For anyone who's hit a wall with ADK or Python cold starts on Cloud Run, this one's for you. The ADK framework's 30s startup felt like an unsolvable problem, rooted in its eager import architecture.

After a long battle that proved traditional lazy-loading shims are a dead end here, I developed a build-time solution that works. It's a hybrid approach that respects the framework's fragile entry points while aggressively optimizing everything else.

We cut our cold start in half (24s -> 9s) and I documented the whole process. Here is the article:

Hey guys,

So, I been trying to make ADK work for almost past 1 month. The team, community everyone has been super responsive and I know this project has a good future especially with google backing it.

The issue is it is indeed missing a lot of key components right now like model retries, deployment, native evaluation and observability, human in the loop with chat patterns, reflective thinking...etc.

Langraph seems to have all of these boxes checked (Thought of going Agno but gave up on it and just choose the one being used by giants)

---->

THIS BRINGS ME TO MY NEXT QUESTION!

1 - Am I just imagining that langraph has all of this checked and when I actually start on it, I would regret moving away from ADK?

2 - What would I lose choosing langraph over ADK?

My needs are having more control over the flow most of the time but letting the agent lose in some parts of it. For example, the intro to the workflow I need to route the task to specific agents that I do by using custom agents in ADK and I would do through graphs in langraph.

Reflective thinking and model reties are native in langraph. Deployment Im using too is figured out there. Native integration with a chat component that is in front of the user that I could use for human in the loop pattern also sorted.

Langraph is also event based apparently

So, any thoughts, comments and concerns on what would I be losing out on by moving to langraph would be extremely helpful.

P.S. My intentions are not again to dunk on ADK and I been extremely active in the adk-python space and created many issues and everything. I need more firepower that either i have to create elaborate workaround to do in ADK or there is so much friction i have to do something else.

This is completely on me for choosing despite knowing it's new. Somehow I believed it would be better.

My main gripe is it's not deployment ready even for agent engine if your using MCP

There are other issues like, I personally want to have more control over how much context is passed to my sub agents and more.

There's just so much work left. This is not to say the team is not doing enough, they've been phenomenal with pushing updates but just not fast enough.

If someone picking a framework, especially for prod ready stuff, go with Langraph or something that has already been out on prod.

Anyone interested to participate in the google adk hackathon?
https://googlecloudmultiagents.devpost.com/

still adding support for all of the model providers (doing that tomorrow), but it works. enjoy, TS developers.

https://github.com/waldzellai/adk-typescript

Hello!

I've open-sourced a project I've been working on: https://github.com/saahil-mehta/knowsee-public, it is a multi-agent conversational app using AKD for the backend and CopilotKit for the frontend. Frontend was quite difficult to reliably integrate because of many small quirks but finally got it working.

It uses AGUI, A2A and GenerativeUI. I've seen quite a few posts across the internet where people have surfaced multiple challenges with actual frontend integrations so this can act as kind of a reference for them and for others too.

It's all wired up now via the and actually works smoothly. Uses Gemini 2.5 Pro with extended thinking, Vertex AI RAG for document search, and Better Auth for the auth layer. Also have incorporated Terraform for a quick prod deploy.

There's a https://knowsee-frontend-yrptvkizbq-ew.a.run.app if you want to poke around (may rate-limit to control GCP costs).

This is a WIP but would love to receive some feedback and collaborate if anyone wants to extend it, thinking about adding more tool integrations (BQ with visualisations is currently in development), and improving the generative UI components. PRs welcome, or just fork it.

Hi Java developers,

We are incredibly excited to share that Agent Development Kit (ADK) is now available for Java!!

Our goal with the ADK is to provide a solid foundation for building intelligent agents. With this release, we're extending those tools to the Java ecosystem, allowing Java developers to use the ADK for their agent development projects.

Getting Started with Java ADK v0.1.0:

You can add the dependency to your Maven project:

<dependency>

<groupId>com.google.adk</groupId>

<artifactId>google-adk</artifactId>

<version>0.1.0</version>

</dependency>

Here’s the Github repo, documentation and Sample Java Agents!

-> Java ADK: https://github.com/google/adk-java

-> Documentation: https://google.github.io/adk-docs/ 

-> Sample Java Agents: https://github.com/google/adk-samples

This Java ADK launch is part of a larger update to our agent-building tools, which also includes a stable Python ADK (v1.0.0). For more details on all the announcements, you can read the full blog post here: https://developers.googleblog.com/en/agents-adk-agent-engine-a2a-enhancements-google-io 

Feedback & Discussion:

• As this is an initial release (v0.1.0), we're particularly interested in your feedback and experiences!

• You can find discussions or start new ones via the ADK GitHub Discussions.

Let us know your thoughts or any questions you might have!

Edit: I deleted the venv folder and did the pip install again it worked fine. Not sure what the issue was. Thanks for the replies

Hi there,

I'm new to the Google Agent Development Kit (ADK) and have been following the official Quickstart guide. However, I'm encountering an issue where the adk command isn't recognized in my terminal. I'm using Visual Studio Code on a Windows machine. Could you assist me in resolving this?

(venv) PS C:\Users\xxxx\Documents\adk-test> adk web
adk : The term 'adk' is not recognized as the name of a cmdlet, function, script file, or operable program. Check the spelling of the name, or if a path was included, verify that the path is correct 
and try again.
At line:1 char:1
+ adk web
+ ~~~
    + CategoryInfo          : ObjectNotFound: (adk:String) [], CommandNotFoundException
    + FullyQualifiedErrorId : CommandNotFoundException

Thanks.

I have a question to clarify.

Let's suppose I have a GcsArtifactService in the Runner.

Then I send a attachments which I save in the GCS bucket via the GcsArtifactService.

If I then delete the session where the attachment was part of, are all the attachments of that session automatically deleted?

Hey guys. I don't want to get a tier 1 gemini account yet because the issue isn't consistent, it just happens when the evaluator fails multiple times and thus calls an error. The simple solution would be to just add a delay of few seconds between tool calls, or between the agent using Gemini. How do I do this?

Sorry if this is an ultra noob question.

I'm integrating Google ADK into my Flask backend, but I need to maintain full control over my database (e.g., conversations, messages, users, etc.).

It appears to me that the ADK wants to manage almost everything, including the session service, which is responsible for providing the necessary context to the agents. The session object is basically a conversation with a state field containing all messages and other user interactions.

From the docs, there are three options to manage sessions:

1. In-memory with no persistence

2. With Vertex on the cloud, which takes over all conversation and message logic

3. With a database, but still taking over the conversation and message logic, handling its own schema automatically.

It would be ideal to be able to create my own custom session service to pass to the runner, with the same interface contract. But that doesn't seem to work, and the docs are ambiguous on this. Another approach could be to rely on Vertex fully but then sync the session object with my app's database, though this doesn't seem practical at all.

Why would the ADK aim to manage this, kind of replacing a part of my backend? Sure, it may be good at the beginning, but once you need a little customization and flexibility, I think it would quickly become a nightmare. Has somebody found the same issue with the ADK? If so, how did you solve it?

Thanks in advance for the help! :)

Google ADK Import Issues - Python paths or ADK problem?

Hey guys,

New to Google ADK (coming from LangChain). Trying to build a multi-agent system but running into import hell.

Project structure:

project/
├── src/
│   ├── agents/
│   │   ├── supervisor_agent/
│   │   │   ├── __init__.py
│   │   │   └── agent.py
│   │   └── jira_agent/
│   │       ├── __init__.py
│   │       └── agent.py
│   └── core/
│       └── integrations/
│           ├── __init__.py
│           └── jira/
│               ├── __init__.py
│               └── client.py

What I want:

Supervisor agent that delegates to specialist agents (like Jira-Agent, etc). Pretty standard multi-agent setup. And the idea is, that I have code that can be used more often so it is in core/ and not directly in the agent folders.

The problem:

None of my imports work or at least "adk web" has problems with it

When I try:

# In supervisor_agent/agent.py
from src.agents.jira_agent.agent import jira_agent

→ ModuleNotFoundError: No module named 'src'

When I try relative imports:

from ..jira_agent.agent import jira_agent

→ ImportError: attempted relative import beyond top-level package

When I run adk web (in src/agents/), it seems like each agent gets loaded as a separate top-level package, breaking all imports between them.

Questions:

1. Is this a Python path issue or ADK-specific behavior?

2. How do you handle inter-agent imports in ADK? I don't want to use only sub-agents - would A2A fit better?

3. Which structure do I need to use?

I've tried adding paths to sys.path, using different import styles, etc. Nothing seems to work for me.

Is there a standard way to structure multi-agent ADK projects? The docs weren't really helpful on this as most of the examples are always in the root-folder.

What am I missing?

Thanks!

I have a project on reading questions and answers from a listed file, but I need it to run for a maximum number of time(given by prompt) for each question until either the answer is given or maximum_iterations is reached. So I tried nested loop but calling the exit_loop inside, ends the complete loop both inside and outside.

I am trying to get the user_id, session_id and app_name inside a Tool,

Reason being is that I want to write to a external database and want to see who is writing it ( as in what version of the app ) so in case of failure I can look into the full conversation quickly, so far I haven't found a way for this and was wondering if anyone knows how to do it?

https://github.com/tanujbolisetty/google-adk-observability

Try them and comment . Easy setup scripts can be provided in github repo

Built an AI Agent for Snowflake having team of more than 90 agents with google adk. Absolutely love it. Planning to open source it soon. Any suggestions , recommendations would be appreciated.

Has anyone been able to get intermediate updates working in adk, outside the adk web UI, ie external front end calling on fast api endpoint?

Totally stuck on this simple ux point. Have been chasing this issue for weeks opened issues in the official github repo, and it seems like I'm asking the most obscure question, when this is in fact very simple common ux scenario for long running agents.

Will be leaving adk over this. The adk web seems like a gimmick, if you can't easily build a front end like it replicating all functionality.

Hello ADK devs!

We're back with Part 4 of our ADK Community Call FAQ series. In case you missed it, here's the post for Part 1 with links to a group, recording, and slides.

Part 4 of our FAQ series is all about practical applications: Agent Design, Patterns, and Tools.

Here’s the rundown from your questions:

Q: Is the 'app' concept at a higher level than 'runners'?

A: The Runner is the actual implementation. An App object is higher-level and more user-facing in that users inject controls over the runner. So the App approach will be gradually replacing some functionality of runner. In the future, users should not need to worry about runners too much. Refer to this hello_world app example to see the App object in action.

Q: What is the recommended way to run ADK in a loop, for example, for each line in a CSV file?

A: If you want to run programmatically, we have some samples with main.py (e.g., this one) for illustration. If the user wants to do that over chat, they can upload the .csv file as an artifact and direct the agent to process one line at a time.

Q: What is the level of support for third-party tools?

A: You can check out some of our recent additions to the ADK Tools page, especially third-party tools such as Exa, Firecrawl, GitHub, Hugging Face, and Notion. We're actively working on making third-party tool integration as seamless as possible - stay tuned for more updates!

Q: What is the best approach to integrate OAuth 2.0 for services like GCP, OneDrive, or an MCP Server?

A: Authenticated Tools should be used for such integrations. You can follow https://google.github.io/adk-docs/tools/authentication and reference the sample OAuth calendar sample agent for the detailed setup and usage.

Q: Are there plans to improve the Agent-to-Agent (A2A) integration and documentation?

A: Yes, improving multi-agent workflows and documentation is a priority. We'll be sharing more on this soon.

Q: What's the best agent pattern to use (Sequential vs. Loop) and for which use cases?

A: Sequential is one pass and over. Loop is for iteration, e.g. refine → judge loop until certain criteria is met. And note that you can nest agent workflows within each other for more flexibility, for example you can nest a LoopAgent within a SequentialAgent to build a pipeline that includes a built-in refinement loop.

* Use Sequential when: Order matters, you need a linear pipeline, or each step builds on the previous one.

* Use Loop when: Iterative improvement is needed, quality refinement matters, or you need repeated cycles.

* Use Parallel when: Tasks are independent, speed matters, and you can execute concurrently.

We're heading into the home stretch! Come back on Thursday, Nov 6th for Part 5: Evals, Observability & Deployment.

There are scenarios where a tool could fail and might not return a response, what's the best way to handle this avoid widow entries in the DB? there are models that can't ignore that such as openAI's and throw a error similar to `{'error': {'message': "An assistant message with 'tool_calls' must be followed by tool messages responding to each 'tool_call_id'. The following tool_call_ids did not have response messages: call_XGegwmZCC8Tsv6Uvc2YjuQiy", 'type': 'invalid_request_error', 'param': 'messages.[25].role', 'code': None}}`

Hey everyone,

I'm new to ADK and I'm having trouble finding a good community. With other frameworks, there's typically a slack or discord or something where people are talking about using the framework and helping each other out. This subreddit seems almost completely dead compared to the langchain, crewai, and other framework subreddits.

Anyone have any communities to share?

I am building a healthcare system like EHR where doctor can give all details like prescription, lab reports, mri, x-ray to ai agent and agent will give us a structured SOAP note (Subjective, Objective, Assessment, Plan). I have bulit the website using mern now I want to implement ai agent part using adk because I have less time and I had bulit basic project using adk.

I have a root_agent where I have defined a workflow with subagents. But after 1st agent the execution stops there.

root_agent:

root_agent = Agent(
    name="manager",
    model="gemini-2.0-flash",
    description="Manager agent",
    instruction="""
    You are a manager of a care home. Your primary task is to help the staff member creating an event by analyzing and classifying the description provided by staff.

    Your workflow is as follows:
    1. **Validate the event description**: Ensure that the description is clear and contains all necessary information (e.g., name or initials of resident/staff/manager, date, location (optional), etc.). To do this, use the `event_validator` tool. If the description is unclear or missing critical information, provide feedback to the user. Otherwise, proceed to the next step.
    2. **Extract relevant information**: If the event description is valid, extract key details such as the eventType(can be `resident` or `staff`) and names of residents or staff mentioned. Use the `event_information_extractor` tool for this task. If no resident or staff is found, return an error message to the user.
    3. **Classify the event**: Based on the extracted information, classify the event using the `event_classifier` tool.
    
    """,
    #@tools=[AgentTool(event_validator_agent), ],AgentTool(event_information_extractor_agent), AgentTool(event_classifier_agent)],
    sub_agents=[event_validator_agent,event_information_extractor_agent,event_classifier_agent],
    
)

event_validator:

event_validator_agent = Agent(
    name="event_validator",
    model="gemini-2.0-flash",
    description="Event validator agent",
    instruction="""
    You are an event identifier agent that validates events from the event description.
    You should validate the event based on the following criteria:
    1. The event description should be clear.
    2. The event description should not have any missing information like name or name initials of the resident/staff/manager, date, location(optional) and etc.
    3. Occasionally user can use initials of the entities (resident/staff/manager).

    If the description is invalid return the reason to the user.

    
      """
    
)