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 SandboxWebSearchTool
from dotenv import load_dotenv
from utils.config import config
from flags.flags import is_enabled
from agent.agent_builder_prompt import get_agent_builder_prompt
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.tools.expand_msg_tool import ExpandMessageTool
from agent.prompt import get_system_prompt
from utils.logger 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
from agent.tools.sb_image_edit_tool import SandboxImageEditTool
from services.langfuse import langfuse
from langfuse.client import StatefulTraceClient
from services.langfuse import langfuse
from agent.gemini_prompt import get_gemini_system_prompt
from agent.tools.mcp_tool_wrapper import MCPToolWrapper
from agentpress.tool import SchemaType
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 = 100,
model_name: str = "anthropic/claude-sonnet-4-20250514",
enable_thinking: Optional[bool] = False,
reasoning_effort: Optional[str] = 'low',
enable_context_manager: bool = True,
agent_config: Optional[dict] = None,
trace: Optional[StatefulTraceClient] = None,
is_agent_builder: Optional[bool] = False,
target_agent_id: Optional[str] = None
):
"""Run the development agent with specified configuration."""
logger.info(f"š Starting agent with model: {model_name}")
if agent_config:
logger.info(f"Using custom agent: {agent_config.get('name', 'Unknown')}")
if not trace:
trace = langfuse.trace(name="run_agent", session_id=thread_id, metadata={"project_id": project_id})
thread_manager = ThreadManager(trace=trace, is_agent_builder=is_agent_builder or False, target_agent_id=target_agent_id, agent_config=agent_config)
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
# Get enabled tools from agent config, or use defaults
enabled_tools = None
if agent_config and 'agentpress_tools' in agent_config:
enabled_tools = agent_config['agentpress_tools']
logger.info(f"Using custom tool configuration from agent")
# Register tools based on configuration
# If no agent config (enabled_tools is None), register ALL tools for full Suna capabilities
# If agent config exists, only register explicitly enabled tools
if is_agent_builder:
logger.info("Agent builder mode - registering only update agent tool")
from agent.tools.update_agent_tool import UpdateAgentTool
from services.supabase import DBConnection
db = DBConnection()
thread_manager.add_tool(UpdateAgentTool, thread_manager=thread_manager, db_connection=db, agent_id=target_agent_id)
if enabled_tools is None:
# No agent specified - register ALL tools for full Suna experience
logger.info("No agent specified - registering all tools for full Suna capabilities")
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(ExpandMessageTool, thread_id=thread_id, thread_manager=thread_manager)
thread_manager.add_tool(MessageTool)
thread_manager.add_tool(SandboxWebSearchTool, project_id=project_id, thread_manager=thread_manager)
thread_manager.add_tool(SandboxVisionTool, project_id=project_id, thread_id=thread_id, thread_manager=thread_manager)
thread_manager.add_tool(SandboxImageEditTool, project_id=project_id, thread_id=thread_id, thread_manager=thread_manager)
if config.RAPID_API_KEY:
thread_manager.add_tool(DataProvidersTool)
else:
logger.info("Custom agent specified - registering only enabled tools")
thread_manager.add_tool(ExpandMessageTool, thread_id=thread_id, thread_manager=thread_manager)
thread_manager.add_tool(MessageTool)
if enabled_tools.get('sb_shell_tool', {}).get('enabled', False):
thread_manager.add_tool(SandboxShellTool, project_id=project_id, thread_manager=thread_manager)
if enabled_tools.get('sb_files_tool', {}).get('enabled', False):
thread_manager.add_tool(SandboxFilesTool, project_id=project_id, thread_manager=thread_manager)
if enabled_tools.get('sb_browser_tool', {}).get('enabled', False):
thread_manager.add_tool(SandboxBrowserTool, project_id=project_id, thread_id=thread_id, thread_manager=thread_manager)
if enabled_tools.get('sb_deploy_tool', {}).get('enabled', False):
thread_manager.add_tool(SandboxDeployTool, project_id=project_id, thread_manager=thread_manager)
if enabled_tools.get('sb_expose_tool', {}).get('enabled', False):
thread_manager.add_tool(SandboxExposeTool, project_id=project_id, thread_manager=thread_manager)
if enabled_tools.get('web_search_tool', {}).get('enabled', False):
thread_manager.add_tool(SandboxWebSearchTool, project_id=project_id, thread_manager=thread_manager)
if enabled_tools.get('sb_vision_tool', {}).get('enabled', False):
thread_manager.add_tool(SandboxVisionTool, project_id=project_id, thread_id=thread_id, thread_manager=thread_manager)
if config.RAPID_API_KEY and enabled_tools.get('data_providers_tool', {}).get('enabled', False):
thread_manager.add_tool(DataProvidersTool)
# Register MCP tool wrapper if agent has configured MCPs or custom MCPs
mcp_wrapper_instance = None
if agent_config:
# Merge configured_mcps and custom_mcps
all_mcps = []
# Add standard configured MCPs
if agent_config.get('configured_mcps'):
all_mcps.extend(agent_config['configured_mcps'])
# Add custom MCPs
if agent_config.get('custom_mcps'):
for custom_mcp in agent_config['custom_mcps']:
# Transform custom MCP to standard format
custom_type = custom_mcp.get('customType', custom_mcp.get('type', 'sse'))
mcp_config = {
'name': custom_mcp['name'],
'qualifiedName': f"custom_{custom_type}_{custom_mcp['name'].replace(' ', '_').lower()}",
'config': custom_mcp['config'],
'enabledTools': custom_mcp.get('enabledTools', []),
'instructions': custom_mcp.get('instructions', ''),
'isCustom': True,
'customType': custom_type
}
all_mcps.append(mcp_config)
if all_mcps:
logger.info(f"Registering MCP tool wrapper for {len(all_mcps)} MCP servers (including {len(agent_config.get('custom_mcps', []))} custom)")
thread_manager.add_tool(MCPToolWrapper, mcp_configs=all_mcps)
for tool_name, tool_info in thread_manager.tool_registry.tools.items():
if isinstance(tool_info['instance'], MCPToolWrapper):
mcp_wrapper_instance = tool_info['instance']
break
if mcp_wrapper_instance:
try:
await mcp_wrapper_instance.initialize_and_register_tools()
logger.info("MCP tools initialized successfully")
updated_schemas = mcp_wrapper_instance.get_schemas()
logger.info(f"MCP wrapper has {len(updated_schemas)} schemas available")
for method_name, schema_list in updated_schemas.items():
if method_name != 'call_mcp_tool':
for schema in schema_list:
if schema.schema_type == SchemaType.OPENAPI:
thread_manager.tool_registry.tools[method_name] = {
"instance": mcp_wrapper_instance,
"schema": schema
}
logger.info(f"Registered dynamic MCP tool: {method_name}")
# Log all registered tools for debugging
all_tools = list(thread_manager.tool_registry.tools.keys())
logger.info(f"All registered tools after MCP initialization: {all_tools}")
mcp_tools = [tool for tool in all_tools if tool not in ['call_mcp_tool', 'sb_files_tool', 'message_tool', 'expand_msg_tool', 'web_search_tool', 'sb_shell_tool', 'sb_vision_tool', 'sb_browser_tool', 'computer_use_tool', 'data_providers_tool', 'sb_deploy_tool', 'sb_expose_tool', 'update_agent_tool']]
logger.info(f"MCP tools registered: {mcp_tools}")
except Exception as e:
logger.error(f"Failed to initialize MCP tools: {e}")
# Continue without MCP tools if initialization fails
# Prepare system prompt
# First, get the default system prompt
if "gemini-2.5-flash" in model_name.lower() and "gemini-2.5-pro" not in model_name.lower():
default_system_content = get_gemini_system_prompt()
else:
# Use the original prompt - the LLM can only use tools that are registered
default_system_content = get_system_prompt()
# Add sample response for non-anthropic models
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()
default_system_content = default_system_content + "\n\n " + sample_response + ""
# Handle custom agent system prompt
if agent_config and agent_config.get('system_prompt'):
custom_system_prompt = agent_config['system_prompt'].strip()
# Completely replace the default system prompt with the custom one
# This prevents confusion and tool hallucination
system_content = custom_system_prompt
logger.info(f"Using ONLY custom agent system prompt for: {agent_config.get('name', 'Unknown')}")
elif is_agent_builder:
system_content = get_agent_builder_prompt()
logger.info("Using agent builder system prompt")
else:
# Use just the default system prompt
system_content = default_system_content
logger.info("Using default system prompt only")
if await is_enabled("knowledge_base"):
try:
from services.supabase import DBConnection
kb_db = DBConnection()
kb_client = await kb_db.client
current_agent_id = agent_config.get('agent_id') if agent_config else None
kb_result = await kb_client.rpc('get_combined_knowledge_base_context', {
'p_thread_id': thread_id,
'p_agent_id': current_agent_id,
'p_max_tokens': 4000
}).execute()
if kb_result.data and kb_result.data.strip():
logger.info(f"Adding combined knowledge base context to system prompt for thread {thread_id}, agent {current_agent_id}")
system_content += "\n\n" + kb_result.data
else:
logger.debug(f"No knowledge base context found for thread {thread_id}, agent {current_agent_id}")
except Exception as e:
logger.error(f"Error retrieving knowledge base context for thread {thread_id}: {e}")
if agent_config and (agent_config.get('configured_mcps') or agent_config.get('custom_mcps')) and mcp_wrapper_instance and mcp_wrapper_instance._initialized:
mcp_info = "\n\n--- MCP Tools Available ---\n"
mcp_info += "You have access to external MCP (Model Context Protocol) server tools.\n"
mcp_info += "MCP tools can be called directly using their native function names in the standard function calling format:\n"
mcp_info += '\n'
mcp_info += '\n'
mcp_info += 'value1\n'
mcp_info += 'value2\n'
mcp_info += '\n'
mcp_info += '\n\n'
# List available MCP tools
mcp_info += "Available MCP tools:\n"
try:
# Get the actual registered schemas from the wrapper
registered_schemas = mcp_wrapper_instance.get_schemas()
for method_name, schema_list in registered_schemas.items():
if method_name == 'call_mcp_tool':
continue # Skip the fallback method
# Get the schema info
for schema in schema_list:
if schema.schema_type == SchemaType.OPENAPI:
func_info = schema.schema.get('function', {})
description = func_info.get('description', 'No description available')
# Extract server name from description if available
server_match = description.find('(MCP Server: ')
if server_match != -1:
server_end = description.find(')', server_match)
server_info = description[server_match:server_end+1]
else:
server_info = ''
mcp_info += f"- **{method_name}**: {description}\n"
# Show parameter info
params = func_info.get('parameters', {})
props = params.get('properties', {})
if props:
mcp_info += f" Parameters: {', '.join(props.keys())}\n"
except Exception as e:
logger.error(f"Error listing MCP tools: {e}")
mcp_info += "- Error loading MCP tool list\n"
# Add critical instructions for using search results
mcp_info += "\nšØ CRITICAL MCP TOOL RESULT INSTRUCTIONS šØ\n"
mcp_info += "When you use ANY MCP (Model Context Protocol) tools:\n"
mcp_info += "1. ALWAYS read and use the EXACT results returned by the MCP tool\n"
mcp_info += "2. For search tools: ONLY cite URLs, sources, and information from the actual search results\n"
mcp_info += "3. For any tool: Base your response entirely on the tool's output - do NOT add external information\n"
mcp_info += "4. DO NOT fabricate, invent, hallucinate, or make up any sources, URLs, or data\n"
mcp_info += "5. If you need more information, call the MCP tool again with different parameters\n"
mcp_info += "6. When writing reports/summaries: Reference ONLY the data from MCP tool results\n"
mcp_info += "7. If the MCP tool doesn't return enough information, explicitly state this limitation\n"
mcp_info += "8. Always double-check that every fact, URL, and reference comes from the MCP tool output\n"
mcp_info += "\nIMPORTANT: MCP tool results are your PRIMARY and ONLY source of truth for external data!\n"
mcp_info += "NEVER supplement MCP results with your training data or make assumptions beyond what the tools provide.\n"
system_content += mcp_info
system_message = { "role": "system", "content": system_content }
iteration_count = 0
continue_execution = True
latest_user_message = await client.table('messages').select('*').eq('thread_id', thread_id).eq('type', 'user').order('created_at', desc=True).limit(1).execute()
if latest_user_message.data and len(latest_user_message.data) > 0:
data = latest_user_message.data[0]['content']
if isinstance(data, str):
data = json.loads(data)
trace.update(input=data['content'])
while continue_execution and iteration_count < max_iterations:
iteration_count += 1
logger.info(f"š Running iteration {iteration_count} of {max_iterations}...")
# 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}"
trace.event(name="billing_limit_reached", level="ERROR", status_message=(f"{error_msg}"))
# 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':
logger.info(f"Last message was from assistant, stopping execution")
trace.event(name="last_message_from_assistant", level="DEFAULT", status_message=(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 = latest_browser_state_msg.data[0]["content"]
if isinstance(browser_content, str):
browser_content = json.loads(browser_content)
screenshot_base64 = browser_content.get("screenshot_base64")
screenshot_url = browser_content.get("image_url")
# Create a copy of the browser state without screenshot data
browser_state_text = browser_content.copy()
browser_state_text.pop('screenshot_base64', None)
browser_state_text.pop('image_url', 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)}"
})
# Only add screenshot if model is not Gemini, Anthropic, or OpenAI
if 'gemini' in model_name.lower() or 'anthropic' in model_name.lower() or 'openai' in model_name.lower():
# Prioritize screenshot_url if available
if screenshot_url:
temp_message_content_list.append({
"type": "image_url",
"image_url": {
"url": screenshot_url,
"format": "image/jpeg"
}
})
trace.event(name="screenshot_url_added_to_temporary_message", level="DEFAULT", status_message=(f"Screenshot URL added to temporary message."))
elif screenshot_base64:
# Fallback to base64 if URL not available
temp_message_content_list.append({
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{screenshot_base64}",
}
})
trace.event(name="screenshot_base64_added_to_temporary_message", level="WARNING", status_message=(f"Screenshot base64 added to temporary message. Prefer screenshot_url if available."))
else:
logger.warning("Browser state found but no screenshot data.")
trace.event(name="browser_state_found_but_no_screenshot_data", level="WARNING", status_message=(f"Browser state found but no screenshot data."))
else:
logger.warning("Model is Gemini, Anthropic, or OpenAI, so not adding screenshot to temporary message.")
trace.event(name="model_is_gemini_anthropic_or_openai", level="WARNING", status_message=(f"Model is Gemini, Anthropic, or OpenAI, so not adding screenshot to temporary message."))
except Exception as e:
logger.error(f"Error parsing browser state: {e}")
trace.event(name="error_parsing_browser_state", level="ERROR", status_message=(f"{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 = latest_image_context_msg.data[0]["content"] if isinstance(latest_image_context_msg.data[0]["content"], dict) else 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}")
trace.event(name="error_parsing_image_context", level="ERROR", status_message=(f"{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():
# Claude 3.5 Sonnet has a limit of 8192 tokens
max_tokens = 8192
elif "gpt-4" in model_name.lower():
max_tokens = 4096
elif "gemini-2.5-pro" in model_name.lower():
# Gemini 2.5 Pro has 64k max output tokens
max_tokens = 64000
generation = trace.generation(name="thread_manager.run_thread")
try:
# Make the LLM call and process the response
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,
generation=generation
)
if isinstance(response, dict) and "status" in response and response["status"] == "error":
logger.error(f"Error response from run_thread: {response.get('message', 'Unknown error')}")
trace.event(name="error_response_from_run_thread", level="ERROR", status_message=(f"{response.get('message', 'Unknown error')}"))
yield response
break
# Track if we see ask, complete, or web-browser-takeover tool calls
last_tool_call = None
agent_should_terminate = False
# Process the response
error_detected = False
try:
full_response = ""
async for chunk in response:
# If we receive an error chunk, we should stop after this iteration
if isinstance(chunk, dict) and chunk.get('type') == 'status' and chunk.get('status') == 'error':
logger.error(f"Error chunk detected: {chunk.get('message', 'Unknown error')}")
trace.event(name="error_chunk_detected", level="ERROR", status_message=(f"{chunk.get('message', 'Unknown error')}"))
error_detected = True
yield chunk # Forward the error chunk
continue # Continue processing other chunks but don't break yet
# Check for termination signal in status messages
if chunk.get('type') == 'status':
try:
# Parse the metadata to check for termination signal
metadata = chunk.get('metadata', {})
if isinstance(metadata, str):
metadata = json.loads(metadata)
if metadata.get('agent_should_terminate'):
agent_should_terminate = True
logger.info("Agent termination signal detected in status message")
trace.event(name="agent_termination_signal_detected", level="DEFAULT", status_message="Agent termination signal detected in status message")
# Extract the tool name from the status content if available
content = chunk.get('content', {})
if isinstance(content, str):
content = json.loads(content)
if content.get('function_name'):
last_tool_call = content['function_name']
elif content.get('xml_tag_name'):
last_tool_call = content['xml_tag_name']
except Exception as e:
logger.debug(f"Error parsing status message for termination check: {e}")
# Check for XML versions like , , or 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', '')
full_response += assistant_text
if isinstance(assistant_text, str):
if '' in assistant_text or '' in assistant_text or '' in assistant_text:
if '' in assistant_text:
xml_tool = 'ask'
elif '' in assistant_text:
xml_tool = 'complete'
elif '' in assistant_text:
xml_tool = 'web-browser-takeover'
last_tool_call = xml_tool
logger.info(f"Agent used XML tool: {xml_tool}")
trace.event(name="agent_used_xml_tool", level="DEFAULT", status_message=(f"Agent used XML tool: {xml_tool}"))
except json.JSONDecodeError:
# Handle cases where content might not be valid JSON
logger.warning(f"Warning: Could not parse assistant content JSON: {chunk.get('content')}")
trace.event(name="warning_could_not_parse_assistant_content_json", level="WARNING", status_message=(f"Warning: Could not parse assistant content JSON: {chunk.get('content')}"))
except Exception as e:
logger.error(f"Error processing assistant chunk: {e}")
trace.event(name="error_processing_assistant_chunk", level="ERROR", status_message=(f"Error processing assistant chunk: {e}"))
yield chunk
# Check if we should stop based on the last tool call or error
if error_detected:
logger.info(f"Stopping due to error detected in response")
trace.event(name="stopping_due_to_error_detected_in_response", level="DEFAULT", status_message=(f"Stopping due to error detected in response"))
generation.end(output=full_response, status_message="error_detected", level="ERROR")
break
if agent_should_terminate or last_tool_call in ['ask', 'complete', 'web-browser-takeover']:
logger.info(f"Agent decided to stop with tool: {last_tool_call}")
trace.event(name="agent_decided_to_stop_with_tool", level="DEFAULT", status_message=(f"Agent decided to stop with tool: {last_tool_call}"))
generation.end(output=full_response, status_message="agent_stopped")
continue_execution = False
except Exception as e:
# Just log the error and re-raise to stop all iterations
error_msg = f"Error during response streaming: {str(e)}"
logger.error(f"Error: {error_msg}")
trace.event(name="error_during_response_streaming", level="ERROR", status_message=(f"Error during response streaming: {str(e)}"))
generation.end(output=full_response, status_message=error_msg, level="ERROR")
yield {
"type": "status",
"status": "error",
"message": error_msg
}
# Stop execution immediately on any error
break
except Exception as e:
# Just log the error and re-raise to stop all iterations
error_msg = f"Error running thread: {str(e)}"
logger.error(f"Error: {error_msg}")
trace.event(name="error_running_thread", level="ERROR", status_message=(f"Error running thread: {str(e)}"))
yield {
"type": "status",
"status": "error",
"message": error_msg
}
# Stop execution immediately on any error
break
generation.end(output=full_response)
langfuse.flush()