KataGo's public distributed training run is ongoing! See https://katagotraining.org/ for more details, to download the latest and strongest neural nets, or to learn how to contribute if you want to help KataGo improve further! Also check out the computer Go discord channel!
As of 2026, KataGo remains one of the strongest open source Go bots available online. KataGo was trained using an AlphaZero-like process with many enhancements and improvements, and is capable of reaching top levels rapidly and entirely from scratch with no outside data, improving only via self-play. Some of these improvements take advantage of game-specific features and training targets, but also many of the techniques are general and could be applied in other games. As a result, early training is immensely faster than in other self-play-trained bots - with only a few strong GPUs for a few days, any researcher/enthusiast should be able to train a neural net from nothing to high amateur dan strength on the full 19x19 board. If tuned well, a training run using only a single top-end consumer GPU could possibly train a bot from scratch to superhuman strength within a few months.
Experimentally, KataGo did also try some limited ways of using external data at the end of its June 2020 run, and has continued to do so into its most recent public distributed run, "kata1" at https://katagotraining.org/. External data is not necessary for reaching top levels of play, but still appears to provide some mild benefits against some opponents, and noticeable benefits in a useful analysis tool for a variety of kinds of situations that don't occur in self-play but that do occur in human games and games that users wish to analyze.
KataGo's engine aims to be a useful tool for Go players and developers, and supports the following features: * Estimates territory and score, rather than only "winrate", helping analyze kyu and amateur dan games besides only on moves that actually would swing the game outcome at pro/superhuman-levels of play. * Cares about maximizing score, enabling strong play in handicap games when far behind, and reducing slack play in the endgame when winning. * Supports alternative values of komi (including integer values) and good high-handicap game play. * Supports board sizes ranging from 7x7 to 19x19, and as of May 2020 may be the strongest open-source bot on both 9x9 and 13x13 as well. * Supports a wide variety of rules, including rules that match Japanese rules in almost all common cases, and ancient stone-counting-like rules. * For tool/back-end developers - supports a JSON-based analysis engine that can batch multiple-game evaluations efficiently and be easier to use than GTP.
Here are some links to some docs/papers/posts about KataGo's research and training!
Paper about the major new ideas and techniques used in KataGo: Accelerating Self-Play Learning in Go (arXiv). Many of the specific parameters are outdated, but the general methods continue to be used.
Many major further improvements have been found since then, which have been incorporated into KataGo's more recent runs and are documented here: KataGoMethods.md.
A description of the neural network sizes and architectures used in KataGo's main public run is: NetworkArchitectures.md.
KataGo has a fully working implementation of Monte-Carlo Graph Search, extending MCTS to operate on graphs instead of just trees! An explanation can be found here Monte-Carlo Graph Search from First Principles. This explanation is written to be general (not specific to KataGo) and to fill a big gap in explanatory material in the academic literature and hopefully it can be useful to others!
Many thanks to Jane Street for supporting the training of KataGo's major earlier published runs, as well as numerous many smaller testing runs and experiments. Blog posts about the initial release and some interesting subsequent experiments:
For more details about KataGo's older training runs, including comparisons to other bots, see Older Training History and Research!
Also if you're looking to ask about general information about KataGo or how it works, or about some past Go bots besides KataGo, consider the computer Go discord channel.
Precompiled executables for KataGo can be found at the releases page for Windows and Linux.
And the latest neural nets are available at https://katagotraining.org/.
KataGo implements just a GTP engine, which is a simple text protocol that Go software uses. It does NOT have a graphical interface on its own. So generally, you will want to use KataGo along with a GUI or analysis program. A few of them bundle KataGo in their download so that you can get everything from one place rather than downloading separately and managing the file paths and commands.
This is by no means a complete list - there are lots of things out there. But, writing as of 2026, a few of the easier and/or popular ones might be:
Generally, for GUIs that don't offer an all-in-one package, you will need to download KataGo (or any other Go engine of your choice!) and tell the GUI the proper command line to run to invoke your engine, with the proper file paths involved. See How To Use below for details on KataGo's command line interface.
KataGo currently officially supports both Windows and Linux, with precompiled executables provided each release. On Windows, the executables should generally work out of the box, on Linux if you encounter issues with system library versions, as an alternative building from source is usually straightforward. Not all different OS versions and compilers have been tested, so if you encounter problems, feel free to open an issue. KataGo can also, of course, be compiled from source on Windows or Linux. On Windows it supports MSVC or MinGW compilers, on Linux it supports usual compilers like g++, documented further down.
The community also provides KataGo packages for Homebrew on MacOS - releases there may lag behind official releases slightly.
Use brew install katago. The latest config files and networks are installed in KataGo's share directory. Find them via brew list --verbose katago. A basic way to run katago will be katago gtp -config $(brew list --verbose katago | grep 'gtp.*\.cfg') -model $(brew list --verbose katago | grep .gz | head -1). You should choose the Network according to the release notes here and customize the provided example config as with every other way of installing KataGo.
KataGo has six backends, OpenCL (GPU), CUDA (GPU), TensorRT (GPU), ROCm (GPU), ONNX Runtime (varied hardware), and Eigen (CPU). (On macOS there is also a Metal backend, most easily obtained via homebrew - see above.)
As of v1.17, KataGo supports transformer neural nets, which are generally much stronger for the same compute cost and which the main training run is switching to. Transformer models are more demanding on the GPU backend than the older convolutional nets, so the backend recommendations below matter more for them - in particular OpenCL is noticeably slower on transformers, and on NVIDIA the CUDNN and TensorRT versions make a large difference.
The quick summary is: * **To
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