🧠
Multi Agent Book Writer model by EimanTahir027
⭐ 39.3
🧠
Model

Multi Agent Book Writer

by EimanTahir027 eimantahir027/multi-agent-book-writer
Free2AITools Nexus Index
39.3
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 45
P: Popularity 0
R: Recency 75
Q: Quality 70
Tech Context
Vital Performance —
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 39.3 FNI Score
Tiny - Params
- Context
0 Downloads
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID eimantahir027/multi-agent-book-writer
License MIT
Provider github
πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{eimantahir027_multi_agent_book_writer,
  author = {EimanTahir027},
  title = {Multi Agent Book Writer Model},
  year = {2026},
  howpublished = {\url{https://github.com/EimanTahir027/Multi-Agent-Book-Writer}},
  note = {Accessed via Free2AITools.}
}
APA Style
EimanTahir027. (2026). Multi Agent Book Writer [Model]. Free2AITools. https://github.com/EimanTahir027/Multi-Agent-Book-Writer

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ™ Git Clone
git clone https://github.com/EimanTahir027/Multi-Agent-Book-Writer

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 45
Popularity (P) 0
Recency (R) 75
Quality (Q) 70

πŸ’¬ Index Insight

FNI V2.0 for Multi Agent Book Writer: Authority (A:45), Popularity (P:0), Recency (R:75), Quality (Q:70). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data
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Deployment reference

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Technical Deep Dive

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.

description ## 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

text 
multiagent-book-writer/
β”œβ”€β”€ main.py                 # Main pipeline orchestrator
β”œβ”€β”€ agents/                 # Agent implementations
β”‚   β”œβ”€β”€ planner.py         # Chapter planning agent
β”‚   β”œβ”€β”€ researcher.py      # Research gathering agent
β”‚   β”œβ”€β”€ writer.py          # Content writing agent
β”‚   └── editor.py          # Content editing agent
β”œβ”€β”€ shared/
β”‚   └── context.py         # Shared state management
β”œβ”€β”€ output/
β”‚   └── draft.txt          # Final output (generated)
β”œβ”€β”€ config.yaml            # Configuration file
β”œβ”€β”€ 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

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

  2. Pull an LLM model with Ollama

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

3

⚠️ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source β†’

πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ FNI scores are relative rankings and may change as new models are added.

Social Proof

GitHub Repository
10Stars
Repo Issues
πŸ™ Data Source: GitHub β†—
πŸ”„ Updated daily

Source summary: Based on GitHub metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
gh-model--eimantahir027--multi-agent-book-writer
slug
eimantahir027--multi-agent-book-writer
source
github
author
EimanTahir027
license
MIT
tags
agentic, agents, ai-agents, python

βš™οΈ Technical Specs

pipeline tag
other

πŸ“Š Engagement & Metrics

downloads
0
stars
10
forks
0

Data indexed from public sources. Updated daily.