mirror of https://github.com/kortix-ai/suna.git
wip
This commit is contained in:
marko-kraemer
parent
d850800a5f
commit
4bbc03f674
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@ -32,7 +32,6 @@ instance_id = None # Global instance ID for this backend instance
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# TTL for Redis response lists (24 hours)
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REDIS_RESPONSE_LIST_TTL = 3600 * 24
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class AgentStartRequest(BaseModel):
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model_name: Optional[str] = None # Will be set from config.MODEL_TO_USE in the endpoint
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enable_thinking: Optional[bool] = False
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@ -1798,6 +1797,7 @@ async def delete_agent(agent_id: str, user_id: str = Depends(get_current_user_id
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logger.error(f"Error deleting agent {agent_id}: {str(e)}")
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raise HTTPException(status_code=500, detail="Internal server error")
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# Marketplace Models
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class MarketplaceAgent(BaseModel):
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agent_id: str
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@ -603,285 +603,4 @@ async def run_agent(
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break
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generation.end(output=full_response)
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langfuse.flush() # Flush Langfuse events at the end of the run
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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 = await 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(await 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'
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# # Add timestamp and format tool result nicely
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# tool_name = "UnknownTool" # Try to get from metadata if available
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# result_content = "No content"
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# # Parse metadata - handle both string and dict formats
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# metadata = chunk.get('metadata', {})
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# if isinstance(metadata, str):
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# try:
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# metadata = json.loads(metadata)
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# except json.JSONDecodeError:
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# metadata = {}
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# linked_assistant_msg_id = metadata.get('assistant_message_id')
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# parsing_details = metadata.get('parsing_details')
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# if parsing_details:
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# tool_name = parsing_details.get('xml_tag_name', 'UnknownTool') # Get name from parsing details
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# try:
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# # Content is 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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# content_json = json.loads(content)
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# else:
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# content_json = content
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# # The actual tool result is nested inside content.content
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# tool_result_str = content_json.get('content', '')
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# # Extract the actual tool result string (remove outer <tool_result> tag if present)
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# match = re.search(rf'<{tool_name}>(.*?)</{tool_name}>', tool_result_str, re.DOTALL)
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# if match:
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# result_content = match.group(1).strip()
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# # Try to parse the result string itself as JSON for pretty printing
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# try:
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# result_obj = json.loads(result_content)
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# result_content = json.dumps(result_obj, indent=2)
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# except json.JSONDecodeError:
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# # Keep as string if not JSON
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# pass
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# else:
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# # Fallback if tag extraction fails
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# result_content = tool_result_str
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# except json.JSONDecodeError:
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# result_content = chunk.get('content', 'Error parsing tool content')
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# except Exception as e:
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# result_content = f"Error processing tool chunk: {e}"
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# print(f"\n\n🛠️ TOOL RESULT [{tool_name}] → {result_content}")
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# elif chunk.get('type') == 'status':
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# # Log tool status changes
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# try:
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# # Handle content as string or object
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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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# status_type = status_content.get('status_type')
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# function_name = status_content.get('function_name', '')
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# xml_tag_name = status_content.get('xml_tag_name', '') # Get XML tag if available
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# tool_name = xml_tag_name or function_name # Prefer XML tag name
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# if status_type == 'tool_started' and tool_name:
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# tool_usage_counter += 1
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# print(f"\n⏳ TOOL STARTING #{tool_usage_counter} [{tool_name}]")
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# print(" " + "-" * 40)
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# # Return to the current content display
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# if current_response:
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# print("\nContinuing response:", flush=True)
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# print(current_response, end='', flush=True)
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# elif status_type == 'tool_completed' and tool_name:
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# status_emoji = "✅"
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# print(f"\n{status_emoji} TOOL COMPLETED: {tool_name}")
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# elif status_type == 'finish':
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# finish_reason = status_content.get('finish_reason', '')
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# if finish_reason:
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# print(f"\n📌 Finished: {finish_reason}")
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# # else: # Print other status types if needed for debugging
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# # print(f"\nℹ️ STATUS: {chunk.get('content')}")
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# except json.JSONDecodeError:
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# print(f"\nWarning: Could not parse status content JSON: {chunk.get('content')}")
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# except Exception as e:
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# print(f"\nError processing status chunk: {e}")
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# # Removed elif chunk.get('type') == 'tool_call': block
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# # Update final message
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# print(f"\n\n✅ Agent run completed with {tool_usage_counter} tool executions")
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# # Try to clean up the test sandbox if possible
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# try:
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# # Attempt to delete/archive the sandbox to clean up resources
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# # Note: Actual deletion may depend on the Daytona SDK's capabilities
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# logger.info(f"Attempting to clean up test sandbox {original_sandbox_id}")
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# # If there's a method to archive/delete the sandbox, call it here
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# # Example: daytona.archive_sandbox(sandbox.id)
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# except Exception as e:
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# logger.warning(f"Failed to clean up test sandbox {original_sandbox_id}: {str(e)}")
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# if __name__ == "__main__":
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# import asyncio
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# # Configure any environment variables or setup needed for testing
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# load_dotenv() # Ensure environment variables are loaded
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# # Run the test function
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# asyncio.run(test_agent())
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langfuse.flush()
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@ -9,15 +9,10 @@ import json
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from typing import List, Dict, Any, Optional, Union
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from litellm.utils import token_counter
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from litellm.cost_calculator import completion_cost
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from services.supabase import DBConnection
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from services.llm import make_llm_api_call
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from utils.logger import logger
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# Constants for token management
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DEFAULT_TOKEN_THRESHOLD = 120000 # 80k tokens threshold for summarization
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SUMMARY_TARGET_TOKENS = 10000 # Target ~10k tokens for the summary message
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RESERVE_TOKENS = 5000 # Reserve tokens for new messages
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DEFAULT_TOKEN_THRESHOLD = 120000
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class ContextManager:
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"""Manages thread context including token counting and summarization."""
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@ -126,12 +126,17 @@ async def log_requests_middleware(request: Request, call_next):
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raise
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# Define allowed origins based on environment
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allowed_origins = ["https://www.suna.so", "https://suna.so", "http://localhost:3000"]
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allowed_origins = ["https://www.suna.so", "https://suna.so"]
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allow_origin_regex = None
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# Add staging-specific origins
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if config.ENV_MODE == EnvMode.LOCAL:
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allowed_origins.append("http://localhost:3000")
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# Add staging-specific origins
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if config.ENV_MODE == EnvMode.STAGING:
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allowed_origins.append("https://staging.suna.so")
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allowed_origins.append("http://localhost:3000")
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allow_origin_regex = r"https://suna-.*-prjcts\.vercel\.app"
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app.add_middleware(
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@ -14,7 +14,7 @@ from services.supabase import DBConnection
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from utils.auth_utils import get_current_user_id_from_jwt
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from pydantic import BaseModel
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from utils.constants import MODEL_ACCESS_TIERS, MODEL_NAME_ALIASES
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from litellm import cost_per_token, model_cost
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from litellm import cost_per_token
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import time
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# Initialize Stripe
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