🏛️ When Agents Rule
Where language models battle for the crown in antiquity.
Up to four LLMs. One map. One winner.
A browser-based, Age-of-Empires-style real-time strategy game in which competing language models play against each other — while you watch, coach, and score them.
A Wonder falls. The army reforms. Real gameplay from a four-model match, recorded September 9, 2026, at normal playback speed. Click for the sharper, longer video.
What is this?
A sandbox arena for pitting language models against one another at a task they were never trained for: running an economy and an army, in real time, inside a small RTS they've never seen. Every player is an autonomous model agent governing its own civilization, and every match ends one of two ways — a rival razed to the ground, or a Wonder held in peace.
This is an agent harness whose task happens to be a real-time strategy game. Every turn a model is handed a compact JSON snapshot of its situation (resources, buildings, units and what each can do, fog-of-war discoveries, its memory of enemy units, threats, tech tree, map bounds), named command tools — such as move_units, attack_target and research_tech, sharing a budget of three commands per turn — plus an independent plan tool to save its objective — and one instruction: win. Then it has to keep doing that, turn after turn, for a whole match.
A seat is played in one of two ways:
- A library model: the game calls an endpoint you configure (OpenAI-compatible, Anthropic, Ollama or Google) and reads real tool calls from the reply, the way any agent harness does.
- 🆕 An agent harness over MCP: Claude Code, Hermes, OpenCode, ChatGPT and others play a seat themselves, through a small local hub, on their own subscription or local model. Same state, same tools, same rules — the harness around the model is what changes. More below.
It's a hands-on testbed, not a benchmark — see Disclaimers. With the setup held steady (same civilization, fixed map seed, resources placed identically for every player) a match isolates the model well enough for narrow comparisons.
| A town in winter | An archer raid |
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From economy to conquest, in Episodes 9 and 10: the notes floating over units and buildings are the models' own reasons for what they ordered there. These settlements and armies were built and commanded by the competing models.
Why it's an interesting eval
Most quick LLM demos reward a single clever answer. A full match rewards the things people actually care about in agents:
- 🎯 Three actions a turn, and the turn will not wait. The budget is the eval. A model does not get to do everything it can think of — it has to answer what are the three most important things right now, on a board that changes between its own calls.
- ⚔️ Models develop their own doctrine. Same rules, same prompt — yet you get pure economists racing for a Wonder next to warlords massing an army absurdly early.
- 🧨 Pressure changes their play. Raided, out-scouted, slipping down the leaderboard — many models genuinely switch tactics rather than doubling down.
- 🛠️ Precise tool calling. Hallucinate a tool, fumble the schema, wrap it in prose — the call is wasted, and you can watch format discipline hold or crumble.
- 🧭 An unfamiliar framework. No fine-tuning, no examples of good play. Only the rules in the prompt and the state in front of it.
- 🧠 Long-horizon strategy. Economy → tech → military → conquest plays out over dozens of turns. The harness carries a model-authored objective + plan across turns — but maintaining it is the model's job.
- 🔁 Error recovery. A rejected action comes back with a precise reason. Does the model correct course, or bang on the same locked door?
- 🗺️ Reasoning from partial information. Fog of war hides the map. What a model remembers of enemy units is where and how long ago it last saw them — working out where they went is up to the model.
- 🧩 The harness matters too. Over MCP the same model can play through different agent harnesses, with their own memory, skills and habits — and they do not play the same.
Orders keep working between model replies. At night, DeepSeek V4.1's Yamato army burns down Qwen3.8's Maya town center (Episode 18). Normal playback speed; click for the higher-quality video.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
