How I Built a Multi-Agent Geopolitical Simulator with FastAPI + LiteLLM

What happens when you give four LLM agents their own strategic doctrines, red lines, and constraints — then throw them into a geopolitical crisis?

I built Strait of Hormuz Simulator to find out. It's a multi-agent sandbox where four nations (Iran, US, Israel, Gulf States) are each controlled by an independent LLM, and the results are surprisingly realistic.

The Architecture

Each country is a markdown file (a "soul") that defines its strategic doctrine and default parameters:

backend/souls/ ├── iran.md # Asymmetric warfare, Strait control ├── us.md # Three bad options, escalation management ├── israel.md # Nuclear red line, preemptive calculus └── gulf_states.md # Oil leverage, diplomatic survival

backend/souls/ ├── iran.md # Asymmetric warfare, Strait control ├── us.md # Three bad options, escalation management ├── israel.md # Nuclear red line, preemptive calculus └── gulf_states.md # Oil leverage, diplomatic survival

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The backend runs each agent sequentially — every round, each nation receives:

• A shared situation briefing

• Its own doctrine (system prompt)

• Rolling memory of prior rounds

• Active scenario modifiers

Then it decides what to do. No script. No predetermined outcomes.

Why LiteLLM?

I wanted users to bring their own API key and pick any model. LiteLLM gave me a unified interface across GPT-4o, Claude, Gemini, and DeepSeek with zero provider-specific code. One litellm.completion() call handles all of them.

The Chaos Slider

A single float (0-1) injected into every agent's prompt:

• Low chaos → agents seek diplomatic exits

• High chaos → bold moves, miscommunication, escalation spirals

Emergent Behavior That Surprised Me

In one run, Israel independently chose not to strike Iranian nuclear facilities — reasoning that US strikes made unilateral action diplomatically counterproductive. Nobody programmed that.

Gulf States withheld spare oil capacity to extract security guarantees, creating price feedback loops the oil market agent had to price in real-time.

The Oil Market Agent

A separate LLM agent watches all military and diplomatic actions, then sets the oil price. Blockade the Strait? Price spikes. Ceasefire talks? Price dips. It adds a layer of economic consequences that feeds back into each nation's decision-making.

Scenario Tags

11 toggleable modifiers that inject additional context:

• 🚀 Nuclear Brinkmanship

• 🏴‍☠️ Houthi Wildcard

• 🇨🇳 China Mediator

• 💰 Market Patience Clock

• 🖥️ Cyber Offensive

• ...and 6 more

Mix and match for different simulation dynamics.

Stack

• Backend: Python 3.11, FastAPI, LiteLLM, Pydantic

• Frontend: Vue 3, Vite, TailwindCSS, DaisyUI

• Streaming: SSE for real-time agent output

• Deployment: Docker + Cloudflare

Try It

• 🎮 Play free (20 rounds on DeepSeek V3)

• 📦 GitHub (MIT license)

Adding a new country is just creating a markdown file in backend/souls/. The system auto-discovers it on restart.

Would love feedback — especially on agent behavior and scenario design.