I Spent $47 testing OpenClaw for a week: Here's What's Actually Happening
Three weeks ago, I'd never heard of OpenClaw. Last week, my feed was full of it. people calling it the future of work, others warning it's a security nightmare. The discourse was so polarized that I did what any developer would do: I set up a test environment and tried it myself.
I spun up an old laptop, isolated it from my main network, and spent a week actually using OpenClaw for daily tasks. I also dove into the security reports, GitHub issues, and talked to people running it in production. What I found surprised me, not because it's revolutionary or terrible, but because it's both, depending on who's sitting at the keyboard.
This isn't a hit piece. It's also not a love letter. It's what I learned after actually installing and testing this thing everyone's talking about.
What It Actually Is
Strip away the hype and OpenClaw is fundamentally this: an open-source bridge between LLMs and your computer.
It runs on your own hardware and connects language models (Claude, GPT-4, DeepSeek, etc.) to your messaging apps. You text it on WhatsApp or Slack, and it executes tasks on your computer: manages your inbox, schedules meetings, browses websites, runs terminal commands, organizes files.
Think of it like having a developer friend SSH'd into your machine who's always available via text message. Except it's an AI, it never sleeps, and it remembers every conversation you've ever had with it.
The Setup Reality
Here's where theory meets practice. The documentation says "installation is straightforward," and I can confirm - if you're comfortable with Node.js, Docker, and terminal commands, it is. For everyone else, not so much.
My setup took about 30 minutes on an Ubuntu laptop. Here's what I had to navigate:
- Installing Node.js version 22+ (had to upgrade from v20)
- Setting up Docker containers
- Managing API keys for Claude and GPT-4
- Configuring OAuth flows for WhatsApp and Telegram
- Setting environment variables and editing config files
- Understanding WSL2 requirements (if you're on Windows)
The documentation is actually decent, but it assumes you already know what npm install, docker-compose up, and environment variables are. There's no Zapier-style wizard. You're reading YAML files and setting permissions manually.
The marketing suggests "personal AI assistant" when the reality is "developer power tool." That distinction matters.
What I Actually Tested
Once I got it running, I spent days using OpenClaw for real tasks. Here's what worked, what didn't, and what surprised me:
Email Automation (Worked Well)
I connected it to a throwaway Gmail account with ~2,000 emails accumulated over six months. I asked it to: "Unsubscribe me from all newsletters and promotional emails, keep only transactional emails and personal correspondence."
It processed everything in about 20 minutes. Out of 847 newsletter emails, it successfully unsubscribed from 203 lists and categorized the rest. I spot-checked about 50 emails - accuracy was around 90%. It missed a few legitimate emails from a client who uses a marketing platform, but overall, impressive.
Cost: ~$8 in API calls (using Claude Sonnet 3.5)
Calendar Management
I tested having it schedule a meeting with three colleagues based on their availability. I gave it access to my test calendar and said: "Schedule a 1-hour team sync with Alex, Jordan, and Sam sometime next week, preferably mornings."
It... kind of worked. It found a slot that was free for me and sent calendar invites. But it didn't actually check the other calendars (because I didn't set up those OAuth permissions). When I granted proper access and tried again, it worked but took few minutes to propose a time.
File Organization (Surprisingly Good)
I dumped 300 random files (PDFs, screenshots, documents) into a folder and asked it to: "Organize these by category and rename them with descriptive names."
It created a folder structure (Documents/Work, Documents/Personal, Images/Screenshots, etc.) and renamed files with actually useful names. A screenshot of a GitHub issue became "github-issue-authentication-error-screenshot.png" instead of "Screenshot_2026_01_15.png".
This genuinely saved me time. I'd use this feature regularly.
Web Research (The Expensive Part)
I asked it to: "Research the top 5 project management tools for remote teams, compare pricing and features, and create a summary document."
It spent the next 30 minutes browsing websites, taking screenshots, reading documentation, and compiling information. The output was actually useful, a well-structured comparison with pricing tiers and key features.
Cost: ~$22 (!!!) because of all the browser automation and vision model calls
This is where the costs can spiral. Every screenshot it takes, every page it reads with vision models that adds up fast.
Code Review Automation (Didn't Work Well)
I tried having it review a pull request and provide feedback. It read the diff, provided some generic comments about code structure, but missed an actual bug I'd intentionally introduced. It also suggested changes that would've broken the build.
The Pattern I Noticed
After a week of testing, here's what I figured out: OpenClaw excels at structured, repetitive tasks with clear success criteria.
It's not going to "figure out your business strategy". But for the boring, automatable stuff like email cleanup, file organization, research compilation,etc.. actually works.
The failures came from tasks requiring judgment calls or deep domain knowledge. It's a tool, not a replacement for thinking.
The Cost Reality
OpenClaw itself is free. But that free software isn't actually free to run.
Over my five-day testing period, I spent approximately $47 in API costs.
The expensive stuff? Anything involving browser automation with screenshots. Every time OpenClaw takes a screenshot to "see" what's on a page, that's a vision model API call. That $22 research task? It took 30 minutes and involved visiting 15+ websites with screenshots of each.
I was using Claude Sonnet 3.5 for most tasks. If I'd used Opus for everything, those costs would've easily doubled or tripled.
Here's what I learned: The model you choose dramatically affects costs. For simple tasks (email sorting, file renaming), cheaper models work fine. For complex tasks (research, multi-step workflows), you need the expensive models - and the costs add up fast.
I also discovered that runaway processes are a real concern. I set up a scheduled task to check my inbox every hour. It ran perfectly, but by morning it had made 24 API calls I hadn't budgeted for. Not catastrophically expensive (about $6), but it showed how costs creep up if you're not monitoring.
Pro tip from experience: Set usage limits on your API keys from day one.
The Security Situation
Here's the part that made me nervous during testing. I ran OpenClaw on an isolated laptop specifically because of what I'd read about security issues. After using it for a week, I understand why people are concerned.
Let me be direct: the security concerns are real and architectural, not just implementation bugs.
What I Observed
During setup, I noticed OpenClaw requested pretty extensive permissions:
- Full filesystem access
- Ability to run shell commands
- Access to my messaging apps
- Browser control with full navigation rights
- Memory persistence across sessions
This is necessary for it to work, but it also means OpenClaw has essentially root-level access to everything on that machine. If something goes wrong or if someone compromises the instance they have access to everything too.
The Problem I see
The fundamental issue isn't sloppy coding, it's architectural. OpenClaw needs system-level access to be useful. But giving an LLM that much power creates inherent risks.
LLMs can be tricked through prompt injection. If OpenClaw visits a malicious website or reads a crafted document, hidden instructions can override your actual commands. I tested this lightly (in my isolated environment) and confirmed it's possible to confuse it with carefully worded instructions embedded in content.
During my testing week, I kept OpenClaw strictly sandboxed:
- Dedicated laptop with no personal data
- No connection to my main network
- No access to real email or calendar accounts (used throwaway accounts)
- Firewall rules preventing inbound connections
The Pattern
Tasks with clear, verifiable outputs worked reliably:
- "Unsubscribe from newsletters" → I can check my subscriptions
- "Rename files with descriptive names" → I can see the results
- "Sort emails by category" → Easy to verify
Tasks with ambiguous success criteria were hit-or-miss:
- "Make this document more professional" → Subjective
- "Find the best options for X" → Depends on priorities I didn't specify
- "Schedule a convenient meeting" → Convenient for whom?
The biggest issue: OpenClaw sometimes reports success when it hasn't actually completed the task. That calendar scheduling failure? It told me everything worked. I only discovered the problem because I'm paranoid and double-check things.
In a production environment, that's dangerous. You can't just trust the output, you need verification mechanisms. I agree with something I saw in another review: you're not removing human effort - you're changing it from execution to babysitting.
My Honest Verdict
After a week of actual hands-on testing, here's what I think:
The concept works: That email cleanup saving me hours of manual work? That file organization actually being useful? Those are real wins. Personal AI assistants with system access aren't vaporware they exist and they can deliver value.
The security model makes me nervous: Even in my isolated test environment, I was constantly aware of how much access OpenClaw had. The architectural risks like prompt injection, malicious skills, exposed credentials aren't fixable with patches. They're inherent to giving an LLM system-level permissions.
It's genuinely useful... for specific tasks: Structured, repetitive work with clear success criteria? OpenClaw handles it well. Anything requiring judgment, deep context, or nuanced decision-making? Not ready.
Reliability needs verification: The fact that it reported successful calendar scheduling when nothing actually happened concerns me. You can't just trust the output—you need to verify every automated action.
Costs are manageable but need monitoring: My $47 in five days extrapolates to roughly $280/month if I used it daily. For automation that saves 10+ hours monthly, maybe worth it. But runaway processes and unexpected bills are real risks.
Would I Use It Going Forward?
For experimentation and learning? Absolutely. It's fascinating technology and I learned a lot.
For production automation on my main systems? No way. Not with current security and reliability.
On a dedicated, isolated machine for specific automation? Possibly. If I had a clear, repetitive task that saved significant time, I might set it up carefully: dedicated hardware, whitelist-only skills, extensive monitoring, verification for every automated task.
The gap between "this technically works" and "I trust this with my actual data" is still too wide for me.
Should You Try It?
Based on my testing experience:
Consider it if you:
- Are comfortable with Docker, security configs, and troubleshooting
- Have a dedicated machine you can isolate for testing
- Understand and accept the security risks deliberately
- Will audit any skills before installing them
- Have specific, repetitive automation needs
- Can monitor API costs actively
Final Thoughts
After spending a week actually using OpenClaw, I can say this: it's both more capable and problematic than the headlines suggest. Is it a breakthrough? For specific automation tasks, yes.
Is it overhyped? Absolutely. The TikTok demos and viral tweets make it look safer and easier than the reality.
Is it the future of work? Not yet. But it's showing us what that future might look like and more importantly, what problems we need to solve to get there. The conversations OpenClaw forces about security, reliability, governance, what we're comfortable delegating to AI are exactly the ones we need to have as autonomous agents become more capable.
This was a hands-on test in a controlled environment. Your experience may vary. I really appreciate you taking the time to read through all of this!"
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