🧠 Built an AI OS that controls your entire computer powered by Groq's blazing-fast inference

Build with Groq
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Hey Groq community! I’ve been building NeuronOS — an open-source AI layer that sits on top of your operating system and lets you control it entirely through natural language.

You type (or speak) what you want. The agent figures out how to do it.

What it can actually do:

  • :desktop_computer: Shell control — runs PowerShell/bash commands, reads/writes files, searches your drive

  • :globe_with_meridians: Browser automation — opens URLs, clicks elements, fills forms, scrapes pages

  • :computer_mouse: Desktop control — moves the mouse, types keystrokes, takes screenshots, focuses windows

  • :bar_chart: System monitoring — CPU/RAM stats, process list, kill processes, network info

  • :brain: Memory — remembers past tasks using SQLite (pgvector optional for semantic search)

Why Groq?

I started with Ollama (local models) — latency was painful for agentic loops. Every tool call roundtrip added up.

Switching to Groq with Llama 4 Scout (openai/gpt-oss-120b) was a night-and-day difference. The agent completes multi-step tasks — find a file, read it, summarize it, open the result in a browser — in seconds.

For an agentic system that runs 3–6 LLM calls per task, Groq’s speed isn’t just a nice-to-have. It’s what makes the UX feel like magic.

Tech stack:

  • Backend: FastAPI + LangGraph (tool-calling loop) + LangChain

  • Frontend: React + Vite (streaming WebSocket UI)

  • LLM: Groq (default) — switchable to Ollama, Anthropic, OpenAI via one env var

Switching providers is literally one line in .env:

LLM_PROVIDER=groq
GROQ_API_KEY=your_key_here
GROQ_MODEL=meta-llama/llama-4-scout-17b-16e-instruct

One challenge I hit with Groq (and how I fixed it):

Groq’s API is stricter than others about tool role messages — it rejects empty content. When a tool returns [] or {}, LangChain passes that directly and Groq throws a 400.

Fixed it with a sanitizer that runs before every LLM call:

def _sanitize_tool_messages(messages):
    for msg in messages:
        if isinstance(msg, ToolMessage):
            if not msg.content or msg.content == []:
                msg = ToolMessage(content="(no output)", ...)
    return messages

Groq’s strictness actually caught a real bug in my tool return types — so I consider it a feature :grinning_face_with_smiling_eyes:

GitHub: GitHub - pratikskarnik/NeuronOS · GitHub
Medium: https://medium.com/@pratikskarnik/neuronos-build-your-own-ai-powered-operating-system-in-the-browser-188bf4384ce7

Demo: https://www.youtube.com/watch?v=VC1zkbDVhDY

Would love feedback from this community — especially on model choices. I’m currently using Llama 4 Scout. Has anyone benchmarked it vs Llama 3.3 70B Versatile for multi-step tool-calling tasks? Curious what others are seeing.

Built with :heart: and Groq speed.

#NeuronOS #Groq #LangGraph #AgenticAI #OpenSource #LlamaOnGroq

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