Story-Story: An AI-Driven Storytelling System for African Historical Narratives

Introduction

Storytelling plays a crucial role in cultural preservation, education, and entertainment. However, many African historical figures remain underrepresented in children's literature. Secondly, in Nigeria, the curriculum does not give appropriate attention to history which is very vital. As a result, children grow uninterested in learning about their roots and national heroes.

Story-Story is AI-powered storytelling system designed to generate engaging and factual narratives about African historical figures for young readers. The system employs an agentic workflow powered by large language models (LLMs) and diffusion-based image generation techniques to create comprehensive, child-friendly stories. The system's architecture, agent roles, and implementation details are outlined and discussions on its potential impact on education and cultural representation are made.

Our system takes in the name of a historical figure and autonomously generates a 10-page illustrated story, ensuring factual accuracy and cultural relevance. The key contributions of this work include:

1. An AI-driven storytelling pipeline that generates structured, coherent, and historically accurate narratives.
2. A multimodal approach combining text-based story generation with AI-assisted illustrations.
3. A content verification agent to ensure safe child-friendly storytelling.

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2. System Architecture

Story-Story is built on an ensemble of AI agents orchestrated using LangGraph. The system consists of five primary agents:

2.1 Planner Agent

• Role: Generates a structured outline (chapter titles) for the story.
• Input: historical_figure
• Output: chapter_title
• Model: GPT-4o

2.2 Storyteller Agent

• Role: Generates detailed, historically accurate chapter content.
• Input: historical_figure, chapter_title
• Output: chapter_content
• Model: GPT-4o

2.3 Illustrator Agent

• Role: Creates prompts for generating illustrations based on chapter content.
• Input: historical_figure, chapter_title
• Output: prompt
• Model: GPT-4o

2.4 Image Generator

• Role: Produces AI-generated illustrations from the prompts.
• Input: historical_figure, prompt
• Output: image
• Model: Flux.1 Schnell

2.5 Publisher Agent

• Role: Ensures the final story and images adhere to child-friendly content guidelines.
• Input: historical_figure, chapter_content, image
• Output: score
• Model: GPT-4v

2.6 Workflow Overview

The entire process follows a sequential workflow:

1. The Planner Agent generates a structured outline for the story.
2. The Storyteller Agent expands each chapter title into a detailed narrative.
3. The Illustrator Agent formulates image generation prompts.
4. The Image Generator creates illustrations using diffusion models.
5. The Publisher Agent verifies the appropriateness of content before finalization.

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3. Implementation

3.1 Technology Stack

The system is built using:

• LangGraph for agent coordination
• OpenAI GPT-4o & GPT-4v for story generation and verification
• Flux.1 Schnell for AI-assisted illustration
• Supabase for database storage
• Python for backend logic

3.2 Running the System

3.2.1 Installation

pip install -r requirements.txt

3.2.2 Running the Application

python main.py --historical_figure "Ahmadu Bello"

This command generates a complete story with illustrations, storing the output in a Supabase database.

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4. Database Structure

The system uses Supabase to store generated stories. The database consists of two main tables:

4.1 Stories Table

4.2 StoryPages Table

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5. Results and Discussion

5.1 Generated Stories

Story-Story successfully produces engaging and informative narratives for African historical figures. The content is structured, historically accurate, and suitable for children. The generated illustrations complement the stories, enhancing engagement.

5.2 Strengths

• Automated Story Generation: The system autonomously produces full-length, illustrated stories from a single input.
• Child-Friendly Curation: Ensures content is appropriate and engaging.
• Cultural Representation: Promotes African historical figures in children's literature.

5.3 Limitations & Future Work

While Story-Story provides a robust storytelling framework, the following enhancements are planned:

• Implementing Retrieval-Augmented Generation to improve factual accuracy by integrating a bigger pool of documented history.
• Adding text-to-speech with Kokoro TTS for an immersive audio experience.
• Expanding to other African languages.

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6. Conclusion

Story-Story showcases the power of AI-driven storytelling in bringing African historical narratives to children. Given the recent advancements in LLM agents and multimodal AI, a platform such as Story-Story is feasible and provides a fresh and engaging way to automate storytelling, enhancing education and cultural understanding.

Future enhancements, such as RAG-boosted accuracy and the addition of multiple languages, will solidify its integrity and make the delivery of African literature more seamless.

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