Hack temporal-direct into working

examples/temporal-direct/main.py

@@ -46,6 +46,29 @@
     import ai
 
 
+MODEL_ID = "gateway:anthropic/claude-sonnet-4.6"
+
+
+# ── Workflow-safe model placeholder ──────────────────────────────
+#
+# ``agent.run`` requires a ``Model``, but a real one can't be built
+# inside the workflow: ``ai.get_model("gateway:...")`` constructs an
+# ``httpx.AsyncClient`` at provider-init time, which imports
+# httpcore/anyio and trips the Temporal sandbox (``threading.local``
+# at module load). Our loop never calls the model directly anyway --
+# every LLM call is delegated to ``llm_call_activity``, which runs
+# outside the sandbox and resolves the real model by id there.
+#
+# So hand the workflow a placeholder ``Model`` whose provider builds
+# no client. It carries the real model id (so the activity can
+# resolve it) but is safe to construct inside the sandbox.
+class WorkflowModelProvider(ai.Provider[Any]):
+    """A clientless provider, safe to construct in a workflow sandbox."""
+
+    def __init__(self) -> None:
+        super().__init__(name="workflow-placeholder", base_url="")
+
+
 # ── Tool definitions ─────────────────────────────────────────────
 #
 # Declared with @ai.tool so the framework can extract JSON schemas
@@ -93,6 +116,7 @@ async def get_population_activity(city: str) -> int:
 
 @dataclasses.dataclass
 class LLMParams:
+    model_id: str
     messages: list[dict[str, Any]]
     tool_schemas: list[dict[str, Any]]
 
@@ -105,7 +129,7 @@ class LLMResult:
 @temporalio.activity.defn
 async def llm_call_activity(params: LLMParams) -> LLMResult:
     """Call the LLM, drain the stream, return the final message."""
-    model = ai.get_model("gateway:anthropic/claude-sonnet-4.6")
+    model = ai.get_model(params.model_id)
     messages = [ai.messages.Message.model_validate(m) for m in params.messages]
     tools = [
         ai.Tool(
@@ -148,6 +172,7 @@ async def loop(
             result = await temporalio.workflow.execute_activity(
                 llm_call_activity,
                 LLMParams(
+                    model_id=context.model.id,
                     messages=[m.model_dump() for m in context.messages],
                     tool_schemas=tool_schemas,
                 ),
@@ -213,7 +238,7 @@ async def _call() -> ai.events.ToolCallResult:
 class WeatherWorkflow:
     @temporalio.workflow.run
     async def run(self, user_query: str) -> str:
-        model = ai.get_model("gateway:anthropic/claude-sonnet-4.6")
+        model = ai.Model(MODEL_ID, provider=WorkflowModelProvider())
         messages: list[ai.messages.Message] = [
             ai.system_message(
                 "Answer questions using the weather and population tools."