hermes-agent/run_agent.py

#!/usr/bin/env python3
"""
AI Agent Runner with Tool Calling

This module provides a clean, standalone agent that can execute AI models
with tool calling capabilities. It handles the conversation loop, tool execution,
and response management.

Features:
- Automatic tool calling loop until completion
- Configurable model parameters
- Error handling and recovery
- Message history management
- Support for multiple model providers

Usage:
    from run_agent import AIAgent
    
    agent = AIAgent(base_url="http://localhost:30000/v1", model="claude-opus-4-20250514")
    response = agent.run_conversation("Tell me about the latest Python updates")
"""

import json
import logging
import os
import time
from typing import List, Dict, Any, Optional
from openai import OpenAI
import fire
from datetime import datetime
from pathlib import Path

# Load environment variables from .env file
from dotenv import load_dotenv

# Load .env file if it exists
env_path = Path(__file__).parent / '.env'
if env_path.exists():
    load_dotenv(dotenv_path=env_path)
    print(f"✅ Loaded environment variables from {env_path}")
else:
    print(f"ℹ️  No .env file found at {env_path}. Using system environment variables.")

# Import our tool system
from model_tools import get_tool_definitions, handle_function_call, check_toolset_requirements
from tools.terminal_tool import cleanup_vm


class AIAgent:
    """
    AI Agent with tool calling capabilities.
    
    This class manages the conversation flow, tool execution, and response handling
    for AI models that support function calling.
    """
    
    def __init__(
        self,
        base_url: str = None,
        api_key: str = None,
        model: str = "gpt-4",
        max_iterations: int = 10,
        tool_delay: float = 1.0,
        enabled_toolsets: List[str] = None,
        disabled_toolsets: List[str] = None,
        save_trajectories: bool = False,
        verbose_logging: bool = False,
        ephemeral_system_prompt: str = None
    ):
        """
        Initialize the AI Agent.

        Args:
            base_url (str): Base URL for the model API (optional)
            api_key (str): API key for authentication (optional, uses env var if not provided)
            model (str): Model name to use (default: "gpt-4")
            max_iterations (int): Maximum number of tool calling iterations (default: 10)
            tool_delay (float): Delay between tool calls in seconds (default: 1.0)
            enabled_toolsets (List[str]): Only enable tools from these toolsets (optional)
            disabled_toolsets (List[str]): Disable tools from these toolsets (optional)
            save_trajectories (bool): Whether to save conversation trajectories to JSONL files (default: False)
            verbose_logging (bool): Enable verbose logging for debugging (default: False)
            ephemeral_system_prompt (str): System prompt used during agent execution but NOT saved to trajectories (optional)
        """
        self.model = model
        self.max_iterations = max_iterations
        self.tool_delay = tool_delay
        self.save_trajectories = save_trajectories
        self.verbose_logging = verbose_logging
        self.ephemeral_system_prompt = ephemeral_system_prompt

        # Store toolset filtering options
        self.enabled_toolsets = enabled_toolsets
        self.disabled_toolsets = disabled_toolsets
        
        # Configure logging
        if self.verbose_logging:
            logging.basicConfig(
                level=logging.DEBUG,
                format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
                datefmt='%H:%M:%S'
            )
            # Keep OpenAI and httpx at INFO level to avoid massive base64 logs
            # Even in verbose mode, we don't want to see full request/response bodies
            logging.getLogger('openai').setLevel(logging.INFO)
            logging.getLogger('httpx').setLevel(logging.WARNING)
            print("🔍 Verbose logging enabled (OpenAI/httpx request bodies suppressed)")
        else:
            # Set logging to INFO level for important messages only
            logging.basicConfig(
                level=logging.INFO,
                format='%(asctime)s - %(levelname)s - %(message)s',
                datefmt='%H:%M:%S'
            )
            # Reduce OpenAI client logging
            logging.getLogger('openai').setLevel(logging.WARNING)
            logging.getLogger('httpx').setLevel(logging.WARNING)
        
        # Initialize OpenAI client
        client_kwargs = {}
        if base_url:
            client_kwargs["base_url"] = base_url
        if api_key:
            client_kwargs["api_key"] = api_key
        else:
            client_kwargs["api_key"] = os.getenv("ANTHROPIC_API_KEY", "dummy-key")
        
        try:
            self.client = OpenAI(**client_kwargs)
            print(f"🤖 AI Agent initialized with model: {self.model}")
            if base_url:
                print(f"🔗 Using custom base URL: {base_url}")
        except Exception as e:
            raise RuntimeError(f"Failed to initialize OpenAI client: {e}")
        
        # Get available tools with filtering
        self.tools = get_tool_definitions(
            enabled_toolsets=enabled_toolsets,
            disabled_toolsets=disabled_toolsets
        )
        
        # Show tool configuration
        if self.tools:
            tool_names = [tool["function"]["name"] for tool in self.tools]
            print(f"🛠️  Loaded {len(self.tools)} tools: {', '.join(tool_names)}")
            
            # Show filtering info if applied
            if enabled_toolsets:
                print(f"   ✅ Enabled toolsets: {', '.join(enabled_toolsets)}")
            if disabled_toolsets:
                print(f"   ❌ Disabled toolsets: {', '.join(disabled_toolsets)}")
        else:
            print("🛠️  No tools loaded (all tools filtered out or unavailable)")
        
        # Check tool requirements
        if self.tools:
            requirements = check_toolset_requirements()
            missing_reqs = [name for name, available in requirements.items() if not available]
            if missing_reqs:
                print(f"⚠️  Some tools may not work due to missing requirements: {missing_reqs}")
        
        # Show trajectory saving status
        if self.save_trajectories:
            print("📝 Trajectory saving enabled")
        
        # Show ephemeral system prompt status
        if self.ephemeral_system_prompt:
            prompt_preview = self.ephemeral_system_prompt[:60] + "..." if len(self.ephemeral_system_prompt) > 60 else self.ephemeral_system_prompt
            print(f"🔒 Ephemeral system prompt: '{prompt_preview}' (not saved to trajectories)")
    
    def _format_tools_for_system_message(self) -> str:
        """
        Format tool definitions for the system message in the trajectory format.
        
        Returns:
            str: JSON string representation of tool definitions
        """
        if not self.tools:
            return "[]"
        
        # Convert tool definitions to the format expected in trajectories
        formatted_tools = []
        for tool in self.tools:
            func = tool["function"]
            formatted_tool = {
                "name": func["name"],
                "description": func.get("description", ""),
                "parameters": func.get("parameters", {}),
                "required": None  # Match the format in the example
            }
            formatted_tools.append(formatted_tool)
        
        return json.dumps(formatted_tools, ensure_ascii=False)
    
    def _convert_to_trajectory_format(self, messages: List[Dict[str, Any]], user_query: str, completed: bool) -> List[Dict[str, Any]]:
        """
        Convert internal message format to trajectory format for saving.
        
        Args:
            messages (List[Dict]): Internal message history
            user_query (str): Original user query
            completed (bool): Whether the conversation completed successfully
            
        Returns:
            List[Dict]: Messages in trajectory format
        """
        trajectory = []
        
        # Add system message with tool definitions
        system_msg = (
            "You are a function calling AI model. You are provided with function signatures within <tools> </tools> XML tags. "
            "You may call one or more functions to assist with the user query. If available tools are not relevant in assisting "
            "with user query, just respond in natural conversational language. Don't make assumptions about what values to plug "
            "into functions. After calling & executing the functions, you will be provided with function results within "
            "<tool_response> </tool_response> XML tags. Here are the available tools:\n"
            f"<tools>\n{self._format_tools_for_system_message()}\n</tools>\n"
            "For each function call return a JSON object, with the following pydantic model json schema for each:\n"
            "{'title': 'FunctionCall', 'type': 'object', 'properties': {'name': {'title': 'Name', 'type': 'string'}, "
            "'arguments': {'title': 'Arguments', 'type': 'object'}}, 'required': ['name', 'arguments']}\n"
            "Each function call should be enclosed within <tool_call> </tool_call> XML tags.\n"
            "Example:\n<tool_call>\n{'name': <function-name>,'arguments': <args-dict>}\n</tool_call>"
        )
        
        trajectory.append({
            "from": "system",
            "value": system_msg
        })
        
        # Add the initial user message
        trajectory.append({
            "from": "human",
            "value": user_query
        })
        
        # Process remaining messages
        i = 1  # Skip the first user message as we already added it
        while i < len(messages):
            msg = messages[i]
            
            if msg["role"] == "assistant":
                # Check if this message has tool calls
                if "tool_calls" in msg and msg["tool_calls"]:
                    # Format assistant message with tool calls
                    content = ""
                    if msg.get("content") and msg["content"].strip():
                        content = msg["content"] + "\n"
                    
                    # Add tool calls wrapped in XML tags
                    for tool_call in msg["tool_calls"]:
                        tool_call_json = {
                            "name": tool_call["function"]["name"],
                            "arguments": json.loads(tool_call["function"]["arguments"]) if isinstance(tool_call["function"]["arguments"], str) else tool_call["function"]["arguments"]
                        }
                        content += f"<tool_call>\n{json.dumps(tool_call_json, ensure_ascii=False)}\n</tool_call>\n"
                    
                    trajectory.append({
                        "from": "gpt",
                        "value": content.rstrip()
                    })
                    
                    # Collect all subsequent tool responses
                    tool_responses = []
                    j = i + 1
                    while j < len(messages) and messages[j]["role"] == "tool":
                        tool_msg = messages[j]
                        # Format tool response with XML tags
                        tool_response = f"<tool_response>\n"
                        
                        # Try to parse tool content as JSON if it looks like JSON
                        tool_content = tool_msg["content"]
                        try:
                            if tool_content.strip().startswith(("{", "[")):
                                tool_content = json.loads(tool_content)
                        except (json.JSONDecodeError, AttributeError):
                            pass  # Keep as string if not valid JSON
                        
                        tool_response += json.dumps({
                            "tool_call_id": tool_msg.get("tool_call_id", ""),
                            "name": msg["tool_calls"][len(tool_responses)]["function"]["name"] if len(tool_responses) < len(msg["tool_calls"]) else "unknown",
                            "content": tool_content
                        }, ensure_ascii=False)
                        tool_response += "\n</tool_response>"
                        tool_responses.append(tool_response)
                        j += 1
                    
                    # Add all tool responses as a single message
                    if tool_responses:
                        trajectory.append({
                            "from": "tool",
                            "value": "\n".join(tool_responses)
                        })
                        i = j - 1  # Skip the tool messages we just processed
                
                else:
                    # Regular assistant message without tool calls
                    trajectory.append({
                        "from": "gpt",
                        "value": msg["content"] or ""
                    })
            
            elif msg["role"] == "user":
                trajectory.append({
                    "from": "human",
                    "value": msg["content"]
                })
            
            i += 1
        
        return trajectory
    
    def _save_trajectory(self, messages: List[Dict[str, Any]], user_query: str, completed: bool):
        """
        Save conversation trajectory to JSONL file.
        
        Args:
            messages (List[Dict]): Complete message history
            user_query (str): Original user query
            completed (bool): Whether the conversation completed successfully
        """
        if not self.save_trajectories:
            return
        
        # Convert messages to trajectory format
        trajectory = self._convert_to_trajectory_format(messages, user_query, completed)
        
        # Determine which file to save to
        filename = "trajectory_samples.jsonl" if completed else "failed_trajectories.jsonl"
        
        # Create trajectory entry
        entry = {
            "conversations": trajectory,
            "timestamp": datetime.now().isoformat(),
            "model": self.model,
            "completed": completed
        }
        
        # Append to JSONL file
        try:
            with open(filename, "a", encoding="utf-8") as f:
                f.write(json.dumps(entry, ensure_ascii=False) + "\n")
            print(f"💾 Trajectory saved to {filename}")
        except Exception as e:
            print(f"⚠️ Failed to save trajectory: {e}")
    
    def run_conversation(
        self,
        user_message: str,
        system_message: str = None,
        conversation_history: List[Dict[str, Any]] = None,
        task_id: str = None
    ) -> Dict[str, Any]:
        """
        Run a complete conversation with tool calling until completion.

        Args:
            user_message (str): The user's message/question
            system_message (str): Custom system message (optional, overrides ephemeral_system_prompt if provided)
            conversation_history (List[Dict]): Previous conversation messages (optional)
            task_id (str): Unique identifier for this task to isolate VMs between concurrent tasks (optional, auto-generated if not provided)

        Returns:
            Dict: Complete conversation result with final response and message history
        """
        # Generate unique task_id if not provided to isolate VMs between concurrent tasks
        import uuid
        effective_task_id = task_id or str(uuid.uuid4())
        # Initialize conversation
        messages = conversation_history or []
        
        # Add user message
        messages.append({
            "role": "user",
            "content": user_message
        })
        
        print(f"💬 Starting conversation: '{user_message[:60]}{'...' if len(user_message) > 60 else ''}'")
        
        # Determine which system prompt to use for API calls (ephemeral)
        # Priority: explicit system_message > ephemeral_system_prompt > None
        active_system_prompt = system_message if system_message is not None else self.ephemeral_system_prompt
        
        # Main conversation loop
        api_call_count = 0
        final_response = None
        
        while api_call_count < self.max_iterations:
            api_call_count += 1
            print(f"\n🔄 Making API call #{api_call_count}...")
            
            # Log request details if verbose
            if self.verbose_logging:
                logging.debug(f"API Request - Model: {self.model}, Messages: {len(messages)}, Tools: {len(self.tools) if self.tools else 0}")
                logging.debug(f"Last message role: {messages[-1]['role'] if messages else 'none'}")
            
            api_start_time = time.time()
            retry_count = 0
            max_retries = 3
            
            while retry_count <= max_retries:
                try:
                    # Prepare messages for API call
                    # If we have an ephemeral system prompt, prepend it to the messages
                    api_messages = messages.copy()
                    if active_system_prompt:
                        # Insert system message at the beginning
                        api_messages = [{"role": "system", "content": active_system_prompt}] + api_messages
                    
                    # Make API call with tools
                    response = self.client.chat.completions.create(
                        model=self.model,
                        messages=api_messages,
                        tools=self.tools if self.tools else None,
                        timeout=60.0  # Add explicit timeout
                    )
                    
                    api_duration = time.time() - api_start_time
                    print(f"⏱️  API call completed in {api_duration:.2f}s")
                    
                    if self.verbose_logging:
                        logging.debug(f"API Response received - Usage: {response.usage if hasattr(response, 'usage') else 'N/A'}")
                    
                    break  # Success, exit retry loop
                    
                except Exception as api_error:
                    retry_count += 1
                    if retry_count > max_retries:
                        raise api_error
                    
                    wait_time = min(2 ** retry_count, 10)  # Exponential backoff, max 10s
                    print(f"⚠️  API call failed (attempt {retry_count}/{max_retries}): {str(api_error)[:100]}")
                    print(f"⏳ Retrying in {wait_time}s...")
                    logging.warning(f"API retry {retry_count}/{max_retries} after error: {api_error}")
                    time.sleep(wait_time)
            
            try:
                assistant_message = response.choices[0].message
                
                # Handle assistant response
                if assistant_message.content:
                    print(f"🤖 Assistant: {assistant_message.content[:100]}{'...' if len(assistant_message.content) > 100 else ''}")
                
                # Check for tool calls
                if assistant_message.tool_calls:
                    print(f"🔧 Processing {len(assistant_message.tool_calls)} tool call(s)...")
                    
                    if self.verbose_logging:
                        for tc in assistant_message.tool_calls:
                            logging.debug(f"Tool call: {tc.function.name} with args: {tc.function.arguments[:200]}...")
                    
                    # Add assistant message with tool calls to conversation
                    messages.append({
                        "role": "assistant",
                        "content": assistant_message.content,
                        "tool_calls": [
                            {
                                "id": tool_call.id,
                                "type": tool_call.type,
                                "function": {
                                    "name": tool_call.function.name,
                                    "arguments": tool_call.function.arguments
                                }
                            }
                            for tool_call in assistant_message.tool_calls
                        ]
                    })
                    
                    # Execute each tool call
                    for i, tool_call in enumerate(assistant_message.tool_calls, 1):
                        function_name = tool_call.function.name
                        
                        try:
                            function_args = json.loads(tool_call.function.arguments)
                        except json.JSONDecodeError as e:
                            print(f"❌ Invalid JSON in tool call arguments: {e}")
                            function_args = {}
                        
                        # Preview tool call arguments (first 20 chars)
                        args_str = json.dumps(function_args, ensure_ascii=False)
                        args_preview = args_str[:20] + "..." if len(args_str) > 20 else args_str
                        print(f"  📞 Tool {i}: {function_name}({list(function_args.keys())}) - {args_preview}")

                        tool_start_time = time.time()

                        # Execute the tool with task_id to isolate VMs between concurrent tasks
                        function_result = handle_function_call(function_name, function_args, effective_task_id)

                        tool_duration = time.time() - tool_start_time
                        result_preview = function_result[:200] if len(function_result) > 200 else function_result

                        if self.verbose_logging:
                            logging.debug(f"Tool {function_name} completed in {tool_duration:.2f}s")
                            logging.debug(f"Tool result preview: {result_preview}...")

                        # Add tool result to conversation
                        messages.append({
                            "role": "tool",
                            "content": function_result,
                            "tool_call_id": tool_call.id
                        })

                        # Preview tool response (first 20 chars)
                        response_preview = function_result[:20] + "..." if len(function_result) > 20 else function_result
                        print(f"  ✅ Tool {i} completed in {tool_duration:.2f}s - {response_preview}")
                        
                        # Delay between tool calls
                        if self.tool_delay > 0 and i < len(assistant_message.tool_calls):
                            time.sleep(self.tool_delay)
                    
                    # Continue loop for next response
                    continue
                
                else:
                    # No tool calls - this is the final response
                    final_response = assistant_message.content or ""
                    
                    # Add final assistant message
                    messages.append({
                        "role": "assistant", 
                        "content": final_response
                    })
                    
                    print(f"🎉 Conversation completed after {api_call_count} API call(s)")
                    break
                
            except Exception as e:
                error_msg = f"Error during API call #{api_call_count}: {str(e)}"
                print(f"❌ {error_msg}")
                
                if self.verbose_logging:
                    logging.exception("Detailed error information:")
                
                # Add error to conversation and try to continue
                messages.append({
                    "role": "assistant",
                    "content": f"I encountered an error: {error_msg}. Let me try a different approach."
                })
                
                # If we're near the limit, break to avoid infinite loops
                if api_call_count >= self.max_iterations - 1:
                    final_response = f"I apologize, but I encountered repeated errors: {error_msg}"
                    break
        
        # Handle max iterations reached
        if api_call_count >= self.max_iterations:
            print(f"⚠️  Reached maximum iterations ({self.max_iterations}). Stopping to prevent infinite loop.")
            if final_response is None:
                final_response = "I've reached the maximum number of iterations. Here's what I found so far."
        
        # Determine if conversation completed successfully
        completed = final_response is not None and api_call_count < self.max_iterations

        # Save trajectory if enabled
        self._save_trajectory(messages, user_message, completed)

        # Clean up VM for this task after conversation completes
        try:
            cleanup_vm(effective_task_id)
        except Exception as e:
            if self.verbose_logging:
                logging.warning(f"Failed to cleanup VM for task {effective_task_id}: {e}")

        return {
            "final_response": final_response,
            "messages": messages,
            "api_calls": api_call_count,
            "completed": completed
        }
    
    def chat(self, message: str) -> str:
        """
        Simple chat interface that returns just the final response.
        
        Args:
            message (str): User message
            
        Returns:
            str: Final assistant response
        """
        result = self.run_conversation(message)
        return result["final_response"]


def main(
    query: str = None,
    model: str = "claude-opus-4-20250514", 
    api_key: str = None,
    base_url: str = "https://api.anthropic.com/v1/",
    max_turns: int = 10,
    enabled_toolsets: str = None,
    disabled_toolsets: str = None,
    list_tools: bool = False,
    save_trajectories: bool = False,
    verbose: bool = False
):
    """
    Main function for running the agent directly.
    
    Args:
        query (str): Natural language query for the agent. Defaults to Python 3.13 example.
        model (str): Model name to use. Defaults to claude-opus-4-20250514.
        api_key (str): API key for authentication. Uses ANTHROPIC_API_KEY env var if not provided.
        base_url (str): Base URL for the model API. Defaults to https://api.anthropic.com/v1/
        max_turns (int): Maximum number of API call iterations. Defaults to 10.
        enabled_toolsets (str): Comma-separated list of toolsets to enable. Supports predefined 
                              toolsets (e.g., "research", "development", "safe"). 
                              Multiple toolsets can be combined: "web,vision"
        disabled_toolsets (str): Comma-separated list of toolsets to disable (e.g., "terminal")
        list_tools (bool): Just list available tools and exit
        save_trajectories (bool): Save conversation trajectories to JSONL files. Defaults to False.
        verbose (bool): Enable verbose logging for debugging. Defaults to False.
        
    Toolset Examples:
        - "research": Web search, extract, crawl + vision tools
    """
    print("🤖 AI Agent with Tool Calling")
    print("=" * 50)
    
    # Handle tool listing
    if list_tools:
        from model_tools import get_all_tool_names, get_toolset_for_tool, get_available_toolsets
        from toolsets import get_all_toolsets, get_toolset_info
        
        print("📋 Available Tools & Toolsets:")
        print("-" * 50)
        
        # Show new toolsets system
        print("\n🎯 Predefined Toolsets (New System):")
        print("-" * 40)
        all_toolsets = get_all_toolsets()
        
        # Group by category
        basic_toolsets = []
        composite_toolsets = []
        scenario_toolsets = []
        
        for name, toolset in all_toolsets.items():
            info = get_toolset_info(name)
            if info:
                entry = (name, info)
                if name in ["web", "terminal", "vision", "creative", "reasoning"]:
                    basic_toolsets.append(entry)
                elif name in ["research", "development", "analysis", "content_creation", "full_stack"]:
                    composite_toolsets.append(entry)
                else:
                    scenario_toolsets.append(entry)
        
        # Print basic toolsets
        print("\n📌 Basic Toolsets:")
        for name, info in basic_toolsets:
            tools_str = ', '.join(info['resolved_tools']) if info['resolved_tools'] else 'none'
            print(f"  • {name:15} - {info['description']}")
            print(f"    Tools: {tools_str}")
        
        # Print composite toolsets
        print("\n📂 Composite Toolsets (built from other toolsets):")
        for name, info in composite_toolsets:
            includes_str = ', '.join(info['includes']) if info['includes'] else 'none'
            print(f"  • {name:15} - {info['description']}")
            print(f"    Includes: {includes_str}")
            print(f"    Total tools: {info['tool_count']}")
        
        # Print scenario-specific toolsets
        print("\n🎭 Scenario-Specific Toolsets:")
        for name, info in scenario_toolsets:
            print(f"  • {name:20} - {info['description']}")
            print(f"    Total tools: {info['tool_count']}")
        
        
        # Show legacy toolset compatibility
        print("\n📦 Legacy Toolsets (for backward compatibility):")
        legacy_toolsets = get_available_toolsets()
        for name, info in legacy_toolsets.items():
            status = "✅" if info["available"] else "❌"
            print(f"  {status} {name}: {info['description']}")
            if not info["available"]:
                print(f"    Requirements: {', '.join(info['requirements'])}")
        
        # Show individual tools
        all_tools = get_all_tool_names()
        print(f"\n🔧 Individual Tools ({len(all_tools)} available):")
        for tool_name in sorted(all_tools):
            toolset = get_toolset_for_tool(tool_name)
            print(f"  📌 {tool_name} (from {toolset})")
        
        print(f"\n💡 Usage Examples:")
        print(f"  # Use predefined toolsets")
        print(f"  python run_agent.py --enabled_toolsets=research --query='search for Python news'")
        print(f"  python run_agent.py --enabled_toolsets=development --query='debug this code'")
        print(f"  python run_agent.py --enabled_toolsets=safe --query='analyze without terminal'")
        print(f"  ")
        print(f"  # Combine multiple toolsets")
        print(f"  python run_agent.py --enabled_toolsets=web,vision --query='analyze website'")
        print(f"  ")
        print(f"  # Disable toolsets")
        print(f"  python run_agent.py --disabled_toolsets=terminal --query='no command execution'")
        print(f"  ")
        print(f"  # Run with trajectory saving enabled")
        print(f"  python run_agent.py --save_trajectories --query='your question here'")
        return
    
    # Parse toolset selection arguments
    enabled_toolsets_list = None
    disabled_toolsets_list = None
    
    if enabled_toolsets:
        enabled_toolsets_list = [t.strip() for t in enabled_toolsets.split(",")]
        print(f"🎯 Enabled toolsets: {enabled_toolsets_list}")
    
    if disabled_toolsets:
        disabled_toolsets_list = [t.strip() for t in disabled_toolsets.split(",")]
        print(f"🚫 Disabled toolsets: {disabled_toolsets_list}")
    
    if save_trajectories:
        print(f"💾 Trajectory saving: ENABLED")
        print(f"   - Successful conversations → trajectory_samples.jsonl")
        print(f"   - Failed conversations → failed_trajectories.jsonl")
    
    # Initialize agent with provided parameters
    try:
        agent = AIAgent(
            base_url=base_url,
            model=model,
            api_key=api_key,
            max_iterations=max_turns,
            enabled_toolsets=enabled_toolsets_list,
            disabled_toolsets=disabled_toolsets_list,
            save_trajectories=save_trajectories,
            verbose_logging=verbose
        )
    except RuntimeError as e:
        print(f"❌ Failed to initialize agent: {e}")
        return
    
    # Use provided query or default to Python 3.13 example
    if query is None:
        user_query = (
            "Tell me about the latest developments in Python 3.13 and what new features "
            "developers should know about. Please search for current information and try it out."
        )
    else:
        user_query = query
    
    print(f"\n📝 User Query: {user_query}")
    print("\n" + "=" * 50)
    
    # Run conversation
    result = agent.run_conversation(user_query)
    
    print("\n" + "=" * 50)
    print("📋 CONVERSATION SUMMARY")
    print("=" * 50)
    print(f"✅ Completed: {result['completed']}")
    print(f"📞 API Calls: {result['api_calls']}")
    print(f"💬 Messages: {len(result['messages'])}")
    
    if result['final_response']:
        print(f"\n🎯 FINAL RESPONSE:")
        print("-" * 30)
        print(result['final_response'])
    
    print("\n👋 Agent execution completed!")


if __name__ == "__main__":
    fire.Fire(main)