Debug agentops attribute error in trace

docs/v2/concepts/core-concepts.mdx

@@ -19,7 +19,7 @@ The [decorators](/v2/concepts/decorators) system allows you to add tracing to yo
 
 AgentOps is built on [OpenTelemetry](https://opentelemetry.io/), a widely-adopted standard for observability instrumentation. This provides a robust and standardized approach to collecting, processing, and exporting telemetry data.
 
-# Sessions
+## Sessions
 
 A [Session](/v2/concepts/sessions) represents a single user interaction with your agent. When you initialize AgentOps using the `init` function, a session is automatically created for you:
 
@@ -30,14 +30,16 @@ import agentops
 agentops.init(api_key="YOUR_API_KEY")
 ```
 
-By default, all events and API calls will be associated with this session. For more advanced use cases, you can control session creation manually:
+By default, all events and API calls will be associated with this session. For workflows where you want explicit lifecycle control, prefer creating traces explicitly:
 
 ```python
-# Initialize without auto-starting a session
+# Initialize without starting a trace automatically
 agentops.init(api_key="YOUR_API_KEY", auto_start_session=False)
 
-# Later, manually start a session when needed
-agentops.start_session(tags=["customer-query"])
+# Later, wrap a workflow in a trace context manager
+with agentops.start_trace(trace_name="Customer Workflow", tags=["customer-query", "high-priority"]):
+    # your workflow code
+    pass
 ```
 
 # Span Hierarchy
@@ -109,14 +111,14 @@ response = client.chat.completions.create(
 
 # Tags
 
-[Tags](/v2/concepts/tags) help you organize and filter your sessions. You can add tags when initializing AgentOps or when starting a session:
+[Tags](/v2/concepts/tags) help you organize and filter your sessions and traces. You can add tags when initializing AgentOps or when starting a trace:
 
 ```python
 # Add tags when initializing
-agentops.init(api_key="YOUR_API_KEY", tags=["production", "web-app"])
+agentops.init(api_key="YOUR_API_KEY", default_tags=["production", "web-app"])
 
-# Or when manually starting a session
-agentops.start_session(tags=["customer-service", "tier-1"])
+# Or when manually starting a trace
+agentops.start_trace(trace_name="customer-service-workflow", tags=["customer-service", "tier-1"])
 ```
 
 # Host Environment

docs/v2/concepts/tags.mdx

@@ -4,7 +4,7 @@ description: 'Organize and filter your sessions with customizable tags'
 ---
 ## Adding Tags
 
-You can add tags when initializing AgentOps, whicreh is the most common approach:
+You can add tags when initializing AgentOps, which is the most common approach:
 
 ```python
 import agentops
@@ -23,7 +23,7 @@ Alternatively, when using manual trace creation:
 agentops.init(api_key="YOUR_API_KEY", auto_start_session=False)
 
 # Later start a trace with specific tags (modern approach)
-trace = agentops.start_trace(trace_name="test_workflow", default_tags=["development", "testing", "claude-3"])
+trace = agentops.start_trace(trace_name="test_workflow", tags=["development", "testing", "claude-3"])
 ```
 
 <Note>

docs/v2/concepts/traces.mdx

@@ -48,14 +48,15 @@ You can use the `@trace` decorator to create a trace for a specific function:
 
 ```python
 import agentops
+from agentops.sdk.decorators import trace
 
-@agentops.trace
+@trace
 def process_customer_data(customer_id):
     # This entire function execution will be tracked as a trace
     return analyze_data(customer_id)
 
 # Or with custom parameters
-@agentops.trace(name="data_processing", tags=["analytics"])
+@trace(name="data_processing", tags=["analytics"])
 def analyze_user_behavior(user_data):
     return perform_analysis(user_data)
 ```

docs/v2/examples/crewai.mdx

@@ -261,7 +261,7 @@ class JobPostingAgentCrew():
 
 ### Step 7: Update the Main Execution File
 
-Edit `src/job_posting_agent/main.py` to add AgentOps session management:
+Edit `src/job_posting_agent/main.py` to add AgentOps trace management:
 
 ```python
 #!/usr/bin/env python
@@ -276,36 +276,28 @@ def run():
     """
     Run the crew with AgentOps tracking.
     """
-    # Start AgentOps session
-    session = agentops.start_session(tags=["crewai", "job-posting"])
-
-    try:
+    # Start an AgentOps trace for this run
+    with agentops.start_trace(trace_name="CrewAI Job Posting", tags=["crewai", "job-posting"]):
         # Define the inputs for the crew
         inputs = {
             'company_description': 'A fast-paced tech startup focused on AI-driven solutions for e-commerce.',
             'company_domain': 'https://agentops.ai',
             'hiring_needs': 'Senior Software Engineer with experience in Python, AI, and cloud platforms.',
             'specific_benefits': 'Competitive salary, stock options, remote work flexibility, and great team culture.'
         }
-        
+
         # Initialize and run the crew
         crew = JobPostingAgentCrew().crew()
         result = crew.kickoff(inputs=inputs)
-        
+
         print("\n" + "="*50)
         print("Job Posting Creation Completed!")
         print("Result:")
         print(result)
         print("\nCheck 'job_posting.md' for the generated job posting.")
-
-        # End session successfully
-        agentops.end_session("Success")
+
         return result
-
-    except Exception as e:
-        print(f"An error occurred: {e}")
-        agentops.end_session("Failed", end_state_reason=str(e))
-        raise
+
 
 def train():
     """
@@ -322,6 +314,7 @@ def train():
     except Exception as e:
         raise Exception(f"An error occurred while training the crew: {e}")
 
+
 def replay():
     """
     Replay the crew execution from a specific task.
@@ -390,13 +383,9 @@ After running your job posting generator, visit your [AgentOps Dashboard](https:
 
 **AgentOps Integration Points:**
 - `agentops.init()` in `crew.py` - Enables automatic instrumentation
-- `agentops.start_session()` in `main.py` - Begins tracking
-- `agentops.end_session()` in `main.py` - Completes the session
+- `agentops.start_trace()` in `main.py` - Wraps execution with a trace
+- Context manager automatically completes the trace on exit
 
 ## Next Steps
 
-- Customize the input parameters in `main.py`
-- Modify agent configurations in `agents.yaml`
-- Add more tasks or change the workflow in `tasks.yaml`
-- Add custom tools in the `tools/` directory
-- Use AgentOps analytics to optimize agent performance
+- Customize the input parameters in `

docs/v2/examples/google_adk.mdx

@@ -243,20 +243,18 @@ Create the function to run the approval workflow with AgentOps tracking:
 ```python
 async def run_approval_workflow(user_request: str, session_id: str):
     """Run the complete approval workflow with AgentOps tracking"""
-    # Start AgentOps session
-    session = agentops.start_session(tags=["google-adk", "approval-workflow"])
-
-    try:
+    # Start AgentOps trace
+    with agentops.start_trace(trace_name="Google ADK Approval Workflow", tags=["google-adk", "approval-workflow"]):
         print(f"{'=' * 60}")
         print(f" Starting Approval Workflow for Session: {session_id}")
         print(f"{'=' * 60}")
         print(f"User Request: {user_request}")
-        
+
         # Create user message
         user_content = types.Content(role="user", parts=[types.Part(text=user_request)])
         step_count = 0
         final_response = "No response received"
-        
+
         # Run the workflow
         async for event in workflow_runner.run_async(
             user_id=USER_ID,
@@ -268,31 +266,12 @@ async def run_approval_workflow(user_request: str, session_id: str):
                 print(f"📋 Step {step_count} - {event.author}:")
                 if event.content.parts:
                     response_text = event.content.parts[0].text
-                    print(f"   {response_text}")
-                    if event.is_final_response():
-                        final_response = response_text
-
-        # Display session state
-        session_data = await session_service.get_session(
-            app_name=APP_NAME,
-            user_id=USER_ID,
-            session_id=session_id,
-        )
-        print(f"{'=' * 60}")
-        print(f"📊 Workflow Complete - Session State ({session_id}):")
-        print(f"{'=' * 60}")
-        for key, value in session_data.state.items():
-            print(f"   {key}: {value}")
-        print(f"🎯 Final Response: {final_response}")
-
-        # End AgentOps session successfully
-        agentops.end_session("Success")
+                    print(response_text)
+                    final_response = response_text
+
+        print(f"\n✅ Workflow Completed in {step_count} steps")
+        print(f"Final Response: {final_response}")
         return final_response
-
-    except Exception as e:
-        print(f"Error occurred: {e}")
-        agentops.end_session("Failed", end_state_reason=str(e))
-        raise
 ```
 
 Finally, add the main execution logic:
@@ -374,8 +353,7 @@ After running your approval workflow, visit your [AgentOps Dashboard](https://ap
 
 **AgentOps Integration Points:**
 - `agentops.init()` - Enables automatic instrumentation
-- `agentops.start_session()` - Begins tracking each workflow run
-- `agentops.end_session()` - Completes the session with status
+- `agentops.start_trace()` - Wraps each workflow run in a trace
 
 ## Next Steps
 

docs/v2/examples/langgraph.mdx

@@ -162,13 +162,11 @@ def stream_graph_updates(user_input: str):
 
 def run_chatbot():
     """Main function to run the chatbot with AgentOps tracking."""
-    # Start AgentOps session
-    session = agentops.start_session(tags=["langgraph", "chatbot"])
-
-    try:
+    # Start AgentOps trace for this chat session
+    with agentops.start_trace(trace_name="LangGraph Chatbot", tags=["langgraph", "chatbot"]):
         print("🤖 LangGraph Chatbot Started!")
         print("Type 'quit', 'exit', or 'q' to stop.\n")
-        
+
         while True:
             try:
                 user_input = input("User: ")
@@ -183,14 +181,6 @@ def run_chatbot():
             except Exception as e:
                 print(f"Error: {e}")
                 break
-
-        # End session successfully
-        agentops.end_session("Success")
-
-    except Exception as e:
-        print(f"Error occurred: {e}")
-        agentops.end_session("Failed", end_state_reason=str(e))
-        raise
 
 # Main execution
 if __name__ == "__main__":
@@ -207,12 +197,12 @@ python chatbot.py
 ```
 
 **What happens:**
-1. AgentOps session starts automatically
+1. AgentOps trace starts
 2. Interactive chat loop begins
 3. Each user message flows through: START → chatbot node → END
 4. LLM generates responses based on conversation history
 5. AgentOps captures all state transitions and LLM interactions
-6. Session ends when you type 'quit'
+6. Trace ends when you type 'quit'
 
 **Example conversation:**
 ```
@@ -248,8 +238,7 @@ After running your chatbot, visit your [AgentOps Dashboard](https://app.agentops
 
 **AgentOps Integration Points:**
 - `agentops.init()` - Enables automatic LangGraph instrumentation
-- `agentops.start_session()` - Begins tracking each chat session
-- `agentops.end_session()` - Completes the session with status
+- `agentops.start_trace()` - Wraps each chat session in a trace
 
 ## Next Steps
 

docs/v2/examples/watsonx.mdx

@@ -116,19 +116,17 @@ print(f"Chat Response:\n{chat_response['results'][0]['generated_text']}")
 
 ## Clean Up
 
-Finally, let's close the persistent connection with the models if they were established and end the AgentOps session.
+Finally, let's close the persistent connection with the models if they were established. The AgentOps trace will be closed automatically when the context ends.
 ```python
 # Close connections if persistent connections were used.
 # This is good practice if the SDK version/usage implies persistent connections.
 try:
-    gen_model.close_persistent_connection()
-    chat_model.close_persistent_connection()
+	gen_model.close_persistent_connection()
+	chat_model.close_persistent_connection()
 except AttributeError:
-    # Handle cases where this method might not exist (e.g. newer SDK versions or stateless calls)
-    print("Note: close_persistent_connection not available or needed for one or more models.")
-    pass
-
-agentops.end_session("Success") # Manually end session
+	# Handle cases where this method might not exist (e.g. newer SDK versions or stateless calls)
+	print("Note: close_persistent_connection not available or needed for one or more models.")
+	pass
 ```
 
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