Why you should use OpenClaw
In this blog post, I dive deep into the topic of using OpenClaw, your personal AI agent that does your work for you, I talk about common issues, their solutions, and how OpenClaw can supercharge your workflow and make you extremely powerful
Imagine a world where every ambitious person: a developer, researcher, founder, student has a mentor who never sleeps, never judges, and never charges by the hour. An advisor who can write your code, review your research, draft your pitch, and explain a zero-day exploit at 3am without breaking a sweat
That world isn’t coming. It’s already here. The only question is: are you set up to use it properly?
I stumbled into OpenClaw out of frustration more than curiosity. And honestly, it fixed a problem I didn’t even know had a name until it was solved
The need for a centralized AI environment
Here’s what my browser looked like on a typical day: a Gemini 3 tab for reasoning-heavy tasks, a Kimi k2.5 tab because it’s been punching way above its weight class lately, Anthropic’s Sonnet for the nuanced writing stuff, and of course ChatGPT because… habit. Four tabs. Four different contexts. Four different conversation histories. Four times the chaos
As much as it’s an inconvenience. It’s a real productivity drain. You’re constantly re-explaining your project, your preferences, your stack. You’re playing model roulette: “which one do I paste this into?” instead of actually building things. And the worst part? None of them remember you
What if the model didn’t matter? What if you had a single interface with persistent memory that actually works for you, where you can swap the underlying model like changing a gear; not rebuilding the entire engine?
That’s the core idea behind OpenClaw. You set it up once, it learns your context over time, and it routes your requests intelligently regardless of which model is powering it under the hood. No more re-introducing yourself to a new tab. No more copy-pasting your project README into every new conversation. Your AI co-pilot knows you
“But Nomaan, Managing multiple API Keys is a Nightmare”
Fair point. Multiple models means multiple providers, multiple keys, multiple billing dashboards. It’s the same tab problem but for your .env file.
The answer here is dead simple: just use OpenRouter. One API key. One billing dashboard. Access to basically every frontier model and a growing list of open-source ones. You integrate once, and OpenClaw handles the routing. Done
This is where a lot of people stop, and I get it. API costs can spiral if you’re not intentional about it. But here’s how I think about it:
Batch your requests. Don’t fire off a fresh API call every time you have a half-formed thought. Queue them up, process them together, and you’ll slash your costs significantly
Use Ollama for the boring stuff. Cron jobs, heartbeat checks, status summaries, log parsing — these don’t need GPT-5. Run a local model via Ollama and save the premium tokens for work that actually needs a frontier brain
And when you genuinely have no budget? gpt-oss-120b:free on OpenRouter is a free tier that’s shockingly capable. It’s not going to replace your paid tier for complex reasoning, but for scaffolding, drafting, and light automation? It holds up
“Which Model Should I Actually Use?”
This is the question that wastes the most time in AI communities — people arguing about benchmarks they’ve never run on their own tasks. My advice: stop trusting vibes and go to Artificial Analysis. It’s the most rigorous independent benchmarking site out there, tracking intelligence scores, output speed, cost per million tokens, and context windows across 100+ models. Find what fits your use case and budget, not what’s trending on X
These aren’t permanent. The landscape moves fast. But as of today, this is what I’d actually reach for:
1. Gemini 3 Flash is the best value model on the market right now, full stop. Ridiculously fast, massive context window, solid reasoning, and cheap enough that you can throw it at almost anything without watching your wallet. If you’re building something that needs to process long documents or maintain long conversational context, this is your default
2. GLM-5 is criminally underrated outside of China. Strong benchmark performance, available via OpenRouter, and genuinely competitive with models that cost 3-4x more. Great for coding tasks and structured outputs. Worth having in your rotation
3. Claude Sonnet I know, I know, I’m biased because I’m literally writing this in it. But the writing quality, the nuance in reasoning, and especially the instruction-following are still best-in-class for the kinds of tasks that matter most to me — research synthesis, technical writing, code review. When quality counts more than cost, this is where I go
The Bigger Picture
None of this is complicated. The barrier to having an AI co-pilot that actually works for you isn’t technical. It’s just setup friction and tab addiction. OpenClaw removes the friction. OpenRouter removes the API headache. Artificial Analysis removes the guesswork. And Ollama keeps your costs sane
The people building the most interesting things right now aren’t the ones with access to the best models everyone has access to the best models. They’re the ones who’ve built systems around those models that compound over time
Set it up once. Let it remember. Get back to building