mirror of https://github.com/kortix-ai/suna.git
636 lines
36 KiB
Python
636 lines
36 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 SandboxWebSearchTool
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from dotenv import load_dotenv
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from utils.config import config
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from flags.flags import is_enabled
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from agent.agent_builder_prompt import get_agent_builder_prompt
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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.tools.expand_msg_tool import ExpandMessageTool
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from agent.prompt import get_system_prompt
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from utils.logger 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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from agent.tools.sb_image_edit_tool import SandboxImageEditTool
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from services.langfuse import langfuse
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from langfuse.client import StatefulTraceClient
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from services.langfuse import langfuse
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from agent.gemini_prompt import get_gemini_system_prompt
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from agent.tools.mcp_tool_wrapper import MCPToolWrapper
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from agentpress.tool import SchemaType
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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 = 100,
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model_name: str = "anthropic/claude-sonnet-4-20250514",
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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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agent_config: Optional[dict] = None,
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trace: Optional[StatefulTraceClient] = None,
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is_agent_builder: Optional[bool] = False,
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target_agent_id: Optional[str] = None
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):
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"""Run the development agent with specified configuration."""
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logger.info(f"🚀 Starting agent with model: {model_name}")
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if agent_config:
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logger.info(f"Using custom agent: {agent_config.get('name', 'Unknown')}")
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if not trace:
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trace = langfuse.trace(name="run_agent", session_id=thread_id, metadata={"project_id": project_id})
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thread_manager = ThreadManager(trace=trace, is_agent_builder=is_agent_builder or False, target_agent_id=target_agent_id, agent_config=agent_config)
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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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# Get enabled tools from agent config, or use defaults
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enabled_tools = None
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if agent_config and 'agentpress_tools' in agent_config:
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enabled_tools = agent_config['agentpress_tools']
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logger.info(f"Using custom tool configuration from agent")
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# Register tools based on configuration
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# If no agent config (enabled_tools is None), register ALL tools for full Suna capabilities
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# If agent config exists, only register explicitly enabled tools
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if is_agent_builder:
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logger.info("Agent builder mode - registering only update agent tool")
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from agent.tools.update_agent_tool import UpdateAgentTool
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from services.supabase import DBConnection
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db = DBConnection()
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thread_manager.add_tool(UpdateAgentTool, thread_manager=thread_manager, db_connection=db, agent_id=target_agent_id)
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if enabled_tools is None:
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# No agent specified - register ALL tools for full Suna experience
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logger.info("No agent specified - registering all tools for full Suna capabilities")
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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(ExpandMessageTool, thread_id=thread_id, thread_manager=thread_manager)
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thread_manager.add_tool(MessageTool)
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thread_manager.add_tool(SandboxWebSearchTool, project_id=project_id, thread_manager=thread_manager)
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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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thread_manager.add_tool(SandboxImageEditTool, project_id=project_id, thread_id=thread_id, thread_manager=thread_manager)
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if config.RAPID_API_KEY:
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thread_manager.add_tool(DataProvidersTool)
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else:
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logger.info("Custom agent specified - registering only enabled tools")
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thread_manager.add_tool(ExpandMessageTool, thread_id=thread_id, thread_manager=thread_manager)
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thread_manager.add_tool(MessageTool)
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if enabled_tools.get('sb_shell_tool', {}).get('enabled', False):
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thread_manager.add_tool(SandboxShellTool, project_id=project_id, thread_manager=thread_manager)
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if enabled_tools.get('sb_files_tool', {}).get('enabled', False):
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thread_manager.add_tool(SandboxFilesTool, project_id=project_id, thread_manager=thread_manager)
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if enabled_tools.get('sb_browser_tool', {}).get('enabled', False):
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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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if enabled_tools.get('sb_deploy_tool', {}).get('enabled', False):
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thread_manager.add_tool(SandboxDeployTool, project_id=project_id, thread_manager=thread_manager)
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if enabled_tools.get('sb_expose_tool', {}).get('enabled', False):
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thread_manager.add_tool(SandboxExposeTool, project_id=project_id, thread_manager=thread_manager)
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if enabled_tools.get('web_search_tool', {}).get('enabled', False):
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thread_manager.add_tool(SandboxWebSearchTool, project_id=project_id, thread_manager=thread_manager)
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if enabled_tools.get('sb_vision_tool', {}).get('enabled', False):
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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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if config.RAPID_API_KEY and enabled_tools.get('data_providers_tool', {}).get('enabled', False):
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thread_manager.add_tool(DataProvidersTool)
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# Register MCP tool wrapper if agent has configured MCPs or custom MCPs
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mcp_wrapper_instance = None
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if agent_config:
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# Merge configured_mcps and custom_mcps
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all_mcps = []
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# Add standard configured MCPs
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if agent_config.get('configured_mcps'):
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all_mcps.extend(agent_config['configured_mcps'])
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# Add custom MCPs
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if agent_config.get('custom_mcps'):
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for custom_mcp in agent_config['custom_mcps']:
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# Transform custom MCP to standard format
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custom_type = custom_mcp.get('customType', custom_mcp.get('type', 'sse'))
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mcp_config = {
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'name': custom_mcp['name'],
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'qualifiedName': f"custom_{custom_type}_{custom_mcp['name'].replace(' ', '_').lower()}",
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'config': custom_mcp['config'],
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'enabledTools': custom_mcp.get('enabledTools', []),
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'instructions': custom_mcp.get('instructions', ''),
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'isCustom': True,
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'customType': custom_type
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}
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all_mcps.append(mcp_config)
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if all_mcps:
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logger.info(f"Registering MCP tool wrapper for {len(all_mcps)} MCP servers (including {len(agent_config.get('custom_mcps', []))} custom)")
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thread_manager.add_tool(MCPToolWrapper, mcp_configs=all_mcps)
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for tool_name, tool_info in thread_manager.tool_registry.tools.items():
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if isinstance(tool_info['instance'], MCPToolWrapper):
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mcp_wrapper_instance = tool_info['instance']
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break
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if mcp_wrapper_instance:
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try:
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await mcp_wrapper_instance.initialize_and_register_tools()
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logger.info("MCP tools initialized successfully")
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updated_schemas = mcp_wrapper_instance.get_schemas()
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logger.info(f"MCP wrapper has {len(updated_schemas)} schemas available")
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for method_name, schema_list in updated_schemas.items():
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if method_name != 'call_mcp_tool':
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for schema in schema_list:
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if schema.schema_type == SchemaType.OPENAPI:
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thread_manager.tool_registry.tools[method_name] = {
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"instance": mcp_wrapper_instance,
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"schema": schema
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}
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logger.info(f"Registered dynamic MCP tool: {method_name}")
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# Log all registered tools for debugging
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all_tools = list(thread_manager.tool_registry.tools.keys())
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logger.info(f"All registered tools after MCP initialization: {all_tools}")
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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']]
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logger.info(f"MCP tools registered: {mcp_tools}")
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except Exception as e:
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logger.error(f"Failed to initialize MCP tools: {e}")
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# Continue without MCP tools if initialization fails
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# Prepare system prompt
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# First, get the default system prompt
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if "gemini-2.5-flash" in model_name.lower() and "gemini-2.5-pro" not in model_name.lower():
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default_system_content = get_gemini_system_prompt()
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else:
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# Use the original prompt - the LLM can only use tools that are registered
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default_system_content = get_system_prompt()
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# Add sample response for non-anthropic models
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if "anthropic" not in model_name.lower():
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sample_response_path = os.path.join(os.path.dirname(__file__), 'sample_responses/1.txt')
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with open(sample_response_path, 'r') as file:
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sample_response = file.read()
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default_system_content = default_system_content + "\n\n <sample_assistant_response>" + sample_response + "</sample_assistant_response>"
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# Handle custom agent system prompt
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if agent_config and agent_config.get('system_prompt'):
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custom_system_prompt = agent_config['system_prompt'].strip()
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# Completely replace the default system prompt with the custom one
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# This prevents confusion and tool hallucination
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system_content = custom_system_prompt
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logger.info(f"Using ONLY custom agent system prompt for: {agent_config.get('name', 'Unknown')}")
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elif is_agent_builder:
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system_content = get_agent_builder_prompt()
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logger.info("Using agent builder system prompt")
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else:
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# Use just the default system prompt
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system_content = default_system_content
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logger.info("Using default system prompt only")
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if await is_enabled("knowledge_base"):
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try:
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from services.supabase import DBConnection
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kb_db = DBConnection()
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kb_client = await kb_db.client
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current_agent_id = agent_config.get('agent_id') if agent_config else None
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kb_result = await kb_client.rpc('get_combined_knowledge_base_context', {
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'p_thread_id': thread_id,
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'p_agent_id': current_agent_id,
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'p_max_tokens': 4000
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}).execute()
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if kb_result.data and kb_result.data.strip():
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logger.info(f"Adding combined knowledge base context to system prompt for thread {thread_id}, agent {current_agent_id}")
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system_content += "\n\n" + kb_result.data
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else:
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logger.debug(f"No knowledge base context found for thread {thread_id}, agent {current_agent_id}")
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except Exception as e:
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logger.error(f"Error retrieving knowledge base context for thread {thread_id}: {e}")
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if agent_config and (agent_config.get('configured_mcps') or agent_config.get('custom_mcps')) and mcp_wrapper_instance and mcp_wrapper_instance._initialized:
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mcp_info = "\n\n--- MCP Tools Available ---\n"
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mcp_info += "You have access to external MCP (Model Context Protocol) server tools.\n"
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mcp_info += "MCP tools can be called directly using their native function names in the standard function calling format:\n"
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mcp_info += '<function_calls>\n'
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mcp_info += '<invoke name="{tool_name}">\n'
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mcp_info += '<parameter name="param1">value1</parameter>\n'
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mcp_info += '<parameter name="param2">value2</parameter>\n'
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mcp_info += '</invoke>\n'
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mcp_info += '</function_calls>\n\n'
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# List available MCP tools
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mcp_info += "Available MCP tools:\n"
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try:
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# Get the actual registered schemas from the wrapper
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registered_schemas = mcp_wrapper_instance.get_schemas()
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for method_name, schema_list in registered_schemas.items():
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if method_name == 'call_mcp_tool':
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continue # Skip the fallback method
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# Get the schema info
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for schema in schema_list:
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if schema.schema_type == SchemaType.OPENAPI:
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func_info = schema.schema.get('function', {})
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description = func_info.get('description', 'No description available')
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# Extract server name from description if available
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server_match = description.find('(MCP Server: ')
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if server_match != -1:
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server_end = description.find(')', server_match)
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server_info = description[server_match:server_end+1]
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else:
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server_info = ''
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mcp_info += f"- **{method_name}**: {description}\n"
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# Show parameter info
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params = func_info.get('parameters', {})
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props = params.get('properties', {})
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if props:
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mcp_info += f" Parameters: {', '.join(props.keys())}\n"
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except Exception as e:
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logger.error(f"Error listing MCP tools: {e}")
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mcp_info += "- Error loading MCP tool list\n"
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# Add critical instructions for using search results
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mcp_info += "\n🚨 CRITICAL MCP TOOL RESULT INSTRUCTIONS 🚨\n"
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mcp_info += "When you use ANY MCP (Model Context Protocol) tools:\n"
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mcp_info += "1. ALWAYS read and use the EXACT results returned by the MCP tool\n"
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mcp_info += "2. For search tools: ONLY cite URLs, sources, and information from the actual search results\n"
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mcp_info += "3. For any tool: Base your response entirely on the tool's output - do NOT add external information\n"
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mcp_info += "4. DO NOT fabricate, invent, hallucinate, or make up any sources, URLs, or data\n"
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mcp_info += "5. If you need more information, call the MCP tool again with different parameters\n"
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mcp_info += "6. When writing reports/summaries: Reference ONLY the data from MCP tool results\n"
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mcp_info += "7. If the MCP tool doesn't return enough information, explicitly state this limitation\n"
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mcp_info += "8. Always double-check that every fact, URL, and reference comes from the MCP tool output\n"
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mcp_info += "\nIMPORTANT: MCP tool results are your PRIMARY and ONLY source of truth for external data!\n"
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mcp_info += "NEVER supplement MCP results with your training data or make assumptions beyond what the tools provide.\n"
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system_content += mcp_info
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system_message = { "role": "system", "content": system_content }
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iteration_count = 0
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continue_execution = True
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latest_user_message = await client.table('messages').select('*').eq('thread_id', thread_id).eq('type', 'user').order('created_at', desc=True).limit(1).execute()
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if latest_user_message.data and len(latest_user_message.data) > 0:
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data = latest_user_message.data[0]['content']
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if isinstance(data, str):
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data = json.loads(data)
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if trace:
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trace.update(input=data['content'])
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while continue_execution and iteration_count < max_iterations:
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iteration_count += 1
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logger.info(f"🔄 Running iteration {iteration_count} of {max_iterations}...")
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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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if trace:
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trace.event(name="billing_limit_reached", level="ERROR", status_message=(f"{error_msg}"))
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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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logger.info(f"Last message was from assistant, stopping execution")
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if trace:
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trace.event(name="last_message_from_assistant", level="DEFAULT", status_message=(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 = latest_browser_state_msg.data[0]["content"]
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if isinstance(browser_content, str):
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browser_content = json.loads(browser_content)
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screenshot_base64 = browser_content.get("screenshot_base64")
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screenshot_url = browser_content.get("image_url")
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# Create a copy of the browser state without screenshot data
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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('image_url', 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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# Only add screenshot if model is not Gemini, Anthropic, or OpenAI
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if 'gemini' in model_name.lower() or 'anthropic' in model_name.lower() or 'openai' in model_name.lower():
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# Prioritize screenshot_url if available
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if screenshot_url:
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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": screenshot_url,
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"format": "image/jpeg"
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}
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})
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if trace:
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trace.event(name="screenshot_url_added_to_temporary_message", level="DEFAULT", status_message=(f"Screenshot URL added to temporary message."))
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elif screenshot_base64:
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# Fallback to base64 if URL not available
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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}",
|
|
}
|
|
})
|
|
if trace:
|
|
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.")
|
|
if trace:
|
|
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.")
|
|
if trace:
|
|
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}")
|
|
if trace:
|
|
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}")
|
|
if trace:
|
|
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") if trace else None
|
|
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')}")
|
|
if trace:
|
|
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
|
|
full_response = ""
|
|
try:
|
|
# Check if response is iterable (async generator) or a dict (error case)
|
|
if hasattr(response, '__aiter__') and not isinstance(response, dict):
|
|
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')}")
|
|
if trace:
|
|
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")
|
|
if trace:
|
|
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 <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', '')
|
|
full_response += assistant_text
|
|
if isinstance(assistant_text, str):
|
|
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
|
|
logger.info(f"Agent used XML tool: {xml_tool}")
|
|
if trace:
|
|
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')}")
|
|
if trace:
|
|
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}")
|
|
if trace:
|
|
trace.event(name="error_processing_assistant_chunk", level="ERROR", status_message=(f"Error processing assistant chunk: {e}"))
|
|
|
|
yield chunk
|
|
else:
|
|
# Response is not iterable, likely an error dict
|
|
logger.error(f"Response is not iterable: {response}")
|
|
error_detected = True
|
|
|
|
# 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")
|
|
if trace:
|
|
trace.event(name="stopping_due_to_error_detected_in_response", level="DEFAULT", status_message=(f"Stopping due to error detected in response"))
|
|
if generation:
|
|
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}")
|
|
if trace:
|
|
trace.event(name="agent_decided_to_stop_with_tool", level="DEFAULT", status_message=(f"Agent decided to stop with tool: {last_tool_call}"))
|
|
if generation:
|
|
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}")
|
|
if trace:
|
|
trace.event(name="error_during_response_streaming", level="ERROR", status_message=(f"Error during response streaming: {str(e)}"))
|
|
if generation:
|
|
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}")
|
|
if trace:
|
|
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
|
|
if generation:
|
|
generation.end(output=full_response)
|
|
|
|
langfuse.flush() |