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Animora
The high-level, declarative animation framework built on Manim for computer science, mathematics, and technical storytelling.
Binary Search Tree Insertion Animation in Animora
$ pip install animora

⚡ Value Proposition: Manim, Elevated

from animora.core import Scene
from animora.datastructures import BST
from animora.theme import ModernDark, use_theme


class BSTDemoScene(Scene):
    def construct(self) -> None:
        with use_theme(ModernDark):
            # 1. Declaratively initialize a Binary Search Tree
            bst = BST([50, 30, 70, 20, 40])
            self.play(bst.animate_create())

            # 2. Insert with animated path tracing: 50 -> 30 -> 40 -> 35
            self.play(bst.animate_insert(35))

            # 3. Search target key with instant visual highlight
            self.play(bst.animate_search(35))

🤖 Built for AI & Machine Learning Engineers

Animora now features a dedicated, production-grade AI & Machine Learning visualization suite. Render complex scalar loss surfaces, backpropagation gradient waves, multi-layer networks, CNN convolutions, and attention heatmaps in a single line of code.

Animora AI/ML Suite Showcase

Explore the AI & ML Visualizations →

📦 What Makes Animora Distinct?

High-level Text, Shape, Connector, Arrow, Group, and Panel primitives with semantic state animations (animate_highlight, animate_transform, animate_create).

Pure geometric layout engines (Horizontal, Vertical, Grid, Circular, Tree, Graph, Flow) that arrange components automatically with zero manual coordinate math.

Centralized design token themes (ModernDark, PaperLight, Cyberpunk, Monokai) with dynamic context resolution via use_theme().

Dual-correctness educational chart components: Axes, BarChart, LineChart, ScatterPlot, Histogram, and Table.

Stateful structures pairing computational Python models with animation layers: Array, Stack, Queue, LinkedList, Heap, Tree, BST, Graph, HashTable.

Explicit OperationTrace event tracking for Search, 5 Sorting algorithms, BFS/DFS, Dijkstra, A*, DP, and Backtracking.

Complete, one-call visualizers for optimization surfaces, classic machine learning (regression, clustering, trees, SVM, PCA), deep learning (forward pass, backprop, SGD/Momentum/Adam, CNNs, RNNs), and attention mechanisms.

🗺️ Next Steps