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(

src/langbot/pkg/api/mcp/server.py

@@ -165,7 +165,7 @@ class LangBotMCPServer:
             return _dump(ap.box_service.get_recent_errors(context))
 
         @mcp.tool(
-            description='Read bounded Workspace runtime records. Filters follow the monitoring service: bot_ids, pipeline_ids, session_ids, start_time, end_time, mode (all/real/debug), execution_statuses (owning execution status groups), knowledge_base_id, user_query, is_active as supported by the record kind.'
+            description='Read bounded Workspace runtime records. LLM calls include optional details (timing in ms, complete provider usage, structured error); older records have null details. Filters follow the monitoring service: bot_ids, pipeline_ids, session_ids, start_time, end_time, mode (all/real/debug), execution_statuses (owning execution status groups), knowledge_base_id, user_query, is_active as supported by the record kind.'
         )
         async def get_monitoring_records(
             kind: typing.Literal['messages', 'llm_calls', 'tool_calls', 'embedding_calls', 'sessions', 'errors'],
@@ -513,7 +513,13 @@ class LangBotMCPServer:
             context = _authorized(Permission.RESOURCE_VIEW)
             return _dump(await ap.llm_model_service.get_llm_models(context, include_secret=False))
 
-        @mcp.tool(description='Get a single LLM model by UUID.')
+        @mcp.tool(
+            description=(
+                'Get a single LLM model by UUID, including reasoning capabilities. '
+                'Optional budget_tokens min/max describes numeric thinking-budget support; '
+                'reasoning_config and extra_args retain the existing model API shape.'
+            )
+        )
         async def get_llm_model(model_uuid: str) -> str:
             context = _authorized(Permission.RESOURCE_VIEW)
             return _dump(await ap.llm_model_service.get_llm_model(context, model_uuid, include_secret=False))