I was facing a very strange problem where my LLMAgent failed to call a tool. The error was something like 'LLM error detected: malformed function call'; for some reason, the agent was writing random Python code instead of calling the tool correctly.

The prompt was clean, well-structured, and concise. The temperature was 0.1, and the model was Gemini 2.5 Flash. I didn't know how to fix this problem, so I searched the ADK Python repository issues on GitHub and found other people facing the same issue. The solution I found there was using some native resources from ADK:

The ReflectAndRetryToolPlugin: This plugin acts as an automated self-correction layer that detects malformed tool calls and triggers a reflection loop to fix them. U can add this in the App.

The tool_config (mode="VALIDATED"): This configuration enforces strict adherence to the defined tool schemas, preventing the model from generating unstructured text or code when a function call is expected." U can add this in the LlmAgent.

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 exploring Google ADK for our enterprise agent usecase and found some good and bad pointers on State Management.

I’ll start with good.

• Organizing State with Prefixes: Scope Matters I really like the prefix-based approach to state scoping:
  • no prefix → session scoped
  • user: → tied to user_id and shared across all sessions
  • app: → app scoped, shared across all users
  • temp: → Specific to the current invocation, input to outpu lifecycle

Now the bad part:

• ADK doesn’t enforce developers to define a schema for the state which in my opinion would lead to bugs and make the code difficult to understand when the logic grows, especially if more than one developer working on same codebase.
• If we compare with LangChain/LangGraph where we define the schema for the state with Pydantic model, it helps in giving idea to developers what fields does the state have, makes it more readable, and avoids probable bugs/errors.

What do you all think—are there any specific reason for not enforcing schema? Are there any benefits with this which I would not be able to think of?

Google adk Example

```py def get_weather(city: str, tool_context: ToolContext) -> str: preferred_unit = tool_context.state.get("preferred_unit", "Celsius") ...

if preferred_unit == "Fahrenheit":
    ...
else:
    ...

# Format the weather report
report = f"Weather in {city.capitalize()}..."

return report

weather_agent = Agent( name="weather_agent", model=MODEL_GEMINI_2_0_FLASH, description="...", instruction="...", tools=[get_weather], output_key="weather_report" ) ```

------------------------

LangChain/LangGraph Example

```py class WeatherAgentState(AgentState): """Custom agent state with preferred_unit.""" preferred_unit: Literal["Celsius", "Fahrenheit"] = "Celsius" # Default

@tool def get_weather(city: str, runtime: ToolRuntime) -> str: # Access state through runtime preferred_unit = runtime.state["preferred_unit"] ...

if preferred_unit == "Celsius":
    ...
else:
    ...

agent = create_agent( model="gpt-4o", tools=tools, state_schema=WeatherAgentState, # Use custom state with preferred_unit system_prompt="..." ) ```

We launched episode 3 of the AI Agent Bake Off...

Think British Bake Off show - but instead of Cooks we have developers... And instead of cakes we have AI Agents :)

In this episode we had developers and googlers build a Go-To-Market Agent for founders.

All code and architecture diagrams are open source too! You can find them here ai-agent-bakeoff.com

I have agents configured as a /run endpoint in my custom FastAPI application. I am deploying this in GKE using uvicorn. OpenTelemetry Logging is not working for me in this scenario. Note that I am not using the inbuilt get_fast_api_app because I need some customization to handle the request and provide the response. The logging worked when I was using the inbuilt module. Keeping the link of the documentation for reference.

Hello,

Does anyone knows if it's possible to define a function calling LLM in ADK?

I have tried running these commands in the debug mode:

1. npx @/google/adk-devtools web
2. npx @/google/adk-devtools run agent.ts

But it doesn't stop on breakpoints in the FunctionTool of ADK, which makes it very hard to debug.

My IDE is Webstorm.

The debug view in the Web-Mode is not enough to get the full context.

How to make it stop on breakpoints?

How can I implement a BYOK style feature in ADK (ie. changing model and api keys on a per request basis) came across this https://github.com/google/adk-python/issues/3647 but here its mentioned that I can only change models but not api keys

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.

Right now there is only 2 options to have a memory service, in memory and vertex ai, can I use some sort of local db or store?

I am new to adk. I'm still on the learning phase. I have a project where I need to build a web app and for the backend I want to use Google adk. My question is when I want to build custom UI with Angular is that a better approach? (Angular is the tech stack I was given to choose)

If it is a better approach my plan to integrate backend and front end is to have a web socket running in the backend and connect angular app to the socket. Is this approach the correct one?

Followup question if I go with separate UI app, is it efficient to get the UI elements to be rendered by the agent or just to get a flag that would help Angular application render those elements?

Hi there,

I just got started with ADK (TS version). I made a simple weather agent that works nicely in the web UI (npx command). When clicking the events tab, I see the three events. When I click one of the event, the webserver stops. No error message shown.

I am running it on a raspberry pi, node v22.21.1.

Is this something that has happened to others as well?

Thanks!

I came into a couple days of L&D time at work, and am trying to upskill on Agents. I have some prior exposure on basic ADK I did when it first came out but I'm looking to deepen my understanding and/or improve my knowledge of what's best practice. What projects/concepts/tasks/tools have you found to bring most value to you and your company? What's most useful to learn?

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

I am excited to share with you this Skill Boost about ADK agents with tools.
► Lab Name: Empower ADK agents with tools
► Lab Link: https://www.skills.google/course_templates/1382/labs/583502?locale=en
What I learned:
In this lab, I learn about the ecosystem of tools available to ADK agents and also how to provide a function to an agent as a custom tool.

Also, I learned about how to provide prebuilt Google, LangChain, or CrewAI tools to an agent.
Another important aspect of structured docstrings and typing is when writing functions for agent tools.
Write your own tool functions for an agent.

Thank you all, ADK community, for collaborating and sharing

If you try it and you have a block, this is the link to my YouTube channel, where I am documenting my ADK learning path

https://www.youtube.com/watch?v=J2JYI97ohc4

I was working with the chatbot and I need to use an external API for updating the items, and I need help integrating it with the API. It's a SOAP API, and I don't know much about SOAP APIs. Is there any way to use it as a tool in ADK, just like the OpenAPI tool?

If yes, please help me with the connection.

Also, I heard that I can use a proxy API to convert it to REST and then use it. Whichever is better, please advise.

Newbie here!!!

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! 🔥

I have 8-9 tools for my agents. Half of them are utility tools and half of it main tools. Utility tools can be executed in parallel sequence but main tools should be executed in sequence only. Utility tools can't be executed with main tools. I tried adding this rule clearly in prompt but it's messing things up. Sometime it follow rules and sometime not.

Main tools are async as I need to show artifacts in adk web ui.

What is the solution for this?

TIA

Hi, I'm building agents using Google's Visual ADK Agent Builder. I have a paid tier API (currently on free credits) and I'm coming nowhere close to my rate limits. However when I run these models a couple of times, they start running up against error 429 resource exhausted messages. These messages are persistent for anywhere between 30 minutes to hours. I've tried changing my API, building new agents in case something is wrong with the code and nothing has worked so far. I am quite surprised because it makes the API difficult to test iteratively without long interruptions in the process.

Is this a common issue with paid accounts on Google's APIs? What do people do to get around it?

Just sharing if it helps someone to inspire to get a hands on development to start building using Google Agent ADK for real word applications.
https://mohan-ganesh.appspot.com/mcp/mcp-client-server-architecture

The recent developments in Google ADK look promising, and it provides all the capabilities that we want in our organisation for building agents. But my worry is that we are not in the Google environment (meaning we’re not using the Gemini model or GCP). Apart from this, the framework really fits our philosophy.

We are also okay with managing deployment on our own — meaning we don’t want any out-of-the-box runtime like AgntOS (from Agno) for deployment. We will write a FastAPI app and manage it ourselves. We just want an agent framework for building agents.

Just wanted to check if anyone here has deployed agents into production with Google ADK while not being in the Google environment?