Emmanuel Mumba

Posted on Feb 4

I Tried OpenClaw as a Virtual Assistant Here’s My Honest Take

#webdev #programming #ai #devops

When you read about agentic AI frameworks, they all promise the same thing: “Your AI, but proactive.”Not just answering questions, but planning, acting, and handling real tasks on your behalf.

So instead of reading another announcement or skimming a GitHub README, I decided to actually try one.

I spent time running OpenClaw (formerly Clawdbot / MoltBot) as a real virtual assistant not just installing it, but integrating it into my daily workflow and seeing how it held up beyond the first “wow” moment. I used it for real tasks, real conversations, and real automation.

Here’s what worked, what surprised me, and where it still feels a bit rough around the edges.

First Impressions: This Is Not Just Another Chatbot

The biggest difference you notice immediately is that OpenClaw doesn’t just respond it acts.

Instead of asking an LLM questions in a browser tab, you talk to OpenClaw through Telegram or Slack. When you say something like:

“Check this repo and summarize the open issues.”

It doesn’t just answer. It:

Plans steps
Opens files
Runs commands
Calls APIs
Sends results back to chat

That shift from chatting to delegating is where OpenClaw really stands out.

What OpenClaw Does Well

After a few days of use, three strengths became very clear.

1. It Feels Like a Real Assistant (Not a Demo)

Because OpenClaw runs locally and remembers context, conversations don’t reset every time. I could follow up with:

“Okay, now draft a reply based on that.”

And it knew exactly what “that” meant.

That continuity alone makes it feel far closer to a junior assistant than a traditional AI tool.

2. Chat-Based Control Is Surprisingly Powerful

Using Telegram or Slack as the interface felt natural. No dashboards, no new UI to learn.

You just message it like a person and under the hood, it’s:

Editing files
Running scripts
Fetching data
Triggering workflows

Once you get used to this, going back to prompt-only tools feels limiting.

3. Full Control Over Data and Environment

Since OpenClaw runs on your own machine or server:

Your data stays local
You decide what it can access
You choose the model (Claude, OpenAI, etc.)

For developers who care about privacy and control, this is a big advantage.

Where OpenClaw Gets Tricky

This isn’t a plug-and-play product and that’s both its strength and its weakness.

Setup Takes Real Effort

You’ll need to be comfortable with:

Node.js
Environment variables
Messaging platform APIs
Docker or VM-based isolation

If you expect “install and go,” this may feel heavy.

If you enjoy building and customizing tools, it’s actually quite rewarding.

Security Is on You

OpenClaw can run shell commands and access files. That’s powerful and potentially risky.

I wouldn’t recommend running it with full permissions on your primary machine. Containerization or a VM isn’t optional here; it’s essential.

That’s not a flaw in OpenClaw, but it does raise the bar for responsible usage.

Want to Try It Yourself? (Setup Guides)

If reading this makes you curious and you want to run OpenClaw yourself, these step-by-step guides walk through the full setup:

How to Run OpenClaw as Your Virtual Assistant

https://apidog.com/blog/how-to-run-openclaw/

Installing OpenClaw on a Mac mini with Cloudflare

https://apidog.com/blog/install-openclaw-mac-mini-openclaw-cloudflare/

Both guides cover installation, environment setup, and messaging app integration in detail, so you can skip the guesswork.

Extending OpenClaw with APIs (Where It Gets Really Interesting)

The real “aha” moment for me came when I started extending OpenClaw with custom APIs.

Once you give it tools that connect to:

Internal services
Calendars
Weather data
Your own backend

…it stops being a generic assistant and becomes your assistant.

This is where Apidog was genuinely helpful. Instead of juggling OpenAPI specs, test scripts, and documentation across different tools, I used Apidog to design, mock, test, and document APIs in one place then connected those APIs directly to OpenClaw’s tools.

What made the difference for me:

Design-first APIs – I could sketch the API contract before writing any backend code, which made it much easier to reason about what tools OpenClaw should have.
Instant mocking – I tested OpenClaw’s tool calls against mocked endpoints before the real services were ready, so I wasn’t blocked on backend work.
Built-in testing & debugging – Sending requests, inspecting responses, and fixing edge cases all happened in the same interface.
Always-in-sync documentation – The API docs updated automatically as the spec evolved, which saved me from manual cleanup later.
Cleaner agent tools – Because the API behavior was well-defined, OpenClaw’s tool definitions ended up simpler and more reliable.

That tighter feedback loop made experimentation much faster and far less frustrating. Instead of fighting tooling, I could focus on what actually mattered: teaching the agent how to use the right tools at the right time.