NousResearch/autonovel

NousResearch/autonovel

Description: An autonomous novel writing pipeline, by Hermes Agent

Language: Python

Stars: 1117

Forks: 217

Open issues: 14

Created: 2026-03-14T04:51:20Z

Pushed: 2026-03-20T16:17:11Z

Default branch: master

Fork: no

Archived: no

README:

autonovel

An autonomous pipeline for writing, revising, typesetting, illustrating, and narrating a complete novel. From a seed concept to a print-ready PDF, ePub, audiobook, and landing page — all generated by AI agents.

Inspired by karpathy/autoresearch: the same modify-evaluate-keep/discard loop, applied to fiction.

First novel produced: *The Second Son of the House of Bells* — 19 chapters, 79,456 words. See the autonovel/bells branch.

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Quick Start

# Clone and setup
git clone && cd autonovel
cp .env.example .env # Add your API keys

# Install dependencies
uv sync

# Generate a seed concept (or write your own in seed.txt)
uv run python seed.py

# Run the full pipeline
uv run python run_pipeline.py --from-scratch

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The Pipeline

Phase 1: Foundation

Build the world, characters, outline, voice, and canon from a seed concept. Loop until foundation_score > 7.5.

Phase 2: First Draft

Write chapters sequentially. Evaluate each one. Keep if score > 6.0, retry if not. Forward progress over perfection.

Phase 3a: Automated Revision

Adversarial editing → apply cuts → reader panel → generate briefs → rewrite chapters. Plateau detection stops the loop when scores stabilize.

Phase 3b: Opus Review Loop

Send the full manuscript to Claude Opus for dual-persona review (literary critic + professor of fiction). Parse actionable items. Fix the top issues. Repeat until the reviewer runs out of major items.

Phase 4: Export

Rebuild docs, typeset in LaTeX, generate art, produce audiobook scripts, build ePub, create landing page.

See [PIPELINE.md](PIPELINE.md) for the full technical specification.

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Tools (27 Python scripts)

Foundation

| Tool | Purpose | |------|---------| | seed.py | Generate seed concepts | | gen_world.py | Seed → world bible | | gen_characters.py | Seed + world → character registry | | gen_outline.py | Outline with beats and foreshadowing | | gen_outline_part2.py | Foreshadowing ledger | | gen_canon.py | Cross-reference hard facts | | voice_fingerprint.py | Voice analysis and discovery |

Drafting

| Tool | Purpose | |------|---------| | draft_chapter.py | Write a single chapter with anti-pattern rules | | run_drafts.py | Batch sequential chapter drafter |

Evaluation

| Tool | Purpose | |------|---------| | evaluate.py | Mechanical slop scorer + LLM judge | | adversarial_edit.py | "Cut 500 words" analysis → classified cuts | | compare_chapters.py | Head-to-head Elo tournament | | reader_panel.py | 4-persona novel-level evaluation | | review.py | Opus dual-persona review with stopping conditions |

Revision

| Tool | Purpose | |------|---------| | gen_brief.py | Auto-generate revision briefs from feedback | | gen_revision.py | Rewrite a chapter from a revision brief | | apply_cuts.py | Batch adversarial cut applicator |

Art & Cover

| Tool | Purpose | |------|---------| | gen_art.py | Art pipeline: style, curate, ornaments, vectorize | | gen_art_directions.py | Generate diverse art directions for curation | | gen_cover_composite.py | Text overlay on cover art | | gen_cover_print.py | Print-ready full-wrap cover (Lulu/KDP specs) |

Audiobook

| Tool | Purpose | |------|---------| | gen_audiobook_script.py | Parse chapters into speaker-attributed scripts | | gen_audiobook.py | Generate multi-voice audio via ElevenLabs |

Orchestration

| Tool | Purpose | |------|---------| | run_pipeline.py | Full pipeline orchestrator (seed → finished novel) | | build_arc_summary.py | Regenerate arc summary from chapters | | build_outline.py | Regenerate outline from chapters |

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File Structure

FRAMEWORK (reusable, on master):
program.md — Agent instructions per phase
CRAFT.md — Craft education (plot, character, world, prose)
ANTI-SLOP.md — Word-level AI tell detection
ANTI-PATTERNS.md — Structural AI pattern detection
PIPELINE.md — Full automation specification
WORKFLOW.md — Step-by-step human guide

TEMPLATES (filled per-novel on a branch):
voice.md — Part 1: guardrails. Part 2: discovered per novel
world.md — World bible template
characters.md — Character registry template
outline.md — Chapter outline template
canon.md — Hard facts database
MYSTERY.md — Central mystery (author-only)
state.json — Pipeline state tracker

TYPESETTING:
typeset/novel.tex — LaTeX template (EB Garamond, trade paperback)
typeset/build_tex.py — Chapters → LaTeX with vector ornaments
typeset/epub_* — ePub metadata, CSS, and front matter

ART:
audiobook_voices.json — Character → ElevenLabs voice mapping
landing/index.html — Responsive landing page template

CONFIG:
.env.example — API keys (Anthropic, fal.ai, ElevenLabs)
pyproject.toml — Python dependencies

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How It Works

The novel is five co-evolving layers:

Layer 5: voice.md — HOW we write
Layer 4: world.md — WHAT exists
Layer 3: characters.md — WHO acts
Layer 2: outline.md — WHAT HAPPENS
Layer 1: chapters/ch_NN.md — THE ACTUAL PROSE
Cross-cutting: canon.md — WHAT IS TRUE

Changes propagate both down (lore change → outline change → chapter revision) and up (writing reveals a gap → update lore → check downstream). The pipeline tracks propagation debts in state.json.

Two Immune Systems

1. Mechanical (evaluate.py, no LLM): regex scans for banned words, fiction clichés, show-don't-tell violations, sentence uniformity.

2. LLM Judge (evaluate.py, separate model): scores prose quality, voice adherence, character distinctiveness, beat coverage.

The Opus Review Loop

After automated revision cycles, the full manuscript goes to Claude Opus with this prompt:

> "Read the below novel. Review it first as a literary critic and then > as a professor of fiction. Give specific, actionable suggestions for > any defects you find. Be fair but honest. You don't have to find defects."

The dual-persona review catches what automated tools can't: prose-level repetition, character thinness, ethical gaps, structural monotony. The loop continues until the reviewer's items are mostly qualified hedges rather than real problems.

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API Keys

The pipeline uses three external services:…