diff --git a/config.example.yaml b/config.example.yaml
new file mode 100644
index 0000000..c2efd00
--- /dev/null
+++ b/config.example.yaml
@@ -0,0 +1,96 @@
+# Opus Orchestrator AI Configuration
+# Copy this file to .env or config.yaml and configure
+
+# =============================================================================
+# LLM Configuration
+# =============================================================================
+
+# OpenAI Configuration (recommended)
+openai_api_key: sk-your-openai-key-here
+
+# MiniMax Configuration (alternative)
+# minimax_api_key: sk-your-minimax-key-here
+
+# =============================================================================
+# API Configuration
+# =============================================================================
+
+# Default LLM provider: openai, anthropic, or minimax
+agent_provider: openai
+
+# Model to use
+agent_model: gpt-4o
+
+# Temperature for generation (0.0 - 1.0)
+agent_temperature: 0.7
+
+# Maximum tokens per response (null = unlimited)
+# agent_max_tokens: null
+
+# =============================================================================
+# GitHub Configuration
+# =============================================================================
+
+# GitHub token for private repository access
+github_token: ghp_your_token_here
+
+# =============================================================================
+# S3/MinIO Configuration
+# =============================================================================
+
+# AWS Credentials
+aws_access_key_id: your-access-key
+aws_secret_access_key: your-secret-key
+aws_region: us-east-1
+
+# Custom S3 Endpoint (for MinIO, DigitalOcean Spaces, Wasabi, etc.)
+# s3_endpoint_url: https://nyc3.digitaloceanspaces.com
+
+# =============================================================================
+# Iteration Configuration
+# =============================================================================
+
+# Minimum critique rounds
+iteration_min_critic_rounds: 2
+
+# Maximum critique rounds
+iteration_max_critic_rounds: 5
+
+# Score threshold for approval (0.0 - 1.0)
+iteration_approval_threshold: 0.8
+
+# =============================================================================
+# Output Configuration
+# =============================================================================
+
+# Output format: markdown, html, txt
+output_format: markdown
+
+# Include table of contents
+output_include_toc: true
+
+# Output directory
+output_dir: ./output
+
+# =============================================================================
+# Server Configuration
+# =============================================================================
+
+# API Server Host
+server_host: 0.0.0.0
+
+# API Server Port
+server_port: 8000
+
+# Web UI Port
+web_ui_port: 8080
+
+# Enable CORS
+server_cors_enabled: true
+
+# =============================================================================
+# Logging
+# =============================================================================
+
+# Log level: DEBUG, INFO, WARNING, ERROR
+log_level: INFO
diff --git a/opus_orchestrator/workflow.py b/opus_orchestrator/workflow.py
deleted file mode 100644
index f21a91f..0000000
--- a/opus_orchestrator/workflow.py
+++ /dev/null
@@ -1,614 +0,0 @@
-"""LangGraph workflow nodes for Opus Orchestrator.
-
-Contains the workflow graph with:
-- Stage nodes (one sentence, character sheets, outline, etc.)
-- Validation nodes
-- Iteration loops for writing
-- Checkpoint management
-"""
-
-import json
-import os
-from datetime import datetime
-from pathlib import Path
-from typing import Any, Callable, Optional
-
-from opus_orchestrator.agents.fiction import (
-    ArchitectAgent,
-    CharacterLeadAgent,
-    EditorAgent,
-    VoiceAgent,
-    WorldsmithAgent,
-)
-from opus_orchestrator.config import AgentConfig
-from opus_orchestrator.frameworks import get_framework_prompt, StoryFramework
-from opus_orchestrator.langgraph_state import (
-    OpusState,
-    Stage,
-    Character,
-    ChapterPlan,
-    PlotBeat,
-    PreWriting,
-    WritingState,
-    create_initial_state,
-    get_progress,
-    validate_character_sheets,
-    validate_one_paragraph,
-    validate_one_sentence,
-    validate_prewriting_complete,
-    validate_scene_list,
-)
-from opus_orchestrator.schemas import BookIntent, BookType
-
-
-# Checkpoint management
-
-CHECKPOINT_DIR = Path("./checkpoints")
-
-
-def save_checkpoint(state: OpusState, checkpoint_id: str = "default") -> Path:
-    """Save state checkpoint to disk."""
-    CHECKPOINT_DIR.mkdir(parents=True, exist_ok=True)
-    
-    checkpoint_file = CHECKPOINT_DIR / f"checkpoint_{checkpoint_id}.json"
-    
-    # Convert to JSON-serializable dict
-    data = {
-        "stage": state.stage.value,
-        "framework": state.framework,
-        "genre": state.genre,
-        "target_word_count": state.target_word_count,
-        "seed_concept": state.seed_concept,
-        "prewriting": state.prewriting.model_dump() if state.prewriting else {},
-        "style_guide": state.style_guide,
-        "writing": state.writing.model_dump() if state.writing else {},
-        "manuscript": state.manuscript,
-        "total_word_count": state.total_word_count,
-        "validation_errors": state.validation_errors,
-        "warnings": state.warnings,
-        "progress": state.progress,
-        "last_updated": datetime.utcnow().isoformat(),
-    }
-    
-    with open(checkpoint_file, "w") as f:
-        json.dump(data, f, indent=2)
-    
-    return checkpoint_file
-
-
-def load_checkpoint(checkpoint_id: str = "default") -> Optional[OpusState]:
-    """Load state checkpoint from disk."""
-    checkpoint_file = CHECKPOINT_DIR / f"checkpoint_{checkpoint_id}.json"
-    
-    if not checkpoint_file.exists():
-        return None
-    
-    with open(checkpoint_file, "r") as f:
-        data = json.load(f)
-    
-    # Reconstruct state
-    prewriting_data = data.get("prewriting", {})
-    prewriting = PreWriting(**prewriting_data) if prewriting_data else PreWriting()
-    
-    writing_data = data.get("writing", {})
-    writing = WritingState(**writing_data) if writing_data else WritingState()
-    
-    state = OpusState(
-        stage=Stage(data.get("stage", "seed")),
-        framework=data.get("framework", "snowflake"),
-        genre=data.get("genre", "general"),
-        target_word_count=data.get("target_word_count", 80000),
-        seed_concept=data.get("seed_concept", ""),
-        prewriting=prewriting,
-        style_guide=data.get("style_guide", ""),
-        writing=writing,
-        manuscript=data.get("manuscript", ""),
-        total_word_count=data.get("total_word_count", 0),
-        validation_errors=data.get("validation_errors", []),
-        warnings=data.get("warnings", []),
-        progress=data.get("progress", 0.0),
-    )
-    
-    return state
-
-
-# Workflow nodes
-
-class OpusWorkflow:
-    """LangGraph workflow for Opus Orchestrator."""
-    
-    def __init__(
-        self,
-        framework: str = "snowflake",
-        genre: str = "general",
-        target_word_count: int = 80000,
-        api_key: Optional[str] = None,
-    ):
-        self.framework = framework
-        self.genre = genre
-        self.target_word_count = target_word_count
-        self.api_key = api_key
-        
-        # Initialize agents
-        agent_config = AgentConfig(api_key=api_key)
-        self.architect = ArchitectAgent(agent_config)
-        self.character_lead = CharacterLeadAgent(agent_config)
-        self.voice = VoiceAgent(agent_config)
-        self.editor = EditorAgent(agent_config)
-    
-    def stage_1_one_sentence(self, state: OpusState) -> OpusState:
-        """Generate one sentence summary."""
-        # Use framework-specific prompting
-        framework_prompt = get_framework_prompt(StoryFramework(self.framework))
-        
-        user_prompt = f"""Create a ONE SENTENCE summary of this story concept.
-
-The sentence should contain:
-- Protagonist's name (or descriptor)
-- Their goal
-- The conflict/obstacle
-- The stakes
-
-Example: "In a world where magic is forbidden, a young mage must master forbidden arts to save her dying brother, even if it means sparking a war with the ruling theocracy."
-
-## Your seed concept:
-{state.seed_concept}
-
-## Task:
-Write ONE compelling sentence that captures the entire story.
-"""
-        
-        # Call LLM
-        import asyncio
-        result = asyncio.run(self.architect.call_llm(framework_prompt, user_prompt))
-        
-        # Parse and validate
-        state.prewriting.one_sentence = result.strip()
-        state.validation_errors = validate_one_sentence(state)
-        state.stage = Stage.ONE_SENTENCE
-        state.progress = get_progress(Stage.ONE_SENTENCE)
-        state.last_updated = datetime.utcnow().isoformat()
-        
-        # Save checkpoint
-        save_checkpoint(state)
-        
-        return state
-    
-    def stage_2_one_paragraph(self, state: OpusState) -> OpusState:
-        """Generate one paragraph outline."""
-        framework_prompt = get_framework_prompt(StoryFramework(self.framework))
-        
-        user_prompt = f"""Expand this one-sentence summary into a full one-paragraph story outline.
-
-Include:
-- Opening image (the "before" state)
-- Setup (normal world, who the protagonist is)
-- Catalyst (what changes everything)
-- Rising action (attempts to solve the problem)
-- Midpoint (major twist or revelation)
-- Complications (things get worse)
-- Crisis (lowest point)
-- Resolution (how it ends)
-
-## One sentence:
-{state.prewriting.one_sentence}
-
-## Task:
-Write one detailed paragraph (4-8 sentences) that tells the complete story arc.
-"""
-        
-        import asyncio
-        result = asyncio.run(self.architect.call_llm(framework_prompt, user_prompt))
-        
-        state.prewriting.one_paragraph = result.strip()
-        state.validation_errors = validate_one_paragraph(state)
-        state.stage = Stage.ONE_PARAGRAPH
-        state.progress = get_progress(Stage.ONE_PARAGRAPH)
-        state.last_updated = datetime.utcnow().isoformat()
-        
-        save_checkpoint(state)
-        
-        return state
-    
-    def stage_3_character_sheets(self, state: OpusState) -> OpusState:
-        """Generate character sheets (structured)."""
-        user_prompt = f"""Create character sheets for all major characters in this story.
-
-For each character, provide:
-- Name
-- Role (protagonist, antagonist, love interest, mentor, etc.)
-- Age and physical description
-- Background/history (2-3 sentences)
-- Want (external goal)
-- Need (internal growth)
-- Fear
-- Secret (if any)
-- Character arc (how do they change?)
-
-## Story outline:
-{state.prewriting.one_paragraph}
-
-## Task:
-Create comprehensive character sheets. Return as a list with each character clearly defined.
-"""
-        
-        import asyncio
-        result = asyncio.run(self.character_lead.execute(
-            {"characters": [], "raw_content": state.prewriting.one_paragraph},
-            {},
-        ))
-        
-        # Parse characters from result (basic parsing - could be improved)
-        text = result.output if isinstance(result.output, str) else str(result.output)
-        
-        # Extract characters (simplified - in production would use better parsing)
-        characters = self._parse_characters(text)
-        
-        state.prewriting.characters = characters
-        state.prewriting.framework_used = self.framework
-        state.validation_errors = validate_character_sheets(state)
-        state.stage = Stage.CHARACTER_SHEETS
-        state.progress = get_progress(Stage.CHARACTER_SHEETS)
-        state.last_updated = datetime.utcnow().isoformat()
-        
-        save_checkpoint(state)
-        
-        return state
-    
-    def stage_4_four_page_outline(self, state: OpusState) -> OpusState:
-        """Generate four-page outline."""
-        framework_prompt = get_framework_prompt(StoryFramework(self.framework))
-        
-        user_prompt = f"""Expand this one-paragraph outline into a detailed four-page outline.
-
-For each major section, provide:
-- Multiple scenes
-- Character motivations
-- Plot developments
-- World details
-- Dialogue hooks
-
-## Current outline:
-{state.prewriting.one_paragraph}
-
-## Characters:
-{', '.join(c.name for c in state.prewriting.characters)}
-
-## Task:
-Write a comprehensive four-page outline covering the entire story.
-"""
-        
-        import asyncio
-        result = asyncio.run(self.architect.call_llm(framework_prompt, user_prompt))
-        
-        # Parse outline sections
-        state.prewriting.outline_sections = [s.strip() for s in result.split("\n\n") if s.strip()]
-        state.stage = Stage.FOUR_PAGE_OUTLINE
-        state.progress = get_progress(Stage.FOUR_PAGE_OUTLINE)
-        state.last_updated = datetime.utcnow().isoformat()
-        
-        save_checkpoint(state)
-        
-        return state
-    
-    def stage_5_character_charts(self, state: OpusState) -> OpusState:
-        """Generate detailed character charts."""
-        user_prompt = f"""Create detailed character charts for all major characters.
-
-For each character include:
-- Full backstory
-- Psychological profile
-- Speech patterns (with sample dialogue)
-- Character quirks
-- Relationships with other characters
-- How they appear to others vs. who they really are
-- Key scenes they're in
-
-## Characters (basic):
-{chr(10).join(f"- {c.name}: {c.role}" for c in state.prewriting.characters)}
-
-## Task:
-Write comprehensive, detailed character charts.
-"""
-        
-        import asyncio
-        result = asyncio.run(self.character_lead.execute(
-            {"characters": [], "raw_content": state.prewriting.one_paragraph},
-            {},
-        ))
-        
-        text = result.output if isinstance(result.output, str) else str(result.output)
-        
-        # Store character details
-        for char in state.prewriting.characters:
-            state.prewriting.character_details[char.name] = text
-        
-        state.stage = Stage.CHARACTER_CHARTS
-        state.progress = get_progress(Stage.CHARACTER_CHARTS)
-        state.last_updated = datetime.utcnow().isoformat()
-        
-        save_checkpoint(state)
-        
-        return state
-    
-    def stage_6_scene_list(self, state: OpusState) -> OpusState:
-        """Generate scene list (structured)."""
-        framework_prompt = get_framework_prompt(StoryFramework(self.framework))
-        
-        words_per_scene = 1500
-        num_scenes = max(10, self.target_word_count // words_per_scene)
-        
-        user_prompt = f"""Create a SCENE LIST for this story.
-
-For each scene, provide:
-- Scene name/number
-- What happens (brief description)
-- POV character
-- Location
-- Purpose (advances plot? reveals character?)
-
-Target: {num_scenes} scenes
-
-## Outline:
-{chr(10).join(state.prewriting.outline_sections[:3])}
-
-## Characters:
-{', '.join(c.name for c in state.prewriting.characters)}
-
-## Task:
-Create a comprehensive scene list.
-"""
-        
-        import asyncio
-        result = asyncio.run(self.architect.call_llm(framework_prompt, user_prompt))
-        
-        # Parse scenes
-        scenes = self._parse_scenes(result)
-        state.prewriting.scene_list = scenes
-        
-        # Also create chapter plans
-        num_chapters = max(3, self.target_word_count // 3000)
-        state.prewriting.chapter_plans = self._create_chapter_plans(num_chapters, scenes)
-        
-        state.validation_errors = validate_scene_list(state)
-        state.stage = Stage.SCENE_LIST
-        state.progress = get_progress(Stage.SCENE_LIST)
-        state.last_updated = datetime.utcnow().isoformat()
-        
-        save_checkpoint(state)
-        
-        return state
-    
-    def stage_7_scene_descriptions(self, state: OpusState) -> OpusState:
-        """Generate scene descriptions."""
-        user_prompt = f"""Expand key scenes into detailed descriptions.
-
-For each key scene, provide:
-- Opening beat
-- Key dialogue points
-- Conflict moment
-- Turning point
-- Closing beat
-
-## Scene list:
-{chr(10).join(f"- {s.name}: {s.description}" for s in state.prewriting.scene_list[:10])}
-
-## Task:
-Write detailed descriptions for at least 10 key scenes.
-"""
-        
-        import asyncio
-        result = asyncio.run(self.architect.call_llm(
-            "You are an expert story architect. Create vivid scene descriptions.",
-            user_prompt,
-        ))
-        
-        # Parse into dict
-        state.prewriting.scene_descriptions = self._parse_scene_descriptions(result)
-        
-        state.stage = Stage.SCENE_DESCRIPTIONS
-        state.progress = get_progress(Stage.SCENE_DESCRIPTIONS)
-        state.last_updated = datetime.utcnow().isoformat()
-        
-        save_checkpoint(state)
-        
-        return state
-    
-    def create_style_guide(self, state: OpusState) -> OpusState:
-        """Create style guide."""
-        user_prompt = f"""Create a voice/style guide for this story.
-
-- Genre: {self.genre}
-- Target audience: adult readers
-
-## One sentence:
-{state.prewriting.one_sentence}
-
-## Task:
-Create a comprehensive style guide.
-"""
-        
-        import asyncio
-        result = asyncio.run(self.voice.execute(
-            {"genre": self.genre, "tone": "neutral", "target_audience": "adult readers"},
-            {},
-        ))
-        
-        state.style_guide = result.output if isinstance(result.output, str) else str(result.output)
-        
-        state.stage = Stage.STYLE_GUIDE
-        state.progress = get_progress(Stage.STYLE_GUIDE)
-        state.last_updated = datetime.utcnow().isoformat()
-        
-        save_checkpoint(state)
-        
-        return state
-    
-    # Helper parsing functions
-    
-    def _parse_characters(self, text: str) -> list[Character]:
-        """Parse characters from LLM output."""
-        characters = []
-        lines = text.split("\n")
-        current_char = {}
-        
-        for line in lines:
-            line = line.strip()
-            if not line:
-                continue
-            
-            # Simple parsing - look for patterns
-            lower = line.lower()
-            if "name:" in lower or (line and line[0].isupper() and len(line) < 30):
-                if current_char and "name" in current_char:
-                    characters.append(Character(**current_char))
-                current_char = {"name": line.split(":")[-1].strip() if ":" in line else line}
-            elif "role:" in lower:
-                current_char["role"] = line.split(":")[-1].strip()
-            elif "want:" in lower:
-                current_char["want"] = line.split(":")[-1].strip()
-            elif "need:" in lower:
-                current_char["need"] = line.split(":")[-1].strip()
-            elif "fear:" in lower:
-                current_char["fear"] = line.split(":")[-1].strip()
-            elif "arc:" in lower:
-                current_char["arc"] = line.split(":")[-1].strip()
-        
-        # Add last character
-        if current_char and "name" in current_char:
-            characters.append(Character(**current_char))
-        
-        # Ensure at least one character
-        if not characters:
-            characters.append(Character(
-                name="Protagonist",
-                role="protagonist",
-                description="Main character",
-                want="Complete the quest",
-                need="Learn to trust others",
-                fear="Failure",
-                arc="Grows from isolated to connected",
-            ))
-        
-        return characters
-    
-    def _parse_scenes(self, text: str) -> list[PlotBeat]:
-        """Parse scenes from LLM output."""
-        scenes = []
-        lines = text.split("\n")
-        
-        for i, line in enumerate(lines):
-            line = line.strip()
-            if not line:
-                continue
-            
-            # Extract scene info
-            parts = line.split("-", 1)
-            if len(parts) > 1:
-                scenes.append(PlotBeat(
-                    name=f"Scene {i+1}",
-                    description=parts[1].strip()[:200],
-                ))
-        
-        # Ensure minimum scenes
-        if not scenes:
-            scenes = [PlotBeat(name=f"Scene {i+1}", description=f"Story beat {i+1}") 
-                      for i in range(10)]
-        
-        return scenes[:20]  # Limit to 20
-    
-    def _parse_scene_descriptions(self, text: str) -> dict[str, str]:
-        """Parse scene descriptions from LLM output."""
-        descriptions = {}
-        sections = text.split("\n\n")
-        
-        for i, section in enumerate(sections):
-            if section.strip():
-                descriptions[f"scene_{i+1}"] = section.strip()[:500]
-        
-        return descriptions
-    
-    def _create_chapter_plans(self, num_chapters: int, scenes: list[PlotBeat]) -> list[ChapterPlan]:
-        """Create chapter plans from scenes."""
-        scenes_per_chapter = max(1, len(scenes) // num_chapters)
-        plans = []
-        
-        for i in range(num_chapters):
-            start_idx = i * scenes_per_chapter
-            end_idx = min(start_idx + scenes_per_chapter, len(scenes))
-            chapter_scenes = scenes[start_idx:end_idx] if scenes else []
-            
-            plans.append(ChapterPlan(
-                chapter_number=i + 1,
-                title=f"Chapter {i + 1}",
-                summary=f"Chapter {i + 1} with {len(chapter_scenes)} scenes",
-                word_count_target=self.target_word_count // num_chapters,
-                beats=[s.name for s in chapter_scenes],
-            ))
-        
-        return plans
-
-
-# Simplified workflow runner (would use actual LangGraph in production)
-
-def run_workflow(
-    seed_concept: str,
-    framework: str = "snowflake",
-    genre: str = "general",
-    target_word_count: int = 80000,
-    api_key: Optional[str] = None,
-    checkpoint_id: Optional[str] = None,
-) -> OpusState:
-    """Run the complete workflow.
-    
-    In production, this would use actual LangGraph graph.walk()
-    For now, uses sequential execution with checkpoints.
-    """
-    
-    # Try to load checkpoint
-    if checkpoint_id:
-        state = load_checkpoint(checkpoint_id)
-        if state:
-            print(f"📂 Loaded checkpoint: {state.stage}")
-    else:
-        state = None
-    
-    # Create workflow
-    workflow = OpusWorkflow(
-        framework=framework,
-        genre=genre,
-        target_word_count=target_word_count,
-        api_key=api_key,
-    )
-    
-    # Run stages in order (skipping completed)
-    if not state or state.stage == Stage.SEED:
-        state = create_initial_state(seed_concept, framework, genre, target_word_count)
-        state = workflow.stage_1_one_sentence(state)
-    
-    if state.stage == Stage.ONE_SENTENCE:
-        state = workflow.stage_2_one_paragraph(state)
-    
-    if state.stage == Stage.ONE_PARAGRAPH:
-        state = workflow.stage_3_character_sheets(state)
-    
-    if state.stage == Stage.CHARACTER_SHEETS:
-        state = workflow.stage_4_four_page_outline(state)
-    
-    if state.stage == Stage.FOUR_PAGE_OUTLINE:
-        state = workflow.stage_5_character_charts(state)
-    
-    if state.stage == Stage.CHARACTER_CHARTS:
-        state = workflow.stage_6_scene_list(state)
-    
-    if state.stage == Stage.SCENE_LIST:
-        state = workflow.stage_7_scene_descriptions(state)
-    
-    if state.stage == Stage.SCENE_DESCRIPTIONS:
-        state = workflow.create_style_guide(state)
-    
-    state.stage = Stage.COMPLETE
-    state.progress = 1.0
-    save_checkpoint(state, checkpoint_id or "final")
-    
-    return state