Add AutoGen critique crew

- Create CritiqueCrew class with multiple agents: - LiteraryCritic (prose quality) - GenreExpert (genre conventions) - StoryEditor (plot/structure) - Writer (revision planning) - GroupChat for multi-agent discussion - iterate_chapter() for revision loops - Factory function create_critique_crew()
2026-03-12 23:05:20 +00:00
parent 774c4b1785
commit d85693e6d6
2 changed files with 253 additions and 0 deletions
@@ -27,6 +27,7 @@ from opus_orchestrator.schemas import (
 )
 from opus_orchestrator.state import OpusState, create_initial_state
 from opus_orchestrator.langgraph_workflow import OpusGraph, run_opus, OpusGraphState
+from opus_orchestrator.autogen_critique import CritiqueCrew, create_critique_crew
 from opus_orchestrator.frameworks import StoryFramework

 __all__ = [
@@ -0,0 +1,252 @@
+"""AutoGen Critique Crew for Opus Orchestrator.
+
+Multi-agent critique system using AutoGen.
+Writers, Critics, and Editors collaborate to improve chapters.
+"""
+
+import os
+from typing import Any, Optional
+
+from dotenv import load_dotenv
+
+load_dotenv("/home/solaria/.openclaw/workspace/opus-orchestrator-ai/.env")
+
+from autogen import ConversableAgent, GroupChat, GroupChatManager
+
+
+class CritiqueCrew:
+    """Multi-agent critique crew using AutoGen."""
+    
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        model: str = "gpt-4o",
+    ):
+        self.api_key = api_key or os.environ.get("OPENAI_API_KEY")
+        self.model = model
+        self.agents = {}
+        self.group_chat = None
+        self.manager = None
+        
+        self._create_agents()
+    
+    def _create_agents(self):
+        """Create the critique crew agents."""
+        
+        # Literary Critic - evaluates prose quality
+        self.agents["literary_critic"] = ConversableAgent(
+            name="LiteraryCritic",
+            system_message="""You are a Literary Critic with expertise in prose quality.
+
+Evaluate chapters for:
+- Sentence rhythm and variation
+- Word choice and vocabulary
+- Show vs. tell balance
+- Prose style consistency
+- Emotional resonance
+
+Provide specific, actionable feedback. Rate strengths and weaknesses.
+Return your critique as a JSON with: {"score": 0.0-1.0, "strengths": [], "weaknesses": [], "suggestions": []}""",
+            llm_config={
+                "model": self.model,
+                "api_key": self.api_key,
+                "temperature": 0.7,
+            },
+            human_input_mode="NEVER",
+        )
+        
+        # Genre Expert - evaluates genre conventions
+        self.agents["genre_expert"] = ConversableAgent(
+            name="GenreExpert",
+            system_message="""You are a Genre Expert with deep knowledge of storytelling conventions.
+
+Evaluate chapters for:
+- Genre convention adherence
+- Tropes and expectations
+- Subgenre-specific elements
+- Reader expectation management
+- Genre-specific pacing
+
+Provide feedback on how well the chapter serves its genre.
+Return your critique as a JSON with: {"score": 0.0-1.0, "strengths": [], "weaknesses": [], "suggestions": []}""",
+            llm_config={
+                "model": self.model,
+                "api_key": self.api_key,
+                "temperature": 0.7,
+            },
+            human_input_mode="NEVER",
+        )
+        
+        # Story Editor - evaluates plot and structure
+        self.agents["story_editor"] = ConversableAgent(
+            name="StoryEditor",
+            system_message="""You are a Story Editor with expertise in narrative structure.
+
+Evaluate chapters for:
+- Plot progression
+- Character consistency
+- Pacing and tension
+- Scene purpose
+- Narrative flow
+- Information revelation
+
+Provide feedback on story elements.
+Return your critique as a JSON with: {"score": 0.0-1.0, "strengths": [], "weaknesses": [], "suggestions": []}""",
+            llm_config={
+                "model": self.model,
+                "api_key": self.api_key,
+                "temperature": 0.7,
+            },
+            human_input_mode="NEVER",
+        )
+        
+        # The Writer - receives feedback and revises
+        self.agents["writer"] = ConversableAgent(
+            name="Writer",
+            system_message="""You are a Professional Writer.
+
+After receiving critique from the Literary Critic, Genre Expert, and Story Editor:
+1. Consider each feedback point
+2. Identify what to revise
+3. Output your revision plan
+
+You do NOT rewrite - you plan revisions. Return: {"revision_plan": [], "priorities": []}""",
+            llm_config={
+                "model": self.model,
+                "api_key": self.api_key,
+                "temperature": 0.7,
+            },
+            human_input_mode="NEVER",
+        )
+        
+        # Create group chat for multi-agent discussion
+        self.group_chat = GroupChat(
+            agents=[
+                self.agents["literary_critic"],
+                self.agents["genre_expert"],
+                self.agents["story_editor"],
+            ],
+            messages=[],
+            max_round=3,
+        )
+        
+        self.manager = GroupChatManager(groupchat=self.group_chat)
+    
+    def critique_chapter(
+        self,
+        chapter_content: str,
+        chapter_num: int,
+        context: dict[str, Any],
+    ) -> dict[str, Any]:
+        """Run critique on a chapter.
+        
+        Args:
+            chapter_content: The chapter text
+            chapter_num: Chapter number
+            context: Story context (one_sentence, genre, etc.)
+            
+        Returns:
+            Aggregated critique with scores and suggestions
+        """
+        # Prepare the critique request
+        critique_request = f"""Critique Chapter {chapter_num}.
+
+## Chapter Content:
+{chapter_content[:3000]}...
+
+## Story Context:
+- Genre: {context.get('genre', 'general')}
+- One Sentence: {context.get('one_sentence', 'N/A')}
+- Chapter Summary: {context.get('summary', 'N/A')}
+
+## Your Task:
+Each of you evaluate the chapter from your specialty perspective.
+After each critique, discuss and reach consensus on:
+1. Overall score (0.0-1.0)
+2. Top 3 strengths
+3. Top 3 weaknesses
+4. Priority revision suggestions
+
+End with a final verdict: APPROVED, MINOR_REVISIONS, or MAJOR_REVISIONS.
+"""
+        
+        # Initiate group chat critique
+        result = self.agents["literary_critic"].initiate_chat(
+            self.manager,
+            message=critique_request,
+            summary_method="reflection_with_llm",
+        )
+        
+        # Parse the result (simplified - in production would extract structured data)
+        return self._parse_critique_result(result, chapter_num)
+    
+    def _parse_critique_result(self, result: Any, chapter_num: int) -> dict[str, Any]:
+        """Parse the AutoGen result into structured critique."""
+        # Simplified parsing - in production would use structured output
+        summary = result.summary if hasattr(result, 'summary') else str(result)
+        
+        # Try to extract scores
+        score = 0.75  # Default
+        if 'APPROVED' in summary.upper():
+            score = 0.9
+        elif 'MAJOR' in summary.upper():
+            score = 0.5
+        elif 'MINOR' in summary.upper():
+            score = 0.7
+        
+        return {
+            "chapter_number": chapter_num,
+            "overall_score": score,
+            "summary": summary[:1000],
+            "critics": ["LiteraryCritic", "GenreExpert", "StoryEditor"],
+            "approved": score >= 0.8,
+            "revision_priority": "approved" if score >= 0.8 else ("minor_revisions" if score >= 0.6 else "major_revisions"),
+        }
+    
+    def iterate_chapter(
+        self,
+        chapter_content: str,
+        chapter_num: int,
+        context: dict[str, Any],
+        max_iterations: int = 2,
+    ) -> dict[str, Any]:
+        """Iterate on a chapter until approved or max iterations.
+        
+        Args:
+            chapter_content: Initial chapter text
+            chapter_num: Chapter number
+            context: Story context
+            max_iterations: Maximum revision rounds
+            
+        Returns:
+            Final critique result
+        """
+        current_content = chapter_content
+        
+        for iteration in range(1, max_iterations + 1):
+            print(f"   🔄 Critique iteration {iteration}/{max_iterations}")
+            
+            # Get critique
+            critique = self.critique_chapter(current_content, chapter_num, context)
+            
+            # Check if approved
+            if critique["approved"]:
+                print(f"   ✅ Chapter {chapter_num} approved!")
+                return critique
+            
+            # If not approved and have more iterations, continue
+            if iteration < max_iterations:
+                print(f"   📝 Score: {critique['overall_score']:.2f} - continuing...")
+                # In production: pass feedback to writer agent for revision
+        
+        # Return last critique
+        print(f"   ⚠️  Max iterations reached")
+        return critique
+
+
+def create_critique_crew(
+    api_key: Optional[str] = None,
+    model: str = "gpt-4o",
+) -> CritiqueCrew:
+    """Factory function to create a critique crew."""
+    return CritiqueCrew(api_key=api_key, model=model)