# google-deepmind/open_spiel

OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

Repository: https://github.com/google-deepmind/open_spiel
Canonical: https://ross.abutalabs.com/products/open_spiel
Language: C++
License: Apache-2.0
License Family: permissive
Topics: games, reinforcement-learning, multiagent, cpp, python
Last push: 2026-08-25T22:34:39+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 97, longevity 100
- inputs: {"age_days": 2599, "days_push": 8, "days_rel": 21, "gap_med": 11, "n_releases_24m": 18}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5435, forks 1171 (observed 2026-08-28T04:09:18.149436+00:00)

## What it is
OpenSpiel is a collection of game environments and reinforcement learning/search/planning algorithms for research in general RL and multiagent games. The core API and games are implemented in C++ with Python bindings, supporting a wide variety of game types and analysis tools.

## Use cases
- research reinforcement learning in games
- implement multi-agent RL algorithms
- study game-theoretic learning dynamics
- benchmark planning and search algorithms on games
- model imperfect-information games like poker
- teach RL and game theory concepts

## When to choose
- you need a standardized collection of game environments for RL research
- you want both C++ performance and Python usability
- you need support for many game classes (perfect/imperfect information, simultaneous moves, general-sum)
- you want to analyze learning dynamics across games

## When to avoid
- you want to build a production game or game engine
- you need high-throughput 3D or physics-based environments
- you only need a simple single-agent RL gym without game-theoretic features

## Facets
- artifact type: framework
- maturity: active
- function: reinforcement-learning, simulation, game-engine, machine-learning, benchmarking
- domain: reinforcement-learning, artificial-intelligence
- platform: windows, cpp, python
- tags: multi-agent, game-theory, search-and-planning, extensive-form-games, research-framework, game-development, research, algorithms, linux, macos

## Member repositories
- google-deepmind/open_spiel (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:18.149436+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 2026-08-29T17:56:52.739611+00:00, confidence not recorded.
  - readme: https://github.com/google-deepmind/open_spiel (fetched 2026-08-28T04:09:18.149436+00:00, sha d1f996f8cb6f)
- Data as of 2026-08-30T08:39:29.467469+00:00.