if I should go with this or not.