perf(llm): harden request policy and optimize connections, reasoning and prompt caching (#2661)

* fix(llm): centralize request policy and preserve stream diagnostics

* test(llm): dispose SQLite engine after migration regression

* perf(llm): reuse connections and unify reasoning and prompt cache policy

* fix(llm): preserve explicit native prompt cache options

* perf(llm): fix measured TLS setup and streaming reuse overhead

* fix(llm): resolve plugin request-chain regressions found by E2E

---------

Co-authored-by: Hyu <[email protected]>

src/langbot/pkg/api/http/service/monitoring.py

@@ -605,6 +605,7 @@ class MonitoringService(ExecutionDetailsMixin):
         cost: float | None = None,
         error_message: str | None = None,
         message_id: str | None = None,
+        details: dict | None = None,
     ) -> str:
         """Record an LLM call"""
         workspace_uuid = self._require_write_context(context)
@@ -616,7 +617,7 @@ class MonitoringService(ExecutionDetailsMixin):
             'model_name': model_name,
             'input_tokens': input_tokens,
             'output_tokens': output_tokens,
-            'total_tokens': input_tokens + output_tokens,
+            'total_tokens': ((details or {}).get('usage') or {}).get('total_tokens') or (input_tokens + output_tokens),
             'duration': duration,
             'cost': cost,
             'status': status,
@@ -627,6 +628,7 @@ class MonitoringService(ExecutionDetailsMixin):
             'session_id': session_id,
             'error_message': error_message,
             'message_id': message_id,
+            'details': details,
         }
 
         await self.ap.persistence_mgr.execute_async(