feat(telemetry): add isolated beta quality diagnostics and release identity

src/langbot/pkg/provider/modelmgr/requester.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+from langbot.pkg.telemetry import diagnostics
+
 import abc
 import typing
 import time
@@ -98,6 +100,7 @@ class RuntimeProvider:
             raise WorkspaceInvariantError('LLM invocation requires an ExecutionContext when query is absent')
         return execution_context
 
+    @diagnostics.observe('api', 'model.invoke_llm', source='agent')
     async def invoke_llm(
         self,
         query: pipeline_query.Query | None,
@@ -182,6 +185,7 @@ class RuntimeProvider:
                 except Exception as monitor_err:
                     self.requester.ap.logger.error(f'[Monitoring] Failed to record LLM call: {monitor_err}')
 
+    @diagnostics.observe('api', 'model.invoke_llm_stream', source='agent')
     async def invoke_llm_stream(
         self,
         query: pipeline_query.Query | None,
@@ -264,6 +268,7 @@ class RuntimeProvider:
                 except Exception as monitor_err:
                     self.requester.ap.logger.error(f'[Monitoring] Failed to record LLM stream call: {monitor_err}')
 
+    @diagnostics.observe('api', 'model.invoke_embedding', source='agent')
     async def invoke_embedding(
         self,
         model: RuntimeEmbeddingModel,
@@ -331,6 +336,7 @@ class RuntimeProvider:
             except Exception as monitor_err:
                 self.requester.ap.logger.error(f'[Monitoring] Failed to record embedding call: {monitor_err}')
 
+    @diagnostics.observe('api', 'model.invoke_rerank', source='agent')
     async def invoke_rerank(
         self,
         model: RuntimeRerankModel,

src/langbot/pkg/provider/tools/toolmgr.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+from langbot.pkg.telemetry import diagnostics
+
 import typing
 import time
 import inspect
@@ -473,6 +475,7 @@ class ToolManager:
         )
         return result
 
+    @diagnostics.observe('api', 'tool.execute', source='agent', stage='execute')
     async def execute_func_call(
         self,
         name: str,