google antigravity vs other ai workflow agents
Google Just Entered The Agentic IDE Space
and I have opinions about it
Field Notes from a Fintech Engineer · April 2, 2026 · 14 min read · ☕ Fuelled by zero coffee (Lent)
Seven years at Nubank. Built lending systems at Wio Bank. Currently jobless in Poznań, spending too much time running AI agents on a Mac Mini. Here’s my take on Google Antigravity vs the current field.
The Situation: Everyone is building agents now. That’s the problem.
It’s April 2026. I resigned from Wio Bank yesterday. My Karta Pobytu is stuck in Polish bureaucracy somewhere between “processing” and “heat death of the universe.” And the one thing I cannot stop doing is obsessing over AI agents.
So when Google dropped Antigravity back in November 2025 alongside Gemini 3, I installed it within the hour. And then spent the next few months watching OpenClaw go from 0 to 250k GitHub stars, Anthropic quietly ship Cowork to Max subscribers, n8n accelerate its AI workflow features, and Hermes Agent from NousResearch come out of nowhere.
The agentic space exploded in a way that even I — a guy who literally runs OpenClaw on a dedicated Mac Mini named Pola — didn’t fully anticipate. So let me do what I do best: break it down properly, skip the hype, and tell you what actually matters for engineers.
// me when Antigravity shipped with Claude Opus 4.6 support
Google Antigravity: The Agent-First IDE
Antigravity is Google’s answer to the agentic development era. It’s not just an editor. It’s a development platform that combines a familiar, AI-powered coding experience with a new agent-first interface, allowing you to deploy agents that autonomously plan, execute, and verify complex tasks across your editor, terminal, and browser.
The thesis is simple: you stop being the typist and start being the architect. In the Antigravity workflow, the developer transitions from a “writer of code” to an “Architect” or “Mission Controller.” You don’t type function login();, you issue a directive: “Refactor the login flow to support OAuth and verify it against these design specs.”
Technically, it’s a heavily modified fork of VS Code (yes, Cursor lineage, yes, the VS Code ecosystem is devouring everything). There’s debate as to whether it’s a direct fork or a fork of Windsurf, another AI-oriented code editor which is itself a fork of VS Code. At this point I don’t care. It runs well and ships with Gemini 3 Pro, Claude Sonnet 4.6, Claude Opus 4.6, and OpenAI models. Model optionality is table stakes now.
Key distinction: Antigravity has two modes. The Editor View is your standard AI-powered IDE with tab completions — basically Cursor. The Manager View is where things get interesting: it’s a control center for dispatching parallel agents to independent workspaces. You can have five agents fixing five different bugs simultaneously.
The killer feature here is Artifacts. Delegating work to an agent requires trust, but scrolling through raw tool calls is tedious. Antigravity solves this by having agents generate Artifacts — tangible deliverables like task lists, implementation plans, screenshots, and browser recordings. These Artifacts allow you to verify the agent’s logic at a glance. If something looks off, you can leave feedback directly on the Artifact — similar to commenting on a doc — and the agent will incorporate your input without stopping its execution flow.
// manager view dispatching agents in parallel like it’s nothing
The honest critique? The lack of MCP support and occasional agent reliability issues keep it from dethroning Claude Code for complex work or Cursor for daily IDE use. As someone who runs Claude Code professionally, that MCP gap is significant. My entire workflow is built around MCP servers — if I can’t hook Antigravity into my toolchain the same way, it’s a second-tier tool regardless of how impressive the Manager View is.
As of early 2026, Google Antigravity remains in public preview, free of charge for users with personal Gmail accounts. That’s a strong free tier play. For prototyping? Genuinely excellent. For production-grade complex work? You’ll hit the ceiling.
Now let’s talk about the competition
Google didn’t enter a vacuum. The space already had serious players. Here’s how I see each one — and yes, I have actual experience with all of them, not just Twitter takes.
🦞 OpenClaw
formerly Clawdbot, formerly Moltbot · 250k+ ⭐ GitHub · Open source
I run this on a Mac Mini M4. I called it Pola. Jensen Huang called it “probably the single most important release of software, you know, probably ever.” The hype is real and also kind of exhausting.
OpenClaw is a free and open-source autonomous AI agent that can execute tasks via large language models, using messaging platforms as its main user interface. You talk to it via WhatsApp, Telegram, Discord. It talks back. And then it executes shell commands on your machine while you eat dinner.
The Skills system is the architectural choice I appreciate most — a directory with a SKILL.md file that tells the agent how to use a capability. Very composable, very extensible. The whole thing is Node.js-based and model-agnostic. I run it with Claude because, well, it’s better.
The downsides are real: because OpenClaw requires access to email accounts, calendars, messaging platforms, and system-level commands, it exposes users to numerous security vulnerabilities. And yes, Cisco’s team found supply chain attacks in the skills registry. The creator himself warned that if you can’t understand how to run a command line, this is far too dangerous for you to use safely. I take that seriously. This is a power tool, not a productivity app.
Pros: Fully open source and self-hosted · Works via any messaging app · Skills system is elegant · Massive community · Model agnostic
Cons: Security nightmare out of the box · No enterprise-grade governance · Needs a dedicated machine · Not for non-technical users · Creator left for OpenAI
🤝 Claude Cowork
Anthropic · “Claude Code for the rest of your work” · $20–200/mo (Pro / Max / Team)
Anthropic noticed people were using Claude Code for non-coding tasks — recovering wedding photos, cancelling subscriptions, monitoring plant growth. So they built Cowork: the same agentic architecture, minus the terminal requirement.
Claude Cowork handles tasks autonomously. Give it a goal and Claude works on your computer, local files, and applications to return a finished deliverable. Most AI tools are built around the prompt. Claude Cowork is built around the outcome.
The implementation detail I love: Anthropic built a powerful coding agent first — Claude Code — and is now abstracting its capabilities for broader audiences. This technical lineage may give Cowork more robust agentic behavior from the start. Bottoms-up architecture > top-down product design. Every time.
My honest POV: this is the one I’d recommend to non-technical colleagues. It’s sandboxed, Anthropic-backed, and the skills system is the same one powering all their desktop tools. The limitation is it’s local — it doesn’t deploy to the cloud, it just executes locally on your machine.
Pros: No terminal required · Same architecture as Claude Code · Filesystem sandboxed by default · Plugin ecosystem (n8n connector!) · Backed by Anthropic
Cons: Paid plan required · Still research preview · Local only (no cloud execution) · Mac bias (Windows is newer) · HIPAA / regulated workloads: nope
⚙️ n8n
The workflow automation veteran · Open source + Cloud
n8n is the old guard in this comparison. It predates the “agentic” buzzword by years. But it’s evolved: native LLM nodes, AI agent workflows, memory tools, and yes — it’s a connector in Claude Cowork’s marketplace. That tells you everything about where it sits in the ecosystem.
The audience is different. n8n is for workflow automation at the integration layer — Slack to Airtable to Salesforce pipelines, webhook-triggered automation, scheduled jobs. It’s less “give me a goal” and more “here’s the exact DAG of what I want to happen.” That’s not a weakness, it’s a different tool for a different problem.
For a backend engineer like me, n8n is the thing I reach for when I need reliable, observable, repeatable automation with proper error handling and retry logic. It’s not an agent — it’s a workflow engine that increasingly has AI nodes. The distinction matters.
Pros: Mature, battle-tested · Self-hostable · 500+ integrations · Proper error handling / observability · AI nodes are genuinely good
Cons: Not truly “agentic” by default · Visual DAG can get messy at scale · Requires explicit workflow design · Heavy for simple agent tasks
🧬 Hermes Agent
NousResearch · “The agent that grows with you” · Open source · Python
This is the one most people are sleeping on. Hermes Agent is an open-source autonomous AI agent built by Nous Research. It’s not a coding copilot tethered to an IDE or a chatbot wrapper around a single API. Hermes lives on your server, remembers what it learns, and gets more capable the longer it runs.
The self-improvement mechanism is architecturally elegant: after complex tasks, it writes reusable skill documents. Memory is curated across sessions in MEMORY.md and USER.md files. It’s not weight retraining — it’s retrieval-based learning. Practically, that means repeated task types genuinely get better over time.
The NousResearch angle is underrated. This isn’t just a personal assistant — it’s research infrastructure. What distinguishes Hermes Agent from the broader personal agent ecosystem is its dual identity: it’s both a practical daily-driver assistant and a research platform for generating training data and running reinforcement learning experiments. That’s a different kind of moat.
It also ships with a one-liner migration from OpenClaw: hermes claw migrate. Respect.
Pros: True self-improvement loop · $5 VPS deployable · MCP support · Research-grade RL tooling · OpenClaw migration built-in
Cons: Smaller community (~8.7k stars) · Docs have gaps · Windows: WSL2 only · Early stage, reliability varies
// me trying to explain to my wife why I need 5 AI agent subscriptions
Head to Head: The Actual Comparison
| Dimension | Antigravity | OpenClaw | Cowork | n8n | Hermes |
|---|---|---|---|---|---|
| Paradigm | Agent-first IDE | Personal agent | Desktop agent | Workflow engine | Self-hosting agent |
| Pricing | Free preview | Open source | $20–200/mo | OS + Cloud | Open source |
| Primary use | Code / Dev | Life automation | Knowledge work | Integration pipelines | Persistent assistant |
| Interface | IDE (VS Code fork) | Telegram / WhatsApp | Desktop GUI | Visual DAG + API | Telegram / CLI |
| Multi-agent | ✅ Manager View | ⚠️ Limited | ✅ Sub-agents | ✅ Parallel nodes | ✅ Subagent spawn |
| MCP support | ❌ Not yet | ✅ | ✅ Connectors | ✅ Native integrations | ✅ |
| Self-hostable | ⚠️ Local IDE | ✅ Required | ⚠️ Local only | ✅ | ✅ |
| Persistent memory | ⚠️ Knowledge base | ⚠️ Session-based | ✅ Global instructions | ❌ Stateless by default | ✅ Cross-session + FTS |
| Security posture | Medium | 🔴 Risky | 🟢 Sandboxed | 🟢 Mature | 🟡 Docker isolation |
| Non-dev friendly | ⚠️ Still dev-heavy | ❌ Absolutely not | ✅ By design | ⚠️ Visual but complex | ❌ Dev only |
The industry has moved through three phases: autocomplete era, agent-assisted era, agent-first era. Whether you like it or not, you’re in phase three now.
What I actually use (and recommend)
I have a confession: I don’t think there’s a winner. The right question is “winner for what?” Different tools for different contexts.
My personal stack (as of April 2026):
| Use case | Tool | Notes |
|---|---|---|
| Daily coding | Cowork | + Claude Code CLI |
| Life automation | OpenClaw | Mac Mini “Pola” |
| Prototyping | Antigravity | Manager View |
| Pipelines | n8n | Self-hosted |
| Experimenting | Hermes | NousResearch RL |
The optimal setup right now is Antigravity for rapid prototyping + Claude Code / Cowork for serious work. The $0 entry on Antigravity makes it a no-brainer addition to your toolbox. Hermes is the sleeper pick for engineers who care about privacy and persistent agents. n8n is for deterministic workflows. OpenClaw is for brave people who enjoy reading Cisco vulnerability reports.
For engineers in fintech / distributed systems specifically: The MCP gap in Antigravity is a blocker for production-grade tooling. Claude Code + Cowork wins on integration depth and security model. Until Antigravity ships MCP, it’s a prototyping tool, not a professional one.
// when the agent actually works end to end without any hallucinations
The real disruption isn’t the tools. It’s the mindset shift.
I’ve been writing software for 15+ years across PHP, Clojure, Java, TypeScript, and a healthy dose of “whatever the company decided before I joined.” The honest truth is this: the jump from “AI autocomplete” to “agent that executes your intent autonomously” is qualitatively different in a way that took me a while to internalize.
Google Antigravity is a strong statement from a company with the resources to make it stick. The Manager View is genuinely impressive. The multi-model support shows they’re not betting the house on Gemini alone (smart). The free tier is the right go-to-market for developer adoption.
But it’s not dethroning anyone yet. OpenClaw proved that community-driven, open, local-first agents have a massive audience. Cowork proved that the best agent UX might be no terminal at all. Hermes is quietly building the infrastructure for agents that actually improve. And n8n is sitting comfortably as the backbone of deterministic automation that agentic tools are still not reliable enough to replace.
My advice: don’t pick one. Pick based on context. Use Antigravity to explore what’s possible. Use Cowork or Claude Code when you need it to actually ship. Use n8n when you need it to actually run reliably in production at 3am. Use OpenClaw when you want to feel like Tony Stark. Use Hermes when you want your agent to get smarter over time without selling your soul to a cloud provider.
A note on security across all of these: Every single tool in this list that touches your filesystem, email, calendar, or terminal is a prompt injection vector. We’re all early here. Be thoughtful about what you grant access to, run in sandboxes where possible, and read the security advisories. The “move fast” era of agentic AI is exciting and also kind of terrifying.
Now if you’ll excuse me, I’m going to go ask Pola to organize my job application spreadsheet while I argue with Polish bureaucracy about my residency permit.
— Paulo
paulo.dev · Poznań, Poland · April 2026
Written by a Senior Technical Lead currently between jobs. Opinions are my own, not those of any past or future employer. Found an error? Found me a job? Both: hello [at] paulo.dev