# facebookresearch/darkforestGo

DarkForest, the Facebook Go engine.

Repository: https://github.com/facebookresearch/darkforestGo
Canonical: https://ross.abutalabs.com/products/darkforestgo
Language: C
License: NOASSERTION
License Family: other
Archived: true
Last push: 2019-12-26T09:04:19+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3770, "days_push": 2442, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2095, forks 320 (observed 2026-08-28T04:06:13.217796+00:00)

## What it is
DarkForest is a computer Go game engine developed at Facebook AI Research, powered by deep neural networks and Monte Carlo tree search. It achieved strong amateur dan ranks on KGS and its training code and pre-trained models were open-sourced, later being incorporated into the ELF platform.

## Use cases
- play Go against a deep learning engine
- study neural network based computer Go
- train a Go playing neural network
- research MCTS with policy and value networks
- benchmark computer Go engines

## When to choose
- you need a historical reference implementation of deep learning Go
- you want to reproduce ICLR 2016 computer Go research
- you have a CUDA GPU and Torch7 environment

## When to avoid
- you need a maintained or modern Go engine
- you cannot build legacy Torch7 and CUDA dependencies
- you want a general game AI framework - use ELF instead

## Facets
- artifact type: application
- maturity: abandoned
- function: game-engine, deep-learning, machine-learning, llm-training
- domain: deep-learning, machine-learning, simulation
- platform: cpp
- tags: go-game, mcts, torch7, reinforcement-learning, computer-go, neural-network, game-development, linux, gpu

## Member repositories
- facebookresearch/darkforestGo (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:13.217796+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 2026-08-30T02:54:39.040023+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/darkforestGo (fetched 2026-08-28T04:06:13.217796+00:00, sha 29bffe7c4b97)
- Data as of 2026-08-30T08:39:29.467469+00:00.