Record instructions on the agent run span even when an instructions function is used.

Author: dmontagu

pydantic_ai_slim/pydantic_ai/agent/__init__.py

@@ -685,15 +685,21 @@ async def get_instructions(run_context: RunContext[AgentDepsT]) -> str | None:
         finally:
             try:
                 if instrumentation_settings and run_span.is_recording():
-                    run_span.set_attributes(self._run_span_end_attributes(state, usage, instrumentation_settings))
+                    run_span.set_attributes(
+                        self._run_span_end_attributes(
+                            state, usage, instrumentation_settings, graph_deps.new_message_index
+                        )
+                    )
             finally:
                 run_span.end()
 
     def _run_span_end_attributes(
-        self, state: _agent_graph.GraphAgentState, usage: _usage.RunUsage, settings: InstrumentationSettings
+        self,
+        state: _agent_graph.GraphAgentState,
+        usage: _usage.RunUsage,
+        settings: InstrumentationSettings,
+        new_message_index: int,
     ):
-        literal_instructions, _ = self._get_instructions()
-
         if settings.version == 1:
             attrs = {
                 'all_messages_events': json.dumps(
@@ -704,19 +710,32 @@ def _run_span_end_attributes(
                 )
             }
         else:
-            attrs = {
+            # Store the last instructions here for convenience
+            last_instructions = InstrumentedModel._get_instructions(state.message_history)  # pyright: ignore[reportPrivateUsage]
+            attrs: dict[str, Any] = {
                 'pydantic_ai.all_messages': json.dumps(settings.messages_to_otel_messages(list(state.message_history))),
-                **settings.system_instructions_attributes(literal_instructions),
+                **settings.system_instructions_attributes(last_instructions),
             }
 
+            # Store an attribute that indicates that the instructions from this agent run were not always the same
+            # This can signal to an observability UI that different steps in the agent run had different instructions
+            # Note: We purposely only look at "new" messages because they are the only ones produced by this agent run.
+            for m in state.message_history[new_message_index:]:
+                if (
+                    isinstance(m, _messages.ModelRequest)
+                    and m.instructions is not None
+                    and m.instructions != last_instructions
+                ):
+                    attrs['pydantic_ai.variable_instructions'] = True
+
         return {
             **usage.opentelemetry_attributes(),
             **attrs,
             'logfire.json_schema': json.dumps(
                 {
                     'type': 'object',
                     'properties': {
-                        **{attr: {'type': 'array'} for attr in attrs.keys()},
+                        **{k: {'type': 'array'} if isinstance(v, str) else {} for k, v in attrs.items()},
                         'final_result': {'type': 'object'},
                     },
                 }