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Basic Information

This repository is a Python-based system that uses the AutoGen framework to generate complete books by coordinating multiple specialized AI agents. It provides an example implementation and tooling to plan, outline, write, and edit multi-chapter narratives starting from an initial prompt. The README documents the agent roles, a suggested installation workflow using a virtual environment and requirements file, simple usage examples showing how to create agents and run an outline-to-book generation sequence, configuration options such as LLM endpoint URL and chapter count, and the expected output layout saved into a book_output directory. The project is intended as an experimental generator and developer-facing reference for building collaborative agent pipelines rather than a polished end-user product.

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App Details

Features
The project documents a multi-agent collaborative writing pipeline with named roles including Story Planner, World Builder, Memory Keeper, Writer, Editor, and Outline Creator. It emphasizes structured chapter generation, story continuity and character development tracking, automated world-building and setting management, and support for complex multi-chapter narratives. The system includes built-in validation, retry logic, error handling, and backup copies to protect generated content. Configuration is centralized in config.py with options for LLM endpoints, number of chapters, agent parameters, and output location. Output is organized into an outline file and per-chapter text files in a book_output directory. Requirements specify Python 3.8+ and AutoGen 0.2.0+ plus other dependencies.
Use Cases
This repo helps developers and researchers prototype automated long-form content production workflows by providing a concrete example of orchestrating multiple specialized agents to produce coherent books. It accelerates the outline-to-book process by automating plot planning, world-building, continuity tracking, drafting, and editorial passes, and it produces file-based outputs ready for review. Configurability of agent parameters, chapter counts, and LLM endpoints enables experimentation with different models and settings. Built-in validation, retry logic, and backups reduce the risk of data loss during lengthy generations. The README also outlines development and contribution steps, making it straightforward to extend agent behavior, tune pipelines, or integrate other LLMs while acknowledging that human review and compute resources are still required.

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