Openclaw+ Qwen 3.5 Is INSANE!

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OpenClaw just became one of the most powerful local AI automation stacks you can run — and when you pair it with Qwen 3.5, it’s on another level.

Most people are still paying monthly for ChatGPT Plus, Claude Pro, Gemini Advanced and juggling browser tabs. Meanwhile, you can now run a fully local AI agent with vision, tool calling, coding, and automation directly on your own machine.

No subscriptions.
No vendor lock-in.
No unnecessary data exposure.

In this video, I break down exactly how OpenClaw + Qwen 3.5 works and how you can use it to automate real business workflows.

OpenClaw is an open-source, MIT-licensed AI agent framework that runs inside your terminal. It’s local-first, meaning your files stay on your machine unless you explicitly connect an external API. With over 200,000 GitHub stars, it’s not a toy project — it’s serious infrastructure.

It supports tool calling, file access, code execution, Telegram integration, and now vision support. That means your AI doesn’t just answer questions. It takes action.

Qwen 3.5 is Alibaba’s latest multimodal model and it’s extremely capable. The Plus version supports a 1 million token context window, which means you can feed it entire codebases, full business documentation, months of content, and it will still maintain context.

It also supports vision. That means it can analyze screenshots, PDFs, diagrams, UI layouts, and reason about them alongside text.

When you combine OpenClaw’s local agent framework with Qwen 3.5’s multimodal reasoning and tool usage, you get something powerful:

A local AI agent that can see images, read your files, write and run code, automate workflows, and operate like a team member — all from your terminal.

Here’s how you set it up.

Install OpenClaw from GitHub. It runs cross-platform on Mac, Windows, and Linux.

Then connect Qwen 3.5 either via Alibaba’s API or run it locally through Ollama if you want full offline inference.

Enable tool access inside the OpenClaw config so your agent can read/write files, execute code, and use search.

Once configured, you can start automating real workflows.

You can analyze landing pages from screenshots and get conversion improvement suggestions.

You can scan entire folders of content and generate a structured content calendar.

You can generate personalized onboarding messages from member data files.

You can build and test automation scripts directly in your terminal.

You can screenshot competitor pages and have the agent recreate improved versions with better copy and layout.

The 1 million token context window is what makes this even more powerful. You can feed your full SOPs, audience research, past content, analytics data, and documentation into the model at once and get outputs that reflect your entire business context — not just fragments.

This isn’t just chatting with AI.

This is deploying AI.

If you want step-by-step guidance on building real automation systems like this inside your business, start here:

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