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ankigen_core/agents/enhancers.py

@@ -54,7 +54,7 @@ class RevisionAgent(BaseAgentWrapper):
             )
 
             # Execute revision
-            response = await self.execute(user_input)
+            response, usage = await self.execute(user_input)
 
             # Parse revised card
             revised_card = self._parse_revised_card(response, card)
@@ -188,7 +188,7 @@ class EnhancementAgent(BaseAgentWrapper):
             user_input = self._build_enhancement_prompt(card, enhancement_targets)
 
             # Execute enhancement
-            response = await self.execute(user_input)
+            response, usage = await self.execute(user_input)
 
             # Parse enhanced card
             enhanced_card = self._parse_enhanced_card(response, card)

ankigen_core/agents/judges.py

@@ -57,8 +57,8 @@ class ContentAccuracyJudge(BaseAgentWrapper):
         try:
             user_input = f"""Evaluate this flashcard for factual accuracy:
 
-Front: {card.front.content}
-Back: {card.back.content}
+Front: {card.front.question}
+Back: {card.back.answer}
 
 Assess:
 1. Factual correctness
@@ -153,7 +153,7 @@ cognitive levels, and educational best practices.""",
 
         try:
             user_input = self._build_judgment_prompt(card)
-            response = await self.execute(user_input)
+            response, usage = await self.execute(user_input)
 
             decision_data = (
                 json.loads(response) if isinstance(response, str) else response
@@ -263,7 +263,7 @@ to the target audience.""",
 
         try:
             user_input = self._build_judgment_prompt(card)
-            response = await self.execute(user_input)
+            response, usage = await self.execute(user_input)
 
             decision_data = (
                 json.loads(response) if isinstance(response, str) else response
@@ -378,7 +378,7 @@ Verify code syntax, best practices, security considerations, and technical corre
                 )
 
             user_input = self._build_judgment_prompt(card)
-            response = await self.execute(user_input)
+            response, usage = await self.execute(user_input)
 
             decision_data = (
                 json.loads(response) if isinstance(response, str) else response
@@ -504,7 +504,7 @@ and maintain consistent quality standards.""",
 
         try:
             user_input = self._build_judgment_prompt(card)
-            response = await self.execute(user_input)
+            response, usage = await self.execute(user_input)
 
             decision_data = (
                 json.loads(response) if isinstance(response, str) else response

app.py

@@ -306,10 +306,6 @@ def create_ankigen_interface():
                                 label="Generate Cloze Cards (Experimental)",
                                 value=False,
                             )
-                            llm_judge_checkbox = gr.Checkbox(
-                                label="Use LLM Judge",
-                                value=False,
-                            )
 
                             # Agent System Controls (simplified since we're agent-only)
                             if AGENTS_AVAILABLE_APP:
@@ -666,7 +662,6 @@ def create_ankigen_interface():
                 cards_per_topic_val,
                 preference_prompt_val,
                 generate_cloze_checkbox_val,
-                llm_judge_checkbox_val,
                 agent_mode_val,
                 enable_subject_expert_val,
                 enable_generation_coordinator_val,
@@ -774,7 +769,6 @@ def create_ankigen_interface():
                     cards_per_topic_val,
                     preference_prompt_val,
                     generate_cloze_checkbox_val,
-                    llm_judge_checkbox_val,
                 )
                 # Expect 3-tuple return (dataframe, total_cards_html, token_usage_html)
 
@@ -791,7 +785,6 @@ def create_ankigen_interface():
                     cards_per_topic,
                     preference_prompt,
                     generate_cloze_checkbox,
-                    llm_judge_checkbox,
                     agent_mode_dropdown,
                     enable_subject_expert,
                     enable_generation_coordinator,