Most agent demos are one clever prompt. I wanted to see what happens when you organize agents the way a real company is organized: departments, specialists, and someone at the top deciding who does what.
That is Botropolis: 20 specialist agents across 10 departments. Research has Scout. Code has Coder and Reviewer. Finance, legal, health, data, marketing, ops, security, support all have their people. A CEO agent reads your request, routes it to the right departments, runs them, and compiles one report.
Each agent is a YAML spec: name, title, specialty, model, tools, system prompt, example tasks. The registry loads them, the CEO orchestrates, and a FastAPI server exposes it all. There is a browser UI with three views: chat with the CEO, a war room for talking to any single agent directly, and analytics showing per-agent calls, latency, and token usage.
The part I am proudest of is the training pipeline. I trained botropolis-scout-tiny, a 5.5M-parameter transformer, from scratch on CPU on 300 synthetic Q&A pairs. It is a demo-scale model and honest about it: it learned the shape of research answers, not reliable facts. The pipeline is the point. On top of that I built a 2,940-example curated dataset and a Colab script that fine-tunes SmolLM2-135M with LoRA on a free GPU. That is the path to a real model.
What I learned:
- Routing is the whole game. Twenty agents are useless if the wrong ones get the work.
- YAML specs make agents reviewable. You can read the whole company in an afternoon.
- Usage analytics change how you build. Once you see per-agent latency and tokens, you start routing like a manager watching a budget.
Roadmap: streaming responses, tool execution wired into the model clients, and fine-tunes for more agents.
Repo (MIT): https://github.com/kagithamanoj/botropolis
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