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ankigen_core/agents/base.py

@@ -36,7 +36,7 @@ class AgentConfig:
 
     name: str
     instructions: str
-    model: str = "gpt-4.1"
+    model: str = "gpt-5.1"
     temperature: float = 0.7
     max_tokens: Optional[int] = None
     timeout: float = 30.0
@@ -67,8 +67,19 @@ class BaseAgentWrapper:
 
             set_default_openai_client(self.openai_client, use_for_tracing=False)
 
-            # Create model settings with temperature
-            model_settings = ModelSettings(temperature=self.config.temperature)
+            # Create model settings with temperature and GPT-5.1 reasoning support
+            model_settings_kwargs = {"temperature": self.config.temperature}
+
+            # GPT-5.1 (not chat-latest) supports reasoning_effort
+            if (
+                self.config.model.startswith("gpt-5")
+                and "chat-latest" not in self.config.model
+            ):
+                from openai.types.shared import Reasoning
+
+                model_settings_kwargs["reasoning"] = Reasoning(effort="none")
+
+            model_settings = ModelSettings(**model_settings_kwargs)
 
             # Use clean instructions without JSON formatting hacks
             clean_instructions = self.config.instructions

ankigen_core/agents/config.py

@@ -91,7 +91,7 @@ class AgentConfigManager:
 
             # Default models for each agent type
             default_models = {
-                "subject_expert_model": "gpt-4.1",
+                "subject_expert_model": "gpt-5.1",
             }
 
             # Simple mapping: agent_name -> agent_name_model
@@ -111,7 +111,7 @@ class AgentConfigManager:
                 config = AgentConfig(
                     name=agent_data.get("name", agent_name),
                     instructions=agent_data.get("instructions", ""),
-                    model=agent_data.get("model", "gpt-4"),
+                    model=agent_data.get("model", "gpt-5.1"),
                     temperature=agent_data.get("temperature", 0.7),
                     max_tokens=agent_data.get("max_tokens"),
                     timeout=agent_data.get("timeout", 30.0),
@@ -176,7 +176,7 @@ class AgentConfigManager:
                 config = AgentConfig(
                     name=agent_name,
                     instructions=agent_data.get("instructions", ""),
-                    model=agent_data.get("model", "gpt-4.1"),
+                    model=agent_data.get("model", "gpt-5.1"),
                     temperature=agent_data.get("temperature", 0.7),
                     max_tokens=agent_data.get("max_tokens"),
                     timeout=agent_data.get("timeout", 30.0),

ankigen_core/agents/schemas.py

@@ -167,7 +167,7 @@ class AutoConfigSchema(BaseModel):
     )
     model_choice: str = Field(
         ...,
-        description="Recommended model: 'gpt-4.1' for complex topics, 'gpt-4.1-nano' for simpler topics",
+        description="Recommended model: 'gpt-5.1' for all tasks (uses reasoning_effort=none for speed)",
     )
 
     # Analysis metadata

ankigen_core/auto_config.py

@@ -6,6 +6,7 @@ from openai import AsyncOpenAI
 from ankigen_core.logging import logger
 from ankigen_core.context7 import Context7Client
 from ankigen_core.agents.schemas import AutoConfigSchema
+from ankigen_core.llm_interface import structured_agent_call
 
 
 class AutoConfigService:
@@ -70,20 +71,15 @@ Extract:
 Provide a brief rationale for your choices."""
 
         try:
-            response = await openai_client.beta.chat.completions.parse(
-                model="gpt-4.1-nano",  # Use nano for this analysis task
-                messages=[
-                    {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": user_prompt},
-                ],
-                response_format=AutoConfigSchema,
+            config = await structured_agent_call(
+                openai_client=openai_client,
+                model="gpt-5.1",
+                instructions=system_prompt,
+                user_input=user_prompt,
+                output_type=AutoConfigSchema,
                 temperature=0.3,  # Lower temperature for more consistent analysis
             )
 
-            if not response.choices or not response.choices[0].message.parsed:
-                raise ValueError("Failed to get valid response from OpenAI")
-
-            config = response.choices[0].message.parsed
             logger.info(
                 f"Subject analysis complete: library='{config.library_search_term}', "
                 f"topics={config.topic_number}, cards/topic={config.cards_per_topic}"
@@ -100,7 +96,7 @@ Provide a brief rationale for your choices."""
                 cards_per_topic=8,
                 learning_preferences="Focus on fundamental concepts and core principles with practical examples",
                 generate_cloze=False,
-                model_choice="gpt-4.1-nano",
+                model_choice="gpt-5.1",
                 subject_type="concepts",
                 scope="medium",
                 rationale="Using default settings due to analysis error",

ankigen_core/card_generator.py

@@ -29,15 +29,20 @@ logger.info("Agent system loaded successfully")
 
 # --- Constants --- (Moved from app.py)
 AVAILABLE_MODELS = [
+    {
+        "value": "gpt-5.1",
+        "label": "GPT-5.1 (Best Quality)",
+        "description": "Latest model with adaptive reasoning, 400K context",
+    },
     {
         "value": "gpt-4.1",
-        "label": "GPT-4.1 (Best Quality)",
-        "description": "Highest quality, large context window",
+        "label": "GPT-4.1 (Legacy)",
+        "description": "Previous generation, large context window",
     },
     {
         "value": "gpt-4.1-nano",
-        "label": "GPT-4.1 Nano (Ultra Fast)",
-        "description": "Ultra-fast and cost-effective",
+        "label": "GPT-4.1 Nano (Legacy Fast)",
+        "description": "Previous generation, ultra-fast",
     },
 ]
 

ankigen_core/cli.py

@@ -131,7 +131,7 @@ async def generate_cards_from_config(
             generation_mode="subject",
             source_text="",
             url_input="",
-            model_name=config.get("model_choice", "gpt-4.1-nano"),
+            model_name=config.get("model_choice", "gpt-5.1"),
             topic_number=config.get("topic_number", 3),
             cards_per_topic=config.get("cards_per_topic", 5),
             preference_prompt=config.get("preference_prompt", ""),
@@ -207,7 +207,10 @@ def export_cards(
 )
 @click.option(
     "--model",
-    type=click.Choice(["gpt-4.1", "gpt-4.1-nano"], case_sensitive=False),
+    type=click.Choice(
+        ["gpt-5.1", "gpt-4.1", "gpt-4.1-nano"],
+        case_sensitive=False,
+    ),
     help="Model to use for generation (auto-selected if not specified)",
 )
 @click.option(

ankigen_core/exporters.py

@@ -84,7 +84,253 @@ ANKI_CLOZE_MODEL_NAME = "AnkiGen Cloze"
 DEFAULT_BASIC_MODEL_ID = random.randrange(1 << 30, 1 << 31)
 DEFAULT_CLOZE_MODEL_ID = random.randrange(1 << 30, 1 << 31)
 
-# --- Full Model Definitions with CSS (Restored) ---
+# --- Shared CSS with dark mode support ---
+CARD_CSS = """
+    /* CSS Variables - Light Mode (default) */
+    .card {
+        --bg-card: #ffffff;
+        --bg-answer: #f0f9ff;
+        --bg-explanation: #f0fdf4;
+        --bg-example: #fefce8;
+        --bg-back-extra: #eef2ff;
+        --bg-prereq: #f8fafc;
+        --bg-code: #2d2d2d;
+
+        --text-primary: #1a1a1a;
+        --text-secondary: #4b5563;
+        --text-muted: #666666;
+        --text-heading: #1f2937;
+        --text-code: #f8f8f2;
+
+        --accent-blue: #2563eb;
+        --accent-blue-light: #60a5fa;
+        --accent-green: #4ade80;
+        --accent-yellow: #facc15;
+        --accent-indigo: #818cf8;
+        --accent-red: #ef4444;
+
+        --border-light: #e5e7eb;
+        --border-dashed: #cbd5e1;
+
+        --shadow: rgba(0, 0, 0, 0.05);
+    }
+
+    /* Dark Mode Overrides */
+    .nightMode .card,
+    .night_mode .card {
+        --bg-card: #1e1e1e;
+        --bg-answer: #1e293b;
+        --bg-explanation: #14291a;
+        --bg-example: #292518;
+        --bg-back-extra: #1e1b2e;
+        --bg-prereq: #262626;
+        --bg-code: #0d0d0d;
+
+        --text-primary: #e4e4e7;
+        --text-secondary: #a1a1aa;
+        --text-muted: #9ca3af;
+        --text-heading: #f4f4f5;
+        --text-code: #f8f8f2;
+
+        --accent-blue: #60a5fa;
+        --accent-blue-light: #93c5fd;
+        --accent-green: #4ade80;
+        --accent-yellow: #fde047;
+        --accent-indigo: #a5b4fc;
+        --accent-red: #f87171;
+
+        --border-light: #3f3f46;
+        --border-dashed: #52525b;
+
+        --shadow: rgba(0, 0, 0, 0.3);
+    }
+
+    /* Base styles */
+    .card {
+        font-family: 'Inter', system-ui, -apple-system, sans-serif;
+        font-size: 16px;
+        line-height: 1.6;
+        color: var(--text-primary);
+        max-width: 800px;
+        margin: 0 auto;
+        padding: 20px;
+        background: var(--bg-card);
+    }
+
+    @media (max-width: 768px) {
+        .card {
+            font-size: 14px;
+            padding: 15px;
+        }
+    }
+
+    /* Question side */
+    .question-side {
+        position: relative;
+        min-height: 200px;
+    }
+
+    .difficulty-indicator {
+        position: absolute;
+        top: 10px;
+        right: 10px;
+        width: 10px;
+        height: 10px;
+        border-radius: 50%;
+    }
+
+    .difficulty-indicator.beginner { background: var(--accent-green); }
+    .difficulty-indicator.intermediate { background: var(--accent-yellow); }
+    .difficulty-indicator.advanced { background: var(--accent-red); }
+
+    .question {
+        font-size: 1.3em;
+        font-weight: 600;
+        color: var(--accent-blue);
+        margin-bottom: 1.5em;
+    }
+
+    .prerequisites {
+        margin-top: 1em;
+        font-size: 0.9em;
+        color: var(--text-muted);
+    }
+
+    .prerequisites-toggle {
+        color: var(--accent-blue);
+        cursor: pointer;
+        text-decoration: underline;
+    }
+
+    .prerequisites-content {
+        display: none;
+        margin-top: 0.5em;
+        padding: 0.5em;
+        background: var(--bg-prereq);
+        border-radius: 4px;
+    }
+
+    .prerequisites.show .prerequisites-content {
+        display: block;
+    }
+
+    /* Answer side sections */
+    .answer-section,
+    .explanation-section,
+    .example-section,
+    .back-extra-section {
+        margin: 1.5em 0;
+        padding: 1.2em;
+        border-radius: 8px;
+        box-shadow: 0 2px 4px var(--shadow);
+    }
+
+    .answer-section {
+        background: var(--bg-answer);
+        border-left: 4px solid var(--accent-blue);
+    }
+
+    .back-extra-section {
+        background: var(--bg-back-extra);
+        border-left: 4px solid var(--accent-indigo);
+    }
+
+    .explanation-section {
+        background: var(--bg-explanation);
+        border-left: 4px solid var(--accent-green);
+    }
+
+    .example-section {
+        background: var(--bg-example);
+        border-left: 4px solid var(--accent-yellow);
+    }
+
+    .example-section pre {
+        background-color: var(--bg-code);
+        color: var(--text-code);
+        padding: 1em;
+        border-radius: 0.3em;
+        overflow-x: auto;
+        font-family: 'Consolas', 'Monaco', 'Menlo', monospace;
+        font-size: 0.9em;
+        line-height: 1.4;
+    }
+
+    .example-section code {
+        font-family: 'Consolas', 'Monaco', 'Menlo', monospace;
+    }
+
+    .metadata-section {
+        margin-top: 2em;
+        padding-top: 1em;
+        border-top: 1px solid var(--border-light);
+        font-size: 0.9em;
+        color: var(--text-secondary);
+    }
+
+    .metadata-section h3 {
+        font-size: 1em;
+        color: var(--text-heading);
+        margin-bottom: 0.5em;
+    }
+
+    .metadata-section > div {
+        margin-bottom: 0.8em;
+    }
+
+    .source-url a {
+        color: var(--accent-blue);
+        text-decoration: none;
+    }
+    .source-url a:hover {
+        text-decoration: underline;
+    }
+
+    /* Cloze deletion styles */
+    .cloze {
+        font-weight: bold;
+        color: var(--accent-blue);
+    }
+
+    /* General utility */
+    hr {
+        border: none;
+        border-top: 1px dashed var(--border-dashed);
+        margin: 1.5em 0;
+    }
+
+    /* Rich text field styling */
+    .field ul, .field ol {
+        margin-left: 1.5em;
+        padding-left: 0.5em;
+    }
+    .field li {
+        margin-bottom: 0.3em;
+    }
+
+    /* Responsive design */
+    @media (max-width: 640px) {
+        .answer-section,
+        .explanation-section,
+        .example-section,
+        .back-extra-section {
+            padding: 1em;
+            margin: 1em 0;
+        }
+    }
+
+    /* Animations */
+    @keyframes fadeIn {
+        from { opacity: 0; }
+        to { opacity: 1; }
+    }
+
+    .card {
+        animation: fadeIn 0.3s ease-in-out;
+    }
+"""
+
+# --- Full Model Definitions ---
 
 BASIC_MODEL = genanki.Model(
     DEFAULT_BASIC_MODEL_ID,  # Use the generated ID
@@ -167,188 +413,7 @@ BASIC_MODEL = genanki.Model(
             """,
         }
     ],
-    css="""
-        /* Base styles */
-        .card {
-            font-family: 'Inter', system-ui, -apple-system, sans-serif;
-            font-size: 16px;
-            line-height: 1.6;
-            color: #1a1a1a;
-            max-width: 800px;
-            margin: 0 auto;
-            padding: 20px;
-            background: #ffffff;
-        }
-
-        @media (max-width: 768px) {
-            .card {
-                font-size: 14px;
-                padding: 15px;
-            }
-        }
-
-        /* Question side */
-        .question-side {
-            position: relative;
-            min-height: 200px;
-        }
-
-        .difficulty-indicator {
-            position: absolute;
-            top: 10px;
-            right: 10px;
-            width: 10px;
-            height: 10px;
-            border-radius: 50%;
-        }
-
-        .difficulty-indicator.beginner { background: #4ade80; }
-        .difficulty-indicator.intermediate { background: #fbbf24; }
-        .difficulty-indicator.advanced { background: #ef4444; }
-
-        .question {
-            font-size: 1.3em;
-            font-weight: 600;
-            color: #2563eb;
-            margin-bottom: 1.5em;
-        }
-
-        .prerequisites {
-            margin-top: 1em;
-            font-size: 0.9em;
-            color: #666;
-        }
-
-        .prerequisites-toggle {
-            color: #2563eb;
-            cursor: pointer;
-            text-decoration: underline;
-        }
-
-        .prerequisites-content {
-            display: none;
-            margin-top: 0.5em;
-            padding: 0.5em;
-            background: #f8fafc;
-            border-radius: 4px;
-        }
-
-        .prerequisites.show .prerequisites-content {
-            display: block;
-        }
-
-        /* Answer side */
-        .answer-section,
-        .explanation-section,
-        .example-section {
-            margin: 1.5em 0;
-            padding: 1.2em;
-            border-radius: 8px;
-            box-shadow: 0 2px 4px rgba(0,0,0,0.05);
-        }
-
-        .answer-section {
-            background: #f0f9ff;
-            border-left: 4px solid #2563eb;
-        }
-
-        .explanation-section {
-            background: #f0fdf4;
-            border-left: 4px solid #4ade80;
-        }
-
-        .example-section {
-            background: #fefce8; /* Light yellow */
-            border-left: 4px solid #facc15; /* Yellow */
-        }
-        .example-section pre {
-            background-color: #2d2d2d; /* Darker background for code blocks */
-            color: #f8f8f2; /* Light text for contrast */
-            padding: 1em;
-            border-radius: 0.3em;
-            overflow-x: auto; /* Horizontal scroll for long lines */
-            font-family: 'Consolas', 'Monaco', 'Menlo', monospace;
-            font-size: 0.9em;
-            line-height: 1.4;
-        }
-
-        .example-section code {
-             font-family: 'Consolas', 'Monaco', 'Menlo', monospace;
-        }
-
-        .metadata-section {
-            margin-top: 2em;
-            padding-top: 1em;
-            border-top: 1px solid #e5e7eb; /* Light gray border */
-            font-size: 0.9em;
-            color: #4b5563; /* Cool gray */
-        }
-
-        .metadata-section h3 {
-            font-size: 1em;
-            color: #1f2937; /* Darker gray for headings */
-            margin-bottom: 0.5em;
-        }
-
-        .metadata-section > div {
-            margin-bottom: 0.8em;
-        }
-
-        .source-url a {
-            color: #2563eb;
-            text-decoration: none;
-        }
-        .source-url a:hover {
-            text-decoration: underline;
-        }
-
-        /* Styles for cloze deletion cards */
-        .cloze {
-            font-weight: bold;
-            color: blue;
-        }
-        .nightMode .cloze {
-            color: lightblue;
-        }
-
-        /* General utility */
-        hr {
-            border: none;
-            border-top: 1px dashed #cbd5e1; /* Light dashed line */
-            margin: 1.5em 0;
-        }
-
-        /* Rich text field styling (if Anki adds classes for these) */
-        .field ul, .field ol {
-            margin-left: 1.5em;
-            padding-left: 0.5em;
-        }
-        .field li {
-            margin-bottom: 0.3em;
-        }
-
-        /* Responsive design */
-        @media (max-width: 640px) {
-            .answer-section,
-            .explanation-section,
-            .example-section {
-                padding: 1em;
-                margin: 1em 0;
-            }
-        }
-
-        /* Animations */
-        @keyframes fadeIn {
-            from { opacity: 0; }
-            to { opacity: 1; }
-        }
-
-        .card {
-            animation: fadeIn 0.3s ease-in-out;
-        }
-    """,
-    # model_type=genanki.Model.BASIC, # This was still incorrect
-    # No model_type needed, defaults to Basic (0)
+    css=CARD_CSS,
 )
 
 CLOZE_MODEL = genanki.Model(
@@ -432,177 +497,8 @@ CLOZE_MODEL = genanki.Model(
             """,
         }
     ],
-    css="""
-        /* Base styles */
-        .card {
-            font-family: 'Inter', system-ui, -apple-system, sans-serif;
-            font-size: 16px;
-            line-height: 1.6;
-            color: #1a1a1a;
-            max-width: 800px;
-            margin: 0 auto;
-            padding: 20px;
-            background: #ffffff;
-        }
-
-        @media (max-width: 768px) {
-            .card {
-                font-size: 14px;
-                padding: 15px;
-            }
-        }
-
-        /* Question side */
-        .question-side {
-            position: relative;
-            min-height: 200px;
-        }
-
-        .difficulty-indicator {
-            position: absolute;
-            top: 10px;
-            right: 10px;
-            width: 10px;
-            height: 10px;
-            border-radius: 50%;
-        }
-
-        .difficulty-indicator.beginner { background: #4ade80; }
-        .difficulty-indicator.intermediate { background: #fbbf24; }
-        .difficulty-indicator.advanced { background: #ef4444; }
-
-        .question {
-            font-size: 1.3em;
-            font-weight: 600;
-            color: #2563eb;
-            margin-bottom: 1.5em;
-        }
-
-        .prerequisites {
-            margin-top: 1em;
-            font-size: 0.9em;
-            color: #666;
-        }
-
-        .prerequisites-toggle {
-            color: #2563eb;
-            cursor: pointer;
-            text-decoration: underline;
-        }
-
-        .prerequisites-content {
-            display: none;
-            margin-top: 0.5em;
-            padding: 0.5em;
-            background: #f8fafc;
-            border-radius: 4px;
-        }
-
-        .prerequisites.show .prerequisites-content {
-            display: block;
-        }
-
-        /* Answer side */
-        .answer-section,
-        .explanation-section,
-        .example-section {
-            margin: 1.5em 0;
-            padding: 1.2em;
-            border-radius: 8px;
-            box-shadow: 0 2px 4px rgba(0,0,0,0.05);
-        }
-
-        .answer-section { /* Shared with question for cloze, but can be general */
-            background: #f0f9ff;
-            border-left: 4px solid #2563eb;
-        }
-
-        .back-extra-section {
-            background: #eef2ff; /* A slightly different shade for additional info */
-            border-left: 4px solid #818cf8; /* Indigo variant */
-            margin: 1.5em 0;
-            padding: 1.2em;
-            border-radius: 8px;
-            box-shadow: 0 2px 4px rgba(0,0,0,0.05);
-        }
-
-        .explanation-section {
-            background: #f0fdf4;
-            border-left: 4px solid #4ade80;
-        }
-
-        .example-section {
-            background: #fefce8; /* Light yellow */
-            border-left: 4px solid #facc15; /* Yellow */
-        }
-        .example-section pre {
-            background-color: #2d2d2d; /* Darker background for code blocks */
-            color: #f8f8f2; /* Light text for contrast */
-            padding: 1em;
-            border-radius: 0.3em;
-            overflow-x: auto; /* Horizontal scroll for long lines */
-            font-family: 'Consolas', 'Monaco', 'Menlo', monospace;
-            font-size: 0.9em;
-            line-height: 1.4;
-        }
-
-        .example-section code {
-             font-family: 'Consolas', 'Monaco', 'Menlo', monospace;
-        }
-
-        .metadata-section {
-            margin-top: 2em;
-            padding-top: 1em;
-            border-top: 1px solid #e5e7eb; /* Light gray border */
-            font-size: 0.9em;
-            color: #4b5563; /* Cool gray */
-        }
-
-        .metadata-section h3 {
-            font-size: 1em;
-            color: #1f2937; /* Darker gray for headings */
-            margin-bottom: 0.5em;
-        }
-
-        .metadata-section > div {
-            margin-bottom: 0.8em;
-        }
-
-        .source-url a {
-            color: #2563eb;
-            text-decoration: none;
-        }
-        .source-url a:hover {
-            text-decoration: underline;
-        }
-
-        /* Styles for cloze deletion cards */
-        .cloze {
-            font-weight: bold;
-            color: blue;
-        }
-        .nightMode .cloze {
-            color: lightblue;
-        }
-
-        /* General utility */
-        hr {
-            border: none;
-            border-top: 1px dashed #cbd5e1; /* Light dashed line */
-            margin: 1.5em 0;
-        }
-
-        /* Rich text field styling (if Anki adds classes for these) */
-        .field ul, .field ol {
-            margin-left: 1.5em;
-            padding-left: 0.5em;
-        }
-        .field li {
-            margin-bottom: 0.3em;
-        }
-    """,
-    # model_type=genanki.Model.CLOZE, # This was still incorrect
-    model_type=1,  # Corrected to use integer 1 for Cloze
+    css=CARD_CSS,
+    model_type=1,  # Cloze model type
 )
 
 

ankigen_core/llm_interface.py

@@ -1,38 +1,31 @@
 # Module for OpenAI client management and API call logic
 
+import asyncio
+import time
+from typing import Callable, List, Optional, TypeVar
+
+import tiktoken
+from agents import Agent, ModelSettings, Runner, set_default_openai_client
 from openai import (
+    APIConnectionError,
+    APIStatusError,
     AsyncOpenAI,
     OpenAIError,
-    APIConnectionError,  # For more specific retry
-    RateLimitError,  # For more specific retry
-    APIStatusError,  # For retry on 5xx errors
-)  # Added OpenAIError for specific exception handling
-import json
-import time  # Added for process_crawled_pages later, but good to have
-from typing import List, Optional, Callable  # Added List, Optional, Callable
+    RateLimitError,
+)
+from pydantic import BaseModel
 from tenacity import (
     retry,
+    retry_if_exception_type,
     stop_after_attempt,
     wait_exponential,
-    retry_if_exception_type,
 )
-import asyncio  # Import asyncio for gather
-import tiktoken  # Added tiktoken
 
-# Imports from our new core modules
-from ankigen_core.logging import logger  # Updated to use the new logger
-from ankigen_core.utils import ResponseCache  # Removed get_logger
-from ankigen_core.models import (
-    CrawledPage,
-    Card,
-    CardFront,
-    CardBack,
-)  # Added CrawledPage, Card, CardFront, CardBack
-# We will need Pydantic models if response_format is a Pydantic model,
-# but for now, it's a dict like {"type": "json_object"}.
-# from ankigen_core.models import ... # Placeholder if needed later
+from ankigen_core.logging import logger
+from ankigen_core.models import Card, CardBack, CardFront, CrawledPage
+from ankigen_core.utils import ResponseCache
 
-# logger = get_logger() # Removed, using imported logger
+T = TypeVar("T", bound=BaseModel)
 
 
 class OpenAIClientManager:
@@ -121,106 +114,173 @@ class OpenAIClientManager:
                 self._client = None
 
 
-# Retry decorator for API calls - kept similar to original
-@retry(
-    stop=stop_after_attempt(3),
-    wait=wait_exponential(multiplier=1, min=4, max=10),
-    retry=retry_if_exception_type(
-        Exception
-    ),  # Consider refining this to specific network/API errors
-    before_sleep=lambda retry_state: logger.warning(
-        f"Retrying structured_output_completion (attempt {retry_state.attempt_number}) due to {retry_state.outcome.exception() if retry_state.outcome else 'unknown reason'}"
-    ),
-)
+# --- Agents SDK Utility ---
+
+
+async def structured_agent_call(
+    openai_client: AsyncOpenAI,
+    model: str,
+    instructions: str,
+    user_input: str,
+    output_type: type[T],
+    cache: Optional[ResponseCache] = None,
+    cache_key: Optional[str] = None,
+    temperature: float = 0.7,
+    timeout: float = 120.0,
+    retry_attempts: int = 3,
+) -> T:
+    """
+    Make a single-turn structured output call using the agents SDK.
+
+    This is a lightweight wrapper for simple structured output calls,
+    not intended for complex multi-agent workflows.
+
+    Args:
+        openai_client: AsyncOpenAI client instance
+        model: Model name (e.g., "gpt-5.1", "gpt-5.1-chat-latest")
+        instructions: System instructions for the agent
+        user_input: User prompt/input
+        output_type: Pydantic model class for structured output
+        cache: Optional ResponseCache instance
+        cache_key: Cache key (required if cache is provided)
+        temperature: Model temperature (default 0.7)
+        timeout: Request timeout in seconds (default 120)
+        retry_attempts: Number of retry attempts (default 3)
+
+    Returns:
+        Instance of output_type with the structured response
+    """
+    # 1. Check cache first
+    if cache and cache_key:
+        cached = cache.get(cache_key, model)
+        if cached is not None:
+            logger.info(f"Using cached response for model {model}")
+            # Reconstruct Pydantic model from cached dict
+            if isinstance(cached, dict):
+                return output_type.model_validate(cached)
+            return cached
+
+    # 2. Set up the OpenAI client for agents SDK
+    set_default_openai_client(openai_client, use_for_tracing=False)
+
+    # 3. Build model settings with GPT-5.1 reasoning support
+    model_settings_kwargs: dict = {"temperature": temperature}
+
+    # GPT-5.1 (not chat-latest) supports reasoning_effort
+    if model.startswith("gpt-5") and "chat-latest" not in model:
+        from openai.types.shared import Reasoning
+
+        model_settings_kwargs["reasoning"] = Reasoning(effort="none")
+
+    model_settings = ModelSettings(**model_settings_kwargs)
+
+    # 4. Create agent with structured output
+    agent = Agent(
+        name="structured_output_agent",
+        instructions=instructions,
+        model=model,
+        model_settings=model_settings,
+        output_type=output_type,
+    )
+
+    # 5. Execute with retry and timeout
+    last_error: Optional[Exception] = None
+    for attempt in range(retry_attempts):
+        try:
+            result = await asyncio.wait_for(
+                Runner.run(agent, user_input),
+                timeout=timeout,
+            )
+
+            # 6. Extract structured output
+            output = result.final_output
+
+            # 7. Cache successful result (as dict for serialization)
+            if cache and cache_key and output is not None:
+                if isinstance(output, BaseModel):
+                    cache.set(cache_key, model, output.model_dump())
+                else:
+                    cache.set(cache_key, model, output)
+
+            logger.debug(f"Successfully received response from model {model}")
+            return output
+
+        except asyncio.TimeoutError as e:
+            last_error = e
+            if attempt < retry_attempts - 1:
+                wait_time = 4 * (2**attempt)  # Exponential backoff
+                logger.warning(
+                    f"Agent timed out (attempt {attempt + 1}/{retry_attempts}), "
+                    f"retrying in {wait_time}s..."
+                )
+                await asyncio.sleep(wait_time)
+                continue
+            logger.error(f"Agent timed out after {retry_attempts} attempts")
+            raise
+        except Exception as e:
+            last_error = e
+            if attempt < retry_attempts - 1:
+                wait_time = 4 * (2**attempt)
+                logger.warning(
+                    f"Agent failed (attempt {attempt + 1}/{retry_attempts}): {e}, "
+                    f"retrying in {wait_time}s..."
+                )
+                await asyncio.sleep(wait_time)
+                continue
+            logger.error(f"Agent failed after {retry_attempts} attempts: {e}")
+            raise
+
+    raise RuntimeError(f"Retry loop exited without result: {last_error}")
+
+
+# Generic schema for arbitrary JSON structured outputs
+class GenericJsonOutput(BaseModel):
+    """Generic container for JSON output - allows any structure."""
+
+    model_config = {"extra": "allow"}  # Allow arbitrary fields
+
+
 async def structured_output_completion(
-    openai_client: AsyncOpenAI,  # Expecting an initialized AsyncOpenAI client instance
+    openai_client: AsyncOpenAI,
     model: str,
-    response_format: dict,  # e.g., {"type": "json_object"}
+    response_format: dict,  # Legacy parameter - kept for API compatibility
     system_prompt: str,
     user_prompt: str,
-    cache: ResponseCache,  # Expecting a ResponseCache instance
-):
-    """Makes an API call to OpenAI with structured output, retry logic, and caching."""
-
-    # Use the passed-in cache instance
-    cached_response = cache.get(f"{system_prompt}:{user_prompt}", model)
-    if cached_response is not None:
-        logger.info(f"Using cached response for model {model}")
-        return cached_response  # Return cached value directly, not as a coroutine
+    cache: ResponseCache,
+) -> Optional[dict]:
+    """
+    Makes an API call with structured output using agents SDK.
 
-    try:
-        logger.debug(f"Making API call to OpenAI model {model}")
+    Note: response_format parameter is ignored - the agents SDK handles
+    JSON parsing automatically. For typed outputs, use structured_agent_call() directly.
+    """
+    cache_key = f"{system_prompt}:{user_prompt}"
 
-        # Ensure system_prompt includes JSON instruction if response_format is json_object
-        # This was previously done before calling this function, but good to ensure here too.
-        effective_system_prompt = system_prompt
-        if (
-            response_format.get("type") == "json_object"
-            and "JSON object matching the specified schema" not in system_prompt
-        ):
-            effective_system_prompt = f"{system_prompt}\nProvide your response as a JSON object matching the specified schema."
+    # Ensure system_prompt includes JSON instruction
+    effective_system_prompt = system_prompt
+    if "JSON object matching the specified schema" not in system_prompt:
+        effective_system_prompt = f"{system_prompt}\nProvide your response as a JSON object matching the specified schema."
 
-        # Security: Add timeout to prevent indefinite hanging
-        completion = await openai_client.chat.completions.create(
+    try:
+        result = await structured_agent_call(
+            openai_client=openai_client,
             model=model,
-            messages=[
-                {"role": "system", "content": effective_system_prompt.strip()},
-                {"role": "user", "content": user_prompt.strip()},
-            ],
-            response_format=response_format,  # Pass the dict directly
-            temperature=0.7,  # Consider making this configurable
-            timeout=120.0,  # 120 second timeout
+            instructions=effective_system_prompt.strip(),
+            user_input=user_prompt.strip(),
+            output_type=GenericJsonOutput,
+            cache=cache,
+            cache_key=cache_key,
+            temperature=0.7,
         )
 
-        if not hasattr(completion, "choices") or not completion.choices:
-            logger.warning(
-                f"No choices returned in OpenAI completion for model {model}."
-            )
-            return None  # Or raise an error
-
-        first_choice = completion.choices[0]
-        if (
-            not hasattr(first_choice, "message")
-            or first_choice.message is None
-            or first_choice.message.content is None
-        ):
-            logger.warning(
-                f"No message content in the first choice for OpenAI model {model}."
-            )
-            return None  # Or raise an error
-
-        # Parse the JSON response
-        result = json.loads(first_choice.message.content)
-
-        # Cache the successful response using the passed-in cache instance
-        cache.set(f"{system_prompt}:{user_prompt}", model, result)
-        logger.debug(f"Successfully received and parsed response from model {model}")
+        # Convert Pydantic model back to dict for backward compatibility
+        if isinstance(result, BaseModel):
+            return result.model_dump()
         return result
 
-    except OpenAIError as e:  # More specific error handling
-        logger.error(f"OpenAI API call failed for model {model}: {e}", exc_info=True)
-        raise  # Re-raise to be handled by the calling function, potentially as gr.Error
-    except json.JSONDecodeError as e:
-        # Accessing first_choice might be an issue if completion itself failed before choices
-        # However, structure assumes choices are checked before this json.loads typically
-        # For safety, check if first_choice.message.content is available
-        response_content_for_log = "<unavailable>"
-        if (
-            "first_choice" in locals()
-            and first_choice.message
-            and first_choice.message.content
-        ):
-            response_content_for_log = first_choice.message.content[:500]
-        logger.error(
-            f"Failed to parse JSON response from model {model}: {e}. Response: {response_content_for_log}",
-            exc_info=True,
-        )
-        raise ValueError(
-            f"Invalid JSON response from AI model {model}."
-        )  # Raise specific error
     except Exception as e:
         logger.error(
-            f"Unexpected error during structured_output_completion for model {model}: {e}",
+            f"structured_output_completion failed for model {model}: {e}",
             exc_info=True,
         )
         raise  # Re-raise unexpected errors
@@ -431,29 +491,24 @@ Generate a few high-quality Anki cards from this content.
         logger.debug(
             f"Attempting to generate cards for {page.url} using model {model}."
         )
-        response_format_param = {"type": "json_object"}
-        # Security: Add timeout to prevent indefinite hanging
-        response_data = await openai_client.chat.completions.create(
+
+        # Use agents SDK for structured output
+        result = await structured_agent_call(
+            openai_client=openai_client,
             model=model,
-            messages=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": user_prompt},
-            ],
-            response_format=response_format_param,
+            instructions=system_prompt,
+            user_input=user_prompt,
+            output_type=GenericJsonOutput,  # Flexible schema for card generation
             temperature=0.5,
-            timeout=120.0,  # 120 second timeout
+            timeout=120.0,
         )
 
-        if (
-            not response_data.choices
-            or not response_data.choices[0].message
-            or not response_data.choices[0].message.content
-        ):
-            logger.error(f"Invalid or empty response from OpenAI for page {page.url}.")
+        if result is None:
+            logger.error(f"Invalid or empty response from agent for page {page.url}.")
             return []
 
-        cards_json_str = response_data.choices[0].message.content
-        parsed_cards = json.loads(cards_json_str)
+        # Convert Pydantic model to dict for processing
+        parsed_cards = result.model_dump() if isinstance(result, BaseModel) else result
 
         validated_cards: List[Card] = []
 
@@ -471,7 +526,7 @@ Generate a few high-quality Anki cards from this content.
             cards_list_from_json = parsed_cards
         else:
             logger.error(
-                f"LLM response for {page.url} was not a list or valid dict. Response: {cards_json_str[:200]}..."
+                f"LLM response for {page.url} was not a list or valid dict. Response: {str(parsed_cards)[:200]}..."
             )
             return []
 
@@ -546,34 +601,9 @@ Generate a few high-quality Anki cards from this content.
 
         return validated_cards
 
-    except json.JSONDecodeError as e:
-        # cards_json_str might not be defined if json.loads fails early, or if response_data was bad
-        raw_response_content = "<response_content_unavailable>"
-        if "cards_json_str" in locals() and cards_json_str:
-            raw_response_content = cards_json_str[:500]
-        elif (
-            "response_data" in locals()
-            and response_data
-            and response_data.choices
-            and len(response_data.choices) > 0
-            and response_data.choices[0].message
-            and response_data.choices[0].message.content
-        ):
-            raw_response_content = response_data.choices[0].message.content[:500]
-
-        logger.error(
-            f"Failed to decode JSON response from OpenAI for page {page.url}: {e}. Response: {raw_response_content}...",
-            exc_info=True,
-        )
-        return []
-    except OpenAIError as e:
-        logger.error(
-            f"OpenAI API error while processing page {page.url}: {e}", exc_info=True
-        )
-        return []
     except Exception as e:
         logger.error(
-            f"Unexpected error processing page {page.url} with LLM: {e}", exc_info=True
+            f"Error processing page {page.url} with agents SDK: {e}", exc_info=True
         )
         return []
 

pyproject.toml

@@ -12,12 +12,12 @@ authors = [
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
-    "openai>=1.109.1",
-    "openai-agents>=0.3.2",
+    "openai>=2.8.0",
+    "openai-agents>=0.6.1",
     "gradio>=5.49.1",
     "tenacity>=9.1.2",
     "genanki>=0.13.1",
-    "pydantic==2.11.9",
+    "pydantic>=2.12.3",
     "pandas>=2.3.2",
     "beautifulsoup4==4.13.5",
     "lxml>=6.0.2",

requirements.txt

@@ -89,9 +89,9 @@ fsspec==2025.5.1
     #   huggingface-hub
 genanki==0.13.1
     # via ankigen (pyproject.toml)
-gradio==5.49.1
+gradio==6.0.2
     # via ankigen (pyproject.toml)
-gradio-client==1.13.3
+gradio-client==2.0.1
     # via gradio
 griffe==1.7.3
     # via openai-agents
@@ -183,11 +183,11 @@ numpy==2.3.1
     # via
     #   gradio
     #   pandas
-openai==1.109.1
+openai==2.8.1
     # via
     #   ankigen (pyproject.toml)
     #   openai-agents
-openai-agents==0.3.2
+openai-agents==0.6.1
     # via ankigen (pyproject.toml)
 openapi-core==0.19.5
     # via fastmcp
@@ -226,7 +226,7 @@ py-key-value-shared==0.2.8
     # via py-key-value-aio
 pycparser==2.23
     # via cffi
-pydantic==2.11.9
+pydantic==2.12.4
     # via
     #   ankigen (pyproject.toml)
     #   fastapi
@@ -237,7 +237,7 @@ pydantic==2.11.9
     #   openai-agents
     #   openapi-pydantic
     #   pydantic-settings
-pydantic-core==2.33.2
+pydantic-core==2.41.5
     # via pydantic
 pydantic-settings==2.10.1
     # via mcp
@@ -294,9 +294,7 @@ rpds-py==0.26.0
     # via
     #   jsonschema
     #   referencing
-ruff==0.13.1
-    # via gradio
-safehttpx==0.1.6
+safehttpx==0.1.7
     # via gradio
 secretstorage==3.4.0
     # via keyring
@@ -355,7 +353,7 @@ typing-extensions==4.15.0
     #   starlette
     #   typer
     #   typing-inspection
-typing-inspection==0.4.1
+typing-inspection==0.4.2
     # via
     #   pydantic
     #   pydantic-settings
@@ -370,8 +368,6 @@ uvicorn==0.34.3
     #   gradio
     #   mcp
 websockets==15.0.1
-    # via
-    #   fastmcp
-    #   gradio-client
+    # via fastmcp
 werkzeug==3.1.1
     # via openapi-core

uv.lock

@@ -61,11 +61,11 @@ requires-dist = [
     { name = "genanki", specifier = ">=0.13.1" },
     { name = "gradio", specifier = ">=5.49.1" },
     { name = "lxml", specifier = ">=6.0.2" },
-    { name = "openai", specifier = ">=1.109.1" },
-    { name = "openai-agents", specifier = ">=0.3.2" },
+    { name = "openai", specifier = ">=2.8.0" },
+    { name = "openai-agents", specifier = ">=0.6.1" },
     { name = "pandas", specifier = ">=2.3.2" },
     { name = "pre-commit", marker = "extra == 'dev'", specifier = ">=4.3.0" },
-    { name = "pydantic", specifier = "==2.11.9" },
+    { name = "pydantic", specifier = ">=2.12.3" },
     { name = "pytest", marker = "extra == 'dev'", specifier = ">=8.4.2" },
     { name = "pytest-anyio", marker = "extra == 'dev'", specifier = ">=0.0.0" },
     { name = "pytest-cov", marker = "extra == 'dev'", specifier = ">=7.0.0" },
@@ -734,7 +734,7 @@ wheels = [
 
 [[package]]
 name = "gradio"
-version = "5.49.1"
+version = "6.0.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiofiles" },
@@ -758,7 +758,6 @@ dependencies = [
     { name = "pydub" },
     { name = "python-multipart" },
     { name = "pyyaml" },
-    { name = "ruff" },
     { name = "safehttpx" },
     { name = "semantic-version" },
     { name = "starlette" },
@@ -767,14 +766,14 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "uvicorn" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/83/67/17b3969a686f204dfb8f06bd34d1423bcba1df8a2f3674f115ca427188b7/gradio-5.49.1.tar.gz", hash = "sha256:c06faa324ae06c3892c8b4b4e73c706c4520d380f6b9e52a3c02dc53a7627ba9", size = 73784504, upload-time = "2025-10-08T20:18:40.4Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/b8/e8/66612eabc43a792b09ea8080d5581644128823b4f20185881f86a042e470/gradio-6.0.2.tar.gz", hash = "sha256:e3bb128fd9247a49820048cfb8f5b677b59a0da24f6d81bc990ca1a20eb6d1fb", size = 36452504, upload-time = "2025-12-02T01:57:48.397Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8d/95/1c25fbcabfa201ab79b016c8716a4ac0f846121d4bbfd2136ffb6d87f31e/gradio-5.49.1-py3-none-any.whl", hash = "sha256:1b19369387801a26a6ba7fd2f74d46c5b0e2ac9ddef14f24ddc0d11fb19421b7", size = 63523840, upload-time = "2025-10-08T20:18:34.585Z" },
+    { url = "https://files.pythonhosted.org/packages/1c/b0/d4bb7a5c9c2e26a952da06f046c6411fb8678003a0b9c7ed379941aff21f/gradio-6.0.2-py3-none-any.whl", hash = "sha256:bcb8b9d147b313c958f811977527415cfd7871ee9547ccd92ef1911970c49a2c", size = 21560841, upload-time = "2025-12-02T01:57:45.471Z" },
 ]
 
 [[package]]
 name = "gradio-client"
-version = "1.13.3"
+version = "2.0.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "fsspec" },
@@ -782,11 +781,10 @@ dependencies = [
     { name = "huggingface-hub" },
     { name = "packaging" },
     { name = "typing-extensions" },
-    { name = "websockets" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/3e/a9/a3beb0ece8c05c33e6376b790fa42e0dd157abca8220cf639b249a597467/gradio_client-1.13.3.tar.gz", hash = "sha256:869b3e67e0f7a0f40df8c48c94de99183265cf4b7b1d9bd4623e336d219ffbe7", size = 323253, upload-time = "2025-09-26T19:51:21.7Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/4e/cc/b0f04b1c9bf79c7ae9840b9945f5fbd93355719684f83032837695ab1eaf/gradio_client-2.0.1.tar.gz", hash = "sha256:087eb50652370747c0ce66cd0ae79ecb49f9682188d5348e279d44602cbc2814", size = 54792, upload-time = "2025-12-02T01:57:58.685Z" }
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