Address code review feedback: improve error messages and add helper method

Co-authored-by: cauvang32 <113093128+cauvang32@users.noreply.github.com>

.env.example

@@ -18,6 +18,11 @@ OPENAI_API_KEY=your_openai_api_key_here
 # Use OpenAI directly: https://api.openai.com/v1
 OPENAI_BASE_URL=https://models.github.ai/inference
 
+# Anthropic API Key (for Claude models)
+# Get from: https://console.anthropic.com/
+# Leave empty to disable Claude models
+ANTHROPIC_API_KEY=your_anthropic_api_key_here
+
 # ============================================
 # Image Generation (Optional)
 # ============================================

.gitignore

@@ -2,8 +2,8 @@ test.py
 .env
 chat_history.db
 bot_copy.py
-__pycache__/bot.cpython-312.pyc
-tests/__pycache__/test_bot.cpython-312.pyc
+__pycache__/
+*.pyc
 .vscode/settings.json
 chatgpt.zip
 response.txt
@@ -12,4 +12,5 @@ venv
 temp_charts
 .idea
 temp_data_files
-logs/
+logs/
+.pytest_cache/

README.md

@@ -18,11 +18,11 @@
 
 ## 🌟 Overview
 
-**ChatGPT Discord Bot** brings the power of AI directly to your Discord server! Powered by OpenAI's latest models, this bot goes beyond simple chat - it's a complete AI assistant with **code interpretation**, **file management**, **data analysis**, and much more.
+**ChatGPT Discord Bot** brings the power of AI directly to your Discord server! Powered by OpenAI's latest models and Anthropic's Claude, this bot goes beyond simple chat - it's a complete AI assistant with **code interpretation**, **file management**, **data analysis**, and much more.
 
 ### 🎯 What Makes This Bot Special?
 
-- 🧠 **Latest AI Models** - GPT-4o, GPT-5, o1, o3-mini, and more
+- 🧠 **Latest AI Models** - GPT-4o, GPT-5, o1, o3-mini, Claude 4, and more
 - 💻 **Code Interpreter** - Execute Python code like ChatGPT (NEW in v2.0!)
 - 📁 **Smart File Management** - Handle 200+ file types with automatic cleanup
 - 📊 **Data Analysis** - Upload and analyze CSV, Excel, and scientific data
@@ -164,6 +164,15 @@ Set reminders naturally:
 - `o1`
 - `o3-mini`
 
+</td>
+<td>
+
+**Claude (Anthropic)**
+- `claude-sonnet-4-20250514`
+- `claude-opus-4-20250514`
+- `claude-3.5-sonnet`
+- `claude-3.5-haiku`
+
 </td>
 </tr>
 </table>
@@ -179,6 +188,7 @@ Before you begin, ensure you have:
 - 🐳 **Docker** (recommended) or Python 3.13+
 - 🎮 **Discord Bot Token** ([Create one here](https://discord.com/developers/applications))
 - 🔑 **OpenAI API Key** ([Get it here](https://platform.openai.com/api-keys))
+- 🧠 **Anthropic API Key** (Optional, for Claude models - [Get it here](https://console.anthropic.com/))
 - 🎨 **Runware API Key** ([Sign up here](https://runware.ai/))
 - 🔍 **Google API Key** ([Google Cloud Console](https://console.cloud.google.com/))
 - 🗄️ **MongoDB** ([MongoDB Atlas](https://cloud.mongodb.com/) - Free tier available)
@@ -195,6 +205,9 @@ DISCORD_TOKEN=your_discord_bot_token_here
 OPENAI_API_KEY=your_openai_api_key_here
 OPENAI_BASE_URL=https://api.openai.com/v1
 
+# Anthropic (Claude) - Optional
+ANTHROPIC_API_KEY=your_anthropic_api_key_here
+
 # Image Generation
 RUNWARE_API_KEY=your_runware_api_key_here
 

bot.py

@@ -17,7 +17,7 @@ from src.config.config import (
     DISCORD_TOKEN, MONGODB_URI, RUNWARE_API_KEY, STATUSES, 
     LOGGING_CONFIG, ENABLE_WEBHOOK_LOGGING, LOGGING_WEBHOOK_URL, 
     WEBHOOK_LOG_LEVEL, WEBHOOK_APP_NAME, WEBHOOK_BATCH_SIZE, 
-    WEBHOOK_FLUSH_INTERVAL, LOG_LEVEL_MAP
+    WEBHOOK_FLUSH_INTERVAL, LOG_LEVEL_MAP, ANTHROPIC_API_KEY
 )
 
 # Import webhook logger
@@ -124,6 +124,20 @@ async def main():
         logging.error(f"Error initializing OpenAI client: {e}")
         return
     
+    # Initialize the Anthropic client (for Claude models)
+    anthropic_client = None
+    if ANTHROPIC_API_KEY:
+        try:
+            from anthropic import AsyncAnthropic
+            anthropic_client = AsyncAnthropic(api_key=ANTHROPIC_API_KEY)
+            logging.info("Anthropic client initialized successfully")
+        except ImportError:
+            logging.warning("Anthropic package not installed. Claude models will not be available. Install with: pip install anthropic")
+        except Exception as e:
+            logging.warning(f"Error initializing Anthropic client: {e}. Claude models will not be available.")
+    else:
+        logging.info("ANTHROPIC_API_KEY not set - Claude models will not be available")
+    
     # Global references to objects that need cleanup
     message_handler = None
     db_handler = None
@@ -191,14 +205,14 @@ async def main():
             await ctx.send(f"Error: {error_msg}")
         
         # Initialize message handler
-        message_handler = MessageHandler(bot, db_handler, openai_client, image_generator)
+        message_handler = MessageHandler(bot, db_handler, openai_client, image_generator, anthropic_client)
         
         # Attach db_handler to bot for cogs
         bot.db_handler = db_handler
         
         # Set up slash commands
         from src.commands.commands import setup_commands
-        setup_commands(bot, db_handler, openai_client, image_generator)
+        setup_commands(bot, db_handler, openai_client, image_generator, anthropic_client)
         
         # Load file management commands
         try:

requirements.txt

@@ -16,6 +16,7 @@ beautifulsoup4>=4.12.0
 # AI & ML
 runware>=0.4.33
 tiktoken>=0.7.0
+anthropic>=0.40.0
 
 # Data Processing
 pandas>=2.1.0

src/commands/commands.py

@@ -15,6 +15,7 @@ from src.utils.openai_utils import prepare_file_from_path
 from src.utils.token_counter import token_counter
 from src.utils.code_interpreter import delete_all_user_files
 from src.utils.discord_utils import create_info_embed, create_error_embed, create_success_embed
+from src.utils.claude_utils import is_claude_model, call_claude_api
 
 # Dictionary to keep track of user requests and their cooldowns
 user_requests: Dict[int, Dict[str, Any]] = {}
@@ -69,7 +70,7 @@ async def image_model_autocomplete(
         for model in matches[:25]
     ]
 
-def setup_commands(bot: commands.Bot, db_handler, openai_client, image_generator: ImageGenerator):
+def setup_commands(bot: commands.Bot, db_handler, openai_client, image_generator: ImageGenerator, anthropic_client=None):
     """
     Set up all slash commands for the bot.
     
@@ -78,6 +79,7 @@ def setup_commands(bot: commands.Bot, db_handler, openai_client, image_generator
         db_handler: Database handler instance
         openai_client: OpenAI client instance
         image_generator: Image generator instance
+        anthropic_client: Anthropic client instance (optional, for Claude models)
     """
     tree = bot.tree
     
@@ -265,24 +267,46 @@ def setup_commands(bot: commands.Bot, db_handler, openai_client, image_generator
                     f"(text: {input_token_count['text_tokens']}, images: {input_token_count['image_tokens']})"
                 )
 
-                # Send to the AI model
-                api_params = {
-                    "model": model if model in ["openai/gpt-4o", "openai/gpt-4o-mini", "openai/gpt-5", "openai/gpt-5-nano", "openai/gpt-5-mini", "openai/gpt-5-chat"] else "openai/gpt-4o",
-                    "messages": messages
-                }
-                
-                # Add temperature only for models that support it (exclude GPT-5 family)
-                if model not in ["openai/gpt-5", "openai/gpt-5-nano", "openai/gpt-5-mini", "openai/gpt-5-chat"]:
-                    api_params["temperature"] = 0.5
-                
-                response = await openai_client.chat.completions.create(**api_params)
+                # Check if using Claude model
+                if is_claude_model(model):
+                    if anthropic_client is None:
+                        await interaction.followup.send(
+                            "❌ Claude model not available. ANTHROPIC_API_KEY is not configured.",
+                            ephemeral=True
+                        )
+                        return
+                    
+                    # Use Claude API
+                    claude_response = await call_claude_api(
+                        anthropic_client,
+                        messages,
+                        model,
+                        max_tokens=4096,
+                        use_tools=False
+                    )
+                    
+                    reply = claude_response.get("content", "")
+                    actual_input_tokens = claude_response.get("input_tokens", 0)
+                    actual_output_tokens = claude_response.get("output_tokens", 0)
+                else:
+                    # Send to the OpenAI model
+                    api_params = {
+                        "model": model if model in ["openai/gpt-4o", "openai/gpt-4o-mini", "openai/gpt-5", "openai/gpt-5-nano", "openai/gpt-5-mini", "openai/gpt-5-chat"] else "openai/gpt-4o",
+                        "messages": messages
+                    }
+                    
+                    # Add temperature only for models that support it (exclude GPT-5 family)
+                    if model not in ["openai/gpt-5", "openai/gpt-5-nano", "openai/gpt-5-mini", "openai/gpt-5-chat"]:
+                        api_params["temperature"] = 0.5
+                    
+                    response = await openai_client.chat.completions.create(**api_params)
 
-                reply = response.choices[0].message.content
-                
-                # Get actual token usage from API response
-                usage = response.usage
-                actual_input_tokens = usage.prompt_tokens if usage else input_token_count['total_tokens']
-                actual_output_tokens = usage.completion_tokens if usage else token_counter.count_text_tokens(reply, model)
+                    reply = response.choices[0].message.content
+                    
+                    # Get actual token usage from API response
+                    usage = response.usage
+                    actual_input_tokens = usage.prompt_tokens if usage else input_token_count['total_tokens']
+                    actual_output_tokens = usage.completion_tokens if usage else token_counter.count_text_tokens(reply, model)
                 
                 # Calculate cost
                 cost = token_counter.estimate_cost(actual_input_tokens, actual_output_tokens, model)
@@ -362,19 +386,38 @@ def setup_commands(bot: commands.Bot, db_handler, openai_client, image_generator
                         {"role": "user", "content": f"Content from {url}:\n{content}"}
                     ]
 
-                api_params = {
-                    "model": model if model in ["openai/gpt-4o", "openai/gpt-4o-mini", "openai/gpt-5", "openai/gpt-5-nano", "openai/gpt-5-mini", "openai/gpt-5-chat"] else "openai/gpt-4o",
-                    "messages": messages
-                }
-                
-                # Add temperature and top_p only for models that support them (exclude GPT-5 family)
-                if model not in ["openai/gpt-5", "openai/gpt-5-nano", "openai/gpt-5-mini", "openai/gpt-5-chat"]:
-                    api_params["temperature"] = 0.3
-                    api_params["top_p"] = 0.7
-                
-                response = await openai_client.chat.completions.create(**api_params)
+                # Check if using Claude model
+                if is_claude_model(model):
+                    if anthropic_client is None:
+                        await interaction.followup.send(
+                            "❌ Claude model not available. ANTHROPIC_API_KEY is not configured.",
+                            ephemeral=True
+                        )
+                        return
+                    
+                    # Use Claude API
+                    claude_response = await call_claude_api(
+                        anthropic_client,
+                        messages,
+                        model,
+                        max_tokens=4096,
+                        use_tools=False
+                    )
+                    reply = claude_response.get("content", "")
+                else:
+                    api_params = {
+                        "model": model if model in ["openai/gpt-4o", "openai/gpt-4o-mini", "openai/gpt-5", "openai/gpt-5-nano", "openai/gpt-5-mini", "openai/gpt-5-chat"] else "openai/gpt-4o",
+                        "messages": messages
+                    }
+                    
+                    # Add temperature and top_p only for models that support them (exclude GPT-5 family)
+                    if model not in ["openai/gpt-5", "openai/gpt-5-nano", "openai/gpt-5-mini", "openai/gpt-5-chat"]:
+                        api_params["temperature"] = 0.3
+                        api_params["top_p"] = 0.7
+                    
+                    response = await openai_client.chat.completions.create(**api_params)
 
-                reply = response.choices[0].message.content
+                    reply = response.choices[0].message.content
                 
                 # Add the interaction to history
                 history.append({"role": "user", "content": f"Scraped content from {url}"})

src/config/config.py

@@ -101,7 +101,11 @@ MODEL_OPTIONS = [
     "openai/o1",
     "openai/o3-mini",
     "openai/o3",
-    "openai/o4-mini"
+    "openai/o4-mini",
+    "anthropic/claude-sonnet-4-20250514",
+    "anthropic/claude-opus-4-20250514",
+    "anthropic/claude-3.5-sonnet",
+    "anthropic/claude-3.5-haiku",
 ]
 
 # ==================== IMAGE GENERATION MODELS ====================
@@ -175,7 +179,12 @@ MODEL_TOKEN_LIMITS = {
     "openai/gpt-5": 4000,
     "openai/gpt-5-nano": 4000,
     "openai/gpt-5-mini": 4000,
-    "openai/gpt-5-chat": 4000
+    "openai/gpt-5-chat": 4000,
+    # Claude models (200K context window, using conservative limits)
+    "anthropic/claude-sonnet-4-20250514": 16000,
+    "anthropic/claude-opus-4-20250514": 16000,
+    "anthropic/claude-3.5-sonnet": 16000,
+    "anthropic/claude-3.5-haiku": 16000,
 }
 
 # Default token limit for unknown models
@@ -184,7 +193,7 @@ DEFAULT_TOKEN_LIMIT = 4000
 # Default model for new users
 DEFAULT_MODEL = "openai/gpt-4.1"
 
-PDF_ALLOWED_MODELS = ["openai/gpt-4o", "openai/gpt-4o-mini", "openai/gpt-4.1","openai/gpt-4.1-nano","openai/gpt-4.1-mini"]
+PDF_ALLOWED_MODELS = ["openai/gpt-4o", "openai/gpt-4o-mini", "openai/gpt-4.1","openai/gpt-4.1-nano","openai/gpt-4.1-mini", "anthropic/claude-sonnet-4-20250514", "anthropic/claude-opus-4-20250514", "anthropic/claude-3.5-sonnet", "anthropic/claude-3.5-haiku"]
 PDF_BATCH_SIZE = 3
 
 # Prompt templates
@@ -403,6 +412,7 @@ RUNWARE_API_KEY = os.getenv("RUNWARE_API_KEY")
 MONGODB_URI = os.getenv("MONGODB_URI")
 ADMIN_ID = os.getenv("ADMIN_ID")  # Add ADMIN_ID if you're using it
 TIMEZONE = os.getenv("TIMEZONE", "UTC")  # Default to UTC if not specified
+ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")  # Anthropic API key for Claude models
 
 # File management settings
 FILE_EXPIRATION_HOURS = int(os.getenv("FILE_EXPIRATION_HOURS", "48"))  # Hours until files expire (-1 for never)
@@ -416,5 +426,7 @@ if not MONGODB_URI:
     print("WARNING: MONGODB_URI not found in .env file")
 if not RUNWARE_API_KEY:
     print("WARNING: RUNWARE_API_KEY not found in .env file")
+if not ANTHROPIC_API_KEY:
+    print("INFO: ANTHROPIC_API_KEY not found in .env file - Claude models will not be available")
 if ENABLE_WEBHOOK_LOGGING and not LOGGING_WEBHOOK_URL:
     print("WARNING: Webhook logging enabled but LOGGING_WEBHOOK_URL not found in .env file")

src/config/pricing.py

@@ -51,6 +51,14 @@ MODEL_PRICING: Dict[str, ModelPricing] = {
     
     # o4 Family
     "openai/o4-mini": ModelPricing(input=2.00, output=8.00),
+    
+    # Claude 4 Family (Anthropic - latest models)
+    "anthropic/claude-sonnet-4-20250514": ModelPricing(input=3.00, output=15.00),
+    "anthropic/claude-opus-4-20250514": ModelPricing(input=15.00, output=75.00),
+    
+    # Claude 3.5 Family (Anthropic)
+    "anthropic/claude-3.5-sonnet": ModelPricing(input=3.00, output=15.00),
+    "anthropic/claude-3.5-haiku": ModelPricing(input=0.80, output=4.00),
 }
 
 

src/module/message_handler.py

@@ -15,6 +15,7 @@ import base64
 import traceback
 from datetime import datetime, timedelta
 from src.utils.openai_utils import process_tool_calls, prepare_messages_for_api, get_tools_for_model
+from src.utils.claude_utils import is_claude_model, call_claude_api, convert_claude_tool_calls_to_openai
 from src.utils.pdf_utils import process_pdf, send_response
 from src.utils.code_utils import extract_code_blocks
 from src.utils.reminder_utils import ReminderManager
@@ -95,7 +96,7 @@ except ImportError as e:
     logging.warning(f"Data analysis libraries not available: {str(e)}")
 
 class MessageHandler:
-    def __init__(self, bot, db_handler, openai_client, image_generator):
+    def __init__(self, bot, db_handler, openai_client, image_generator, anthropic_client=None):
         """
         Initialize the message handler.
         
@@ -104,10 +105,12 @@ class MessageHandler:
             db_handler: Database handler instance
             openai_client: OpenAI client instance
             image_generator: Image generator instance
+            anthropic_client: Anthropic client instance (optional, for Claude models)
         """
         self.bot = bot
         self.db = db_handler
         self.client = openai_client
+        self.anthropic_client = anthropic_client
         self.image_generator = image_generator
         self.aiohttp_session = None
         
@@ -172,6 +175,26 @@ class MessageHandler:
             logging.warning(f"Failed to initialize tiktoken encoder: {e}")
             self.token_encoder = None
     
+    def _build_claude_tool_result_message(self, tool_call_id: str, content: str) -> Dict[str, Any]:
+        """
+        Build a tool result message for Claude API.
+        
+        Args:
+            tool_call_id: The ID of the tool call this result is for
+            content: The result content from the tool execution
+            
+        Returns:
+            Dict: A properly formatted Claude tool result message
+        """
+        return {
+            "role": "user",
+            "content": [{
+                "type": "tool_result",
+                "tool_use_id": tool_call_id,
+                "content": content
+            }]
+        }
+    
     def _find_user_id_from_current_task(self):
         """
         Utility method to find user_id from the current asyncio task.
@@ -1514,7 +1537,14 @@ print("\\n=== Correlation Analysis ===")
             
             # Determine which models should have tools available
             # openai/o1-mini and openai/o1-preview do not support tools
-            use_tools = model in ["openai/gpt-4o", "openai/gpt-4o-mini", "openai/gpt-5", "openai/gpt-5-nano", "openai/gpt-5-mini", "openai/gpt-5-chat", "openai/o1", "openai/o3-mini", "openai/gpt-4.1", "openai/gpt-4.1-mini", "openai/gpt-4.1-nano", "openai/o3", "openai/o4-mini"]
+            # Claude models support tools
+            use_tools = model in [
+                "openai/gpt-4o", "openai/gpt-4o-mini", "openai/gpt-5", "openai/gpt-5-nano", 
+                "openai/gpt-5-mini", "openai/gpt-5-chat", "openai/o1", "openai/o3-mini", 
+                "openai/gpt-4.1", "openai/gpt-4.1-mini", "openai/gpt-4.1-nano", "openai/o3", "openai/o4-mini",
+                "anthropic/claude-sonnet-4-20250514", "anthropic/claude-opus-4-20250514",
+                "anthropic/claude-3.5-sonnet", "anthropic/claude-3.5-haiku"
+            ]
             
             # Count tokens being sent to API
             total_content_length = 0
@@ -1535,177 +1565,310 @@ print("\\n=== Correlation Analysis ===")
             logging.info(f"API Request Debug - Model: {model}, Messages: {len(messages_for_api)}, "
                         f"Est. tokens: {estimated_tokens}, Content length: {total_content_length} chars")
             
-            # Prepare API call parameters
-            api_params = {
-                "model": model,
-                "messages": messages_for_api,
-                "timeout": 240  # Increased timeout for better response handling
-            }
-            
-            # Add temperature and top_p only for models that support them (exclude GPT-5 family)
-            if model in ["openai/gpt-4o", "openai/gpt-4o-mini"]:
-                api_params["temperature"] = 0.3
-                api_params["top_p"] = 0.7
-            elif model not in ["openai/gpt-5", "openai/gpt-5-nano", "openai/gpt-5-mini", "openai/gpt-5-chat"]:
-                # For other models (not GPT-4o family and not GPT-5 family)
-                api_params["temperature"] = 1
-                api_params["top_p"] = 1
-            
-            # Add tools if using a supported model
-            if use_tools:
-                tools = get_tools_for_model()
-                api_params["tools"] = tools
-            
             # Initialize variables to track tool responses
             image_generation_used = False
             chart_id = None
             image_urls = []  # Will store unique image URLs
             
-            # Make the initial API call
-            try:
-                response = await self.client.chat.completions.create(**api_params)
-            except Exception as e:
-                # Handle 413 Request Entity Too Large error with a user-friendly message
-                if "413" in str(e) or "tokens_limit_reached" in str(e) or "Request body too large" in str(e):
+            # Check if this is a Claude model
+            if is_claude_model(model):
+                # Use Claude API
+                if self.anthropic_client is None:
                     await message.channel.send(
-                        f"❌ **Request too large for {model}**\n"
-                        f"Your conversation history or message is too large for this model.\n"
-                        f"Try:\n"
-                        f"• Using `/reset` to start fresh\n"
-                        f"• Using a model with higher token limits\n"
-                        f"• Reducing the size of your current message\n"
-                        f"• Breaking up large files into smaller pieces"
+                        f"❌ **Claude model not available**\n"
+                        f"The Anthropic API key is not configured. Please set ANTHROPIC_API_KEY in your .env file."
                     )
                     return
-                else:
-                    # Re-raise other errors
-                    raise e
-            
-            # Extract token usage and calculate cost
-            input_tokens = 0
-            output_tokens = 0
-            total_cost = 0.0
-            
-            if hasattr(response, 'usage') and response.usage:
-                input_tokens = getattr(response.usage, 'prompt_tokens', 0)
-                output_tokens = getattr(response.usage, 'completion_tokens', 0)
                 
-                # Calculate cost based on model pricing
-                pricing = MODEL_PRICING.get(model)
-                if pricing:
-                    total_cost = pricing.calculate_cost(input_tokens, output_tokens)
+                try:
+                    claude_response = await call_claude_api(
+                        self.anthropic_client,
+                        messages_for_api,
+                        model,
+                        max_tokens=4096,
+                        use_tools=use_tools
+                    )
                     
-                    logging.info(f"API call - Model: {model}, Input tokens: {input_tokens}, Output tokens: {output_tokens}, Cost: {format_cost(total_cost)}")
+                    # Extract token usage and calculate cost for Claude
+                    input_tokens = claude_response.get("input_tokens", 0)
+                    output_tokens = claude_response.get("output_tokens", 0)
+                    total_cost = 0.0
                     
-                    # Save token usage and cost to database
-                    await self.db.save_token_usage(user_id, model, input_tokens, output_tokens, total_cost)
-            
-            # Process tool calls if any
-            updated_messages = None
-            if use_tools and response.choices[0].finish_reason == "tool_calls":
-                # Process tools
-                tool_calls = response.choices[0].message.tool_calls
-                tool_messages = {}
-                
-                # Track which tools are being called
-                for tool_call in tool_calls:
-                    if tool_call.function.name in self.tool_mapping:
-                        tool_messages[tool_call.function.name] = True
-                        if tool_call.function.name == "generate_image":
-                            image_generation_used = True
-                        elif tool_call.function.name == "edit_image":
-                            # Display appropriate message for image editing
-                            await message.channel.send("🖌️ Editing image...")
-                
-                # Display appropriate messages based on which tools are being called
-                if tool_messages.get("google_search") or tool_messages.get("scrape_webpage"):
-                    await message.channel.send("🔍 Researching information...")
-                
-                if tool_messages.get("execute_python_code") or tool_messages.get("analyze_data_file"):
-                    await message.channel.send("💻 Running code...")
-                
-                if tool_messages.get("generate_image"):
-                    await message.channel.send("🎨 Generating images...")
+                    # Calculate cost based on model pricing
+                    pricing = MODEL_PRICING.get(model)
+                    if pricing:
+                        total_cost = pricing.calculate_cost(input_tokens, output_tokens)
+                        logging.info(f"Claude API call - Model: {model}, Input tokens: {input_tokens}, Output tokens: {output_tokens}, Cost: {format_cost(total_cost)}")
+                        await self.db.save_token_usage(user_id, model, input_tokens, output_tokens, total_cost)
                     
-                if tool_messages.get("set_reminder") or tool_messages.get("get_reminders"):
-                    await message.channel.send("📅 Processing reminders...")
-                
-                if not tool_messages:                        
-                    await message.channel.send("🤔 Processing...")
-                
-                # Process any tool calls and get the updated messages
-                tool_calls_processed, updated_messages = await process_tool_calls(
-                    self.client, 
-                    response, 
-                    messages_for_api, 
-                    self.tool_mapping
-                )
-                
-                # Process tool responses to extract important data (images, charts)
-                if updated_messages:
-                    # Look for image generation and code interpreter tool responses
-                    for msg in updated_messages:
-                        if msg.get('role') == 'tool' and msg.get('name') == 'generate_image':
-                            try:
-                                tool_result = json.loads(msg.get('content', '{}'))
-                                if tool_result.get('image_urls'):
-                                    image_urls.extend(tool_result['image_urls'])
-                            except:
-                                pass
+                    # Process tool calls if any
+                    updated_messages = None
+                    if use_tools and claude_response.get("tool_calls"):
+                        tool_calls = convert_claude_tool_calls_to_openai(claude_response["tool_calls"])
+                        tool_messages = {}
                         
-                        elif msg.get('role') == 'tool' and msg.get('name') == 'edit_image':
-                            try:
-                                tool_result = json.loads(msg.get('content', '{}'))
-                                if tool_result.get('image_url'):
-                                    image_urls.append(tool_result['image_url'])
-                            except:
-                                pass
+                        # Track which tools are being called
+                        for tool_call in tool_calls:
+                            if tool_call.function.name in self.tool_mapping:
+                                tool_messages[tool_call.function.name] = True
+                                if tool_call.function.name == "generate_image":
+                                    image_generation_used = True
+                                elif tool_call.function.name == "edit_image":
+                                    await message.channel.send("🖌️ Editing image...")
                         
-                        elif msg.get('role') == 'tool' and msg.get('name') in ['execute_python_code', 'analyze_data_file']:
-                            try:
-                                tool_result = json.loads(msg.get('content', '{}'))
-                                if tool_result.get('chart_id'):
-                                    chart_id = tool_result['chart_id']
-                            except:
-                                pass
-                
-                # If tool calls were processed, make another API call with the updated messages
-                if tool_calls_processed and updated_messages:
-                    # Prepare API parameters for follow-up call
-                    follow_up_params = {
-                        "model": model,
-                        "messages": updated_messages,
-                        "timeout": 240
-                    }
-                    
-                    # Add temperature only for models that support it (exclude GPT-5 family)
-                    if model in ["openai/gpt-4o", "openai/gpt-4o-mini"]:
-                        follow_up_params["temperature"] = 0.3
-                    elif model not in ["openai/gpt-5", "openai/gpt-5-nano", "openai/gpt-5-mini", "openai/gpt-5-chat"]:
-                        follow_up_params["temperature"] = 1
-                    
-                    response = await self.client.chat.completions.create(**follow_up_params)
-                    
-                    # Extract token usage and calculate cost for follow-up call
-                    if hasattr(response, 'usage') and response.usage:
-                        follow_up_input_tokens = getattr(response.usage, 'prompt_tokens', 0)
-                        follow_up_output_tokens = getattr(response.usage, 'completion_tokens', 0)
+                        # Display appropriate messages
+                        if tool_messages.get("google_search") or tool_messages.get("scrape_webpage"):
+                            await message.channel.send("🔍 Researching information...")
+                        if tool_messages.get("execute_python_code") or tool_messages.get("analyze_data_file"):
+                            await message.channel.send("💻 Running code...")
+                        if tool_messages.get("generate_image"):
+                            await message.channel.send("🎨 Generating images...")
+                        if tool_messages.get("set_reminder") or tool_messages.get("get_reminders"):
+                            await message.channel.send("📅 Processing reminders...")
+                        if not tool_messages:
+                            await message.channel.send("🤔 Processing...")
                         
-                        input_tokens += follow_up_input_tokens
-                        output_tokens += follow_up_output_tokens
+                        # Process tool calls manually for Claude
+                        tool_results = []
+                        for tool_call in tool_calls:
+                            function_name = tool_call.function.name
+                            if function_name in self.tool_mapping:
+                                try:
+                                    function_args = json.loads(tool_call.function.arguments)
+                                    function_response = await self.tool_mapping[function_name](function_args)
+                                    tool_results.append({
+                                        "tool_call_id": tool_call.id,
+                                        "content": str(function_response)
+                                    })
+                                    
+                                    # Extract image URLs if generated
+                                    if function_name == "generate_image":
+                                        try:
+                                            tool_result = json.loads(function_response) if isinstance(function_response, str) else function_response
+                                            if tool_result.get('image_urls'):
+                                                image_urls.extend(tool_result['image_urls'])
+                                        except:
+                                            pass
+                                except Exception as e:
+                                    logging.error(f"Error executing {function_name}: {e}")
+                                    tool_results.append({
+                                        "tool_call_id": tool_call.id,
+                                        "content": f"Error: {str(e)}"
+                                    })
+                        
+                        # Build updated messages with tool results for follow-up call
+                        updated_messages = messages_for_api.copy()
+                        updated_messages.append({
+                            "role": "assistant",
+                            "content": claude_response.get("content", "")
+                        })
+                        for result in tool_results:
+                            updated_messages.append(
+                                self._build_claude_tool_result_message(result["tool_call_id"], result["content"])
+                            )
+                        
+                        # Make follow-up call
+                        follow_up_response = await call_claude_api(
+                            self.anthropic_client,
+                            updated_messages,
+                            model,
+                            max_tokens=4096,
+                            use_tools=False  # Don't need tools for follow-up
+                        )
+                        
+                        # Update token usage
+                        follow_up_input = follow_up_response.get("input_tokens", 0)
+                        follow_up_output = follow_up_response.get("output_tokens", 0)
+                        input_tokens += follow_up_input
+                        output_tokens += follow_up_output
                         
-                        # Calculate additional cost
-                        pricing = MODEL_PRICING.get(model)
                         if pricing:
-                            additional_cost = pricing.calculate_cost(follow_up_input_tokens, follow_up_output_tokens)
+                            additional_cost = pricing.calculate_cost(follow_up_input, follow_up_output)
                             total_cost += additional_cost
+                            await self.db.save_token_usage(user_id, model, follow_up_input, follow_up_output, additional_cost)
+                        
+                        reply = follow_up_response.get("content", "")
+                    else:
+                        reply = claude_response.get("content", "")
+                    
+                except Exception as e:
+                    error_str = str(e)
+                    if "overloaded" in error_str.lower():
+                        await message.channel.send(
+                            f"⚠️ **Claude is currently overloaded**\n"
+                            f"Please try again in a moment or switch to an OpenAI model."
+                        )
+                        return
+                    else:
+                        raise e
+            else:
+                # Use OpenAI API (existing logic)
+                # Prepare API call parameters
+                api_params = {
+                    "model": model,
+                    "messages": messages_for_api,
+                    "timeout": 240  # Increased timeout for better response handling
+                }
+                
+                # Add temperature and top_p only for models that support them (exclude GPT-5 family)
+                if model in ["openai/gpt-4o", "openai/gpt-4o-mini"]:
+                    api_params["temperature"] = 0.3
+                    api_params["top_p"] = 0.7
+                elif model not in ["openai/gpt-5", "openai/gpt-5-nano", "openai/gpt-5-mini", "openai/gpt-5-chat"]:
+                    # For other models (not GPT-4o family and not GPT-5 family)
+                    api_params["temperature"] = 1
+                    api_params["top_p"] = 1
+                
+                # Add tools if using a supported model
+                if use_tools:
+                    tools = get_tools_for_model()
+                    api_params["tools"] = tools
+                
+                # Make the initial API call
+                try:
+                    response = await self.client.chat.completions.create(**api_params)
+                except Exception as e:
+                    # Handle 413 Request Entity Too Large error with a user-friendly message
+                    if "413" in str(e) or "tokens_limit_reached" in str(e) or "Request body too large" in str(e):
+                        await message.channel.send(
+                            f"❌ **Request too large for {model}**\n"
+                            f"Your conversation history or message is too large for this model.\n"
+                            f"Try:\n"
+                            f"• Using `/reset` to start fresh\n"
+                            f"• Using a model with higher token limits\n"
+                            f"• Reducing the size of your current message\n"
+                            f"• Breaking up large files into smaller pieces"
+                        )
+                        return
+                    else:
+                        # Re-raise other errors
+                        raise e
+                
+                # Extract token usage and calculate cost
+                input_tokens = 0
+                output_tokens = 0
+                total_cost = 0.0
+                
+                if hasattr(response, 'usage') and response.usage:
+                    input_tokens = getattr(response.usage, 'prompt_tokens', 0)
+                    output_tokens = getattr(response.usage, 'completion_tokens', 0)
+                    
+                    # Calculate cost based on model pricing
+                    pricing = MODEL_PRICING.get(model)
+                    if pricing:
+                        total_cost = pricing.calculate_cost(input_tokens, output_tokens)
+                        
+                        logging.info(f"API call - Model: {model}, Input tokens: {input_tokens}, Output tokens: {output_tokens}, Cost: {format_cost(total_cost)}")
+                        
+                        # Save token usage and cost to database
+                        await self.db.save_token_usage(user_id, model, input_tokens, output_tokens, total_cost)
+                
+                # Process tool calls if any (OpenAI)
+                updated_messages = None
+                if use_tools and response.choices[0].finish_reason == "tool_calls":
+                    # Process tools
+                    tool_calls = response.choices[0].message.tool_calls
+                    tool_messages = {}
+                    
+                    # Track which tools are being called
+                    for tool_call in tool_calls:
+                        if tool_call.function.name in self.tool_mapping:
+                            tool_messages[tool_call.function.name] = True
+                            if tool_call.function.name == "generate_image":
+                                image_generation_used = True
+                            elif tool_call.function.name == "edit_image":
+                                # Display appropriate message for image editing
+                                await message.channel.send("🖌️ Editing image...")
+                    
+                    # Display appropriate messages based on which tools are being called
+                    if tool_messages.get("google_search") or tool_messages.get("scrape_webpage"):
+                        await message.channel.send("🔍 Researching information...")
+                    
+                    if tool_messages.get("execute_python_code") or tool_messages.get("analyze_data_file"):
+                        await message.channel.send("💻 Running code...")
+                    
+                    if tool_messages.get("generate_image"):
+                        await message.channel.send("🎨 Generating images...")
+                        
+                    if tool_messages.get("set_reminder") or tool_messages.get("get_reminders"):
+                        await message.channel.send("📅 Processing reminders...")
+                    
+                    if not tool_messages:                        
+                        await message.channel.send("🤔 Processing...")
+                    
+                    # Process any tool calls and get the updated messages
+                    tool_calls_processed, updated_messages = await process_tool_calls(
+                        self.client, 
+                        response, 
+                        messages_for_api, 
+                        self.tool_mapping
+                    )
+                    
+                    # Process tool responses to extract important data (images, charts)
+                    if updated_messages:
+                        # Look for image generation and code interpreter tool responses
+                        for msg in updated_messages:
+                            if msg.get('role') == 'tool' and msg.get('name') == 'generate_image':
+                                try:
+                                    tool_result = json.loads(msg.get('content', '{}'))
+                                    if tool_result.get('image_urls'):
+                                        image_urls.extend(tool_result['image_urls'])
+                                except:
+                                    pass
                             
-                            logging.info(f"Follow-up API call - Model: {model}, Input tokens: {follow_up_input_tokens}, Output tokens: {follow_up_output_tokens}, Additional cost: {format_cost(additional_cost)}")
+                            elif msg.get('role') == 'tool' and msg.get('name') == 'edit_image':
+                                try:
+                                    tool_result = json.loads(msg.get('content', '{}'))
+                                    if tool_result.get('image_url'):
+                                        image_urls.append(tool_result['image_url'])
+                                except:
+                                    pass
                             
-                            # Save additional token usage and cost to database
-                            await self.db.save_token_usage(user_id, model, follow_up_input_tokens, follow_up_output_tokens, additional_cost)
-            
-            reply = response.choices[0].message.content
+                            elif msg.get('role') == 'tool' and msg.get('name') in ['execute_python_code', 'analyze_data_file']:
+                                try:
+                                    tool_result = json.loads(msg.get('content', '{}'))
+                                    if tool_result.get('chart_id'):
+                                        chart_id = tool_result['chart_id']
+                                except:
+                                    pass
+                    
+                    # If tool calls were processed, make another API call with the updated messages
+                    if tool_calls_processed and updated_messages:
+                        # Prepare API parameters for follow-up call
+                        follow_up_params = {
+                            "model": model,
+                            "messages": updated_messages,
+                            "timeout": 240
+                        }
+                        
+                        # Add temperature only for models that support it (exclude GPT-5 family)
+                        if model in ["openai/gpt-4o", "openai/gpt-4o-mini"]:
+                            follow_up_params["temperature"] = 0.3
+                        elif model not in ["openai/gpt-5", "openai/gpt-5-nano", "openai/gpt-5-mini", "openai/gpt-5-chat"]:
+                            follow_up_params["temperature"] = 1
+                        
+                        response = await self.client.chat.completions.create(**follow_up_params)
+                        
+                        # Extract token usage and calculate cost for follow-up call
+                        if hasattr(response, 'usage') and response.usage:
+                            follow_up_input_tokens = getattr(response.usage, 'prompt_tokens', 0)
+                            follow_up_output_tokens = getattr(response.usage, 'completion_tokens', 0)
+                            
+                            input_tokens += follow_up_input_tokens
+                            output_tokens += follow_up_output_tokens
+                            
+                            # Calculate additional cost
+                            pricing = MODEL_PRICING.get(model)
+                            if pricing:
+                                additional_cost = pricing.calculate_cost(follow_up_input_tokens, follow_up_output_tokens)
+                                total_cost += additional_cost
+                                
+                                logging.info(f"Follow-up API call - Model: {model}, Input tokens: {follow_up_input_tokens}, Output tokens: {follow_up_output_tokens}, Additional cost: {format_cost(additional_cost)}")
+                                
+                                # Save additional token usage and cost to database
+                                await self.db.save_token_usage(user_id, model, follow_up_input_tokens, follow_up_output_tokens, additional_cost)
+                
+                reply = response.choices[0].message.content
             
             # Add image URLs to assistant content if any were found
             has_images = len(image_urls) > 0
@@ -1724,7 +1887,15 @@ print("\\n=== Correlation Analysis ===")
                     })
             
             # Store the response in history for models that support it
-            if model in ["openai/gpt-4o", "openai/gpt-4o-mini", "openai/gpt-5", "openai/gpt-5-nano", "openai/gpt-5-mini", "openai/gpt-5-chat", "openai/o1", "openai/o1-mini", "openai/o3-mini", "openai/gpt-4.1", "openai/gpt-4.1-nano", "openai/gpt-4.1-mini", "openai/o3", "openai/o4-mini", "openai/o1-preview"]:
+            models_with_history = [
+                "openai/gpt-4o", "openai/gpt-4o-mini", "openai/gpt-5", "openai/gpt-5-nano", 
+                "openai/gpt-5-mini", "openai/gpt-5-chat", "openai/o1", "openai/o1-mini", 
+                "openai/o3-mini", "openai/gpt-4.1", "openai/gpt-4.1-nano", "openai/gpt-4.1-mini", 
+                "openai/o3", "openai/o4-mini", "openai/o1-preview",
+                "anthropic/claude-sonnet-4-20250514", "anthropic/claude-opus-4-20250514",
+                "anthropic/claude-3.5-sonnet", "anthropic/claude-3.5-haiku"
+            ]
+            if model in models_with_history:
                 if model in ["openai/o1-mini", "openai/o1-preview"]:
                     # For models without system prompt support, keep track separately
                     if has_images:

src/utils/claude_utils.py

@@ -0,0 +1,531 @@
+"""
+Claude (Anthropic) API utility functions.
+
+This module provides utilities for interacting with Anthropic's Claude models,
+including message conversion and API calls compatible with the existing bot structure.
+"""
+
+import logging
+import json
+from typing import List, Dict, Any, Optional, Tuple
+
+
+def is_claude_model(model: str) -> bool:
+    """
+    Check if the model is a Claude/Anthropic model.
+    
+    Args:
+        model: Model name (e.g., "anthropic/claude-sonnet-4-20250514")
+        
+    Returns:
+        True if it's a Claude model, False otherwise
+    """
+    return model.startswith("anthropic/")
+
+
+def get_claude_model_id(model: str) -> str:
+    """
+    Extract the Claude model ID from the full model name.
+    
+    Args:
+        model: Full model name (e.g., "anthropic/claude-sonnet-4-20250514")
+        
+    Returns:
+        Claude model ID (e.g., "claude-sonnet-4-20250514")
+    """
+    if model.startswith("anthropic/"):
+        return model[len("anthropic/"):]
+    return model
+
+
+def convert_openai_messages_to_claude(messages: List[Dict[str, Any]]) -> Tuple[Optional[str], List[Dict[str, Any]]]:
+    """
+    Convert OpenAI message format to Claude message format.
+    
+    OpenAI uses:
+    - {"role": "system", "content": "..."}
+    - {"role": "user", "content": "..."}
+    - {"role": "assistant", "content": "..."}
+    
+    Claude uses:
+    - system parameter (separate from messages)
+    - {"role": "user", "content": "..."}
+    - {"role": "assistant", "content": "..."}
+    
+    Args:
+        messages: List of messages in OpenAI format
+        
+    Returns:
+        Tuple of (system_prompt, claude_messages)
+    """
+    system_prompt = None
+    claude_messages = []
+    
+    for msg in messages:
+        role = msg.get("role")
+        content = msg.get("content")
+        
+        # Skip messages with None content
+        if content is None:
+            continue
+        
+        if role == "system":
+            # Claude uses a separate system parameter
+            if isinstance(content, str):
+                system_prompt = content
+            elif isinstance(content, list):
+                # Extract text from list content
+                text_parts = []
+                for item in content:
+                    if isinstance(item, dict) and item.get("type") == "text":
+                        text_parts.append(item.get("text", ""))
+                    elif isinstance(item, str):
+                        text_parts.append(item)
+                system_prompt = " ".join(text_parts)
+        elif role in ["user", "assistant"]:
+            # Convert content format
+            converted_content = convert_content_to_claude(content)
+            if converted_content:
+                claude_messages.append({
+                    "role": role,
+                    "content": converted_content
+                })
+        elif role == "tool":
+            # Claude handles tool results differently - add as user message with tool result
+            tool_call_id = msg.get("tool_call_id", "")
+            tool_name = msg.get("name", "unknown")
+            claude_messages.append({
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": tool_call_id,
+                        "content": str(content)
+                    }
+                ]
+            })
+    
+    # Claude requires alternating user/assistant messages
+    # Merge consecutive messages of the same role
+    merged_messages = merge_consecutive_messages(claude_messages)
+    
+    return system_prompt, merged_messages
+
+
+def convert_content_to_claude(content: Any) -> Any:
+    """
+    Convert content from OpenAI format to Claude format.
+    
+    Args:
+        content: Content in OpenAI format (string or list)
+        
+    Returns:
+        Content in Claude format
+    """
+    if isinstance(content, str):
+        return content
+    
+    if isinstance(content, list):
+        claude_content = []
+        for item in content:
+            if isinstance(item, dict):
+                item_type = item.get("type")
+                
+                if item_type == "text":
+                    claude_content.append({
+                        "type": "text",
+                        "text": item.get("text", "")
+                    })
+                elif item_type == "image_url":
+                    # Convert image_url format to Claude format
+                    image_url_data = item.get("image_url", {})
+                    if isinstance(image_url_data, dict):
+                        url = image_url_data.get("url", "")
+                    else:
+                        url = str(image_url_data)
+                    
+                    if url:
+                        # Claude requires base64 data or URLs
+                        if url.startswith("data:"):
+                            # Parse base64 data URL
+                            try:
+                                media_type, base64_data = parse_data_url(url)
+                                claude_content.append({
+                                    "type": "image",
+                                    "source": {
+                                        "type": "base64",
+                                        "media_type": media_type,
+                                        "data": base64_data
+                                    }
+                                })
+                            except Exception as e:
+                                logging.warning(f"Failed to parse data URL: {e}")
+                        else:
+                            # Regular URL - Claude supports URLs directly
+                            claude_content.append({
+                                "type": "image",
+                                "source": {
+                                    "type": "url",
+                                    "url": url
+                                }
+                            })
+                else:
+                    # Handle other types as text
+                    if "text" in item:
+                        claude_content.append({
+                            "type": "text",
+                            "text": str(item.get("text", ""))
+                        })
+            elif isinstance(item, str):
+                claude_content.append({
+                    "type": "text",
+                    "text": item
+                })
+        
+        return claude_content if claude_content else None
+    
+    return str(content) if content else None
+
+
+def parse_data_url(data_url: str) -> Tuple[str, str]:
+    """
+    Parse a data URL into media type and base64 data.
+    
+    Args:
+        data_url: Data URL (e.g., "data:image/png;base64,...")
+        
+    Returns:
+        Tuple of (media_type, base64_data)
+        
+    Raises:
+        ValueError: If the data URL format is invalid
+    """
+    if not data_url.startswith("data:"):
+        raise ValueError(f"Not a data URL: expected 'data:' prefix, got '{data_url[:20]}...'")
+    
+    # Remove "data:" prefix
+    content = data_url[5:]
+    
+    # Split by semicolon and comma
+    parts = content.split(";base64,")
+    if len(parts) != 2:
+        raise ValueError(f"Invalid data URL format: expected ';base64,' separator, got '{content[:50]}...'")
+    
+    media_type = parts[0]
+    base64_data = parts[1]
+    
+    return media_type, base64_data
+
+
+def merge_consecutive_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """
+    Merge consecutive messages with the same role.
+    Claude requires alternating user/assistant messages.
+    
+    Args:
+        messages: List of messages
+        
+    Returns:
+        List of merged messages
+    """
+    if not messages:
+        return []
+    
+    merged = []
+    current_role = None
+    current_content = []
+    
+    for msg in messages:
+        role = msg.get("role")
+        content = msg.get("content")
+        
+        if role == current_role:
+            # Same role, merge content
+            if isinstance(content, str):
+                if current_content and isinstance(current_content[-1], dict) and current_content[-1].get("type") == "text":
+                    current_content[-1]["text"] += "\n" + content
+                else:
+                    current_content.append({"type": "text", "text": content})
+            elif isinstance(content, list):
+                current_content.extend(content)
+        else:
+            # Different role, save previous and start new
+            if current_role is not None and current_content:
+                merged.append({
+                    "role": current_role,
+                    "content": simplify_content(current_content)
+                })
+            
+            current_role = role
+            if isinstance(content, str):
+                current_content = [{"type": "text", "text": content}]
+            elif isinstance(content, list):
+                current_content = content.copy()
+            else:
+                current_content = []
+    
+    # Don't forget the last message
+    if current_role is not None and current_content:
+        merged.append({
+            "role": current_role,
+            "content": simplify_content(current_content)
+        })
+    
+    return merged
+
+
+def simplify_content(content: List[Dict[str, Any]]) -> Any:
+    """
+    Simplify content list to string if it only contains text.
+    
+    Args:
+        content: List of content items
+        
+    Returns:
+        Simplified content (string or list)
+    """
+    if not content:
+        return ""
+    
+    # If only one text item, return as string
+    if len(content) == 1 and content[0].get("type") == "text":
+        return content[0].get("text", "")
+    
+    # If all items are text, merge them
+    if all(item.get("type") == "text" for item in content):
+        texts = [item.get("text", "") for item in content]
+        return "\n".join(texts)
+    
+    return content
+
+
+def get_claude_tools() -> List[Dict[str, Any]]:
+    """
+    Get tool definitions for Claude API.
+    Claude uses a slightly different tool format than OpenAI.
+    
+    Returns:
+        List of tool definitions in Claude format
+    """
+    return [
+        {
+            "name": "google_search",
+            "description": "Search the web for current information",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "query": {"type": "string", "description": "The search query"},
+                    "num_results": {"type": "integer", "description": "Number of results (max 10)", "maximum": 10}
+                },
+                "required": ["query"]
+            }
+        },
+        {
+            "name": "scrape_webpage",
+            "description": "Extract and read content from a webpage URL",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "url": {"type": "string", "description": "The webpage URL to scrape"}
+                },
+                "required": ["url"]
+            }
+        },
+        {
+            "name": "execute_python_code",
+            "description": "Run Python code. Packages auto-install. Use load_file('file_id') for user files. Output files auto-sent to user.",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "code": {"type": "string", "description": "Python code to execute"},
+                    "timeout": {"type": "integer", "description": "Timeout in seconds", "maximum": 300}
+                },
+                "required": ["code"]
+            }
+        },
+        {
+            "name": "generate_image",
+            "description": "Create/generate images from text. Models: flux (best), flux-dev, sdxl, realistic (photos), anime, dreamshaper.",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "prompt": {"type": "string", "description": "Detailed description of the image to create"},
+                    "model": {"type": "string", "description": "Model to use", "enum": ["flux", "flux-dev", "sdxl", "realistic", "anime", "dreamshaper"]},
+                    "num_images": {"type": "integer", "description": "Number of images (1-4)", "maximum": 4},
+                    "aspect_ratio": {"type": "string", "description": "Aspect ratio preset", "enum": ["1:1", "16:9", "9:16", "4:3", "3:4", "3:2", "2:3", "21:9"]}
+                },
+                "required": ["prompt"]
+            }
+        },
+        {
+            "name": "set_reminder",
+            "description": "Set a reminder",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "content": {"type": "string", "description": "Reminder content"},
+                    "time": {"type": "string", "description": "Reminder time"}
+                },
+                "required": ["content", "time"]
+            }
+        },
+        {
+            "name": "get_reminders",
+            "description": "List all reminders",
+            "input_schema": {
+                "type": "object",
+                "properties": {}
+            }
+        },
+        {
+            "name": "upscale_image",
+            "description": "Enlarge/upscale an image to higher resolution. Pass 'latest_image' to use the user's most recently uploaded image.",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "image_url": {"type": "string", "description": "Pass 'latest_image' to use the user's most recently uploaded image"},
+                    "scale_factor": {"type": "integer", "description": "Scale factor (2 or 4)", "enum": [2, 4]},
+                    "model": {"type": "string", "description": "Upscale model", "enum": ["clarity", "ccsr", "sd-latent", "swinir"]}
+                },
+                "required": ["image_url"]
+            }
+        },
+        {
+            "name": "remove_background",
+            "description": "Remove background from an image. Pass 'latest_image' to use the user's most recently uploaded image.",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "image_url": {"type": "string", "description": "Pass 'latest_image' to use the user's most recently uploaded image"},
+                    "model": {"type": "string", "description": "Background removal model", "enum": ["bria", "rembg", "birefnet-base", "birefnet-general", "birefnet-portrait"]}
+                },
+                "required": ["image_url"]
+            }
+        },
+        {
+            "name": "image_to_text",
+            "description": "Generate a text description/caption of an image. Pass 'latest_image' to use the user's most recently uploaded image.",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "image_url": {"type": "string", "description": "Pass 'latest_image' to use the user's most recently uploaded image"}
+                },
+                "required": ["image_url"]
+            }
+        }
+    ]
+
+
+async def call_claude_api(
+    anthropic_client,
+    messages: List[Dict[str, Any]],
+    model: str,
+    max_tokens: int = 4096,
+    use_tools: bool = True
+) -> Dict[str, Any]:
+    """
+    Call the Claude API with the given messages.
+    
+    Args:
+        anthropic_client: Anthropic client instance
+        messages: List of messages in OpenAI format
+        model: Model name (e.g., "anthropic/claude-sonnet-4-20250514")
+        max_tokens: Maximum tokens in response
+        use_tools: Whether to include tools
+        
+    Returns:
+        Dict with response data including:
+        - content: Response text
+        - input_tokens: Number of input tokens
+        - output_tokens: Number of output tokens
+        - tool_calls: Any tool calls made
+        - stop_reason: Why the response stopped
+    """
+    try:
+        # Convert messages
+        system_prompt, claude_messages = convert_openai_messages_to_claude(messages)
+        
+        # Get Claude model ID
+        model_id = get_claude_model_id(model)
+        
+        # Build API parameters
+        api_params = {
+            "model": model_id,
+            "max_tokens": max_tokens,
+            "messages": claude_messages
+        }
+        
+        if system_prompt:
+            api_params["system"] = system_prompt
+        
+        if use_tools:
+            api_params["tools"] = get_claude_tools()
+        
+        # Make API call
+        response = await anthropic_client.messages.create(**api_params)
+        
+        # Extract response data
+        result = {
+            "content": "",
+            "input_tokens": response.usage.input_tokens if response.usage else 0,
+            "output_tokens": response.usage.output_tokens if response.usage else 0,
+            "tool_calls": [],
+            "stop_reason": response.stop_reason
+        }
+        
+        # Process content blocks
+        for block in response.content:
+            if block.type == "text":
+                result["content"] += block.text
+            elif block.type == "tool_use":
+                result["tool_calls"].append({
+                    "id": block.id,
+                    "type": "function",
+                    "function": {
+                        "name": block.name,
+                        "arguments": json.dumps(block.input)
+                    }
+                })
+        
+        return result
+        
+    except Exception as e:
+        logging.error(f"Error calling Claude API: {e}")
+        raise
+
+
+def convert_claude_tool_calls_to_openai(tool_calls: List[Dict[str, Any]]) -> List[Any]:
+    """
+    Convert Claude tool calls to OpenAI format for compatibility with existing code.
+    
+    Args:
+        tool_calls: Tool calls from Claude API
+        
+    Returns:
+        Tool calls in OpenAI format
+    """
+    from dataclasses import dataclass
+    
+    @dataclass
+    class FunctionCall:
+        name: str
+        arguments: str
+    
+    @dataclass
+    class ToolCall:
+        id: str
+        type: str
+        function: FunctionCall
+    
+    result = []
+    for tc in tool_calls:
+        result.append(ToolCall(
+            id=tc["id"],
+            type=tc["type"],
+            function=FunctionCall(
+                name=tc["function"]["name"],
+                arguments=tc["function"]["arguments"]
+            )
+        ))
+    
+    return result

tests/test_comprehensive.py

@@ -89,6 +89,10 @@ class TestPricingModule:
             "openai/gpt-4.1",
             "openai/gpt-5",
             "openai/o1",
+            "anthropic/claude-sonnet-4-20250514",
+            "anthropic/claude-opus-4-20250514",
+            "anthropic/claude-3.5-sonnet",
+            "anthropic/claude-3.5-haiku",
         ]
         
         for model in expected_models:
@@ -105,6 +109,10 @@ class TestPricingModule:
         # Test smaller amounts
         cost = calculate_cost("openai/gpt-4o", 1000, 1000)
         assert cost == pytest.approx(0.025, rel=1e-6)  # $0.005 + $0.020
+        
+        # Test Claude model
+        cost = calculate_cost("anthropic/claude-3.5-sonnet", 1_000_000, 1_000_000)
+        assert cost == 18.00  # $3 + $15
     
     def test_calculate_cost_unknown_model(self):
         """Test that unknown models return 0 cost."""
@@ -404,6 +412,92 @@ class TestCodeInterpreterSecurity:
         assert fm._detect_file_type("unknown.xyz") == "binary"
 
 
+# ============================================================
+# Claude Utils Tests
+# ============================================================
+
+class TestClaudeUtils:
+    """Tests for Claude utility functions."""
+    
+    def test_is_claude_model(self):
+        """Test Claude model detection."""
+        from src.utils.claude_utils import is_claude_model
+        
+        # Claude models
+        assert is_claude_model("anthropic/claude-sonnet-4-20250514") == True
+        assert is_claude_model("anthropic/claude-opus-4-20250514") == True
+        assert is_claude_model("anthropic/claude-3.5-sonnet") == True
+        assert is_claude_model("anthropic/claude-3.5-haiku") == True
+        
+        # Non-Claude models
+        assert is_claude_model("openai/gpt-4o") == False
+        assert is_claude_model("openai/gpt-4o-mini") == False
+        assert is_claude_model("gpt-4") == False
+    
+    def test_get_claude_model_id(self):
+        """Test Claude model ID extraction."""
+        from src.utils.claude_utils import get_claude_model_id
+        
+        assert get_claude_model_id("anthropic/claude-sonnet-4-20250514") == "claude-sonnet-4-20250514"
+        assert get_claude_model_id("anthropic/claude-3.5-sonnet") == "claude-3.5-sonnet"
+        assert get_claude_model_id("claude-3.5-sonnet") == "claude-3.5-sonnet"
+    
+    def test_convert_openai_messages_to_claude(self):
+        """Test message conversion from OpenAI to Claude format."""
+        from src.utils.claude_utils import convert_openai_messages_to_claude
+        
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "Hi there!"},
+            {"role": "user", "content": "How are you?"},
+        ]
+        
+        system, claude_messages = convert_openai_messages_to_claude(messages)
+        
+        # System should be extracted
+        assert system == "You are a helpful assistant."
+        
+        # Messages should not contain system
+        assert all(m.get("role") != "system" for m in claude_messages)
+        
+        # Should have user and assistant messages
+        assert len(claude_messages) >= 2
+    
+    def test_convert_content_to_claude(self):
+        """Test content conversion."""
+        from src.utils.claude_utils import convert_content_to_claude
+        
+        # String content
+        assert convert_content_to_claude("Hello") == "Hello"
+        
+        # List content with text
+        list_content = [
+            {"type": "text", "text": "Hello"},
+            {"type": "text", "text": "World"}
+        ]
+        result = convert_content_to_claude(list_content)
+        assert isinstance(result, list)
+        assert len(result) == 2
+    
+    def test_merge_consecutive_messages(self):
+        """Test merging consecutive messages with same role."""
+        from src.utils.claude_utils import merge_consecutive_messages
+        
+        messages = [
+            {"role": "user", "content": "Hello"},
+            {"role": "user", "content": "How are you?"},
+            {"role": "assistant", "content": "Hi!"},
+        ]
+        
+        merged = merge_consecutive_messages(messages)
+        
+        # Should merge two user messages into one
+        assert len(merged) == 2
+        assert merged[0]["role"] == "user"
+        assert merged[1]["role"] == "assistant"
+
+
 # ============================================================
 # OpenAI Utils Tests
 # ============================================================