Making summary generation more robust

temporalio/contrib/openai_agents/_temporal_model_stub.py

@@ -8,7 +8,7 @@
 
 logger = logging.getLogger(__name__)
 
-from typing import Any, AsyncIterator, Sequence, Union, cast
+from typing import Any, AsyncIterator, Union, cast
 
 from agents import (
     AgentOutputSchema,
@@ -54,7 +54,7 @@ def __init__(
     async def get_response(
         self,
         system_instructions: Optional[str],
-        input: Union[str, list[TResponseInputItem], dict[str, str]],
+        input: Union[str, list[TResponseInputItem]],
         model_settings: ModelSettings,
         tools: list[Tool],
         output_schema: Optional[AgentOutputSchemaBase],
@@ -64,28 +64,6 @@ async def get_response(
         previous_response_id: Optional[str],
         prompt: Optional[ResponsePromptParam],
     ) -> ModelResponse:
-        def get_summary(
-            input: Union[str, list[TResponseInputItem], dict[str, str]],
-        ) -> str:
-            ### Activity summary shown in the UI
-            try:
-                max_size = 100
-                if isinstance(input, str):
-                    return input[:max_size]
-                elif isinstance(input, list):
-                    seq_input = cast(Sequence[Any], input)
-                    last_item = seq_input[-1]
-                    if isinstance(last_item, dict):
-                        return last_item.get("content", "")[:max_size]
-                    elif hasattr(last_item, "content"):
-                        return str(getattr(last_item, "content"))[:max_size]
-                    return str(last_item)[:max_size]
-                elif isinstance(input, dict):
-                    return input.get("content", "")[:max_size]
-            except Exception as e:
-                logger.error(f"Error getting summary: {e}")
-            return ""
-
         def make_tool_info(tool: Tool) -> ToolInput:
             if isinstance(tool, (FileSearchTool, WebSearchTool)):
                 return tool
@@ -150,7 +128,7 @@ def make_tool_info(tool: Tool) -> ToolInput:
         return await workflow.execute_activity_method(
             ModelActivity.invoke_model_activity,
             activity_input,
-            summary=self.model_params.summary_override or get_summary(input),
+            summary=self.model_params.summary_override or _extract_summary(input),
             task_queue=self.model_params.task_queue,
             schedule_to_close_timeout=self.model_params.schedule_to_close_timeout,
             schedule_to_start_timeout=self.model_params.schedule_to_start_timeout,
@@ -176,3 +154,34 @@ def stream_response(
         prompt: ResponsePromptParam | None,
     ) -> AsyncIterator[TResponseStreamEvent]:
         raise NotImplementedError("Temporal model doesn't support streams yet")
+
+
+def _extract_summary(input: Union[str, list[TResponseInputItem]]) -> str:
+    ### Activity summary shown in the UI
+    try:
+        max_size = 100
+        if isinstance(input, str):
+            return input[:max_size]
+        elif isinstance(input, list):
+            # Find all message inputs, which are reasonably summarizable
+            messages: list[TResponseInputItem] = [
+                item for item in input if item.get("type", "message") == "message"
+            ]
+            if not messages:
+                return ""
+
+            content: Any = messages[-1].get("content", "")
+
+            # In the case of multiple contents, take the last one
+            if isinstance(content, list):
+                if not content:
+                    return ""
+                content = content[-1]
+
+            # Take the text field from the content if present
+            if isinstance(content, dict) and content.get("text") is not None:
+                content = content.get("text")
+            return str(content)[:max_size]
+    except Exception as e:
+        logger.error(f"Error getting summary: {e}")
+    return ""

temporalio/contrib/openai_agents/workflow.py

@@ -134,7 +134,7 @@ async def run_activity(ctx: RunContextWrapper[Any], input: str) -> Any:
             cancellation_type=cancellation_type,
             activity_id=activity_id,
             versioning_intent=versioning_intent,
-            summary=summary,
+            summary=summary or schema.description,
             priority=priority,
         )
         try:

tests/contrib/openai_agents/test_openai.py

@@ -44,12 +44,16 @@
 )
 from openai import APIStatusError, AsyncOpenAI, BaseModel
 from openai.types.responses import (
+    EasyInputMessageParam,
     ResponseFunctionToolCall,
+    ResponseFunctionToolCallParam,
     ResponseFunctionWebSearch,
+    ResponseInputTextParam,
     ResponseOutputMessage,
     ResponseOutputText,
 )
 from openai.types.responses.response_function_web_search import ActionSearch
+from openai.types.responses.response_input_item_param import Message
 from openai.types.responses.response_prompt_param import ResponsePromptParam
 from pydantic import ConfigDict, Field, TypeAdapter
 
@@ -63,6 +67,7 @@
     TestModel,
     TestModelProvider,
 )
+from temporalio.contrib.openai_agents._temporal_model_stub import _extract_summary
 from temporalio.contrib.pydantic import pydantic_data_converter
 from temporalio.exceptions import ApplicationError, CancelledError
 from temporalio.testing import WorkflowEnvironment
@@ -680,7 +685,8 @@ async def test_research_workflow(client: Client, use_local_model: bool):
     new_config["plugins"] = [
         openai_agents.OpenAIAgentsPlugin(
             model_params=ModelActivityParameters(
-                start_to_close_timeout=timedelta(seconds=30)
+                start_to_close_timeout=timedelta(seconds=120),
+                schedule_to_close_timeout=timedelta(seconds=120),
             ),
             model_provider=TestModelProvider(TestResearchModel())
             if use_local_model
@@ -1687,7 +1693,7 @@ class WorkflowToolModel(StaticTestModel):
                     id="",
                     content=[
                         ResponseOutputText(
-                            text="",
+                            text="Workflow tool was used",
                             annotations=[],
                             type="output_text",
                         )
@@ -1938,3 +1944,37 @@ async def test_heartbeat(client: Client, env: WorkflowEnvironment):
             execution_timeout=timedelta(seconds=5.0),
         )
         await workflow_handle.result()
+
+
+def test_summary_extraction():
+    input: list[TResponseInputItem] = [
+        EasyInputMessageParam(
+            content="First message",
+            role="user",
+        )
+    ]
+
+    assert _extract_summary(input) == "First message"
+
+    input.append(
+        Message(
+            content=[
+                ResponseInputTextParam(
+                    text="Second message",
+                    type="input_text",
+                )
+            ],
+            role="user",
+        )
+    )
+    assert _extract_summary(input) == "Second message"
+
+    input.append(
+        ResponseFunctionToolCallParam(
+            arguments="",
+            call_id="",
+            name="",
+            type="function_call",
+        )
+    )
+    assert _extract_summary(input) == "Second message"