anemone

anemone is a Python library for tree search over valanga game states. It separates structural tree storage, runtime search state, node evaluation, exploration indices, and selector/opening logic so different search policies can share one coherent runtime.

Overview

The main architectural split is:

structure: TreeNode and ITreeNode
runtime: AlgorithmNode and TreeExploration
evaluation: direct evaluation plus backed-up tree evaluation
exploration: per-node exploration-index payloads and tree-wide recomputation
selection: selectors and opening instructions that decide what to expand next

Installation

pip install anemone

Optional extras:

pip install anemone[nn]
pip install anemone[debug]

Debug snapshot export and the debug browser's SVG rendering also require the system Graphviz executable (dot).

Getting started

The canonical public entry point is anemone.factory.create_search.

Use anemone.factory to build a runnable search runtime.
Treat anemone._runtime_assembly and anemone.search_factory as internal wiring layers behind that API.
Use TreeAndValueBranchSelector only when you want the higher-level one-shot recommendation wrapper instead of driving the runtime directly.

from random import Random

from anemone.factory import SearchArgs, create_search

# Domain-specific collaborators omitted for brevity:
# - YourStateType
# - your_dynamics
# - starting_state
# - your_evaluator
# - your_selector_args
# - your_opening_type
# - your_stopping_criterion
# - your_recommender_rule
args = SearchArgs(
    node_selector=your_selector_args,
    opening_type=your_opening_type,
    stopping_criterion=your_stopping_criterion,
    recommender_rule=your_recommender_rule,
)

runtime = create_search(
    state_type=YourStateType,
    dynamics=your_dynamics,
    starting_state=starting_state,
    args=args,
    random_generator=Random(0),
    master_state_value_evaluator=your_evaluator,
    state_representation_factory=None,
)

result = runtime.explore(Random(0))
print(result.branch_recommendation.recommended_name)

Core concepts

TreeNode: the concrete structural node storing state, depth, and links.
AlgorithmNode: the runtime wrapper that adds tree evaluation and exploration-index payloads.
direct_value: the immediate evaluator output for one node.
backed_up_value: the subtree-derived value propagated from children.
exploration index: optional per-node search-priority data recomputed across the tree by a configured strategy.

How it works

create_search(...) assembles the runtime, direct evaluator, selector factory, tree-evaluation factory, and tree manager.
The initial tree is created with a directly evaluated root node.
A selector chooses which node or branch to open next.
The tree manager expands the structure and creates any new runtime nodes.
Direct evaluation fills direct_value on newly created nodes.
Upward propagation recomputes backed_up_value, branch ordering, and principal variation.
Exploration-index payloads are refreshed for the next iteration.

Documentation

Metadata

Release files for algorhino-anemone 0.2.22

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Source distribution (sdist)

Source distribution for algorhino-anemone 0.2.22
File	Size	Uploaded
algorhino_anemone-0.2.22.tar.gz	427.1 kB	Sep 18, 2026	Details

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Table of built distributions (wheels) for algorhino-anemone 0.2.22
File	Interpreter	ABI	Platform	Reset
algorhino_anemone-0.2.22-py3-none-any.whl 428.0 kB Sep 18, 2026	Python 3	none	any	Details

Total release size: 855.1 kB

Release files / algorhino_anemone-0.2.22.tar.gz

Download URL	algorhino_anemone-0.2.22.tar.gz
Size	427.1 kB
Tags	Source
SHA-256 checksum How to use checksums	`5f9ce592501c482530eb5dcd3b5731b61370b157bb42eaaadc94f058d43e7a17`
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Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Publishing platform: GitHub Actions
Publishing repository: github.com/victorgabillon/anemone
Publishing commit: github.com/victorgabillon/anemone/tree/8821d0c2b4711a279f1e29850a30e70db931fb90

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Publishing configuration: github.com/victorgabillon/anemone/blob/8821d0c2b4711a279f1e29850a30e70db931fb90/.github/workflows/release.yml
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Subject: algorhino_anemone-0.2.22.tar.gz
SHA-256 checksum: 5f9ce592501c482530eb5dcd3b5731b61370b157bb42eaaadc94f058d43e7a17 Verifying attestations

Release files / algorhino_anemone-0.2.22-py3-none-any.whl

Download URL	algorhino_anemone-0.2.22-py3-none-any.whl
Size	428.0 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`9393500e8314b54243ee07b0684020eebe2d6be3446f5bfe686f54f6fade6589`
BLAKE2b-256 checksum How to use checksums	`56cf5dfcfb44cf688b66d5c36d728a5722828f7f293a82de61ba17589809b4bf`
Upload date	Sep 18, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/victorgabillon/anemone
Publishing commit: github.com/victorgabillon/anemone/tree/8821d0c2b4711a279f1e29850a30e70db931fb90

Workflow

Publishing configuration: github.com/victorgabillon/anemone/blob/8821d0c2b4711a279f1e29850a30e70db931fb90/.github/workflows/release.yml
Publishing logs: github.com/victorgabillon/anemone/actions/runs/35358512285/attempts/1

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Subject: algorhino_anemone-0.2.22-py3-none-any.whl
SHA-256 checksum: 9393500e8314b54243ee07b0684020eebe2d6be3446f5bfe686f54f6fade6589 Verifying attestations