# google-deepmind/mctx

Monte Carlo tree search in JAX

Repository: https://github.com/google-deepmind/mctx
Canonical: https://ross.abutalabs.com/products/mctx
Language: Python
License: Apache-2.0
License Family: permissive
Topics: jax, reinforcement-learning, monte-carlo-tree-search
Last push: 2026-08-06T15:40:02+00:00

## Health v2 (maintenance only)
Score: 83/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 96, release rhythm 57, longevity 100
- inputs: {"age_days": 1646, "days_push": 27, "days_rel": 79, "gap_med": 286, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2654, forks 215 (observed 2026-08-28T04:07:07.215105+00:00)

## What it is
Mctx is a JAX-native Python library implementing Monte Carlo tree search algorithms such as AlphaZero, MuZero, and Gumbel MuZero. It supports JIT compilation and batched parallel search on accelerators, making it suitable for research on search-based reinforcement learning agents.

## Use cases
- implement mcts in jax
- build a muzero agent
- run alphazero-style search with neural networks
- batched parallel tree search on gpu
- research planning algorithms in python
- combine learned models with tree search

## When to choose
- you need MCTS integrated with JAX-based neural network training
- you want hackable, pure-Python search implementations instead of C++
- you need batched, JIT-compiled search for accelerator efficiency

## When to avoid
- you need a ready-made game engine or complete RL agent rather than search primitives
- your project does not use JAX
- you need CPU-only classical MCTS for board games without learned models

## Facets
- artifact type: library
- maturity: active
- function: reinforcement-learning, machine-learning, simulation
- domain: reinforcement-learning, machine-learning, deep-learning
- platform: python, cross-platform
- tags: mcts, jax, muzero, alphazero, planning, search-algorithms, jit-compilation, algorithms, gpu

## Member repositories
- google-deepmind/mctx (main) score 83

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:07.215105+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 2026-08-30T02:18:38.941510+00:00, confidence not recorded.
  - readme: https://github.com/google-deepmind/mctx (fetched 2026-08-28T04:07:07.215105+00:00, sha c4a869a390d7)
  - registry_pypi: https://pypi.org/pypi/mctx/json (fetched 2026-08-29T10:01:28.833906+00:00, sha 81dc531ca420)
- Data as of 2026-08-30T08:39:29.467469+00:00.