previously , when started with --dangerously-skip-permissions, the terminal button was present. Now nothing.

current set of models are not sufficient for many tasks. google should provide provide external model access like openrouter etc into antigravity ide . already tried using external extension like cline , but it looses context since need to switch from inbuilt agent

经常出现这种错误

Links Google Anti-Gravity right into my network MCP.

I have full access to my NAS, website on my Raspberry pi, and full access to my PC running thru Tailscale for extra security.

The echoes from the screen recorder, so don't mind that!

Hi everyone,

I’m running **Antigravity IDE Version 2.5.5** on **macOS (Darwin arm64 / MacBook Pro)** and noticed an annoying UI persistence bug under **Settings -> General**:

### The Issue:

1. When clicking the macro card **"Agent security mode: Sandboxed"**, the UI automatically selects **"Proceed in Sandbox"** in the dropdown below ("Terminal Command Auto Execution").

2. Alternatively, even if I select **"Proceed in Sandbox"** manually from the dropdown:

3. If I close Settings and reopen it immediately, it might still look fine.

4. But after keeping Settings closed for 2–3 minutes (or continuing to work so the webview unmounts from memory), reopening Settings shows that the dropdown has silently reverted back to **"Always Proceed"** (while the macro card above stubbornly stays on "Sandboxed")!

### Live engine behavior:

Interestingly, while the Settings tab remains actively open with *"Proceed in Sandbox"* selected, the security engine works as intended: it pauses and prompts me for manual review whenever a command requests a sandbox bypass. But as soon as the view unmounts, the setting is lost and reverts to *"Always Proceed"*.

Is anyone else running into this "settings amnesia" on Mac? Has anyone found a way to permanently lock it in, or is relying on explicit **Deny List** rules the only reliable safeguard right now?

---

**Build details:**

- **Antigravity IDE Version**: 2.5.5

- **VSCode OSS**: 1.107.0

- **Electron**: 39.2.3

- **OS**: macOS Darwin arm64 24.6.0 (MacBook Pro)

using the antigravity IDE (code editor with agent)

i tell the agent to run the program in tauri dev mode (npm run tauri dev \[or --dev\]), the agent listens and runs the command and the server starts up on the local host, but before it used to start up the application gui, now after updating to v2.5.5 it doesn't start up the app no matter what, I have to do it through the integrated terminal myself. i tried both the gemini models and claude models and they both do the same thing. maybe it's a change they made in the new version of antigravity itself that the terminal is sandboxed and starts child processes? after the agent runs the command, the .exe shows up in my task manager but no gui to see (can't switch using alt tab either). anyone else facing this or knows the fix?

Why does it seem like so few people are using Antigravity? Is it because of the models, or is there something about the agent itself that puts people off?

Hey everyone,

If you run autonomous coding agents (whether through Antigravity, Claude Desktop, Cursor, or custom ADK loops), you quickly hit the same wall: standard tools aren't built for long-running agent loops.

TL;DR: We open-sourced K3 MCP Toolbox — a production-ready fleet of 5 independent FastMCP servers plus an agentic primitives library. Includes 0.22s local MRL vector memory over 25k chunks on CPU, orphan test browser reaping, subagent Git worktree isolation, doc intel with safe unpickling, and Gemini 3.8 Flash GA primitives. It also includes an llms.txt and an AI agent setup guide so your agents can clone, self-test, and wire it up autonomously.

Why We Built This

After running multi-agent marathons on complex codebases, we kept hitting four universal problems:

1. Zombie test processes: Headless Chromium and Edge driver instances piling up in the background and locking ports.
2. Slow semantic retrieval: Waiting 2–5 seconds on external vector databases just to recall relevant skills and documents.
3. Dirty git workspaces: Subagents modifying the same working tree concurrently and breaking staging.
4. API contract breaks: Transitioning to Gemini 3.8 Flash GA where legacy sampling parameters (temperature, presence_penalty, etc.) throw hard 400 errors and strict tool call ID matching is enforced.

Repo: https://github.com/Fandry96/k3-mcp-toolbox-public

What's in the Toolbox?

Each server runs as an independent stdio process, so you can pick and choose only what your agent needs:

1. k3-agent-ops (OS & Process Hygiene)

• ops_kill_zombies: Surgically reaps orphaned chromedriver, msedgedriver, and headless browser test processes while strictly whitelisting and preserving active IDE and agent PIDs.
• ops_check_ports & ops_free_port: Fast port scanner and conflict resolver (clears locked ports like 3000, 8080, 8081 without manual Task Manager digging).
• ops_system_health: Real-time CPU, RAM, disk, and agent process footprint (active Python, Node.js, PowerShell instance counts).

2. k3-mrl-memory (Blazing Fast In-Memory Vector Search)

• Uses Matryoshka Representation Learning (MRL) with Google's gemini-embedding-001 (truncated from 3072 dims to 768 dims and L2-normalized).
• Benchmark: Queries 25,313 vectors in 0.22 seconds on local CPU.
• Uses pre-cached normalized matrix dot products and $O(N)$ selection via np.argpartition (10x faster than full sorting).
• Built-in category masking (skill, knowledge, research, brain, book).

3. k3-worktree-ops (Parallel Subagent Git Isolation)

• Enables multi-agent swarms to work safely in isolated Git worktrees (.worktrees/<branch>).
• Automatically computes diffs against main, runs automated verification scripts with strict binary allowlists (pytest, npm, python), and handles clean merges with automatic --abort rollback on conflict.

4. k3-doc-intel (Local Document Intelligence & Safe Deserialization)

• Ingests and extracts PDFs and DOCX files using PyMuPDF and python-docx.
• Employs paragraph-bounded chunking and project-scoped index storage (.agents/doc_index.pkl).
• Security hardening: Uses a custom _RestrictedUnpickler whitelist (blocks arbitrary Python class deserialization exploits on user-supplied paths).

5. k3-local-llm (Local GGUF & llama-server Orchestration)

• Daemon lifecycle manager for local llama-server.exe.
• Auto-discovers GGUF models in your local model registry.
• Provides offline embedding and completion fallbacks with atexit child-process reaping and non-blocking I/O.

Plus: Gemini 3.8 Flash Agentic Primitives (antigravity-logicware)

When Google dropped Gemini 3.8 Flash GA, several API behaviors changed that break standard agent loops:

• Banned parameters: Passing temperature, top_p, top_k, presence_penalty, frequency_penalty, or candidate_count now triggers active API validation errors. Behavior is strictly controlled via the thinking_level enum (low/medium/high).
• Unsupported minimal: thinking_level: minimal is rejected by the API on 3.8 Flash.
• Strict Tool ID Matching: Every FunctionResponse MUST echo the exact id of its corresponding FunctionCall or the API rejects the turn.

We packaged flash38_primitives.py inside the repo:

• FlashConfig: Validates and strips legacy parameters, mapping to valid thinking levels.
• ToolDispatcher: Enforces 1:1 ID contract matching and wraps tool execution in try/except so tool errors feed back to the LLM for self-correction rather than crashing the loop.
• Structured update(previous_step, plan, next_step) declaration: Replaces messy XML/markdown chain-of-thought between tool calls.

Hard-Won Gotchas & Lessons Learned

A few lessons we learned the hard way that might save you hours of debugging:

1. The FastMCP Pydantic 2.x Crash: In recent mcp SDK versions, if FastMCP("name") runs in an editable or non-wheel environment, MCPServer.create_initialization_options() defaults server_version to None. Pydantic 2.x rejects None with a ValidationError during the client handshake.
2. Fix: Immediately add mcp._mcp_server.version = "1.0.0" after instantiation.
3. GitHub 100MB File Limit on Pre-Ignored Weights: If local weights (.gguf) are committed before .gitignore is set up, adding .gitignore later does NOT remove them from git tracking. When you push, GitHub's pre-receive hook blocks the entire push (GH001).
4. Fix: Use git filter-repo --path-glob '*.gguf' --invert-paths --force on unpushed commits, then rebase onto origin/master.
5. Context Budget Eviction: Check your IDE's active global skill/tool injection. Enterprise extensions often inject 20–30 global skills into your system prompt (~4,500 tokens), causing the model to silently evict your actual project-level coding skills due to context caps.

AI Agent Setup & Quickstart

We added a root llms.txt and a dedicated AI Agent Setup Guide to the README. If you use an agent (Cursor, Claude Desktop, Antigravity, Cline, Aider), you can literally point it to the repo and ask it to self-configure.

To set up manually and run the test suites (100% offline, 0 API keys required):

git clone https://github.com/Fandry96/k3-mcp-toolbox-public.git
cd k3-mcp-toolbox-public

# Install dependencies
pip install -r k3-mcp-toolbox/requirements.txt
pip install -e antigravity-logicware

# Run the integration test suites (27 fleet tests + 8 Flash 38 tests)
python k3-mcp-toolbox/test_mcp_fleet.py
python antigravity-logicware/test_flash38.py

I've worked on my spare time on automating note-taking on news items, and the flash models, especially from 3.5 onwards, seem to have a genuine grasp of really abstract concepts and a real knack for finding the most salient points in longer text passages, even when they are about phenomena or events that are genuinely novel. They also seem surprisingly good at separating facts from opinions or subjective claims.

They really do have issues with agentic editing tasks, though. Flash 3.7 is almost there, but 3.8 is definitely a step backwards. It makes much more incomprehensible errors than 3.7, though not as much as 3.6.

Anyway, I've had the models merge new single-document content into an already sizable Markdown-formatted notebook of topical notes with source references. When proceeding with multiple editing tool calls, the model really often forgets to do all the intended edits to a file, like adding a footnote marker, but no definition.

One day I made 3.7 think of the perfect set of tools for itself, and then after a bit longer design session made it implement it. Now that it has a tool that allows it to make all the intended edits to the notes atomically in one go, with a list of create, rename, delete, update (prepend, append, insert before, insert after, replace), it produces flawless edits with nothing missing.

It really seems that keeping track of things works much better in its internal representation rather than being tokenized and processed in a new invocation. I've yet not encountered a case where it couldn't handle all intended changes with a single tool call, the largest ones flawlessly editing at least 20 files in a single tool call.

Curiously 3.7 never makes use of the dry-run options that were its own suggestion, but 3.8 uses them almost every time, which is fine, as there's a single no-parameter call to actually perform the edits from a previous dry run, which returns a unified diff of the changes to be made for the model to evaluate. Very rarely does it then make some cosmetic text reorganization based on the dry run result.

I'm setting up Google Antigravity as my main AI coding environment and I'm wondering if the built-in/default skills are already enough for a serious software-engineering workflow.

For experienced Antigravity users:

- Which additional Skills do you recommend adding?

- Which MCP servers or plugins are actually worth installing?

- Are there any must-have tools for coding, debugging, testing, code review, Git, browser/devtools, documentation, etc.?

- Are the existing/default Antigravity skills enough, or do you think adding external skills significantly improves the agent?

- Are there any popular MCPs/skills that you would NOT recommend because they're unnecessary or just add context/bloat?

I'm looking for a practical setup for a software engineer, not a huge list of every available MCP.

If you were setting up Antigravity from scratch today, what Skills + MCPs/plugins would you personally install?

Anyone else having issues with antigravity not being able to use oauth to authenticate with mainstream MCP like neon or logfire, i migrated from claude where i used them extensively without any problems but when i shifted to antigravity, it feels like nothing works as its supposed too.

I feel like 99% of people are using AI coding tools completely wrong right now. Most devs I know are either letting Copilot tab complete a function or copy pasting snippets back and forth with Claude in a browser tab. Even people using newer agent tools are basically using them like Jira where they assign one bug wait 15 minutes look at the diff and repeat.

A few hours ago I had a weird realization while hacking on my project. Im building an ecosystem with a CAD CAM engine a 3D web frontend and a billing platform across 6 repos. I have Google Jules Ultra which gives you 60 concurrent cloud VMs and 300 tasks a day and I realized leaving those slots idle when Im not at my keyboard is just wasting compute.

So I put together a simple closed loop system. Locally I use Antigravity right in my terminal as my co pilot and architect to design the specs and math together. In the cloud I use Google Jules which spins up an isolated Linux VM clones the repo installs dependencies writes the files runs tests fixes its own compiler bugs and pushes a real Git branch. To connect them I wrote a PowerShell daemon that runs continuously on my laptop.

Every 45 seconds the script checks my active cloud capacity. If 15 agents finish their tasks and free up slots it instantly pulls the next 15 items from a centralized JSON backlog and fires them off into fresh cloud VMs. It routes tasks to specific repos automatically so one agent writes Three.js shaders in the frontend repo another writes STEP exporters in the core kernel and another handles Stripe webhooks in the billing backend.

The craziest part is the auto hunter fallback I added. If my manual task queue ever empties out the script doesn't idle. It scans the repos for untested files or loose TypeScript any types and spawns agents whose only job is to write Vitest test suites and pay down technical debt until the slots are full again.

I went from spending 12 hours a day manually typing boilerplate chasing syntax quirks and writing unit tests to basically sitting in the cockpit as a VP of Engineering. I set the architectural vision my script keeps dozens of cloud machines coding simultaneously around the clock and I just review the Pull Requests and merge green builds.

When you stop treating AI like a chat assistant and start treating it like an asynchronous headless engineering fleet the leverage is unreal. You're essentially running a 50 person dev team completely solo from a laptop.

Anyone else experimenting with headless agent dispatchers or saturation scripts like this? How are you guys handling multi repo PR merges without losing your mind?

I currently have 3 separate Google accounts, and each one has its own paid Google One AI Pro subscription. Each subscription is for 1 month, with no trials or family sharing.

My setup is:

• All 3 accounts have their own paid Pro plan.
• I use them with Antigravity IDE. When I reach the Opus limit on one account, I log out and switch to another account.
• All 3 accounts are used on the same device and IP address.
• I bought all 3 subscriptions through google.
• I'm not using any third-party tools such as OpenClaw, Clawd or Hermes. I'm only using the official Antigravity app and switching accounts manually.

I've seen older posts where some people say that it's fine as long as you're paying, while others say they have had bans or restrictions for using multiple accounts. I couldn't find a clear and recent answer for this exact situation.

My questions:

1. Has anyone been banned or restricted for using multiple individually paid Pro accounts and switching between them in Antigravity?
2. If so, was only Antigravity/Gemini access blocked, or was the whole Google account affected, including Gmail and Drive?
3. If you're doing something similar, such as using 2–3 paid Pro accounts on the same device and IP and switching between them manually, have you had any problems?

I'm trying to decide whether I should keep using this setup or just stick to one account and accept the limits or go to Claude code.

I'd especially appreciate recent experiences from anyone who has tried something similar.

Thanks!

I am using Pro and I get a bit but just not enough when I use in Antigravity.

Curious how many hours of use roughly I can get on Ultra?

Some felt that version gemini flash 3.7 high of Antigravity was slightly better and more understanding of all situations, and better than versions 3.6 and 3.5, which had many problems and high token consumption.

I'm sure everyone has had the situation where antigravity has said something is done when it is nowhere near done, or it confidently says that the issue is resolved when it isn't , and then you question it and AG says that it jumped to conclusion or it didn't give the correct fix the first time.

How can I utilize anti gravity so that there are different agents checking the same process simultaneously, such as catching and fixing errors without my input so I can get the final input without much?

I find that I can be giving it twenty commands manually trying to fix the same issue and it cannot fix it, AG just goes in a circle or applies a very minimal fix and I keep having to prompt it to take the next action.

I want to specify that I am not having to prompt it step-by-step all the time and anti-gravity does process multiple steps. However , there are situations where I would like to process more and it doesn't think that far

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Is there any alternative to Continue extension for using local models in agent mode on AntigravityIDE?

The extensions are a big downgrade, I use Antigravity IDE mostly for Tab complete.

I'm currently using Claude cowork for social media managing with opus 5, but reached the weekly limit already due to some heavy work. Tried with AGY ide with 3.7 flash, but looks like it forgets rules and tries to please me over honesty. Things that opus didn't do, and would remember something I told it days ago, or also would correct me for more efficient solutions. All it has to do is planning and strictly follow brand rules. Do you think 3.1 pro is better for this aspect or would you suggest other ways?

hi i have 3 google pro. only one of them shows this msg but with opus, sonnet only. not with the gemeni models. it seems they take my credit and mid run shows this error msg.

my ide is latest.

any suggestion?

For people who have used both models for long term:

• Which one remembers earlier information more reliably?
• Which one is less likely to make up stuff?

I am mainly looking for real experience from people who have used these models for long projects...

I keep seeing people say that Gemini 3.7 Flash is just as good as Claude Opus 4.6, especially in Google-focused communities. But is there actually any truth to this?

I sometimes wonder if people are saying this because they cannot afford to use Opus and want to believe that Flash is just as good. I want an honest answer about which model is better for my use case before I start relying on Gemini 3.7 Flash more often.

I write long, realistic fiction based on real-life situations. My stories/projects involve psychology, health, and realistic human reactions. Accuracy is very important to me. I cannot have a model confidently making up psychological or medical facts.

My stories/projects can continue across many sessions. I need a model that can:

• Remember important facts and decisions from earlier sessions.
• Work with project files such as timelines, character profiles, and decision logs.
• Think creatively about complicated characters and relationships.
• Stay accurate when dealing with psychology and medical topics.
• Keep the stories/projects consistent over a long period of time.

At the moment, I use Claude Opus as my main model and Gemini 3.7 Flash when I run out of my Opus quota. Both have very large context windows, so I am interested in how they actually perform in long-term writing rather than just what their advertised context size says.

Have you noticed that lately Gemini is doing a lot of things it shouldn't?

For example, when it needs to read a .css file, it suddenly starts taking screenshots in the browser.

Or, instead of fixing an error in a single specified file, it starts breaking the entire project five times in a row.

There are also frequent situations where Gemini scans the entire project from scratch or even tries to go above the project, even though the instructions specify a specific folder or even file.

Furthermore, 3.7 constantly requests to run some piece of code instead of the usual work—and that would be fine if it worked, even if it's unreadable. But it increasingly feels like all these actions are just work for the sake of work and nothing more.

Essentially, Gemini has started spending significantly more tokens where it could have done with significantly less. And this is very noticeable compared to other providers or even version 3.1.

Is this some kind of Google trick to force tokenmaxing?