movablegames 0.2.7

AI-powered adaptive game engine for educational platforms

MovableGames Python API

The movablegames package is a thin Python facade over the pybind11-compiled mag_engine C++ wheel. It provides the building blocks for generating educational game environments, producing narrative frame sequences, and dispatching problems across all supported subjects.

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Installation

From wheel (production):

pip install movablegames

Editable / source build (requires CMake 3.25+, vcpkg, a C++17 compiler):

uv pip install -e .
# or
pip install -e .

The build compiles the C++ engine with BUILD_PYTHON_BINDINGS=ON and places the resulting .pyd / .so alongside the pure-Python modules. See engine/README.md for C++ build details.

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Package layout

python/movablegames/
├── __init__.py        re-exports everything from mag_engine (C++ bindings)
├── environment.py     EnvironmentFactory, HistoryEnvironment
├── level.py           MathLevelGenerator, ProblemGenerator
├── sequencer.py       NarrativeManager
└── topics.py          Topic enum, TopicManager

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Core types, from the C++ bindings (mag_engine)

These are re-exported by __init__.py via from .mag_engine import *.

ChargePolarity

from movablegames import ChargePolarity

ChargePolarity.POSITIVE   # numbers > 0
ChargePolarity.NEGATIVE   # numbers < 0
ChargePolarity.NEUTRAL    # variables / dormant entities

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Charge

Represents a number as a physical entity.

from movablegames import Charge, ChargePolarity

c = Charge(magnitude=3.0, polarity=ChargePolarity.POSITIVE, visual_id="c_01")
c.magnitude    # float
c.polarity     # ChargePolarity
c.visual_id    # str, unique identifier for the frontend renderer
c.to_json()    # str, JSON representation

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Environment

A container for charges and mathematical operators (agents). Supports nested sub-environments (parentheses).

from movablegames import Environment

env = Environment("zone_main")

# Add a charge directly
env.add_charge(magnitude=5.0, polarity=ChargePolarity.POSITIVE, visual_id="c_01")

# Add operator agents
env.add_plus_magician("m_plus_01")
env.add_minus_magician("m_minus_01")
env.add_multiply_magician("m_mul_01")
env.add_divide_magician("m_div_01")
env.add_power_sorcerer("m_pow_01")
env.add_root_magician("m_root_01")
env.add_abs_magician("m_abs_01")
env.add_factorial_magician("m_fac_01")
env.add_modulo_magician("m_mod_01")
env.add_log_magician("m_log_01")
env.add_chronomancer("m_chrono_01")       # parenthesis handler

# Add validation gates
env.add_equals_gate("gate_01", target=42.0)
env.add_inequality_gate("gate_02", target=10.0, comparison_type=">")

# Nest a sub-environment (parenthesis bubble)
sub = Environment("zone_sub")
env.add_sub_environment(sub)

# Inspect state
env.id                    # str
env.charges               # list[Charge]
env.agents                # list[MagicianAgent]
env.sub_environments      # list[Environment]
env.is_time_frozen        # bool

env.to_json()             # str, full JSON snapshot of the environment

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NarrativeSequencer

Drives the game loop one step at a time, applying operator priority rules (BIDMAS/BODMAS) and producing a JSON event for each step.

from movablegames import NarrativeSequencer

seq = NarrativeSequencer(env)

while not seq.is_finished:
    frame_json = seq.next_frame()   # str, JSON with status and event data
    print(frame_json)

seq.current_frame    # int, index of the last processed frame
seq.is_finished      # bool

Frame event types (values of the "status" key in frame JSON):

Constant	Meaning
CAST_SPELL	An operator agent acted on charges
COLLAPSE_ZONE	A parenthesis bubble resolved
LEVEL_COMPLETE	All operators finished; gate validates
ERROR	An unrecoverable error occurred
FINISHED	Simulation is done (no more frames)
RUNNING	Step completed; simulation continues

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Python wrappers

EnvironmentFactory, movablegames.environment

Factory that builds typed Environment objects from level data dictionaries.

from movablegames.environment import EnvironmentFactory

env = EnvironmentFactory.create_math_environment(level_data)
env = EnvironmentFactory.create_science_environment(level_data)
env = EnvironmentFactory.create_history_environment(level_data)   # returns HistoryEnvironment
env = EnvironmentFactory.create_english_environment(level_data)
env = EnvironmentFactory.create_technology_environment(level_data)

level_data is the dict returned by MathLevelGenerator.generate_math_problem or any ProblemGenerator.* method. See the Level data schema section below.

Operator role → add_* method mapping (EnvironmentFactory.MAG_MAGICIAN_METHODS):

Role string	Method
"plus"	add_plus_magician
"minus"	add_minus_magician
"multiply"	add_multiply_magician
"divide"	add_divide_magician
"power"	add_power_sorcerer
"root"	add_root_magician
"abs"	add_abs_magician
"factorial"	add_factorial_magician
"modulo"	add_modulo_magician
"log"	add_log_magician

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MathLevelGenerator, movablegames.level

Generates a random, self-consistent math problem and its level data.

from movablegames.level import MathLevelGenerator

expr_str, level_data = MathLevelGenerator.generate_math_problem(
    operators=["+", "-", "*", "/"],   # subset of MAG_OPERATORS keys; None = all
    difficulty="easy",                # "easy" | "medium" | "hard"
)

Supported operator symbols (MathLevelGenerator.MAG_OPERATORS):

Symbol	Role
"+"	plus
"-"	minus
"*"	multiply
"/"	divide
"**" or "^"	power
"root"	root
"abs"	abs
"!"	factorial
"%"	modulo
"log"	log

Difficulty parameters:

Difficulty	Operators	Max value	Max complexity
"easy"	1–2	10	2 (+ − × ÷ abs)
"medium"	2–4	20	3 (adds power, root, modulo)
"hard"	4–6	50	5 (all operators)

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ProblemGenerator, movablegames.level

Placeholder generators for non-math subjects. Return a (problem_string, level_data) tuple suitable for the corresponding EnvironmentFactory.create_* method.

from movablegames.level import ProblemGenerator

problem, data = ProblemGenerator.generate_science_problem(difficulty="easy")
problem, data = ProblemGenerator.generate_history_problem(difficulty="medium")
problem, data = ProblemGenerator.generate_english_problem(difficulty="hard")
problem, data = ProblemGenerator.generate_technology_problem(difficulty="easy")

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NarrativeManager, movablegames.sequencer

Runs the full narrative loop and collects all frames into a list.

from movablegames.sequencer import NarrativeManager

frames = NarrativeManager.get_frames(environment=env, env_type="math")
# Returns list[dict], first entry is the initial state, subsequent entries are
# NarrativeSequencer frames; each dict has a "current_frame" index added.

Raises ValueError for unsupported env_type values.

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Topic / TopicManager, movablegames.topics

from movablegames.topics import Topic, TopicManager

Topic.MATH         # "MATH"
Topic.SCIENCE      # "SCIENCE"
Topic.HISTORY      # "HISTORY"
Topic.ENGLISH      # "ENGLISH"
Topic.TECHNOLOGY   # "TECHNOLOGY"

TopicManager.get_all_topics()
# → ["math", "science", "history", "english", "technology"]

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Level data schema

MathLevelGenerator.generate_math_problem returns a dict with this shape:

{
  "level_id": "gen_easy_3f2a1b4c",
  "environment": {
    "zones": [
      {
        // The primary zone holding all top-level charges and operators
        "id": "zone_main",
        "type": "main_environment",
        "contents": [
          {
            "type": "charge",
            "value": 5.0,
            "polarity": "positive",
            "id": "c_a1b2",
          },
          { "type": "magician", "role": "plus", "id": "m_plus_c3d4" },
        ],
      },
      {
        // Optional: a parenthesis bubble (sub-environment)
        "id": "zone_e5f6",
        "type": "parenthesis_bubble",
        "contents": [
          {
            "type": "charge",
            "value": 3.0,
            "polarity": "positive",
            "id": "c_g7h8",
          },
          {
            "type": "charge",
            "value": 2.0,
            "polarity": "negative",
            "id": "c_i9j0",
          },
          { "type": "magician", "role": "minus", "id": "m_minus_k1l2" },
        ],
      },
      {
        // The goal gate, the equation target
        "id": "gate_main",
        "type": "equals_gate", // or "inequality_gate"
        "target_value": 6.0,
        // For inequality_gate only:
        // "comparison": ">" | "<" | ">=" | "<="
      },
    ],
  },
}

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End-to-end example

from movablegames.level import MathLevelGenerator
from movablegames.environment import EnvironmentFactory
from movablegames.sequencer import NarrativeManager

# 1. Generate a medium-difficulty problem using addition and multiplication
expr_str, level_data = MathLevelGenerator.generate_math_problem(
    operators=["+", "*"],
    difficulty="medium",
)
print(f"Problem: {expr_str}")

# 2. Build the environment from the level data
env = EnvironmentFactory.create_math_environment(level_data)

# 3. Collect all narrative frames
frames = NarrativeManager.get_frames(env, env_type="math")
print(f"Total frames: {len(frames)}")

# 4. Inspect the first and last frames
import json
print("Initial state:", json.dumps(frames[0], indent=2))
print("Final state:  ", json.dumps(frames[-1], indent=2))