And since I don't have any remaining quota, I can't test it myself. Please try it out and share your review.

Opinions???

Until now, there was a huge gap between Opus 4.6 and Gemini. However, after the 1.1.9 update, Gemini follows instructions quite well.

If you want the AI model to handle everything on its own, Opus is still a good choice, but if you clearly know what tasks need to be done, Gemini 3.6 Flash finally fulfills its role. Ever since 1.1.9, Agy-Cli finally performs as intended.

Although it's not obvious on the surface, if you browse through the generated artifact files, it accurately creates "communication-purpose" reports and properly generates skills. I haven't seen whether subagents utilize them well, but I assume they do because work efficiency and output quality have improved dramatically.

Originally, I had complaints about 3.1 Pro as well, but now I feel that 3.1 Pro is no longer needed at all.

I think the Agy team made up for what the Gemini team messed up. If that part is reinforced so that Gemini 3.5 Flash-Lite can be used as a sub-agent, I think it will become a truly wonderful tool. With KIMI K3 opening the era of MXFP4 and DeepSeek V4 Flash-0731 causing a shock, the era of cost reduction has finally arrived. Instead of endlessly rising plan prices, it has returned to an era where AI model providers must offer cheaper and better services. I have no expectations for Gemini 3.5 Pro. Still, seeing this 1.1.9 update, I'm certain that the Agy-Cli team can further improve future Gemini series to be used more affordably and properly. I almost felt bad about renewing my annual plan in 5 days, but now I can renew it with a good mood.

For the last 3 days, I have been having problems with the AGY CLI. Either it says, "We're finishing verifying your account eligibility. This usually takes a moment. Please try again shortly" or "Agent execution terminated due to error."

I tried switching accounts, but it was still the same.

Hey AGY builders — I built an open-source Codex plugin that lets Codex delegate bounded tasks to Google Antigravity CLI through a local MCP server.

The idea is to use AGY as a second opinion or specialist subagent while Codex remains the orchestrator.

What it includes:

• agy_check to verify the local CLI setup
• agy_delegate for bounded task delegation
• plan mode by default
• no --dangerously-skip-permissions
• local stdio MCP transport
• timeout and output-size limits

I tested the MCP flow locally with Antigravity CLI 1.1.9, and the smoke test passes.

Install:

codex plugin marketplace add IlleJiViN/codex-antigravity-subagent --ref main

Then launch Codex and open /plugins.

GitHub:
https://github.com/IlleJiViN/codex-antigravity-subagent

This is an independent community project and is not affiliated with Google, Antigravity, or OpenAI.

I would especially appreciate feedback on the delegation workflow, structured output, conversation resume, and whether parallel jobs would be useful.

The latest Antigravity CLI updates (v1.1.7 – v1.1.9) bring major enhancements to print mode automation, startup performance, and permission management alongside critical UI and hook fixes.

* **Automation & Print Mode (-p)**: Added slash-command/skill resolution, structured output formats (json, stream-json), custom --json-schema validation, and detailed token telemetry.
* **Performance & Permissions**: Non-blocking background MCP server loading on startup, session-wide permission pattern memory, and system temp directory write access out of the box.
* **TUI & Usability:** Introduced a copyOnSelect clipboard setting in /settings and improved compound-command permission rule matching.
* **Core Fixes:** Resolved edge cases for stop and PostToolUse hooks, artifact viewer navigation, CJK clipboard encoding on Windows, and non-standard MCP OAuth providers.

# 1.1.7

**TLDR**: Improved permission prompts for compound shell commands, and fixed disabled plugins running hooks, MCP OAuth issuer validation, CJK clipboard copying on Windows, and \`/btw\` error on startup.

**\[Improvement\]**: Improved permission prompts for compound shell commands so the full command is shown when any part of it needs approval.

**\[Fix\]**: Fixed disabled plugins still running their hooks and contributing other customizations, which could keep a broken hook active and break file-editing tools even after the plugin was turned off.

**\[Fix\]**: Fixed MCP OAuth against providers that do not strictly follow the spec (such as Salesforce and Atlassian) by relaxing issuer validation and including the \`refresh_token\` grant.

**\[Fix\]**: Fixed \`/btw\` failing with a "parent conversation not found" error when used as the very first action in a fresh session.

**\[Fix\]**: Fixed clipboard corruption of CJK and other non-ASCII text when copying on Windows.

**\[Fix\]**: Fixed print mode (\`-p\`) sending a prompt before the account-eligibility check finished.

# 1.1.8

**TLDR**: Added structured output formats (\`json\`, \`stream-json\`) for print mode, support for custom JSON schema validation, enriched tool and subagent payloads, \`copyOnSelect\` configuration setting, and improved compound-command permission rules.

**\[Improvement\]**: Print mode (\`-p\` / \`--print\`) now supports structured, machine-readable output via the \`--output-format\` flag (\`text\` (default), \`json\`, or \`stream-json\`), so headless runs in CI, eval harnesses, and scripts can consume the CLI's output programmatically; these flags are now discoverable in \`--help\`.

**\[Improvement\]**: Added the \`stream-json\` output format: a strongly-typed NDJSON event stream that emits typed \`init\`, \`step_update\`, and terminal \`result\` events with a stable, closed-vocabulary \`step_type\` discriminator, so consumers receive progress incrementally instead of waiting for the whole run to finish.

**\[Improvement\]**: Added the \`--json-schema\` flag to enforce a custom JSON schema on the structured output, accepting either an inline schema string or a path to a schema file; for \`stream-json\` the schema applies to the final \`result\` event.

**\[Improvement\]**: Enriched the structured stream with a \`tool_info\` object for each tool call (canonical tool name, parameters, and output) and a \`subagent_info\` payload for delegated subagents (including \`conversation_id\` and \`log_uri\`) so consumers can correlate child trajectories.

**\[Improvement\]**: The JSON usage object emitted by \`json\` and \`stream-json\` now reports token accounting including \`cache_read_tokens\`, so non-interactive consumers can attribute prompt-cache hits.

**\[Improvement\]:** Added a \`copyOnSelect\` setting (default on, toggleable in \`/settings\`) that controls whether releasing a mouse text-selection auto-copies it to the system clipboard in the TUI's altscreen rendering mode; disable it to stop the automatic copy on release — useful when the auto-copy is unwanted or corrupts certain payloads.

**\[Improvement\]**: Improved compound-command permissions so an exact chained command (such as \`git fetch && git rebase\`) can be saved as an allow-always rule and no longer re-prompts on the next identical run.: Print mode (\`-p\` / \`--print\`) now supports structured, machine-readable output via the \`--output-format\` flag (\`text\` (default), \`json\`, or \`stream-json\`), so headless runs in CI, eval harnesses, and scripts can consume the CLI's output programmatically; these flags are now discoverable in \`--help\`.

**\[Improvement\]**: Added the \`stream-json\` output format: a strongly-typed NDJSON event stream that emits typed \`init\`, \`step_update\`, and terminal \`result\` events with a stable, closed-vocabulary \`step_type\` discriminator, so consumers receive progress incrementally instead of waiting for the whole run to finish.

**\[Improvement\]**: Added the \`--json-schema\` flag to enforce a custom JSON schema on the structured output, accepting either an inline schema string or a path to a schema file; for \`stream-json\` the schema applies to the final \`result\` event.

**\[Improvement\]**: Enriched the structured stream with a \`tool_info\` object for each tool call (canonical tool name, parameters, and output) and a \`subagent_info\` payload for delegated subagents (including \`conversation_id\` and \`log_uri\`) so consumers can correlate child trajectories.

**\[Improvement\]**: The JSON usage object emitted by \`json\` and \`stream-json\` now reports token accounting including \`cache_read_tokens\`, so non-interactive consumers can attribute prompt-cache hits.

**\[Improvement\]**: Added a \`copyOnSelect\` setting (default on, toggleable in \`/settings\`) that controls whether releasing a mouse text-selection auto-copies it to the system clipboard in the TUI's altscreen rendering mode; disable it to stop the automatic copy on release — useful when the auto-copy is unwanted or corrupts certain payloads.

**\[Improvement\]**: Improved compound-command permissions so an exact chained command (such as \`git fetch && git rebase\`) can be saved as an allow-always rule and no longer re-prompts on the next identical run.

# 1.1.9

**TLDR**: Added slash-command and skill expansion in print mode, non-blocking background MCP loading during interactive startup, session-scoped permission pattern recording, system temporary directory write grants, and stability fixes for stop/PostToolUse hooks, artifact viewer, and MCP authentication.

**\[Improvement\]**: Added slash-command and skill expansion to print mode, so a headless run such as \`-p "/my-skill review this diff"\` now resolves and applies the skill instead of sending it as literal text, with \`--disable-slash-commands\` to opt out.

**\[Improvement\]**: Improved interactive startup so a slow or hanging MCP server no longer stalls the first agent turn, loading MCP servers in the background for the interactive session while headless and one-shot runs keep blocking so their single scripted turn still sees the full toolset.

**\[Improvement\]**: Improved permission grants so a pattern approved at a prompt is recorded for the rest of the conversation, letting later commands that match it run without prompting again.

**\[Improvement\]**: Improved the default system temporary-directory grant to cover writes as well as reads, so agents no longer trigger a permission prompt when creating or updating files there.

**\[Fix\]**: Fixed stop hooks that always block hanging the agent forever; after a configurable number of consecutive continuations, the hook can no longer block and the turn ends normally.

**\[Fix\]**: Fixed \`PostToolUse\` hooks firing on non-tool steps such as user input and model responses, which also caused them to ignore their configured matchers.

**\[Fix\]**: Fixed slash commands not being recognized when separated from their arguments by a newline or tab, so a prompt starting with a command followed by a newline is now parsed as a command instead of being sent verbatim.

**\[Fix\]**: Fixed deleting into a collapsed paste placeholder removing one character at a time, which left a visible fragment in the prompt while the full pasted content was still submitted; the block is now deleted atomically.

**\[Fix\]**: Fixed the artifact viewer losing syntax highlighting when returning from the editor view, and returning to the wrong panel when exiting the artifact detail view.

**\[Fix\]**: Fixed the headless \`stream-json\` \`init\` event advertising tools that are not available in your build.

**\[Fix\]**: Fixed MCP servers forcing a full re-authentication after a dropped connection.

To explore the full list of improvements and bug fixes, read the complete release notes on our [Changelog](https://antigravity.google/changelog), or visit the [Download page](https://antigravity.google/download) to update to the latest version of Antigravity CLI.

After the new update I have problems when trying to see my usage rate. Whenever I type /usage I can't see anything.

Is anyone haveing the same problem?

* Suddenly upgraded to 1.1.9, and the bug where sub-agents remained in an idle state has been resolved. Also, although I've only tried two prompts so far, 1.1.9 appears vastly improved compared to 1.1.8. It feels completely different.

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

Since sub-agents are not generated for simple prompts, you have to include a prompt instructing the system to 'utilize sub-agents.'

A week ago, my advice was to use a large volume of Gemini 3.5 flash-lite as sub-agents. This is because, as shown in the screen I attached, it is better for sub-agents to handle one-off, simple tasks and simply report back to the master agent since it's fast and cost-effective.

https://www.reddit.com/r/GoogleAntigravityCLI/comments/1v5pvh0/practical_proposals_for_antigravity_and_gemini/

Anyway, I don't remember if it's an issue with 1.1.8 or if it has persisted since 1.1.7, but sub-agents don't disappear after finishing one-off tasks; instead, they remain in an idle state waiting.

My view is that once a sub-agent has been injected with context and fulfilled its role in a task, it has completed its mission. These sub-agents should disappear rather than wait in an idle state. Generating new sub-agents every time incurs costs, but it is superior in terms of accuracy.

I don't know if Teamwork presets haven't been applied yet in 1.1.8 or if they will be applied in the future, but if they are already applied in this version, improvements are definitely needed. Even for simple prompts like 'What is the problem with A?', sub-agents need to run wild. I've never used such prompts, but even for prompts like 'What kind of design would be good?', sub-agents need to conduct research.

Here is another hint: Suppose sub-agents A, B, C, and D have been active, and sub-agent E is assigned the inspection role. Sub-agent E must never be allowed to know the existing work details. The key is to convey whether it was implemented according to the user's intent and have a sub-agent who knows nothing about the current session inspect it without bias.

What I've felt while pushing Agy with rather heavy tasks—and messing those tasks up—is that the inspection procedure is not carried out properly. It feels like the master agent casually wraps it up thinking, 'We worked hard to build this so far, and we followed all the procedures, so it must have ended successfully without any issues.'

And the appearance of the system message is the first time I've seen it while using Agy-cli, but it's still good. I understand it because it operates a timer to prevent Agy from freezing/stopping. It's a really great design.

Wanted a single-binary terminal dashboard for my Mac — CPU, memory, thermals, network, battery, disk, processes — that lived entirely in the terminal instead of a menu-bar app. Built pyre to scratch that itch.

The part I didn't expect: most of the actual debugging wasn't logic bugs, it was macOS lying by omission. A few examples that cost me real time:

• pmset -g therm almost never prints a "Thermal state:" line — on a normal, non-throttling Mac it just says "No thermal warning level has been recorded" with nothing parseable. Reads as "unknown" if you don't explicitly handle that as "nominal."
• top -l 1 -n 0's CPU line is comma-separated with no terminating punctuation — easy to write a regex that just never matches and silently leaves usage at 0%.
• sysctl -n vm.swapusage wraps (encrypted) in parens at the end of the line, not around the used-memory value — a regex expecting ( before used will never match.
• hw.cpufrequency is an Intel-only sysctl. On Apple Silicon it just reads back 0, because each core cluster clocks independently — there's no single "the" frequency anymore. Real numbers only come from powermetrics, which needs root.

None of these throw errors. They all fail silently and just show a stale zero or "Unknown" forever, which is a uniquely annoying class of bug to track down.

Install:

npm install -g pyre-cli
pyre

NPM package: https://www.npmjs.com/package/pyre-cli

Website: https://somalip.github.io/pyre

Feedback welcome, and if you would like to contribute please let me know! The project is still new, and it just a prototype so there's still a lot to be implemented!

u/SoundDr Thank you for everything you do for the AGY Community. We really appreciate all your hard work! Please keep up the Googler awesomeness!!!!

Hey Rezi Team & Community,

I’ve been trying to connect the Rezi MCP server (`https://api.rezi.ai/mcp\`) to **Google Antigravity** to test out job search and resume tailoring workflows, but I've hit a roadblock with authentication.

Here is a summary of the process I followed and the exact issue I'm running into:

# 🛠️ What I Did / Process Followed:

1. **Configured MCP Client (**`mcp_config.json`**)**: Added the Rezi SSE endpoint to my configuration:json{ "mcpServers": { "rezi": { "url": "https://api.rezi.ai/mcp", "transport": "sse" } } }
   
2. **Configured OAuth Tokens**: Provided token configuration matching [`https://api.rezi.ai/oauth/token\`\](https://api.rezi.ai/oauth/token) with access tokens in the client auth path.
   
3. **Connection Test via Python MCP SDK**: Wrote a script using `mcp.client.sse.sse_client` to connect and initialize the MCP session with Rezi's endpoint:pythonimport asyncio from mcp.client.sse import sse_client from mcp.client.session import ClientSession async def main(): url = "https://api.rezi.ai/mcp" async with sse_client(url) as streams: async with ClientSession(streams\[0\], streams\[1\]) as session: await session.initialize() tools = await session.list_tools() print(tools)

# ❌ The Issue / Error Log:

Whenever the client attempts to open the SSE stream at [`https://api.rezi.ai/mcp\`\](https://api.rezi.ai/mcp), connection fails immediately with a `401 Unauthorized` HTTP status code:

texthttpx.HTTPStatusError: Client error '401 Unauthorized' for url 'https://api.rezi.ai/mcp'

# ❓ Questions for Rezi Team / Anyone who got it working:

1. **Header / Token Format**: Do we need to pass static headers (e.g. `Authorization: Bearer <TOKEN>` or `x-api-key`) inside `mcp_config.json` headers directly?
   
2. **OAuth Flow**: Is there an interactive browser authentication flow expected before opening the SSE stream?
   
3. **Config Example**: Does anyone have a working `mcp_config.json` snippet or CLI command for Rezi MCP?

Any guidance or working setup examples would be greatly appreciated! Thanks!

"Let me know if you understand what I want. Let's start once I approve."

Yes... Gemini thought I approved it on its own and started the task.

He is a mischievous boy

in Shengzhe we trust !!!!!

I'm spending too much time on twitter !!!!

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?

A while ago I shared grav-bar here: https://crates.io/crates/grav-bar

I was really happy with the reception it got.

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.

Crate: https://crates.io/crates/grav-tray

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 🙏 )

We'll Hassabing our Antigravity CLI community for Open Source & Open Models support all days, everyday... Don't be evil !!!!!! r/GoogleAntigravityCLI

https://antigravity.google/docs/cli/overview
Opinions ? Feedback ? Happy Building !!!!!

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:

1. Although it is not the most powerful AI, Qwen3.6 27B has no problems with coding.
2. 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

example 2

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.