Multi-Agent Book Writer

A collaborative AI book writing system using multiple specialized agents. Each agent has a specific role in the writing pipeline to create a complete, polished book draft.

## Project Overview

This project simulates a writing team of AI agents that co-author a book. Each agent has a distinct role:

• Planner: Defines the book structure and chapters
• Researcher: Gathers background information and supporting content
• Writer: Drafts content for each chapter
• Editor: Reviews and polishes the output for grammar and coherence

The agents communicate through shared memory, enabling seamless collaboration.

Tech Stack

Component	Tool/Library
LLMs	Ollama (mistral, llama3, deepseek)
Agent Orchestration	Python classes with sequential pipeline
Context Sharing	In-memory Python objects
HTTP Client	requests library
Configuration	YAML

Project Structure

multiagent-book-writer/
├── main.py                 # Main pipeline orchestrator
├── agents/                 # Agent implementations
│   ├── planner.py         # Chapter planning agent
│   ├── researcher.py      # Research gathering agent
│   ├── writer.py          # Content writing agent
│   └── editor.py          # Content editing agent
├── shared/
│   └── context.py         # Shared state management
├── output/
│   └── draft.txt          # Final output (generated)
├── config.yaml            # Configuration file
├── requirements.txt       # Python dependencies
└── README.md              # This file

Installation

Prerequisites

• Python 3.8+
• Ollama installed and running

Setup Steps

1. Clone or download the project

   cd c:\Users\Dell\Downloads\Multi-Agent Book Writer

2. Pull an LLM model with Ollama

   ollama pull mistral
   # Alternative options: ollama pull llama3, ollama pull deepseek

3. Start Ollama (keep it running in a separate terminal)

   ollama serve

4. Install Python dependencies

   pip install -r requirements.txt

Usage

Basic Execution

Run the complete pipeline:

python main.py

Generate a book with a specific number of chapters:

python main.py 7

Output

The final book draft is saved to output/draft.txt with:

• All chapters with proper formatting
• Edited and polished content
• Coherent flow and consistency

How It Works

Pipeline Flow

1. Planning Phase: Planner agent creates a structured outline with chapter titles and descriptions
2. Research Phase: Researcher agent gathers background information, facts, and context for each chapter
3. Writing Phase: Writer agent creates full chapter drafts based on research data
4. Editing Phase: Editor agent polishes chapters for grammar, clarity, and coherence
5. Output: Final draft is saved to output/draft.txt

Agent Communication

All agents share a central context dictionary that includes:

• title: Book title
• chapters: List of chapter titles and descriptions
• research: Dictionary of research data per chapter
• drafts: List of chapter drafts

Configuration

Edit config.yaml to customize:

• Book settings: Title, number of chapters, output format
• Ollama settings: API URL, model selection, timeout
• Agent parameters: Temperature and behavior for each agent
• Output settings: Directory and filename preferences

Examples

Run with 3 chapters:

python main.py 3

Check the output:

type output\draft.txt

Troubleshooting

Connection Error to Ollama

• Make sure Ollama is running: ollama serve
• Verify it's accessible at http://localhost:11434
• Check firewall settings

Out of Memory

• Try a smaller model: ollama pull orca-mini
• Reduce chapter count: python main.py 2

Slow Generation

• Check if Ollama is using GPU: ollama list --all
• Consider using a smaller, faster model

Extensions & Improvements

Potential enhancements to explore:

• Add LangGraph for advanced orchestration with retries
• Implement feedback loops for iterative refinement
• Build Streamlit UI for real-time monitoring
• Add PDF export functionality
• Integrate with web search APIs for richer research
• Add support for multiple LLMs
• Implement memory persistence (JSON/database)
• Add custom style guides for writing consistency

Requirements

See requirements.txt:

• requests: HTTP library for Ollama API calls
• ollama: Ollama Python client library
• python-dotenv: Environment variable management (optional)

License

This project is open source and available for educational and commercial use.

Notes

• Ollama must be running locally for this project to work
• Generated content quality depends on the selected LLM model
• Generation time depends on model size and hardware capabilities
• All content is generated in-memory before being written to disk

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Getting Started: Install dependencies, start Ollama, and run python main.py to generate your first AI-written book!