# Tencent/PhoenixGo

Go AI program which implements the AlphaGo Zero paper

Repository: https://github.com/Tencent/PhoenixGo
Canonical: https://ross.abutalabs.com/products/phoenixgo
Language: C++
License: NOASSERTION
License Family: other
Last push: 2019-03-11T11:19:25+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3038, "days_push": 2732, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2917, forks 571 (observed 2026-08-28T04:07:30.046005+00:00)

## What it is
PhoenixGo is a Go (weiqi/baduk) AI engine from Tencent that implements the AlphaGo Zero approach of learning the game without human knowledge. It won the World AI Go Tournament 2018 and communicates via the Go Text Protocol (GTP) for use with Go GUIs.

## Use cases
- play go against a strong ai engine
- implement alphago zero from the paper
- connect a go ai to sabaki via gtp
- study monte carlo tree search for board games
- run a go bot on foxgo or cgos
- research neural network go playing

## When to choose
- you need a tournament-strength Go engine on Linux with optional GPU acceleration
- you want to study or reproduce an AlphaGo Zero-style implementation
- you need a GTP-compatible engine for Go GUIs or online servers

## When to avoid
- you need a maintained project with recent updates
- you want an easy build on Windows or macOS
- you need a general game AI framework rather than a Go-specific engine

## Facets
- artifact type: application
- maturity: maintenance
- function: machine-learning, deep-learning, llm-inference, gpu-computing, simulation
- domain: artificial-intelligence, machine-learning
- platform: cpp, cli
- tags: go-game, alpha-go-zero, mcts, reinforcement-learning, gtp-engine, board-games, game-development, linux, gpu

## Member repositories
- Tencent/PhoenixGo (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:30.046005+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 2026-08-30T07:34:04.334204+00:00, confidence not recorded.
  - readme: https://github.com/Tencent/PhoenixGo (fetched 2026-08-28T04:07:30.046005+00:00, sha bfece3bcff72)
- Data as of 2026-08-30T08:39:29.467469+00:00.