Merge: Team 2 - Agent/Workflow

opus_orchestrator/autogen_critique.py

@@ -240,16 +240,58 @@ End with a final verdict: APPROVED, MINOR_REVISIONS, or MAJOR_REVISIONS.
             # Check if approved
             if critique["approved"]:
                 print(f"   ✅ Chapter {chapter_num} approved!")
-                return critique
+                return {
+                    **critique,
+                    "revised_content": current_content,
+                }
             
-            # If not approved and have more iterations, continue
+            # Not approved - get revision suggestions and apply them
             if iteration < max_iterations:
-                print(f"   📝 Score: {critique['overall_score']:.2f} - continuing...")
-                # In production: pass feedback to writer agent for revision
+                print(f"   📝 Score: {critique['overall_score']:.2f} - applying revisions...")
+                
+                # Use the Writer agent to revise based on critique
+                revision_suggestions = critique.get("summary", "")[:2000]
+                
+                try:
+                    # Request revision from Writer agent
+                    revision_request = f"""Revise Chapter {chapter_num} based on critique feedback.
+
+## Current Chapter Content:
+{current_content[:3000]}...
+
+## Critique Feedback:
+{revision_suggestions}
+
+## Your Task:
+Revise the chapter to address the weaknesses identified in the critique.
+Preserve the strengths. Improve the story, pacing, and prose.
+"""
+                    # Use the writer agent to revise
+                    revision_result = self.agents["writer"].initiate_chat(
+                        self.manager,
+                        message=revision_request,
+                        summary_method="reflection_with_llm",
+                    )
+                    
+                    # Extract revised content from the chat
+                    if hasattr(revision_result, 'chat_history'):
+                        # Get the last response as revised content
+                        revised = revision_result.chat_history[-1].get('content', '') if revision_result.chat_history else current_content
+                        if revised and len(revised) > 100:
+                            current_content = revised
+                            print(f"   ✏️  Revision applied, new length: {len(current_content)} chars")
+                        else:
+                            print(f"   ⚠️  No valid revision received, keeping current content")
+                    
+                except Exception as e:
+                    print(f"   ⚠️  Revision failed: {e}, continuing with current content")
         
-        # Return last critique
+        # Return last critique with final content
         print(f"   ⚠️  Max iterations reached")
-        return critique
+        return {
+            **critique,
+            "revised_content": current_content,
+        }
 
 
 def create_critique_crew(

opus_orchestrator/crews/base_crew.py

@@ -18,30 +18,34 @@ def get_crewai_llm(provider: str = "openai", model: str = "gpt-4o") -> LLM:
     """Get a CrewAI LLM instance.
     
     Args:
-        provider: LLM provider (openai, anthropic, etc.)
+        provider: LLM provider (openai, anthropic, minimax)
         model: Model name
         
     Returns:
         Configured CrewAI LLM
     """
-    api_key = os.environ.get("OPENAI_API_KEY")
-    
+    # Get API key based on provider
     if provider == "openai":
-        return LLM(
-            model="openai/" + model,
-            api_key=api_key,
-        )
+        api_key = os.environ.get("OPENAI_API_KEY")
+        model_name = f"openai/{model}"
     elif provider == "anthropic":
-        return LLM(
-            model="anthropic/" + model,
-            api_key=os.environ.get("ANTHROPIC_API_KEY"),
-        )
+        api_key = os.environ.get("ANTHROPIC_API_KEY")
+        model_name = f"anthropic/{model}"
+    elif provider == "minimax":
+        api_key = os.environ.get("MINIMAX_API_KEY")
+        # MiniMax model format
+        model_name = f"minimax/{model}"
     else:
-        # Default to OpenAI
-        return LLM(
-            model="openai/gpt-4o",
-            api_key=api_key,
-        )
+        # Unknown provider - raise error instead of silently using OpenAI
+        raise ValueError(f"Unknown LLM provider: {provider}. Use: openai, anthropic, or minimax")
+    
+    if not api_key:
+        raise ValueError(f"API key not found for provider: {provider}")
+    
+    return LLM(
+        model=model_name,
+        api_key=api_key,
+    )
 
 
 class OpusCrew: