google-deepmind/concordia

What it solves

Concordia is a library for creating generative agent-based models to simulate interactions between entities in physical, social, or digital environments. It provides a framework for grounding agent actions in a simulated world, ensuring that interactions are plausible and consistent with the environment's rules.

How it works

Concordia operates as a game engine for generative agents using three core concepts:

• Entities: The actors in the simulation, which can be player characters (Agents) and system controllers (Game Masters).
• Components: Modular building blocks that implement entity behaviors, such as memory operations, reasoning chains, and logic.
• Engine: The simulation loop that collects actions from entities and uses a Game Master to resolve those actions into outcomes.

The system uses a natural language interaction pattern inspired by tabletop role-playing games, where agents describe their intended actions in natural language and the Game Master translates these into outcomes.

Who it’s for

This library is designed for researchers and developers in social science, AI safety and ethics, cognitive neuroscience, economics, and those needing synthetic data for personalization or performance evaluation of services.

Highlights

• Modular Architecture: Complex behaviors can be assembled from reusable components.
• Game Master Pattern: Uses a specialized entity to maintain environmental grounding and physical plausibility.
• Extensible: Supports custom components and provides pre-assembled "prefabs" for common agent types.
• LLM Agnostic: Works with any standard LLM API and text embedder for associative memory.