# google-deepmind/concordia

A library for generative social simulation

Repository: https://github.com/google-deepmind/concordia
Canonical: https://ross.abutalabs.com/products/concordia
Language: Python
License: Apache-2.0
License Family: permissive
Topics: agent-based-simulation, generative-agents, multi-agent, social-simulation
Last push: 2026-08-14T20:27:00+00:00

## Health v2 (maintenance only)
Score: 84/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 97, release rhythm 73, longevity 72
- inputs: {"age_days": 1016, "days_push": 19, "days_rel": 180, "gap_med": 11, "n_releases_24m": 20}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1647, forks 359 (observed 2026-08-28T04:05:16.499941+00:00)

## What it is
Concordia is a Python library for building generative agent-based social simulations, using a tabletop role-playing game pattern where a Game Master entity simulates environments and resolves natural-language actions from agent entities. It provides modular components, prefabs, and a simulation engine for constructing multi-agent models.

## Use cases
- simulate social interactions between LLM-powered agents
- run generative agent-based models for social science research
- generate synthetic data for personalization
- evaluate real services through simulated usage
- study AI safety and ethics with simulated agents
- model economic or cognitive scenarios with multi-agent simulation

## When to choose
- you need LLM-driven multi-agent simulations with natural-language actions
- you want a modular, component-based agent architecture with a Game Master pattern
- you are doing social science, economics, or AI safety research with simulated agents

## When to avoid
- you need high-performance numeric agent-based modeling without LLMs
- you want a production chatbot or agent deployment framework rather than a research simulation library
- you have no access to an LLM API

## Facets
- artifact type: library
- maturity: active
- function: agent-framework, simulation, llm-inference, data-generation
- domain: artificial-intelligence, simulation, large-language-models, data-science
- platform: python, cross-platform
- tags: generative-agents, agent-based-simulation, social-simulation, game-master, multi-agent, llm-driven-agents, synthetic-data, ai-agents

## Member repositories
- google-deepmind/concordia (main) score 84

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:16.499941+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:45:18.468198+00:00, confidence not recorded.
  - readme: https://github.com/google-deepmind/concordia (fetched 2026-08-28T04:05:16.499941+00:00, sha de7c8cf71361)
- Data as of 2026-08-30T08:39:29.467469+00:00.