Explainify

Explainify – Type any topic, get a narrated educational animation in minutes. Built for neurodivergent learners who absorb information better through visuals and audio.

Demo

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🎬 Generated Video Example

A real video Explainify produced for the topic "How do machines learn to recognize MNIST dataset numbers?" — storyboard → Manim animation → narration → final MP4.

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🧠 The Problem

Most educational content is text‑heavy and static.
For neurodivergent learners (ADHD, dyslexia, autism), this creates friction:

• Too much text → cognitive overload
• Abstract concepts → hard to visualize
• One‑size‑fits‑all explanations → don't always click

Explainify changes that.

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✨ How It Works

1. Type any topic – e.g., "How does a solar cell work?"
2. AI generates a storyboard – breaks the topic into scenes with narration.
3. Manim animations – each scene becomes a professional animated visual.
4. Natural voiceover – synced with the animation (audio duration is passed back into the animation prompt so visuals match the narration).
5. Comprehension check (roadmap) – after watching, the AI asks one micro‑question to verify understanding.
6. Re‑frame loop (roadmap) – on a wrong answer, Explainify picks a new representation strategy (causal diagram, concrete analogy, step‑by‑step) and regenerates the video.

This adaptive loop is our unique differentiator – it discovers how the learner understands and changes the representation until it clicks.

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🔥 Key Features

Feature	Description
Multi‑LLM support	Auto fallback between Claude and OpenAI.
Auto‑storyboard	AI generates a scene‑by‑scene script with timings.
TTS narration	Natural voiceover with audio‑duration‑synced animations.
Manim animations	Professional mathematical and scientific visualizations.
Code repair loop	Generated Manim code is auto‑fixed on render errors (up to 3 retries).
Comprehension check (roadmap)	One micro‑question after each video – tests real understanding.
Re‑frame loop (roadmap)	On wrong answer, AI picks a new representation strategy and re‑renders.
Neurodivergent‑friendly	Low cognitive load, visual‑first, audio‑optional.

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🏗️ Architecture

Explainify is a multi‑agent pipeline that orchestrates several specialized components:

graph TB
    subgraph Input
        A[User Topic]
    end
    
    subgraph "LLM Configuration"
        B[setup_llm_client]
        B1[Claude API]
        B2[OpenAI API]
        B -->|Priority 1| B1
        B -->|Fallback| B2
    end
    
    subgraph "Agent 1: Script Generation"
        C[animations.py]
        C1[generate_script_json]
        C --> C1
    end
    
    subgraph "Agent 2: TTS Generation"
        D[tts_generator.py]
        D1[generate_complete_audio]
        D2[generate_audio_fragment]
        D3[concatenate_audio_fragments]
        D1 --> D2
        D2 --> D3
    end
    
    subgraph "Agent 3: Manim Code Generation"
        E[manim_generator.py]
        E1[generate_manim_code]
        E --> E1
    end
    
    subgraph "Agent 4: Video Compilation"
        F[concat_video.py]
        F1[compile_video]
        F2[concatenate_videos]
        F3[merge_video_and_audio]
        F1 --> F2
        F2 --> F3
    end
    
    subgraph Output
        G[Final Video with Audio]
    end
    
    A --> B
    B --> C1
    C1 -->|video-output.json| D1
    C1 -->|video-output.json| E1
    D1 -->|audio durations| E1
    E1 -->|.py files| F1
    F1 -->|.mp4 fragments| F2
    D3 -->|audio.mp3| F3
    F2 -->|output_silent.mp4| F3
    F3 --> G
    
    style A fill:#e1f5ff
    style G fill:#c8e6c9
    style C fill:#fff9c4
    style D fill:#ffe0b2
    style E fill:#f8bbd0
    style F fill:#d1c4e9

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📦 Installation

Prerequisites

• Python 3.11+
• uv (fast Python package manager)
• Manim, FFmpeg, LaTeX (all included in Docker)

Quick start (using Docker – recommended)

git clone https://github.com/Flowthread/Explainify.git
cd Explainify
docker compose up

Then open http://localhost:5000.

Local setup (without Docker)

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone and setup
git clone https://github.com/Flowthread/Explainify.git
cd Explainify
uv sync
source .venv/bin/activate   # or .venv\Scripts\activate on Windows
cp .env.example .env

# Start Flask server
python src/main.py

Then open your browser and navigate to http://localhost:5000.

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🔑 API Keys

Explainify supports Claude and OpenAI as LLM providers. Configure at least one in .env:

CLAUDE_API_KEY=your_key        # priority 1 (used in auto mode)
OPENAI_API_KEY=your_key        # fallback; also required for TTS

Auto mode uses Claude first, then falls back to OpenAI. You can also force a provider in the UI.

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🎯 Demo Video

Watch a 3‑minute walkthrough (link to be added):
https://youtu.be/your-link-here

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📁 Repository Structure

Explainify/
├── src/
│   ├── animations.py          # Storyboard generation
│   ├── manim_generator.py     # Manim code + repair loop
│   ├── tts_generator.py       # TTS narration
│   ├── concat_video.py        # Video + audio merging
│   ├── video_generator.py     # Job orchestration
│   └── main.py                # Flask API server
├── public/                    # Demo GIFs, logos
├── .env.example
├── Dockerfile
├── docker-compose.yml
├── README.md
└── LICENSE (MIT)

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📄 License

MIT License – free to use, modify, and distribute.

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🙌 Built for IncludAI 2026

Explainify was created for the IncludAI – Neurodiversity Hackathon as a tool that makes learning accessible through visual and auditory content.

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📬 Contact

• GitHub: github.com/Flowthread/Explainify
• Issues: github.com/Flowthread/Explainify/issues
• Developer: @Flowthread