feat: Add test for evaluate step

agents-api/agents_api/activities/task_steps/evaluate_step.py

@@ -1,6 +1,3 @@
-from typing import Any
-
-from beartype import beartype
 from temporalio import activity
 
 from ...activities.task_steps.utils import simple_eval_dict
@@ -12,14 +9,16 @@
 from ...env import testing
 
 
-@beartype
 async def evaluate_step(
     context: StepContext[EvaluateStep],
-) -> StepOutcome[dict[str, Any]]:
-    exprs = context.definition.arguments
+) -> StepOutcome:
+    assert isinstance(context.current_step, EvaluateStep)
+
+    exprs = context.current_step.evaluate
     output = simple_eval_dict(exprs, values=context.model_dump())
 
-    return StepOutcome(output=output)
+    result = StepOutcome(output=output)
+    return result
 
 
 # Note: This is here just for clarity. We could have just imported evaluate_step directly

agents-api/agents_api/activities/task_steps/if_else_step.py

@@ -16,9 +16,9 @@ async def if_else_step(context: StepContext[IfElseWorkflowStep]) -> dict:
     # context_data: dict = context.model_dump()
 
     # next_workflow = (
-    #     context.definition.then
-    #     if simple_eval(context.definition.if_, names=context_data)
-    #     else context.definition.else_
+    #     context.current_step.then
+    #     if simple_eval(context.current_step.if_, names=context_data)
+    #     else context.current_step.else_
     # )
 
     # return {"goto_workflow": next_workflow}

agents-api/agents_api/activities/task_steps/prompt_step.py

@@ -25,9 +25,9 @@ async def prompt_step(context: StepContext[PromptStep]) -> StepOutcome:
 
     # Render template messages
     prompt = (
-        [InputChatMLMessage(content=context.definition.prompt)]
-        if isinstance(context.definition.prompt, str)
-        else context.definition.prompt
+        [InputChatMLMessage(content=context.current_step.prompt)]
+        if isinstance(context.current_step.prompt, str)
+        else context.current_step.prompt
     )
 
     template_messages: list[InputChatMLMessage] = prompt
@@ -47,7 +47,7 @@ async def prompt_step(context: StepContext[PromptStep]) -> StepOutcome:
         for m in messages
     ]
 
-    settings: dict = context.definition.settings.model_dump()
+    settings: dict = context.current_step.settings.model_dump()
     # Get settings and run llm
     response = await litellm.acompletion(
         messages=messages,

agents-api/agents_api/activities/task_steps/tool_call_step.py

@@ -10,10 +10,10 @@
 @beartype
 async def tool_call_step(context: StepContext) -> dict:
     raise NotImplementedError()
-    # assert isinstance(context.definition, ToolCallStep)
+    # assert isinstance(context.current_step, ToolCallStep)
 
-    # context.definition.tool_id
-    # context.definition.arguments
+    # context.current_step.tool_id
+    # context.current_step.arguments
     # # get tool by id
     # # call tool
 

agents-api/agents_api/activities/task_steps/yield_step.py

@@ -1,5 +1,3 @@
-from typing import Any
-
 from beartype import beartype
 from temporalio import activity
 
@@ -12,7 +10,7 @@
 
 
 @beartype
-async def yield_step(context: StepContext[YieldStep]) -> StepOutcome[dict[str, Any]]:
+async def yield_step(context: StepContext[YieldStep]) -> StepOutcome:
     all_workflows = context.execution_input.task.workflows
     workflow = context.current_step.workflow
 

agents-api/agents_api/autogen/openapi_model.py

@@ -1,11 +1,10 @@
 # ruff: noqa: F401, F403, F405
-from typing import Annotated, Generic, Literal, Self, Type, TypeVar
+from typing import Annotated, Any, Generic, Literal, Self, Type, TypeVar
 from uuid import UUID
 
 from litellm.utils import _select_tokenizer as select_tokenizer
 from litellm.utils import token_counter
 from pydantic import AwareDatetime, Field
-from pydantic_partial import create_partial_model
 
 from ..common.utils.datetime import utcnow
 from .Agents import *
@@ -97,17 +96,15 @@ class ListResponse(BaseModel, Generic[DataT]):
 # Create models
 # -------------
 
-CreateTransitionRequest = create_partial_model(
-    Transition,
-    #
-    # The following fields are optional
-    "id",
-    "execution_id",
-    "created_at",
-    "updated_at",
-    "metadata",
-)
-CreateTransitionRequest.model_rebuild()
+
+class CreateTransitionRequest(Transition):
+    # The following fields are optional in this
+
+    id: UUID | None = None
+    execution_id: UUID | None = None
+    created_at: AwareDatetime | None = None
+    updated_at: AwareDatetime | None = None
+    metadata: dict[str, Any] | None = None
 
 
 class CreateEntryRequest(BaseEntry):

agents-api/agents_api/common/protocol/tasks.py

@@ -93,13 +93,13 @@ def current_workflow(self) -> Workflow:
     @computed_field
     @property
     def current_step(self) -> WorkflowStepType:
-        step = self.current_workflow[self.cursor.step]
+        step = self.current_workflow.steps[self.cursor.step]
         return step
 
     @computed_field
     @property
     def is_last_step(self) -> bool:
-        return (self.cursor.step + 1) == len(self.current_workflow)
+        return (self.cursor.step + 1) == len(self.current_workflow.steps)
 
     def model_dump(self, *args, **kwargs) -> dict[str, Any]:
         dump = super().model_dump(*args, **kwargs)
@@ -108,11 +108,8 @@ def model_dump(self, *args, **kwargs) -> dict[str, Any]:
         return dump
 
 
-OutcomeType = TypeVar("OutcomeType", bound=BaseModel)
-
-
-class StepOutcome(BaseModel, Generic[OutcomeType]):
-    output: OutcomeType | None
+class StepOutcome(BaseModel):
+    output: Any
     transition_to: tuple[TransitionType, TransitionTarget] | None = None
 
 

agents-api/agents_api/workflows/task_execution.py

@@ -29,11 +29,11 @@
     )
     from ..common.protocol.tasks import (
         ExecutionInput,
-        # OutcomeType,
         StepContext,
         StepOutcome,
         # Workflow,
     )
+    from ..env import testing
 
 
 STEP_TO_ACTIVITY = {
@@ -59,7 +59,7 @@ async def run(
         context = StepContext(
             execution_input=execution_input,
             inputs=previous_inputs,
-            current=start,
+            cursor=start,
         )
 
         step_type = type(context.current_step)
@@ -69,7 +69,8 @@ async def run(
             outcome = await workflow.execute_activity(
                 activity,
                 context,
-                schedule_to_close_timeout=timedelta(seconds=600),
+                # TODO: This should be a configurable timeout everywhere based on the task
+                schedule_to_close_timeout=timedelta(seconds=3 if testing else 600),
             )
 
         # 2. Then, based on the outcome and step type, decide what to do next

agents-api/tests/fixtures.py

@@ -232,7 +232,7 @@ def test_task(
                 "name": "test task",
                 "description": "test task about",
                 "input_schema": {"type": "object", "additionalProperties": True},
-                "main": [],
+                "main": [{"evaluate": {"hello": '"world"'}}],
             }
         ),
         client=client,

agents-api/tests/test_execution_workflow.py

@@ -25,3 +25,6 @@ async def _(client=cozo_client, developer_id=test_developer_id, task=test_task):
         assert execution.task_id == task.id
         assert execution.input == data.input
         mock_run_task_execution_workflow.assert_called_once()
+
+        result = await handle.result()
+        assert result["hello"] == "world"