fix: capture claude agent sdk session ids

Author: sipercai

instrumentation-loongsuite/loongsuite-instrumentation-claude-agent-sdk/CHANGELOG.md

@@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## Unreleased
 
+### Fixed
+
+- Capture Claude Agent SDK session IDs on agent, LLM, and tool spans.
+
 ## Version 0.6.0 (2026-06-03)
 
 There are no changelog entries for this release.

instrumentation-loongsuite/loongsuite-instrumentation-claude-agent-sdk/src/opentelemetry/instrumentation/claude_agent_sdk/patch.py

@@ -18,6 +18,7 @@
 import time
 from typing import Any, Dict, List, Optional
 
+from opentelemetry import baggage
 from opentelemetry import context as otel_context
 from opentelemetry.instrumentation.claude_agent_sdk.utils import (
     extract_usage_from_result_message,
@@ -27,7 +28,10 @@
 from opentelemetry.trace import set_span_in_context
 from opentelemetry.util.genai.extended_handler import (
     ExtendedTelemetryHandler,
-    get_extended_telemetry_handler,
+)
+from opentelemetry.util.genai.extended_semconv.gen_ai_extended_attributes import (
+    GEN_AI_SESSION_ID,
+    GEN_AI_USER_ID,
 )
 from opentelemetry.util.genai.extended_types import (
     ExecuteToolInvocation,
@@ -46,29 +50,72 @@
 
 logger = logging.getLogger(__name__)
 
-# Storage for tool runs managed by client (created from response stream)
-# Key: tool_use_id, Value: tool_invocation
-_client_managed_runs: Dict[str, ExecuteToolInvocation] = {}
+
+def _current_baggage_value(key: str) -> Optional[str]:
+    try:
+        value = baggage.get_baggage(key)
+    except Exception:
+        return None
+    if value is None:
+        return None
+    text = str(value).strip()
+    return text or None
+
+
+def _entry_baggage_identity_attributes() -> Dict[str, str]:
+    attributes: Dict[str, str] = {}
+    session_id = _current_baggage_value(GEN_AI_SESSION_ID)
+    user_id = _current_baggage_value(GEN_AI_USER_ID)
+    if session_id:
+        attributes[GEN_AI_SESSION_ID] = session_id
+    if user_id:
+        attributes[GEN_AI_USER_ID] = user_id
+    return attributes
+
+
+def _apply_session_identity(
+    invocation: Any, session_id: Optional[str]
+) -> None:
+    """Apply Entry baggage identity first, then Claude's own session fallback."""
+    entry_attributes = _entry_baggage_identity_attributes()
+    effective_session_id = (
+        entry_attributes.get(GEN_AI_SESSION_ID) or session_id
+    )
+
+    if effective_session_id:
+        if hasattr(invocation, "conversation_id"):
+            invocation.conversation_id = effective_session_id
+        invocation.attributes[GEN_AI_SESSION_ID] = effective_session_id
+
+    for key, value in entry_attributes.items():
+        invocation.attributes[key] = value
 
 
-def _clear_client_managed_runs() -> None:
+def _set_session_id(
+    agent_invocation: InvokeAgentInvocation, session_id: Optional[str]
+) -> None:
+    """Set Entry session id or Claude session id on an agent invocation."""
+    _apply_session_identity(agent_invocation, session_id)
+
+
+def _set_llm_session_id(
+    llm_invocation: LLMInvocation, session_id: Optional[str]
+) -> None:
+    """Set Entry session id or Claude session id on an LLM invocation."""
+    _apply_session_identity(llm_invocation, session_id)
+
+
+def _clear_client_managed_runs(
+    handler: ExtendedTelemetryHandler,
+    client_managed_runs: Dict[str, ExecuteToolInvocation],
+) -> None:
     """Clear all client-managed tool runs.
 
     This should be called when a conversation ends to avoid memory leaks
     and to clean up any orphaned tool runs.
     """
-    global _client_managed_runs
-
-    try:
-        handler = get_extended_telemetry_handler()
-    except Exception:
-        # If we can't get the handler (e.g., instrumentation not initialized),
-        # we still need to clear the tracking dictionary to prevent memory leaks.
-        _client_managed_runs.clear()
-        return
-
     # End any orphaned tool runs
-    for tool_use_id, tool_invocation in list(_client_managed_runs.items()):
+    for tool_use_id, tool_invocation in list(client_managed_runs.items()):
         try:
             handler.fail_execute_tool(
                 tool_invocation,
@@ -83,7 +130,7 @@ def _clear_client_managed_runs() -> None:
             # Best effort cleanup: continue processing remaining tools.
             pass
 
-    _client_managed_runs.clear()
+    client_managed_runs.clear()
 
 
 def _extract_message_parts(msg: Any) -> List[Any]:
@@ -112,6 +159,7 @@ def _create_tool_spans_from_message(
     handler: ExtendedTelemetryHandler,
     agent_invocation: InvokeAgentInvocation,
     active_task_stack: List[Any],
+    client_managed_runs: Dict[str, ExecuteToolInvocation],
     exclude_tool_names: Optional[List[str]] = None,
 ) -> None:
     """Create tool execution spans from ToolUseBlocks in an AssistantMessage.
@@ -163,8 +211,11 @@ def _create_tool_spans_from_message(
                     tool_call_arguments=tool_input,
                     tool_description=tool_name,
                 )
+                _apply_session_identity(
+                    tool_invocation, agent_invocation.conversation_id
+                )
                 handler.start_execute_tool(tool_invocation)
-                _client_managed_runs[tool_use_id] = tool_invocation
+                client_managed_runs[tool_use_id] = tool_invocation
 
                 # If this is a Task tool, create a SubAgent span under it
                 # https://platform.claude.com/docs/en/agent-sdk/python#task
@@ -203,6 +254,10 @@ def _create_tool_spans_from_message(
                             agent_description=task_description,
                             input_messages=input_messages,
                         )
+                        _set_session_id(
+                            subagent_invocation,
+                            agent_invocation.conversation_id,
+                        )
 
                         # Start SubAgent span
                         handler.start_invoke_agent(subagent_invocation)
@@ -271,6 +326,7 @@ def _process_assistant_message(
     handler: ExtendedTelemetryHandler,
     collected_messages: List[Dict[str, Any]],
     active_task_stack: List[Any],
+    client_managed_runs: Dict[str, ExecuteToolInvocation],
 ) -> None:
     """Process AssistantMessage: create LLM turn, extract parts, create tool spans."""
     parts = _extract_message_parts(msg)
@@ -353,7 +409,11 @@ def _process_assistant_message(
         turn_tracker.close_llm_turn()
 
     _create_tool_spans_from_message(
-        msg, handler, agent_invocation, active_task_stack
+        msg,
+        handler,
+        agent_invocation,
+        active_task_stack,
+        client_managed_runs,
     )
 
 
@@ -363,6 +423,7 @@ def _process_user_message(
     handler: ExtendedTelemetryHandler,
     collected_messages: List[Dict[str, Any]],
     active_task_stack: List[Any],
+    client_managed_runs: Dict[str, ExecuteToolInvocation],
 ) -> None:
     """Process UserMessage: close tool spans, collect message content, mark next LLM start."""
     user_parts: List[MessagePart] = []
@@ -376,8 +437,8 @@ def _process_user_message(
 
             if block_type == "ToolResultBlock":
                 tool_use_id = getattr(block, "tool_use_id", None)
-                if tool_use_id and tool_use_id in _client_managed_runs:
-                    tool_invocation = _client_managed_runs.pop(tool_use_id)
+                if tool_use_id and tool_use_id in client_managed_runs:
+                    tool_invocation = client_managed_runs.pop(tool_use_id)
 
                     # Set tool response
                     tool_content = getattr(block, "content", None)
@@ -533,7 +594,21 @@ def _process_system_message(
         if hasattr(msg, "data") and isinstance(msg.data, dict):
             session_id = msg.data.get("session_id")
             if session_id:
-                agent_invocation.conversation_id = session_id
+                _set_session_id(agent_invocation, session_id)
+
+
+def _process_stream_event_message(
+    msg: Any,
+    agent_invocation: InvokeAgentInvocation,
+) -> None:
+    """Process StreamEvent: extract session_id when streaming mode exposes it early."""
+    session_id = getattr(msg, "session_id", None)
+    if not session_id:
+        event = getattr(msg, "event", None)
+        if isinstance(event, dict):
+            session_id = event.get("session_id")
+
+    _set_session_id(agent_invocation, session_id)
 
 
 def _process_result_message(
@@ -543,6 +618,8 @@ def _process_result_message(
 ) -> None:
     """Process ResultMessage: update session_id (fallback), token usage, and close any open LLM turn."""
 
+    _set_session_id(agent_invocation, getattr(msg, "session_id", None))
+    turn_tracker.set_session_id(agent_invocation.conversation_id)
     _update_token_usage(agent_invocation, turn_tracker, msg)
 
     if turn_tracker.current_llm_invocation:
@@ -554,6 +631,7 @@ async def _process_agent_invocation_stream(
     handler: ExtendedTelemetryHandler,
     model: str,
     prompt: str,
+    session_id: Optional[str] = None,
 ) -> Any:
     """Unified handler for processing agent invocation stream.
 
@@ -564,18 +642,15 @@ async def _process_agent_invocation_stream(
         provider=infer_provider_from_base_url(),
         agent_name="claude-agent",
         request_model=model,
-        conversation_id="",
+        conversation_id=None,
         input_messages=[
             InputMessage(role="user", parts=[Text(content=prompt)])
         ]
         if prompt
         else [],
     )
+    _set_session_id(agent_invocation, session_id)
 
-    # Attach empty context to clear any previous context, ensuring each query
-    # creates an independent root trace. This is important for scenarios where
-    # multiple queries are called in the same script - each should have its own trace_id.
-    empty_context_token = otel_context.attach(otel_context.Context())
     handler.start_invoke_agent(agent_invocation)
 
     query_start_time = time.time()
@@ -589,13 +664,16 @@ async def _process_agent_invocation_stream(
     # When a Task tool is created, it's pushed here
     # When its ToolResultBlock is received, it's popped
     active_task_stack: List[Any] = []
+    client_managed_runs: Dict[str, ExecuteToolInvocation] = {}
 
     try:
         async for msg in wrapped_stream:
             msg_type = type(msg).__name__
 
             if msg_type == "SystemMessage":
                 _process_system_message(msg, agent_invocation)
+            elif msg_type == "StreamEvent":
+                _process_stream_event_message(msg, agent_invocation)
             elif msg_type == "AssistantMessage":
                 _process_assistant_message(
                     msg,
@@ -606,6 +684,7 @@ async def _process_agent_invocation_stream(
                     handler,
                     collected_messages,
                     active_task_stack,
+                    client_managed_runs,
                 )
             elif msg_type == "UserMessage":
                 _process_user_message(
@@ -614,6 +693,7 @@ async def _process_agent_invocation_stream(
                     handler,
                     collected_messages,
                     active_task_stack,
+                    client_managed_runs,
                 )
             elif msg_type == "ResultMessage":
                 _process_result_message(msg, agent_invocation, turn_tracker)
@@ -648,11 +728,7 @@ async def _process_agent_invocation_stream(
                 # Span closure failure should not break the application
                 pass
 
-        # Detach empty context token to restore the original context.
-        # Note: stop_invoke_agent/fail_invoke_agent already detached invocation.context_token,
-        # which restored to empty context. Now we detach empty_context_token to restore further.
-        otel_context.detach(empty_context_token)
-        _clear_client_managed_runs()
+        _clear_client_managed_runs(handler, client_managed_runs)
 
 
 class AssistantTurnTracker:
@@ -728,8 +804,8 @@ def start_llm_turn(
         # Add conversation_id (session_id) to LLM span attributes
         # This is a custom extension beyond standard GenAI semantic conventions
         if agent_invocation and agent_invocation.conversation_id:
-            llm_invocation.attributes["gen_ai.conversation.id"] = (
-                agent_invocation.conversation_id
+            _set_llm_session_id(
+                llm_invocation, agent_invocation.conversation_id
             )
 
         self.handler.start_llm(llm_invocation)
@@ -774,6 +850,12 @@ def update_usage(
         if output_tokens is not None:
             target_invocation.output_tokens = output_tokens
 
+    def set_session_id(self, session_id: Optional[str]) -> None:
+        """Update the open LLM invocation with a late session id."""
+        target_invocation = self.current_llm_invocation
+        if target_invocation:
+            _set_llm_session_id(target_invocation, session_id)
+
     def close_llm_turn(self) -> None:
         """Close the current LLM invocation span."""
         if self.current_llm_invocation:
@@ -798,6 +880,7 @@ def wrap_claude_client_init(wrapped, instance, args, kwargs, handler=None):
 
     instance._otel_handler = handler
     instance._otel_prompt = None
+    instance._otel_session_id = None
 
     return result
 
@@ -808,6 +891,10 @@ def wrap_claude_client_query(wrapped, instance, args, kwargs, handler=None):
         instance._otel_prompt = str(
             kwargs.get("prompt") or (args[0] if args else "")
         )
+        session_id = kwargs.get("session_id")
+        if session_id is None and len(args) > 1:
+            session_id = args[1]
+        instance._otel_session_id = session_id
 
     return wrapped(*args, **kwargs)
 
@@ -835,6 +922,7 @@ async def wrap_claude_client_receive_response(
         handler=handler,
         model=model,
         prompt=prompt,
+        session_id=getattr(instance, "_otel_session_id", None),
     ):
         yield msg
 
@@ -852,11 +940,13 @@ async def wrap_query(wrapped, instance, args, kwargs, handler=None):
 
     model = get_model_from_options_or_env(options)
     prompt_str = str(prompt) if isinstance(prompt, str) else ""
+    session_id = getattr(options, "resume", None) if options else None
 
     async for message in _process_agent_invocation_stream(
         wrapped(*args, **kwargs),
         handler=handler,
         model=model,
         prompt=prompt_str,
+        session_id=session_id,
     ):
         yield message