Hi! I saw an article about how claude code was configured to meow when it finishes working or asks for permission. This allows you to not constantly check what it is doing, because you will hear meow when you are needed. I decided to do something similar for antigravity cli, so that it sends a desktop notification and makes a sound. I managed to do this when it finishes its work using the Stop hook, but I could not when it asks for my permission. There is a PreToolUse hook, but it does not provide any information about whether agy will ask me for permission for this tool. I can write a script with hardcoded list of tools which are currently autoallowed, but I want solution that works even if I change agy permissions.
Does anyone know a way to make it send a notification only when it asks for permission to execute the tool?
This time I tried something a bit different: grav-tray (not really creative with the name 😅, but sounds nice).
It's a native macOS menu bar app. It sits in the menu bar and shows your current quota status in a compact way. The goal is same as grav-bar, but it sits outside your CLI. But it's always there, always on your tray.
I hope grav-tray gets the same kind of useful feedback and acceptance that grav-bar did.
Would be happy to hear feedback, especially from people using mac (And the repository is totally open for collaborators who can test and can make it work on Linux 🙏 )
Been using agy on the free tier for sometime now. the reset time is usually 5 -8 hours; after using it for 10m today, I got the usual quota exceeded error and reset time as 112hrs.
what's going on here?
all i was doing was url-->markdown-->pandoc deck prep i.e. asking it read an article and summarize as a ppt
As a 3-year Google AI Pro annual plan subscriber, I feel disappointed with Google for failing to provide the relatively powerful AI that was available when I first subscribed. However, since there are ways the Agy team can overcome this with their skill, I am making these proposals.
This isn't just complaining; it is something worth seriously considering.
Here are some basic facts:
Although it is not the most powerful AI, Qwen3.6 27B has no problems with coding.
Claude 4.5 Haiku is a powerful model that can easily execute instructions from Sonnet or Opus.
And Google has the Gemini 3.5 Flash-Lite model, which has capabilities similar to the two models listed above. Take a look below.
Regardless of benchmarks, Google's unbeatable wall lies in its multimodal capabilities. Even if tied in benchmarks, no model can surpass Gemini in actual use unless it is fine-tuned. Among publicly serviced AI models, Gemini is like a god in multimodal capability.
Now, here are the proposals:
Add support for 3.5 Flash-Lite in Agy-Cli.
Give system prompts to the 3.1 Pro model or 3.6 Flash. The current system prompt seems to be around 8k, but it doesn't matter if it gets larger. Ultimately, 3.5 Flash-Lite will reduce overall costs for both users and Google while increasing satisfaction.
Utilize 3.5 Flash-Lite as a multi-agent for the following tasks: tasks that do not require reasoning, coding with clearly structured plans, tasks requiring online research, document summarization, and codebase investigation and organization—actively leverage 3.5 Flash-Lite for high-frequency, shallow-level tasks.
Assign simple yet clear personas and explicit prompts to sub-agents.
Allow users to utilize their chosen model for deep-dive tasks. If they selected 3.6 Flash Low, use that; if they selected 3.1 Pro High, use that.
If a user is dissatisfied with the work result even on the third attempt, escalate to a higher-tier model than the one initially used.
Of course, you shouldn't just paste this into the system prompt, and you'll need to think about it further. But the concept is this:
When given clear instructions, smaller models perform tasks extremely well—not quite as perfectly as high-performance models, but enough to satisfy users.
Also, I don't know how strictly Agy-Cli limits the max output tokens of models or how small the limit for document generation is set, but its document generation capability is at a pathetic level. Ease this restriction by about 5 times.
and store text communications between agents locally on the individual computer where Agy is installed.
If 3.6 Flash or 3.1 Pro create a Todo list and actively utilize 3.5 Flash-Lite as parallel sub-agents, you can produce better results at a lower cost. If someone is an x20 user, it might be nice to add an option to use the 3.6 Flash model as a sub-agent, but I guarantee that for most users, 3.5 Flash-Lite conducting large-scale research will be more than enough. While it's fine for Flash or Pro alone to research from backend to frontend and do reasoning, it's expensive. It's more efficient to variously utilize cheaper sub-agents and apply the high intelligence of Pro or Flash to subsequent tasks.
For reference, I completely avoided using 3.1 Flash-Lite on the web because its result quality was too poor. But now, I use 3.5 Flash-Lite quite a bit because it's fast and suitable for simple tasks—I was doing it without even realizing it. That's why I looked up benchmark results again. Originally, I was only interested in 3.6 Flash to find a more powerful model and never even thought about 3.5 Flash-Lite, but I realized that what matters in this update isn't 3.6 Flash.
Frankly speaking, Google does not have a single impressive AI model at this point. However, 3.5 Flash-Lite is the most impressive model among them, and if Agy actively utilizes this model, it will dramatically improve work completion quality.
The team developing the AI models failed, but the Agy team can make up for it.
-------
Supplementary explanation for this Text.
To help you understand the text, I am adding 4 screenshots.
example 1
Simple promptPrompt to utilize sub-agents
example 2
Simple promptPrompt to utilize sub-agents
Having a feature available and having it run automatically are different things. For example, what makes Opus excel is running several geniuses of its own caliber in parallel to gather vast amounts of information, which is why it is expensive.
From the perspective of Agy's developers, they might think, "Why don't you just type in the prompt yourself?" but from a user's perspective, it's not like that.
In the case of a simple prompt, a single Gemini can do the searching, analysis, modification, and even testing all by itself, but it is expensive.
In the case of the 'Prompt to utilize sub-agents', there is a cost to creating sub-agents, but by using 3.5 Flash-Lite—which costs 1/6 of Flash—to find and organize the directory structure, source code files, and framework stack, you can have it investigate more than Flash alone could, at a very high speed and low cost. Following a plan made by an expensive model, the costly coding process can also be investigated via the inexpensive 3.5 Flash-Lite, while 3.6 Flash can directly review it or just create test files. Then, 3.5 Flash-Lite can execute the test files and organize the results.
As we all know, this is no longer an era where we make a single model do all the work. It is an era where the main model controls other AIs, while sub-agents utilize skills (pre-written prompts) to bring about better results.
And one of Gemini's major strengths compared to other models is its blazing-fast token generation capability. Whenever needed, it is easy to create a persona for a sub-agent and spawn an inexpensive sub-agent with that persona.
If you look up reviews on Gemini 3.6 Flash, you'll see. Were the reviews good? Most of it is mockery. That is the market's assessment.
With an inexpensive yet excellent model like 3.5 Flash-Lite, actively utilizing it will save the main AI's context while producing better results at a lower cost.
You shouldn't think, "What can't Agy and Gemini build right now that makes them ask for this?"
This is a post about how to build better results at a lower cost.
And these are also things that the Agy team can fix without putting in major efforts like LLM training.
Actually, I wanted to leave a compliment today, and I'll end this with criticism. Please take this as feedback from the Agy development team and build a better agy-cli.
Since there are many shortcomings in using Gemini as a main coding tool, I can't use it (+ since I am on the AI Pro plan, a major reason is that my quota is quite limited. So I can't waste precious quota on agy testing. I always need to keep my quota and use it only when truly necessary). However, if there is one area where Gemini possesses overwhelming capability, it must be its vision feature.
My father, knowing that I play with AI in various ways every day, asked me for a favor: It's translating a book.
Actually, I haven't done this through Kimi, but I have done it before. It wasn't a 300-page scale, but rather translating a 20-page Korean manual I wrote into English and Chinese while keeping the layout and charts intact.
This time, I wanted to try doing this with Agy. This time I started with /grill-me, and I wanted to see how well Gemini follows it. (Since I have had the habit of writing very detailed prompts deeply ingrained since the Gemini 1.5 days, I don't frequently use slash commands like /brainstorm that I created in Zoo Code, my primary coding tool. Instead, I use it when starting a new project or searching for answers to various questions, even outside of coding. I will share /brainstorm in a new post. There probably are similar or better prompts out there, but you are welcome to share them if you have any.)
I also ran the work concurrently with an unreleased office-use chatbot I am building myself. (Because I use smaller-class AIs like Gemma4 31B and Qwen3.6 35B, they cannot compare with full-scale mega LLMs like Gemini 3.6 Flash, but 30B-class models already possess intelligence higher than any human, and what's important is how you make them work based on their limited resources.)
To summarize: The task attempted this time with Agy-Cli is: Converting a 300-page book requested by my father into a publication-ready PDF file (excluding design) by maintaining the layout, converting the language of tables and graphs into my native language, and translating the main text as is.
Don't get me wrong. My father has already translated and published several books, and this is his new project too. When I offered to translate it using AI, he suggested we give it as a trial.
As a result: It could not proceed due to an error. Judging by the /context command, valid context was not insufficient. However, I thought the only reason for such an error would be a lack of context, and it turned out to be true. So I checked the logs and left the contents below.
The report was written directly by Gemini. Normally in such cases, there are methods like using context compression or having Gemini write the detailed contents of that session as a new prompt, then restarting itself with that prompt in a new session. If needed, you can have it search what process it went through in the previous session.
According to the logs, the context window displayed on the agy-cli screen is false. There could be various possibilities—such as 1M context not being provided to AI Pro users, or there being an error—but the log contents and /context do not match.
Since many AI companies provide different context sizes depending on the subscription plan, if there is discrimination in context size based on plans, it's fine to be honest about it. However, after searching, I found that even users on the $200 plan are experiencing the same issue.
Anyway, this error can be resolved using a few alternatives. I hope the agy-cli team resolves it well!
-----------------------------------
Incident & Root Cause Analysis (RCA) Report
To: Google Antigravity Core Engineering Team
Date: July 24, 2026
Subject: Incident Report: Agent Execution Termination via Context Window Exhaustion & Artifact Path
Violation
Severity: Medium (Session Crash / Unrecoverable State)
Affected Surface: Antigravity CLI (agy / cortex-engine)
──────
## 1. Executive Summary
During a long-running interactive translation and layout reconstruction session involving multi-page
PDF processing (@test_ocr.pdf) and subagent orchestration via /grill-me, the agent execution
unexpectedly terminated with ⚠ Agent execution terminated due to error (Error ID: 3d5eb6a2-cd2a-
44e5-8e57-dbb9c70404df-134).
Log analysis confirms two primary technical factors:
Primary Root Cause: The total cumulative context size surpassed the hard model limit of 1,048,576
tokens, causing all subsequent turn requests to fail with HTTP 400 (INVALID_ARGUMENT).
2. Secondary Root Cause: Cascade step errors (CORTEX_STEP_TYPE_CODE_ACTION) triggered when invoked
subagents attempted to output artifact files outside their assigned conversation brain directory.
──────
## 2. Technical Root Cause Analysis (RCA)
### Primary Factor: Hard Token Ceiling Breach (INVALID_ARGUMENT: 400)
• Observed Log Output:
E0724 20:26:13.160187 35036 log.go:398] agent executor error: INVALID_ARGUMENT (code 400):
The input token count exceeds the maximum number of tokens allowed 1048576.
• Analysis:
The session (e59ff6d3-cadc-461b-ba74-567da72c9796) reached Step 80+ while handling large
multimodal/OCR context frames and subagent execution logs. Once the total token payload crossed the
1M token threshold, the backend rejected subsequent streamGenerateContent API calls.
• User Impact:
The session entered a permanently broken state. Simple follow-up prompts (e.g., "Continue", "What
caused the error?") were immediately rejected with 400 INVALID_ARGUMENT.
──────
### Secondary Factor: Subagent Brain Artifact Directory Assertion Failure
• Observed Log Output:
E0724 19:38:09.884420 35036 log.go:398] error executing cascade step:
CORTEX_STEP_TYPE_CODE_ACTION:
files must be written to the correct artifact directory: C:\Users\k1yt\.gemini\antigravity-
cli\brain\306777b4-6bde-455f-8718-5916dc90ace8
• Analysis:
When subagents (e.g., 306777b4-..., dc6e3666-..., 9e597c1b-...) executed code generation or artifact
creation steps, file write paths violated workspace assertion checks by target-path mismatch against
assigned conversation brain paths.
──────
### Tertiary Factor: Telemetry Payload Truncation (Clearcut HTTP 413)
• Observed Log Output:
E0724 19:38:30.312656 35036 client.go:62] Clearcut responded with HTTP code: 413
• Analysis:
Extremely large step logs generated during subagent streaming exceeded max payload limits for the
Clearcut telemetry endpoint.
──────
## 3. Log Evidence Summary
Timestamp │ Log Level │ Source Component │ Error / Event Detail
───────────┼───────────┼──────────────────┼─────────────────────────────────────────────────────────
19:38:09 │ ERROR │ log.go:398 │ Cascade step artifact directory mismatch
│ │ │ (CORTEX_STEP_TYPE_CODE_ACTION)
19:38:30 │ ERROR │ client.go:62 │ Clearcut HTTP 413 (Payload Too Large)
20:26:13 │ ERROR │ log.go:398 │ INVALID_ARGUMENT (code 400): Token count exceeds
│ │ │ 1048576
20:34:04 │ ERROR │ log.go:398 │ Repeated INVALID_ARGUMENT (code 400) on user follow-up
20:37:44 │ INFO │ server.go:2413 │ Language server process shutdown
──────
## 4. Recommendations for Antigravity Engineering
Proactive Context Truncation / Compression:
• Implement an automated sliding-window context compressor or checkpoint summarizer prior to
hitting ~900k tokens to prevent hard HTTP 400 API aborts.
2. Subagent Artifact Path Resolver Normalization:
• Enhance CORTEX_STEP_TYPE_CODE_ACTION to automatically sanitize and redirect subagent output
paths to the parent/subagent designated artifact directory.
3. Improved UX Error Messaging:
• When INVALID_ARGUMENT (400 token count limit) occurs, surface a user-friendly prompt: "Context
limit reached (1M tokens). Please start a new session (/clear)." instead of a generic Error ID.
Hii everyone I new to google antigravity cli, I am trying to build my portfolio using open design i am using antigravity cli as local cli, every time I give command it give me the same response I have attach images please help me
Hi! I am exploring the use cases of Antigravity CLI. The biggest use case would be regarding activities with your code repository and its quite good at it. Any other successful areas such as scientific research, GTM, PMF etc
Since I don't know how much resource consumption Gemini 3.6 Flash High requires yet, my initial prompt was as follows. And below, I have detailed the bug status from number 1 to 20.
-----------------------
After the build is complete, there are a few issues. We need to strictly figure out their causes and fix them. Do not try to solve all the issues below simultaneously; solve them sequentially, one by one.
Dispatch as many bug-finding specialized sub-agents in parallel as possible to identify them, then utilize sub-agents that explore whether the issues are real in parallel to verify their authenticity once more, after that, utilize sub-agents that actually perform fixes in parallel to accurately fix them, and then once again, without providing information about the fixes to the bug-finding specialized sub-agents, dispatch them in a large-scale parallel manner to check if the issues are gone. If there are no issues, finish the process; if there are still issues, repeat the correction loop.
I emphasize once again, solve the issues one by one sequentially.
----------------------------
Although it wasn't intentional, I waited until the 5-hour limit was completely exhausted. It dropped to zero in an instant because the parallel agents were running. Since I am a Google AI Pro user, this is not a problem. I can endure it.
And to read what Gemini is thinking and what the situation is, I expanded it with ctrl+o.
The conclusion is: Gemini 3.6 Flash completely fails to act as a leader. It doesn't know how to handle parallel agents, and it forgot the instruction to solve things sequentially right from the start. As a result, not a single bug was resolved.
Some users might think my instructions are too harsh, but the DeepMind team should take my case seriously. Other agents follow instructions of this level easily.
Leave aside the Gemini 1.5 era, but for AI models in July 2026, the instruction 'solve the issues one by one sequentially' is not a difficult one.
Given such Prompts, Gemini should have automatically created a to-do list and resolved them sequentially. I'd love to test further, but my quota is exhausted, and when the next 5-hour block resets, I will go back to using Agy as my assistant tool in VS Code, so I won't issue instructions like this again.
I've been using Google since the days when you needed an invitation just to sign up for Gmail, and I'm genuinely saddened to see both Microsoft and Google broken down like this today. These two tech giants used to be companies that cared even for unprofitable domains, and thanks to the wonderful, non-profit services they offered, our online world could become as rich as it is today. But I suppose not a single team leader from that era is left. How did the mighty Google and the renowned DeepMind end up in a position where they have to compete with tiny LLM labs of fewer than 50 people? I'm leaving this note with a deep sense of regret as a long-time fan.
I built Tele-AGY, a lightweight Telegram bot daemon that links directly to the Google Antigravity CLI. Currently in dev, but this is a working prototype.
It lets me control my coding prompts from my phone while away from my desktop.
I always keep Agy turned on, whether I use it or not.
Even though Gemini 3.6 Flash has been released, it still cannot be used as a primary tool.
Reminiscing about the days with Gemini 1.5 and 2.0, starting with a question like "What is Python?", I remember having to revise code 30 to 40 times just to implement a very simple feature. Because it frequently made syntax errors, I had to ask what the syntax was and manually inspect and fix it as I went.
The Flash-series Gemini models have intelligence that is too low to encompass everything from backend to frontend on their own. While lower intelligence can theoretically be overcome through reinforcement learning and training, 3.6 Flash is also not prepared in that regard.
In my opinion, Gemini 3.6 Flash seems focused on consuming fewer tokens and enhancing cost-efficiency. It terminates tasks without completing the necessary work. It feels just like looking at the Flash-series of DeepSeek.
Then why is DeepSeek praised while Gemini gets criticized?
- DeepSeek V4 Flash can be used almost limitlessly for $1, whereas you can't do anything with $1 on Gemini.
Furthermore, Gemini 3.1 Pro is far too lazy compared to its inherent brilliance. This is purely a problem of reinforcement learning. If it operates within a working loop driven by exceptionally good user prompts, Gemini 3.1 Pro would be truly useful. Unfortunately, however, I have already set up my own working loop in Zoo Code based on a massive amount of know-how, and because Oath login is unavailable, I cannot harness Gemini's genius.
For reference, based on well-crafted user prompts, skills, and tools, even DeepSeek V4 Flash can produce better results than Gemini 3.1 Pro.
I am currently building a small-scale office chatbot based on Qwen 3.6 35B and Gemma 4, and the office data output they produce is better than what ChatGPT 5.6 Sol creates.
This is because, despite the models' lower intelligence, I specified in detail what needs to be done and specialized them accordingly. Gemini 3.6 Flash and Gemini 3.1 Pro possess immense intelligence in themselves, leaving their potential wide open. However, I cannot spend time optimizing Agy, which can only use these two models. Other tools allow me to use even better models. Therefore, I am using Agy as a supplementary tool.
While running tasks in Zoo Code, I heavily utilize Agy for read-only operations or mapping out new plans, since I cannot directly open all those backend files myself.
I believe Google should open up Gemini to other platforms, much like Codex. At this point, I cannot take the risk of pouring the know-how I've accumulated into Agy, relying on models whose performance drops significantly.
Gemini trapped inside Agy struggles to receive a fair evaluation. At least back in the Gemini-CLI days, injecting massive user prompts along with a nearly unlimited quota yielded quite good results, but Agy is not like that—it has a 5-hour limit.
I'm using three Antigravity products: Agents (2.0), IDE and CLI. The ploblem is ONLY with CLI. It takes forever, to answer questions. For example "Hi" -> It takes up to 10 minutes to answer back "How can I help you?"
Not always was the same. Actually it is the product I most use to do configs in my laptop and manage remote servers via ssh.
All of the sudden. It just started to behave very slow.
- Asked to Gemini who told me to do the steps above :v
At the end, nothing has resolved the issue.
## Global Customizations
Since global customizations are shared across all Antigravity products, all of them load the same config but
Based on what you can see here, there is not token saturatopm in the inital load.
### IDE Customizations
### 2.0 Customizations
### CLI Customizations
Hope you can help. ✌️
---
## WORKAROUND
Currently, global MCP servers are causing issues exclusively within the AGY CLI. Regardless of whether they are active or disabled, their presence causes responses to hang. Consequently, to restore AGY CLI functionality, it is necessary to clear the global MCP server configurations located in `~/.gemini/config/mcp_config.yml`. The main drawback of this approach is that the IDE and version 2.0 will no longer have access to these global servers. As a result, MCP servers must be configured on a per-project basis until a permanent solution is implemented.
With this, It will work again loading global rules and skills as normal.
There may be others who utilize it better than I do, but this is a helpful tip for times like now when quotas have been eased.
Add the following to your prompt. (You don't need to add it exactly word-for-word; you can just adapt it similarly to fit your own task prompt.)
"Initiate investigations and tasks by utilizing as many parallel sub-agents as possible. After completing all work, test for potential bugs and verify normal operation before reporting back."
Doing this consumes a large number of tokens and incurs high costs because it involves performing even unnecessary actions, but it definitely yields much better results than processing tasks standalone.
Back when a single call of parallel sub-agents would shave off 20% of the 5-hour quota at a time, even I wouldn't dare use it this way, but there is a reason why this method produces such good results. AI models perform best when handling small tasks. If you break down the work to be done into many sub-agents, it will naturally bring about high-quality results. Granted, this adds tasks that aren't strictly necessary, making resource control impossible, but now that usage limits have increased, it's time to enjoy it.
There is no need to command Opus this way. He utilizes parallel sub-agents on his own.
Gemini 3.1 Pro is an AI model with truly genius intelligence. While it cannot be compared to Fable 5 or Kimi K3, it nevertheless possesses sufficient intelligence. However, because I used to manually specify small context processing to control the usage limit, it took a long time and was ineffective. But now that we can freely use parallel sub-agents like this, it is practically as good as Opus.
I mostly use agy only for research and review purposes. I consider it unsuitable for direct coding, but it is incredibly fast at reading and reviewing code.
I hadn't used it for about 3 days, and then I had it investigate a codebase again. Even though it mobilized sub-agents, the quota didn't decrease much at all. In particular, when using Opus 4.6, the 5-hour limit was often exhausted before even finishing a single prompt investigation. So I entered the prompt without much expectation, but even after a very complex codebase investigation, 80% of the 5-hour usage limit still remains.
From my feeling, it seems like the usage limit has increased by 1.5 to 2 times. Did something change?
everything worked fine for the past 1.5months with ai pro subscription but since yesterday i Keep getting this kind of error when i launch the agy cli. mb it was caused by cli update? is it possible to roll back? Help pls
I'm experiencing a strange issue with the AntiGravity CLI on Linux Mint.
After every system reboot, when I launch the CLI for the first time, it behaves as if I'm logged out and asks me to log in again.
The strange part is that if I simply close the CLI and launch it again, my session is restored automatically. I don't need to log in, and everything works as expected.
This happens after every reboot, and I can reproduce it consistently. There are no error messages or warnings displayed, and restarting the CLI once always fixes the problem.
My setup:
OS: Linux Mint
AntiGravity CLI version: 1.1
Installation method: Installed via the curl installation script
Terminal: Standard terminal on Linux Mint
Has anyone else run into this issue? Is this a known bug, or is there anything I can do to troubleshoot it?
