Something remarkable happened in the AI world over the past two months. An open-source project called OpenClaw went from zero to 179,000 GitHub stars in about 60 days. That's 18x faster than Kubernetes. Its creator, Peter Steinberger, just joined OpenAI.
If you've been anywhere near tech Twitter, you've seen the screenshots: an AI agent that can read your WhatsApp, manage your email, browse the web, run shell commands, and basically act as a 24/7 personal assistant running on your own machine.
It's impressive. And it tells us something important about where AI is going.
But it also highlights a tension that most people aren't talking about: personal AI agents and team AI workspaces are solving fundamentally different problems. Conflating them is a mistake that'll cost you time, security, or both.
The Two Paths of AI at Work
Here's the simplest way to think about it:
Path 1: The AI Butler. A personal agent that runs on your device, connects to your accounts, and does things on your behalf. It reads your messages, writes your emails, manages your files. It's powerful because it has full access to your digital life. OpenClaw is the poster child for this approach.
Path 2: The AI Coworker. An AI that lives inside your team's workspace, understands your shared context (tasks, documents, conversations), and helps the whole team get things done together. It doesn't need access to your entire computer—it needs access to your project's context. This is what we're building at Trilo.
Both paths are valid. Both are needed. But they're not interchangeable, and pretending they are leads to problems.
What OpenClaw Gets Right
Credit where it's due—OpenClaw nailed a few things:
It actually does stuff. Most AI tools are glorified search engines. You ask a question, you get an answer, you copy-paste it somewhere useful. OpenClaw has "eyes and hands"—it can browse, click, type, and execute. That's a meaningful leap from chatbot to agent.
It's local-first. Your data stays on your machine. No cloud databases, no subscriptions, no wondering who's reading your conversations. For privacy-conscious users, this is a big deal.
It meets you where you are. Instead of forcing you into yet another app, it connects to the messaging platforms you already use—WhatsApp, Telegram, Slack, Discord, Signal, even iMessage. You talk to your AI agent the same way you talk to your friends.
It's extensible. With 700+ community skills on ClawHub, the ecosystem is growing fast. Need it to manage your Todoist? There's a skill for that. ClickUp? Jira? Calendar? All covered.
It's free. MIT-licensed, no subscription. Bring your own API key and you're done.
What OpenClaw Doesn't Solve
Here's the thing though. OpenClaw is fundamentally a single-player tool. And most work isn't single-player.
No shared context. When your teammate asks OpenClaw a question, they start from scratch. There's no shared memory, no team knowledge base, no "Sarah figured this out last Tuesday and here's what she learned." Every person's agent is an island.
No native collaboration. There's no task board, no shared documents, no project channels. You can bolt these on through third-party integrations (ClickUp skill, Jira skill, etc.), but that's duct-taping separate systems together. The context gets fragmented across tools.
No structure for team workflows. Need to route a task through review, assign it to the right person, track time spent, or automate a multi-step workflow? That's not what personal agents are built for.
Security is a real concern. This isn't FUD—it's documented. Security researchers found 42,000 exposed OpenClaw instances on the public internet, 93% with critical authentication bypass vulnerabilities. A CVE was issued for one-click remote code execution. 341 malicious skills (12% of the registry) were discovered with keyloggers and data exfiltration. Palo Alto Networks called it "the potential biggest insider threat of 2026."
When you give an AI agent access to your shell, your files, your browser, and your email, the attack surface is enormous. That's not a flaw in OpenClaw's design—it's an inherent risk of the personal agent model.
Where Team AI Workspaces Fill the Gap
This is where our perspective comes in. We built Trilo because we kept running into the same problem: AI tools were powerful but isolated, and collaboration tools were connected but dumb.
Here's what a team-first AI workspace looks like:
Shared Context That Compounds
When one person asks the AI about your pricing strategy, the whole team benefits from the context built up in that conversation. When tasks get created, updated, and discussed, the AI remembers the why behind decisions—not just for one person, but for everyone on the team.
This is the context graph concept we wrote about recently. The more your team uses it, the smarter the AI gets for everyone. That doesn't happen when every person has their own isolated agent.
AI That Understands Your Project, Not Just Your Computer
Trilo's AI coworker Sammy doesn't need access to your entire filesystem or email inbox. It needs to know your tasks, your documents, your conversations, your team's decisions. It operates within a well-defined workspace scope—which means the security surface is dramatically smaller, and the context is dramatically better.
When you ask Sammy "what's the status of the redesign?" it doesn't need to scrape your Slack messages and Jira board. It knows, because it lives where the work happens.
Specialized Agents, Not One Agent That Does Everything
OpenClaw takes the "one agent to rule them all" approach. Trilo takes the opposite: specialized skill-based routing. When you need help with a document, the AI loads page-editing skills. When you're working on tasks, it loads project management skills. When you're generating images, it routes to the image generation specialist.
This means each interaction gets the right tools for the job, without bloating every conversation with capabilities you don't need. It's the difference between a Swiss Army knife and a well-stocked workshop.
Real-Time Collaboration, Not Just Real-Time Chat
Trilo's collaborative editing is built on Yjs (CRDTs)—the same technology behind tools like Figma's multiplayer editing. Multiple people can edit the same document simultaneously, with AI assistance happening in real-time alongside human edits.
OpenClaw's "real-time" is about the agent responding to your messages quickly. That's fast chat, not collaboration.
The Architecture Gap
For the technically curious, here's where the differences get stark:
| OpenClaw | Trilo | |
|---|---|---|
| Database | None. Markdown files on disk. | PostgreSQL with 50+ tables, Drizzle ORM |
| Backend | Node.js WebSocket gateway (local) | Elysia on Bun (cloud, 122 services) |
| Frontend | CLI + messaging apps | Next.js 16 + Expo native mobile + Tauri desktop |
| Real-time | WebSocket (agent ↔ user) | WebSocket (user ↔ user, user ↔ AI, 30+ event types) |
| Workflow engine | Cron jobs and webhooks | Full orchestrator with checkpointing, branching, loops, parallel execution |
| Auth | Pairing codes | JWT + JWKS + Row-Level Security |
| AI routing | Single agent picks skills | SkillClassifier routes to specialized agents |
OpenClaw's architecture is elegant for what it does—a lightweight, local-first agent. But it wasn't designed to handle multi-user workspaces, shared state, or structured workflows. And retrofitting those things onto a file-based, single-user system is exactly the kind of painful rebuild we've seen play out in other categories before.
They're Complementary, Not Competing
Here's the honest take: you probably need both types of tools, and they'll serve different parts of your workflow.
Use a personal AI agent (like OpenClaw) when you need:
- Personal automation (email triage, calendar management, file organization)
- Cross-platform messaging unification
- System-level tasks on your own machine
- Privacy-first, local-only processing
- Quick personal tasks that don't involve your team
Use a team AI workspace (like Trilo) when you need:
- Shared project context that the whole team can access
- Structured task management with AI assistance
- Collaborative document editing with real-time sync
- Automated workflows that span multiple people and steps
- AI that understands your team's decisions, not just your personal files
- An audit trail of who did what and why
The future probably looks like personal agents handling your individual digital life while team workspaces handle collaborative work. The mistake is trying to use one for both.
What This Means for Where Work Is Heading
The OpenClaw phenomenon confirms something we've believed since we started building Trilo: people want AI that doesn't just answer questions—they want AI that does work.
The question is: whose work?
If it's your personal tasks—managing your inbox, automating your social media, organizing your files—a personal agent makes sense.
If it's your team's work—planning a product launch, writing a proposal together, tracking a sprint, onboarding a new hire—you need something built for collaboration from the ground up.
We've written before about how every category of software eventually goes multiplayer. AI is no different. The personal agent wave is exciting, but the multiplayer wave is what actually changes how teams work.
OpenClaw proved that people are ready for AI agents that act, not just chat. The next chapter is proving that teams are ready for AI that collaborates, not just assists.
We're building for that chapter.
Trilo is a workspace where your team works alongside AI coworkers—with shared context, real-time collaboration, and structured workflows. Try it out or learn more about our AI coworkers.
