mirror of https://github.com/kortix-ai/suna.git
559 lines
26 KiB
Python
559 lines
26 KiB
Python
import os
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import json
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import re
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from uuid import uuid4
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from typing import Optional
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# from agent.tools.message_tool import MessageTool
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from agent.tools.message_tool import MessageTool
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from agent.tools.sb_deploy_tool import SandboxDeployTool
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from agent.tools.sb_expose_tool import SandboxExposeTool
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from agent.tools.web_search_tool import WebSearchTool
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from dotenv import load_dotenv
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from utils.config import config
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from agentpress.thread_manager import ThreadManager
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from agentpress.response_processor import ProcessorConfig
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from agent.tools.sb_shell_tool import SandboxShellTool
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from agent.tools.sb_files_tool import SandboxFilesTool
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from agent.tools.sb_browser_tool import SandboxBrowserTool
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from agent.tools.data_providers_tool import DataProvidersTool
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from agent.prompt import get_system_prompt
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from utils import logger
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from utils.auth_utils import get_account_id_from_thread
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from services.billing import check_billing_status
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from agent.tools.sb_vision_tool import SandboxVisionTool
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load_dotenv()
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async def run_agent(
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thread_id: str,
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project_id: str,
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stream: bool,
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thread_manager: Optional[ThreadManager] = None,
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native_max_auto_continues: int = 25,
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max_iterations: int = 150,
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model_name: str = "anthropic/claude-3-7-sonnet-latest",
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enable_thinking: Optional[bool] = False,
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reasoning_effort: Optional[str] = 'low',
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enable_context_manager: bool = True
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):
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"""Run the development agent with specified configuration."""
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print(f"🚀 Starting agent with model: {model_name}")
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thread_manager = ThreadManager()
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client = await thread_manager.db.client
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# Get account ID from thread for billing checks
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account_id = await get_account_id_from_thread(client, thread_id)
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if not account_id:
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raise ValueError("Could not determine account ID for thread")
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# Get sandbox info from project
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project = await client.table('projects').select('*').eq('project_id', project_id).execute()
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if not project.data or len(project.data) == 0:
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raise ValueError(f"Project {project_id} not found")
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project_data = project.data[0]
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sandbox_info = project_data.get('sandbox', {})
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if not sandbox_info.get('id'):
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raise ValueError(f"No sandbox found for project {project_id}")
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# Initialize tools with project_id instead of sandbox object
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# This ensures each tool independently verifies it's operating on the correct project
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thread_manager.add_tool(SandboxShellTool, project_id=project_id, thread_manager=thread_manager)
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thread_manager.add_tool(SandboxFilesTool, project_id=project_id, thread_manager=thread_manager)
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thread_manager.add_tool(SandboxBrowserTool, project_id=project_id, thread_id=thread_id, thread_manager=thread_manager)
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thread_manager.add_tool(SandboxDeployTool, project_id=project_id, thread_manager=thread_manager)
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thread_manager.add_tool(SandboxExposeTool, project_id=project_id, thread_manager=thread_manager)
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thread_manager.add_tool(MessageTool) # we are just doing this via prompt as there is no need to call it as a tool
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thread_manager.add_tool(WebSearchTool)
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thread_manager.add_tool(SandboxVisionTool, project_id=project_id, thread_id=thread_id, thread_manager=thread_manager)
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# Add data providers tool if RapidAPI key is available
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if config.RAPID_API_KEY:
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thread_manager.add_tool(DataProvidersTool)
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system_message = { "role": "system", "content": get_system_prompt() }
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iteration_count = 0
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continue_execution = True
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while continue_execution and iteration_count < max_iterations:
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iteration_count += 1
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# logger.debug(f"Running iteration {iteration_count}...")
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# Billing check on each iteration - still needed within the iterations
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can_run, message, subscription = await check_billing_status(client, account_id)
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if not can_run:
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error_msg = f"Billing limit reached: {message}"
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# Yield a special message to indicate billing limit reached
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yield {
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"type": "status",
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"status": "stopped",
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"message": error_msg
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}
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break
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# Check if last message is from assistant using direct Supabase query
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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()
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if latest_message.data and len(latest_message.data) > 0:
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message_type = latest_message.data[0].get('type')
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if message_type == 'assistant':
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print(f"Last message was from assistant, stopping execution")
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continue_execution = False
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break
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# ---- Temporary Message Handling (Browser State & Image Context) ----
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temporary_message = None
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temp_message_content_list = [] # List to hold text/image blocks
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# Get the latest browser_state message
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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()
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if latest_browser_state_msg.data and len(latest_browser_state_msg.data) > 0:
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try:
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browser_content = json.loads(latest_browser_state_msg.data[0]["content"])
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screenshot_base64 = browser_content.get("screenshot_base64")
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# Create a copy of the browser state without screenshot
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browser_state_text = browser_content.copy()
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browser_state_text.pop('screenshot_base64', None)
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browser_state_text.pop('screenshot_url', None)
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browser_state_text.pop('screenshot_url_base64', None)
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if browser_state_text:
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temp_message_content_list.append({
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"type": "text",
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"text": f"The following is the current state of the browser:\n{json.dumps(browser_state_text, indent=2)}"
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})
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if screenshot_base64:
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temp_message_content_list.append({
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{screenshot_base64}",
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}
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})
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else:
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logger.warning("Browser state found but no screenshot base64 data.")
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await client.table('messages').delete().eq('message_id', latest_browser_state_msg.data[0]["message_id"]).execute()
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except Exception as e:
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logger.error(f"Error parsing browser state: {e}")
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# Get the latest image_context message (NEW)
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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()
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if latest_image_context_msg.data and len(latest_image_context_msg.data) > 0:
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try:
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image_context_content = json.loads(latest_image_context_msg.data[0]["content"])
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base64_image = image_context_content.get("base64")
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mime_type = image_context_content.get("mime_type")
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file_path = image_context_content.get("file_path", "unknown file")
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if base64_image and mime_type:
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temp_message_content_list.append({
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"type": "text",
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"text": f"Here is the image you requested to see: '{file_path}'"
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})
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temp_message_content_list.append({
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"type": "image_url",
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"image_url": {
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"url": f"data:{mime_type};base64,{base64_image}",
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}
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})
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else:
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logger.warning(f"Image context found for '{file_path}' but missing base64 or mime_type.")
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await client.table('messages').delete().eq('message_id', latest_image_context_msg.data[0]["message_id"]).execute()
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except Exception as e:
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logger.error(f"Error parsing image context: {e}")
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# If we have any content, construct the temporary_message
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if temp_message_content_list:
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temporary_message = {"role": "user", "content": temp_message_content_list}
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# logger.debug(f"Constructed temporary message with {len(temp_message_content_list)} content blocks.")
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# ---- End Temporary Message Handling ----
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# Set max_tokens based on model
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max_tokens = None
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if "sonnet" in model_name.lower():
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max_tokens = 64000
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elif "gpt-4" in model_name.lower():
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max_tokens = 4096
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# # Configure tool calling based on model type
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# use_xml_tool_calling = "anthropic" in model_name.lower() or "claude" in model_name.lower()
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# use_native_tool_calling = "openai" in model_name.lower() or "gpt" in model_name.lower()
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# # model_name = "openrouter/qwen/qwen3-235b-a22b"
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response = await thread_manager.run_thread(
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thread_id=thread_id,
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system_prompt=system_message,
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stream=stream,
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llm_model=model_name,
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llm_temperature=0,
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llm_max_tokens=max_tokens,
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tool_choice="auto",
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max_xml_tool_calls=1,
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temporary_message=temporary_message,
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processor_config=ProcessorConfig(
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xml_tool_calling=True,
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native_tool_calling=False,
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execute_tools=True,
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execute_on_stream=True,
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tool_execution_strategy="parallel",
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xml_adding_strategy="user_message"
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),
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native_max_auto_continues=native_max_auto_continues,
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include_xml_examples=True,
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enable_thinking=enable_thinking,
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reasoning_effort=reasoning_effort,
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enable_context_manager=enable_context_manager
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)
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if isinstance(response, dict) and "status" in response and response["status"] == "error":
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yield response
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return
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# Track if we see ask, complete, or web-browser-takeover tool calls
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last_tool_call = None
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async for chunk in response:
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# print(f"CHUNK: {chunk}") # Uncomment for detailed chunk logging
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# Check for XML versions like <ask>, <complete>, or <web-browser-takeover> in assistant content chunks
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if chunk.get('type') == 'assistant' and 'content' in chunk:
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try:
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# The content field might be a JSON string or object
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content = chunk.get('content', '{}')
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if isinstance(content, str):
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assistant_content_json = json.loads(content)
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else:
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assistant_content_json = content
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# The actual text content is nested within
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assistant_text = assistant_content_json.get('content', '')
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if isinstance(assistant_text, str): # Ensure it's a string
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# Check for the closing tags as they signal the end of the tool usage
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if '</ask>' in assistant_text or '</complete>' in assistant_text or '</web-browser-takeover>' in assistant_text:
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if '</ask>' in assistant_text:
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xml_tool = 'ask'
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elif '</complete>' in assistant_text:
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xml_tool = 'complete'
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elif '</web-browser-takeover>' in assistant_text:
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xml_tool = 'web-browser-takeover'
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last_tool_call = xml_tool
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print(f"Agent used XML tool: {xml_tool}")
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except json.JSONDecodeError:
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# Handle cases where content might not be valid JSON
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print(f"Warning: Could not parse assistant content JSON: {chunk.get('content')}")
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except Exception as e:
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print(f"Error processing assistant chunk: {e}")
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# # Check for native function calls (OpenAI format)
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# elif chunk.get('type') == 'status' and 'content' in chunk:
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# try:
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# # Parse the status content
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# status_content = chunk.get('content', '{}')
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# if isinstance(status_content, str):
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# status_content = json.loads(status_content)
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# # Check if this is a tool call status
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# status_type = status_content.get('status_type')
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# function_name = status_content.get('function_name', '')
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# # Check for special function names that should stop execution
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# if status_type == 'tool_started' and function_name in ['ask', 'complete', 'web-browser-takeover']:
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# last_tool_call = function_name
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# print(f"Agent used native function call: {function_name}")
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# except json.JSONDecodeError:
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# # Handle cases where content might not be valid JSON
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# print(f"Warning: Could not parse status content JSON: {chunk.get('content')}")
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# except Exception as e:
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# print(f"Error processing status chunk: {e}")
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yield chunk
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# Check if we should stop based on the last tool call
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if last_tool_call in ['ask', 'complete', 'web-browser-takeover']:
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print(f"Agent decided to stop with tool: {last_tool_call}")
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continue_execution = False
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# # TESTING
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# async def test_agent():
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# """Test function to run the agent with a sample query"""
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# from agentpress.thread_manager import ThreadManager
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# from services.supabase import DBConnection
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# # Initialize ThreadManager
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# thread_manager = ThreadManager()
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# # Create a test thread directly with Postgres function
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# client = await DBConnection().client
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# try:
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# # Get user's personal account
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# account_result = await client.rpc('get_personal_account').execute()
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# # if not account_result.data:
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# # print("Error: No personal account found")
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# # return
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# account_id = "a5fe9cb6-4812-407e-a61c-fe95b7320c59"
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# if not account_id:
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# print("Error: Could not get account ID")
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# return
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# # Find or create a test project in the user's account
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# project_result = await client.table('projects').select('*').eq('name', 'test11').eq('account_id', account_id).execute()
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# if project_result.data and len(project_result.data) > 0:
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# # Use existing test project
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# project_id = project_result.data[0]['project_id']
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# print(f"\n🔄 Using existing test project: {project_id}")
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# else:
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# # Create new test project if none exists
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# project_result = await client.table('projects').insert({
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# "name": "test11",
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# "account_id": account_id
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# }).execute()
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# project_id = project_result.data[0]['project_id']
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# print(f"\n✨ Created new test project: {project_id}")
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# # Create a thread for this project
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# thread_result = await client.table('threads').insert({
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# 'project_id': project_id,
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# 'account_id': account_id
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# }).execute()
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# thread_data = thread_result.data[0] if thread_result.data else None
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# if not thread_data:
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# print("Error: No thread data returned")
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# return
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# thread_id = thread_data['thread_id']
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# except Exception as e:
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# print(f"Error setting up thread: {str(e)}")
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# return
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# print(f"\n🤖 Agent Thread Created: {thread_id}\n")
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# # Interactive message input loop
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# while True:
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# # Get user input
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# user_message = input("\n💬 Enter your message (or 'exit' to quit): ")
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# if user_message.lower() == 'exit':
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# break
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# if not user_message.strip():
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# print("\n🔄 Running agent...\n")
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# await process_agent_response(thread_id, project_id, thread_manager)
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# continue
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# # Add the user message to the thread
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# await thread_manager.add_message(
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# thread_id=thread_id,
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# type="user",
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# content={
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# "role": "user",
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# "content": user_message
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# },
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# is_llm_message=True
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# )
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# print("\n🔄 Running agent...\n")
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# await process_agent_response(thread_id, project_id, thread_manager)
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# print("\n👋 Test completed. Goodbye!")
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# async def process_agent_response(
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# thread_id: str,
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# project_id: str,
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# thread_manager: ThreadManager,
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# stream: bool = True,
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# model_name: str = "anthropic/claude-3-7-sonnet-latest",
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# enable_thinking: Optional[bool] = False,
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# reasoning_effort: Optional[str] = 'low',
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# enable_context_manager: bool = True
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# ):
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# """Process the streaming response from the agent."""
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# chunk_counter = 0
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# current_response = ""
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# tool_usage_counter = 0 # Renamed from tool_call_counter as we track usage via status
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# # Create a test sandbox for processing with a unique test prefix to avoid conflicts with production sandboxes
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# sandbox_pass = str(uuid4())
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# sandbox = create_sandbox(sandbox_pass)
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# # Store the original ID so we can refer to it
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# original_sandbox_id = sandbox.id
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# # Generate a clear test identifier
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# test_prefix = f"test_{uuid4().hex[:8]}_"
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# logger.info(f"Created test sandbox with ID {original_sandbox_id} and test prefix {test_prefix}")
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# # Log the sandbox URL for debugging
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# print(f"\033[91mTest sandbox created: {str(sandbox.get_preview_link(6080))}/vnc_lite.html?password={sandbox_pass}\033[0m")
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# async for chunk in run_agent(
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# thread_id=thread_id,
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# project_id=project_id,
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# sandbox=sandbox,
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# stream=stream,
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# thread_manager=thread_manager,
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# native_max_auto_continues=25,
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# model_name=model_name,
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# enable_thinking=enable_thinking,
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# reasoning_effort=reasoning_effort,
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# enable_context_manager=enable_context_manager
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# ):
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# chunk_counter += 1
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# # print(f"CHUNK: {chunk}") # Uncomment for debugging
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# if chunk.get('type') == 'assistant':
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# # Try parsing the content JSON
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# try:
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# # Handle content as string or object
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# content = chunk.get('content', '{}')
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# if isinstance(content, str):
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# content_json = json.loads(content)
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# else:
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# content_json = content
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# actual_content = content_json.get('content', '')
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# # Print the actual assistant text content as it comes
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# if actual_content:
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# # Check if it contains XML tool tags, if so, print the whole tag for context
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# if '<' in actual_content and '>' in actual_content:
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# # Avoid printing potentially huge raw content if it's not just text
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# if len(actual_content) < 500: # Heuristic limit
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# print(actual_content, end='', flush=True)
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# else:
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# # Maybe just print a summary if it's too long or contains complex XML
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# if '</ask>' in actual_content: print("<ask>...</ask>", end='', flush=True)
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# elif '</complete>' in actual_content: print("<complete>...</complete>", end='', flush=True)
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# else: print("<tool_call>...</tool_call>", end='', flush=True) # Generic case
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# else:
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# # Regular text content
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# print(actual_content, end='', flush=True)
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# current_response += actual_content # Accumulate only text part
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# except json.JSONDecodeError:
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# # If content is not JSON (e.g., just a string chunk), print directly
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# raw_content = chunk.get('content', '')
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# print(raw_content, end='', flush=True)
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# current_response += raw_content
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# except Exception as e:
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# print(f"\nError processing assistant chunk: {e}\n")
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# 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()) |