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A library for generative social simulation
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Today
+5 stars today
This week
+26 stars this week
This month
+86 stars this month
Momentum
46.0
growth rate 1.54%/day
# Concordia
*A library for generative social simulation*
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[](https://pypi.python.org/pypi/gdm-concordia)
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[](../../actions/workflows/test-concordia.yml)
[](../../actions/workflows/test-examples.yml)
<!-- /GITHUB -->
[Concordia Tech Report](https://arxiv.org/abs/2312.03664) | [Concordia Design Pattern](https://arxiv.org/abs/2507.08892) | [Code Cheat Sheet](CHEATSHEET.md)
## About
Concordia is a library for constructing and running generative agent-based
models that simulate interactions among entities in grounded physical, social,
or digital environments. It uses an interaction pattern inspired by tabletop
role-playing games: a special entity called the **Game Master** (GM) simulates
the environment in which player entities interact. Entities describe their
intended actions in natural language, and the GM translates these into
appropriate outcomes e.g. checking physical plausibility in simulated worlds.
Concordia supports a broad range of applications, including social science
research, AI safety and ethics, cognitive neuroscience, economics, synthetic
data generation for personalization, and performance evaluation of real services
through simulated usage.
Concordia requires access to a standard LLM API and may optionally integrate
with external applications and services.
## How it Works
Concordia operates as a **game engine** for generative agents, built around
three core concepts:
* **Entities**: The actors in the simulation—either player characters
(Agents) or system controllers (Game Masters).
* **Components**: Modular building blocks of an Entity. Entity
behaviors e.g. logic, chains of thought, memory operations, etc are all
implemented within components. Concordia comes with a core library of
components and user-created components are also included in the main
library under the contrib directory. It's easy to create your own components
and add them to the library.
* **Engine**: The simulation loop. It solicits actions from entities and
delegates resolution to the Game Master.
This modular architecture enables complex behaviors to be assembled from simple,
reusable parts.
## Folder Structure
* **[`concordia/prefabs`](concordia/prefabs/README.md)**: Pre-assembled
recipes for common agents and Game Masters.
* **[`concordia/components`](concordia/components/README.md)**: Modular
building blocks for agents, including memory systems, reasoning chains, and
sensory modules.
* **[`concordia/environment`](concordia/environment/README.md)**: The "engine"
of the simulation, containing the Game Master and the turn-taking loop.
* **[`concordia/document`](concordia/document/README.md)**: Utilities for
managing LLM prompts and context.
* **[`concordia/language_model`](concordia/language_model/README.md)**: LLM
integration and API wrappers.
* **[`examples/`](examples/)**: Tutorials and example simulations to help you
get started.
> [!TIP]
> The best way to learn is to watch the [Concordia: Building Generative Agent-Based Models](https://youtu.be/2FO5g65mu2I?si=TSk7XTk4gCaadEDs) tutorial on YouTube, run the
> **[`examples/tutorial.ipynb`](examples/tutorial.ipynb)** and then try
> modifying the **Prefabs** to see how agent behavior changes.
## Installation
[Concordia is available on PyPI](https://pypi.python.org/pypi/gdm-concordia)
and can be installed using:
```shell
pip install gdm-concordia
```
After doing this you can then `import concordia` in your own code.
## Development
### CodespacExcerpt of 7,149 characters
Read on GitHub
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Copybara Service · @google
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Hana Joo · https://www.linkedin.com/in/hana-joo-6a0379127/ · Germany
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Austin Welch · AWS · United States
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João Galego · Critical Software · Portugal
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cf9250a0885f4bfd, topic:multi-agent