agent/run.py

import os
import json
import re
from uuid import uuid4
from typing import Optional

# from agent.tools.message_tool import MessageTool
from agent.tools.message_tool import MessageTool
from agent.tools.sb_deploy_tool import SandboxDeployTool
from agent.tools.sb_expose_tool import SandboxExposeTool
from agent.tools.web_search_tool import WebSearchTool
from dotenv import load_dotenv
from utils.config import config

from agentpress.thread_manager import ThreadManager
from agentpress.response_processor import ProcessorConfig
from agent.tools.sb_shell_tool import SandboxShellTool
from agent.tools.sb_files_tool import SandboxFilesTool
from agent.tools.sb_browser_tool import SandboxBrowserTool
from agent.tools.data_providers_tool import DataProvidersTool
from agent.prompt import get_system_prompt
from utils import logger
from utils.auth_utils import get_account_id_from_thread
from services.billing import check_billing_status
from agent.tools.sb_vision_tool import SandboxVisionTool

load_dotenv()

async def run_agent(
    thread_id: str,
    project_id: str,
    stream: bool,
    thread_manager: Optional[ThreadManager] = None,
    native_max_auto_continues: int = 25,
    max_iterations: int = 150,
    model_name: str = "anthropic/claude-3-7-sonnet-latest",
    enable_thinking: Optional[bool] = False,
    reasoning_effort: Optional[str] = 'low',
    enable_context_manager: bool = True
):
    """Run the development agent with specified configuration."""
    print(f"🚀 Starting agent with model: {model_name}")

    thread_manager = ThreadManager()

    client = await thread_manager.db.client

    # Get account ID from thread for billing checks
    account_id = await get_account_id_from_thread(client, thread_id)
    if not account_id:
        raise ValueError("Could not determine account ID for thread")

    # Get sandbox info from project
    project = await client.table('projects').select('*').eq('project_id', project_id).execute()
    if not project.data or len(project.data) == 0:
        raise ValueError(f"Project {project_id} not found")

    project_data = project.data[0]
    sandbox_info = project_data.get('sandbox', {})
    if not sandbox_info.get('id'):
        raise ValueError(f"No sandbox found for project {project_id}")

    # Initialize tools with project_id instead of sandbox object
    # This ensures each tool independently verifies it's operating on the correct project
    thread_manager.add_tool(SandboxShellTool, project_id=project_id, thread_manager=thread_manager)
    thread_manager.add_tool(SandboxFilesTool, project_id=project_id, thread_manager=thread_manager)
    thread_manager.add_tool(SandboxBrowserTool, project_id=project_id, thread_id=thread_id, thread_manager=thread_manager)
    thread_manager.add_tool(SandboxDeployTool, project_id=project_id, thread_manager=thread_manager)
    thread_manager.add_tool(SandboxExposeTool, project_id=project_id, thread_manager=thread_manager)
    thread_manager.add_tool(MessageTool) # we are just doing this via prompt as there is no need to call it as a tool
    thread_manager.add_tool(WebSearchTool)
    thread_manager.add_tool(SandboxVisionTool, project_id=project_id, thread_id=thread_id, thread_manager=thread_manager)
    # Add data providers tool if RapidAPI key is available
    if config.RAPID_API_KEY:
        thread_manager.add_tool(DataProvidersTool)


    # Only include sample response if the model name does not contain "anthropic"
    if "anthropic" not in model_name.lower():
        sample_response_path = os.path.join(os.path.dirname(__file__), 'sample_responses/1.txt')
        with open(sample_response_path, 'r') as file:
            sample_response = file.read()
        
        system_message = { "role": "system", "content": get_system_prompt() + "\n\n <sample_assistant_response>" + sample_response + "</sample_assistant_response>" }
    else:
        system_message = { "role": "system", "content": get_system_prompt() }

    iteration_count = 0
    continue_execution = True

    while continue_execution and iteration_count < max_iterations:
        iteration_count += 1
        # logger.debug(f"Running iteration {iteration_count}...")

        # Billing check on each iteration - still needed within the iterations
        can_run, message, subscription = await check_billing_status(client, account_id)
        if not can_run:
            error_msg = f"Billing limit reached: {message}"
            # Yield a special message to indicate billing limit reached
            yield {
                "type": "status",
                "status": "stopped",
                "message": error_msg
            }
            break
        # Check if last message is from assistant using direct Supabase query
        latest_message = await client.table('messages').select('*').eq('thread_id', thread_id).in_('type', ['assistant', 'tool', 'user']).order('created_at', desc=True).limit(1).execute()
        if latest_message.data and len(latest_message.data) > 0:
            message_type = latest_message.data[0].get('type')
            if message_type == 'assistant':
                print(f"Last message was from assistant, stopping execution")
                continue_execution = False
                break

        # ---- Temporary Message Handling (Browser State & Image Context) ----
        temporary_message = None
        temp_message_content_list = [] # List to hold text/image blocks

        # Get the latest browser_state message
        latest_browser_state_msg = await client.table('messages').select('*').eq('thread_id', thread_id).eq('type', 'browser_state').order('created_at', desc=True).limit(1).execute()
        if latest_browser_state_msg.data and len(latest_browser_state_msg.data) > 0:
            try:
                browser_content = json.loads(latest_browser_state_msg.data[0]["content"])
                screenshot_base64 = browser_content.get("screenshot_base64")
                # Create a copy of the browser state without screenshot
                browser_state_text = browser_content.copy()
                browser_state_text.pop('screenshot_base64', None)
                browser_state_text.pop('screenshot_url', None)
                browser_state_text.pop('screenshot_url_base64', None)

                if browser_state_text:
                    temp_message_content_list.append({
                        "type": "text",
                        "text": f"The following is the current state of the browser:\n{json.dumps(browser_state_text, indent=2)}"
                    })
                if screenshot_base64:
                    temp_message_content_list.append({
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{screenshot_base64}",
                        }
                    })
                else:
                    logger.warning("Browser state found but no screenshot base64 data.")

                await client.table('messages').delete().eq('message_id', latest_browser_state_msg.data[0]["message_id"]).execute()
            except Exception as e:
                logger.error(f"Error parsing browser state: {e}")

        # Get the latest image_context message (NEW)
        latest_image_context_msg = await client.table('messages').select('*').eq('thread_id', thread_id).eq('type', 'image_context').order('created_at', desc=True).limit(1).execute()
        if latest_image_context_msg.data and len(latest_image_context_msg.data) > 0:
            try:
                image_context_content = json.loads(latest_image_context_msg.data[0]["content"])
                base64_image = image_context_content.get("base64")
                mime_type = image_context_content.get("mime_type")
                file_path = image_context_content.get("file_path", "unknown file")

                if base64_image and mime_type:
                    temp_message_content_list.append({
                        "type": "text",
                        "text": f"Here is the image you requested to see: '{file_path}'"
                    })
                    temp_message_content_list.append({
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:{mime_type};base64,{base64_image}",
                        }
                    })
                else:
                    logger.warning(f"Image context found for '{file_path}' but missing base64 or mime_type.")

                await client.table('messages').delete().eq('message_id', latest_image_context_msg.data[0]["message_id"]).execute()
            except Exception as e:
                logger.error(f"Error parsing image context: {e}")

        # If we have any content, construct the temporary_message
        if temp_message_content_list:
            temporary_message = {"role": "user", "content": temp_message_content_list}
            # logger.debug(f"Constructed temporary message with {len(temp_message_content_list)} content blocks.")
        # ---- End Temporary Message Handling ----

        # Set max_tokens based on model
        max_tokens = None
        if "sonnet" in model_name.lower():
            max_tokens = 64000
        elif "gpt-4" in model_name.lower():
            max_tokens = 4096

        response = await thread_manager.run_thread(
            thread_id=thread_id,
            system_prompt=system_message,
            stream=stream,
            llm_model=model_name,
            llm_temperature=0,
            llm_max_tokens=max_tokens,
            tool_choice="auto",
            max_xml_tool_calls=1,
            temporary_message=temporary_message,
            processor_config=ProcessorConfig(
                xml_tool_calling=True,
                native_tool_calling=False,
                execute_tools=True,
                execute_on_stream=True,
                tool_execution_strategy="parallel",
                xml_adding_strategy="user_message"
            ),
            native_max_auto_continues=native_max_auto_continues,
            include_xml_examples=True,
            enable_thinking=enable_thinking,
            reasoning_effort=reasoning_effort,
            enable_context_manager=enable_context_manager
        )

        if isinstance(response, dict) and "status" in response and response["status"] == "error":
            yield response
            return

        # Track if we see ask, complete, or web-browser-takeover tool calls
        last_tool_call = None

        async for chunk in response:
            # print(f"CHUNK: {chunk}") # Uncomment for detailed chunk logging

            # Check for XML versions like <ask>, <complete>, or <web-browser-takeover> in assistant content chunks
            if chunk.get('type') == 'assistant' and 'content' in chunk:
                try:
                    # The content field might be a JSON string or object
                    content = chunk.get('content', '{}')
                    if isinstance(content, str):
                        assistant_content_json = json.loads(content)
                    else:
                        assistant_content_json = content

                    # The actual text content is nested within
                    assistant_text = assistant_content_json.get('content', '')
                    if isinstance(assistant_text, str): # Ensure it's a string
                         # Check for the closing tags as they signal the end of the tool usage
                        if '</ask>' in assistant_text or '</complete>' in assistant_text or '</web-browser-takeover>' in assistant_text:
                           if '</ask>' in assistant_text:
                               xml_tool = 'ask'
                           elif '</complete>' in assistant_text:
                               xml_tool = 'complete'
                           elif '</web-browser-takeover>' in assistant_text:
                               xml_tool = 'web-browser-takeover'

                           last_tool_call = xml_tool
                           print(f"Agent used XML tool: {xml_tool}")
                except json.JSONDecodeError:
                    # Handle cases where content might not be valid JSON
                    print(f"Warning: Could not parse assistant content JSON: {chunk.get('content')}")
                except Exception as e:
                    print(f"Error processing assistant chunk: {e}")

            # # Check for native function calls (OpenAI format)
            # elif chunk.get('type') == 'status' and 'content' in chunk:
            #     try:
            #         # Parse the status content
            #         status_content = chunk.get('content', '{}')
            #         if isinstance(status_content, str):
            #             status_content = json.loads(status_content)

            #         # Check if this is a tool call status
            #         status_type = status_content.get('status_type')
            #         function_name = status_content.get('function_name', '')

            #         # Check for special function names that should stop execution
            #         if status_type == 'tool_started' and function_name in ['ask', 'complete', 'web-browser-takeover']:
            #             last_tool_call = function_name
            #             print(f"Agent used native function call: {function_name}")
            #     except json.JSONDecodeError:
            #         # Handle cases where content might not be valid JSON
            #         print(f"Warning: Could not parse status content JSON: {chunk.get('content')}")
            #     except Exception as e:
            #         print(f"Error processing status chunk: {e}")

            yield chunk

        # Check if we should stop based on the last tool call
        if last_tool_call in ['ask', 'complete', 'web-browser-takeover']:
            print(f"Agent decided to stop with tool: {last_tool_call}")
            continue_execution = False


# # TESTING

# async def test_agent():
#     """Test function to run the agent with a sample query"""
#     from agentpress.thread_manager import ThreadManager
#     from services.supabase import DBConnection

#     # Initialize ThreadManager
#     thread_manager = ThreadManager()

#     # Create a test thread directly with Postgres function
#     client = await DBConnection().client

#     try:
#         # Get user's personal account
#         account_result = await client.rpc('get_personal_account').execute()

#         # if not account_result.data:
#         #     print("Error: No personal account found")
#         #     return

#         account_id = "a5fe9cb6-4812-407e-a61c-fe95b7320c59"

#         if not account_id:
#             print("Error: Could not get account ID")
#             return

#         # Find or create a test project in the user's account
#         project_result = await client.table('projects').select('*').eq('name', 'test11').eq('account_id', account_id).execute()

#         if project_result.data and len(project_result.data) > 0:
#             # Use existing test project
#             project_id = project_result.data[0]['project_id']
#             print(f"\n🔄 Using existing test project: {project_id}")
#         else:
#             # Create new test project if none exists
#             project_result = await client.table('projects').insert({
#                 "name": "test11",
#                 "account_id": account_id
#             }).execute()
#             project_id = project_result.data[0]['project_id']
#             print(f"\n✨ Created new test project: {project_id}")

#         # Create a thread for this project
#         thread_result = await client.table('threads').insert({
#             'project_id': project_id,
#             'account_id': account_id
#         }).execute()
#         thread_data = thread_result.data[0] if thread_result.data else None

#         if not thread_data:
#             print("Error: No thread data returned")
#             return

#         thread_id = thread_data['thread_id']
#     except Exception as e:
#         print(f"Error setting up thread: {str(e)}")
#         return

#     print(f"\n🤖 Agent Thread Created: {thread_id}\n")

#     # Interactive message input loop
#     while True:
#         # Get user input
#         user_message = input("\n💬 Enter your message (or 'exit' to quit): ")
#         if user_message.lower() == 'exit':
#             break

#         if not user_message.strip():
#             print("\n🔄 Running agent...\n")
#             await process_agent_response(thread_id, project_id, thread_manager)
#             continue

#         # Add the user message to the thread
#         await thread_manager.add_message(
#             thread_id=thread_id,
#             type="user",
#             content={
#                 "role": "user",
#                 "content": user_message
#             },
#             is_llm_message=True
#         )

#         print("\n🔄 Running agent...\n")
#         await process_agent_response(thread_id, project_id, thread_manager)

#     print("\n👋 Test completed. Goodbye!")

# async def process_agent_response(
#     thread_id: str,
#     project_id: str,
#     thread_manager: ThreadManager,
#     stream: bool = True,
#     model_name: str = "anthropic/claude-3-7-sonnet-latest",
#     enable_thinking: Optional[bool] = False,
#     reasoning_effort: Optional[str] = 'low',
#     enable_context_manager: bool = True
# ):
#     """Process the streaming response from the agent."""
#     chunk_counter = 0
#     current_response = ""
#     tool_usage_counter = 0 # Renamed from tool_call_counter as we track usage via status

#     # Create a test sandbox for processing with a unique test prefix to avoid conflicts with production sandboxes
#     sandbox_pass = str(uuid4())
#     sandbox = create_sandbox(sandbox_pass)

#     # Store the original ID so we can refer to it
#     original_sandbox_id = sandbox.id

#     # Generate a clear test identifier
#     test_prefix = f"test_{uuid4().hex[:8]}_"
#     logger.info(f"Created test sandbox with ID {original_sandbox_id} and test prefix {test_prefix}")

#     # Log the sandbox URL for debugging
#     print(f"\033[91mTest sandbox created: {str(sandbox.get_preview_link(6080))}/vnc_lite.html?password={sandbox_pass}\033[0m")

#     async for chunk in run_agent(
#         thread_id=thread_id,
#         project_id=project_id,
#         sandbox=sandbox,
#         stream=stream,
#         thread_manager=thread_manager,
#         native_max_auto_continues=25,
#         model_name=model_name,
#         enable_thinking=enable_thinking,
#         reasoning_effort=reasoning_effort,
#         enable_context_manager=enable_context_manager
#     ):
#         chunk_counter += 1
#         # print(f"CHUNK: {chunk}") # Uncomment for debugging

#         if chunk.get('type') == 'assistant':
#             # Try parsing the content JSON
#             try:
#                 # Handle content as string or object
#                 content = chunk.get('content', '{}')
#                 if isinstance(content, str):
#                     content_json = json.loads(content)
#                 else:
#                     content_json = content

#                 actual_content = content_json.get('content', '')
#                 # Print the actual assistant text content as it comes
#                 if actual_content:
#                      # Check if it contains XML tool tags, if so, print the whole tag for context
#                     if '<' in actual_content and '>' in actual_content:
#                          # Avoid printing potentially huge raw content if it's not just text
#                          if len(actual_content) < 500: # Heuristic limit
#                             print(actual_content, end='', flush=True)
#                          else:
#                              # Maybe just print a summary if it's too long or contains complex XML
#                              if '</ask>' in actual_content: print("<ask>...</ask>", end='', flush=True)
#                              elif '</complete>' in actual_content: print("<complete>...</complete>", end='', flush=True)
#                              else: print("<tool_call>...</tool_call>", end='', flush=True) # Generic case
#                     else:
#                         # Regular text content
#                          print(actual_content, end='', flush=True)
#                     current_response += actual_content # Accumulate only text part
#             except json.JSONDecodeError:
#                  # If content is not JSON (e.g., just a string chunk), print directly
#                  raw_content = chunk.get('content', '')
#                  print(raw_content, end='', flush=True)
#                  current_response += raw_content
#             except Exception as e:
#                  print(f"\nError processing assistant chunk: {e}\n")

#         elif chunk.get('type') == 'tool': # Updated from 'tool_result'
#             # Add timestamp and format tool result nicely
#             tool_name = "UnknownTool" # Try to get from metadata if available
#             result_content = "No content"

#             # Parse metadata - handle both string and dict formats
#             metadata = chunk.get('metadata', {})
#             if isinstance(metadata, str):
#                 try:
#                     metadata = json.loads(metadata)
#                 except json.JSONDecodeError:
#                     metadata = {}

#             linked_assistant_msg_id = metadata.get('assistant_message_id')
#             parsing_details = metadata.get('parsing_details')
#             if parsing_details:
#                 tool_name = parsing_details.get('xml_tag_name', 'UnknownTool') # Get name from parsing details

#             try:
#                 # Content is a JSON string or object
#                 content = chunk.get('content', '{}')
#                 if isinstance(content, str):
#                     content_json = json.loads(content)
#                 else:
#                     content_json = content

#                 # The actual tool result is nested inside content.content
#                 tool_result_str = content_json.get('content', '')
#                  # Extract the actual tool result string (remove outer <tool_result> tag if present)
#                 match = re.search(rf'<{tool_name}>(.*?)</{tool_name}>', tool_result_str, re.DOTALL)
#                 if match:
#                     result_content = match.group(1).strip()
#                     # Try to parse the result string itself as JSON for pretty printing
#                     try:
#                         result_obj = json.loads(result_content)
#                         result_content = json.dumps(result_obj, indent=2)
#                     except json.JSONDecodeError:
#                          # Keep as string if not JSON
#                          pass
#                 else:
#                      # Fallback if tag extraction fails
#                      result_content = tool_result_str

#             except json.JSONDecodeError:
#                 result_content = chunk.get('content', 'Error parsing tool content')
#             except Exception as e:
#                 result_content = f"Error processing tool chunk: {e}"

#             print(f"\n\n🛠️  TOOL RESULT [{tool_name}] → {result_content}")

#         elif chunk.get('type') == 'status':
#             # Log tool status changes
#             try:
#                 # Handle content as string or object
#                 status_content = chunk.get('content', '{}')
#                 if isinstance(status_content, str):
#                     status_content = json.loads(status_content)

#                 status_type = status_content.get('status_type')
#                 function_name = status_content.get('function_name', '')
#                 xml_tag_name = status_content.get('xml_tag_name', '') # Get XML tag if available
#                 tool_name = xml_tag_name or function_name # Prefer XML tag name

#                 if status_type == 'tool_started' and tool_name:
#                     tool_usage_counter += 1
#                     print(f"\n⏳ TOOL STARTING #{tool_usage_counter} [{tool_name}]")
#                     print("  " + "-" * 40)
#                     # Return to the current content display
#                     if current_response:
#                         print("\nContinuing response:", flush=True)
#                         print(current_response, end='', flush=True)
#                 elif status_type == 'tool_completed' and tool_name:
#                      status_emoji = "✅"
#                      print(f"\n{status_emoji} TOOL COMPLETED: {tool_name}")
#                 elif status_type == 'finish':
#                      finish_reason = status_content.get('finish_reason', '')
#                      if finish_reason:
#                          print(f"\n📌 Finished: {finish_reason}")
#                 # else: # Print other status types if needed for debugging
#                 #    print(f"\nℹ️ STATUS: {chunk.get('content')}")

#             except json.JSONDecodeError:
#                  print(f"\nWarning: Could not parse status content JSON: {chunk.get('content')}")
#             except Exception as e:
#                 print(f"\nError processing status chunk: {e}")


#         # Removed elif chunk.get('type') == 'tool_call': block

#     # Update final message
#     print(f"\n\n✅ Agent run completed with {tool_usage_counter} tool executions")

#     # Try to clean up the test sandbox if possible
#     try:
#         # Attempt to delete/archive the sandbox to clean up resources
#         # Note: Actual deletion may depend on the Daytona SDK's capabilities
#         logger.info(f"Attempting to clean up test sandbox {original_sandbox_id}")
#         # If there's a method to archive/delete the sandbox, call it here
#         # Example: daytona.archive_sandbox(sandbox.id)
#     except Exception as e:
#         logger.warning(f"Failed to clean up test sandbox {original_sandbox_id}: {str(e)}")

# if __name__ == "__main__":
#     import asyncio

#     # Configure any environment variables or setup needed for testing
#     load_dotenv()  # Ensure environment variables are loaded

#     # Run the test function
#     asyncio.run(test_agent())