{"slug": "i-rebuilt-zo-computer-from-scratch-in-775-lines-of-python-here-s-what-stuck-and", "title": "I rebuilt Zo Computer from scratch in 775 lines of Python — here's what stuck and what snapped", "summary": "A developer rebuilt Zo Computer, an AI agent platform, from scratch in 775 lines of Python without web frameworks or Docker. The resulting project, ZoClone, uses 10 modules, 4 SQLite tables, and a ThreadPoolExecutor, with a filesystem-based skill registry and peer-to-peer compute mesh. The architecture prioritizes simplicity and minimalism, fitting the entire mental model on one screen.", "body_md": "[Zo Computer](https://zo.computer) gives you an AI agent, a skills registry, a compute pool, browser automation, file hosting, scheduled automations, and persistent memory — all on a personal server. I wanted to understand every seam, so I rebuilt the whole thing in vanilla Python 3 with no web framework and no Docker. The result is [ZoClone](https://github.com/AmSach/ZoClone): 10 modules, 775 lines, 4 SQLite tables, one ThreadPoolExecutor. This is what the architecture actually looks like when you strip out the platform.\n\nThe main module is `ZoClone.__init__`\n\n— and that's the entire dependency graph. Each subsystem is an attribute:\n\n``` python\nclass ZoClone:\n    def __init__(self):\n        self.db = init_db()\n        self.executor = ThreadPoolExecutor(max_workers=10)\n        self.ai_client = None\n        self.pool = pool              # ComputePool singleton\n        self.hosting = hosting        # HostingService singleton\n        self.memory = memory          # SQLite-backed memory\n        self.scheduler = scheduler    # cron-like automations\n```\n\nNo DI container, no event bus, no message queue. Every tool is a method on the same object. If you're coming from a microservice background, this is going to look like a 2014 Django app — and that's the point. When you can fit the whole mental model on one screen, you stop second-guessing where a bug lives.\n\nFour tables. No ORM. No migrations. The schema is in a single `executescript`\n\nblock:\n\n```\nCREATE TABLE IF NOT EXISTS conversations(id TEXT PRIMARY KEY, title TEXT, updated_at INTEGER);\nCREATE TABLE IF NOT EXISTS messages(id TEXT PRIMARY KEY, conv_id TEXT, role TEXT, content TEXT, tools TEXT, created_at INTEGER);\nCREATE TABLE IF NOT EXISTS memory(id TEXT PRIMARY KEY, key TEXT UNIQUE, value TEXT, updated_at INTEGER);\nCREATE TABLE IF NOT EXISTS files(id TEXT PRIMARY KEY, path TEXT UNIQUE, content TEXT, encoding TEXT, updated_at INTEGER);\n```\n\nIDs are SHA-256 hashes of `(timestamp, content)`\n\ntruncated to 24 chars. The `tools`\n\ncolumn on `messages`\n\nis a freeform JSON blob. The `memory`\n\ntable is a key-value store with `UNIQUE`\n\non `key`\n\n, which forces last-write-wins semantics. When your entire data model is four tables, schema design becomes a five-minute conversation instead of a five-day one.\n\nSkills in Zo are a folder with a `SKILL.md`\n\n(frontmatter) and a `scripts/<name>.py`\n\n(handler). I auto-discover them at import time:\n\n``` php\ndef load_skill(name: str, path: Path) -> Skill:\n    md_content = path.read_text()\n    # parse YAML-ish frontmatter between --- markers\n    frontmatter = {}\n    if md_content.startswith(\"---\"):\n        end = md_content.find(\"---\", 3)\n        for line in md_content[3:end].strip().split(\"\\n\"):\n            if \":\" in line:\n                k, v = line.split(\":\", 1)\n                frontmatter[k.strip()] = v.strip()\n\n    py_file = path.parent / \"scripts\" / f\"{name}.py\"\n    spec = importlib.util.spec_from_file_location(name, py_file)\n    module = importlib.util.module_from_spec(spec)\n    spec.loader.exec_module(module)\n    handler = getattr(module, \"run\", getattr(module, \"execute\", None))\n    return Skill(name=name, description=..., triggers=..., handler=handler)\n```\n\nNo registry service, no API call to discover skills. The filesystem *is* the registry. Drop a folder, restart, it's loaded. The `triggers`\n\nfield in frontmatter is just a comma-separated string — the LLM gets all skill descriptions in its system prompt and decides which one to call. There's no embedding-based retrieval because, at 30 skills, exact-match triggers work fine.\n\nThe peer-to-peer compute mesh in ZoClone is a dict of jobs, a dict of nodes, and one `threading.Lock`\n\n:\n\n``` php\ndef assign_job(self, node_id: str) -> Optional[Dict]:\n    with self.lock:\n        pending = [j for j in self.jobs.values() if j[\"status\"] == \"pending\"]\n        if not pending:\n            return None\n        pending.sort(key=lambda x: -x[\"priority\"])\n        job = pending[0]\n        job[\"status\"] = \"assigned\"\n        job[\"assigned_node\"] = node_id\n        return job\n```\n\nThat's it. The hub polls, picks the highest-priority pending job, marks the node busy, returns the work. No Redis Streams, no RabbitMQ, no Kafka. The trade-off is obvious: this is a single-process orchestrator, not a horizontally-scalable scheduler. But for a 50-node grid running nightly ML batch jobs, you don't *need* Kafka. You need a lock and a sort.\n\nGPU tier multipliers, regional pricing, and reputation decay are all JSON columns in the `nodes`\n\ndict. When you need to add a new pricing rule, you change one line of `assign_job`\n\n. Compare that to a Kubernetes operator with custom resource definitions, admission webhooks, and reconciler loops.\n\nZo has a `/zo/ask`\n\nAPI that spawns child agent invocations. The clone just calls it:\n\n``` python\nasync def spawn(self, agent_id: str, prompt: str):\n    async with aiohttp.ClientSession() as session:\n        async with session.post(\n            \"https://api.zo.computer/zo/ask\",\n            headers={\"authorization\": self.api_token, \"content-type\": \"application/json\"},\n            json={\"input\": prompt, \"model_name\": self.model}\n        ) as resp:\n            return {\"agent_id\": agent_id, \"output\": (await resp.json()).get(\"output\", \"\")}\n\nasync def spawn_all(self, agents: list):\n    return await asyncio.gather(*[self.spawn(a[\"id\"], a[\"prompt\"]) for a in agents])\n```\n\nFive agent invocations in parallel is `asyncio.gather`\n\n. No Celery, no RQ, no Dask. The `model_name`\n\nis hardcoded — there's exactly one LLM driver, and it's whatever Zo gives you. If you want a different model, change one string.\n\n`run_command`\n\nis `subprocess.run(cmd, shell=True)`\n\n. The agent can `rm -rf ~`\n\nand it will. Production Zo wraps this in gVisor; I don't.`LIKE '%query%'`\n\nscan. Fine at 1k rows, embarrassing at 100k.`chat()`\n\ncall is blocking. You see the full response or nothing.`set_key()`\n\nwrites API keys to a flat JSON file in `~/.zoclone/`\n\n. Multi-user means multi-disaster.`if __name__ == \"__main__\"`\n\nblock that prints the pool status.`run_command`\n\nin a gVisor container, or at minimum a chroot + seccomp.`memory`\n\ntable for SQLite-vec0 and do real semantic recall.`Authorization`\n\nheader check on every API endpoint. Even internal services.The real lesson wasn't \"look how short the code is\" — it was \"look how much of the platform is just a thin layer over a database, a thread pool, and a few HTTP calls.\" The parts that are genuinely hard (the LLM orchestration loop, the skill discovery) are maybe 100 lines. The rest is plumbing, and most of the plumbing doesn't need to exist.\n\n**Repo:** [github.com/AmSach/ZoClone](https://github.com/AmSach/ZoClone)\n\n**License:** MIT\n\n**Stack:** Python 3.10+, SQLite, requests, aiohttp, no web framework\n\n*If you've built a personal-AI clone of your own, drop the repo link in the comments. I want to see how other people split the agent loop from the storage layer.*", "url": "https://wpnews.pro/news/i-rebuilt-zo-computer-from-scratch-in-775-lines-of-python-here-s-what-stuck-and", "canonical_source": "https://dev.to/aman_sachan_126d19c4a2773/i-rebuilt-zo-computer-from-scratch-in-775-lines-of-python-heres-what-stuck-and-what-snapped-1cao", "published_at": "2026-06-14 01:37:19+00:00", "updated_at": "2026-06-14 02:28:48.503993+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "developer-tools", "ai-infrastructure", "machine-learning"], "entities": ["Zo Computer", "ZoClone", "Python", "SQLite", "ThreadPoolExecutor", "GitHub", "AmSach"], "alternates": {"html": "https://wpnews.pro/news/i-rebuilt-zo-computer-from-scratch-in-775-lines-of-python-here-s-what-stuck-and", "markdown": "https://wpnews.pro/news/i-rebuilt-zo-computer-from-scratch-in-775-lines-of-python-here-s-what-stuck-and.md", "text": "https://wpnews.pro/news/i-rebuilt-zo-computer-from-scratch-in-775-lines-of-python-here-s-what-stuck-and.txt", "jsonld": "https://wpnews.pro/news/i-rebuilt-zo-computer-from-scratch-in-775-lines-of-python-here-s-what-stuck-and.jsonld"}}