Analysis updated 2026-05-18
Watch AI agents form civilizations, take on roles like priest or soldier, and interact over time.
Use the Demiurgic Layer panel to pause the simulation and inspect an agent's beliefs and reasoning.
Inject events like famines or plagues to see how a civilization responds and adapts.
| spacecypher/doxa | agno-agi/agent-platform-railway | alexantaluo0/acot-vla-wm | |
|---|---|---|---|
| Stars | 22 | 22 | 22 |
| Language | Python | Python | Python |
| Setup difficulty | hard | moderate | hard |
| Complexity | 4/5 | 4/5 | 5/5 |
| Audience | researcher | developer | researcher |
Figures from each repo's GitHub metadata at analysis time.
Requires Python 3.10, Node.js 18, and an API key from a supported language model provider.
Project Doxa is a simulation where AI-powered agents live as citizens of evolving civilizations. Each agent has physical needs such as hunger, health, and stamina, a collection of memories and beliefs, and a personality profile. At regular intervals this state is sent to a language model, which decides the agent's next action: farm for food, trade with a neighbor, construct a building, or invent a new ideology and try to spread it through the population. Agents form civilizations and take on roles based on what they build. Constructing a temple makes an agent a priest, whose beliefs spread further and decay more slowly. Building barracks creates a soldier with improved combat stats. Farmers grow food more efficiently, and agents who reach old age stop being reassigned to new roles. Trust between civilizations is tracked continuously, and if it falls below a threshold, war is declared automatically, agents traveling through enemy territory during wartime drain health at every step. The world is a grid where terrain affects movement and survival. Resources such as wood, water, stone, and gold appear over time, and crops mature into food that agents can harvest. Scarcity determines whether agents trade cooperatively or fight. A social cohesion index called Asabiyyah (from the medieval historian Ibn Khaldun) tracks how unified a civilization is: high values encourage cooperation, low values can trigger internal conflict. A built-in intervention panel called the Demiurgic Layer lets you pause the simulation, inspect individual agents, view their belief graphs, read the reasoning behind specific decisions, and inject events such as famines, plagues, or miracles to see how the civilization responds. The backend is Python with FastAPI. The frontend is a Next.js interface that shows the world grid and agent states in real time. Running the project requires Python 3.10, Node.js 18, and an API key from any supported language model provider.
A simulation where AI-controlled agents live as citizens of evolving civilizations, farming, trading, building, and going to war based on language-model decisions.
Mainly Python. The stack also includes Python, FastAPI, Next.js.
No license information is stated in the README.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
Verify against the repo before relying on details.