Python: Fix declarative package powerfx import crash and response_format kwarg error (#3841)

* Fix declarative package powerfx import crash and response_format kwarg error

* Address PR feedback. Propagate kwargs for declarative workflows

* move tests

* Fix options merge logic

Author: moonbox3

python/packages/declarative/agent_framework_declarative/_loader.py

@@ -605,10 +605,11 @@ async def _create_agent_with_provider(self, prompt_agent: PromptAgent, mapping:
         # Parse tools
         tools = self._parse_tools(prompt_agent.tools) if prompt_agent.tools else None
 
-        # Parse response format
-        response_format = None
+        # Parse response format into default_options
+        default_options: dict[str, Any] | None = None
         if prompt_agent.outputSchema:
             response_format = _create_model_from_json_schema("agent", prompt_agent.outputSchema.to_json_schema())
+            default_options = {"response_format": response_format}
 
         # Create the agent using the provider
         # The provider's create_agent returns a Agent directly
@@ -620,7 +621,7 @@ async def _create_agent_with_provider(self, prompt_agent: PromptAgent, mapping:
                 instructions=prompt_agent.instructions,
                 description=prompt_agent.description,
                 tools=tools,
-                response_format=response_format,
+                default_options=default_options,
             ),
         )
 

python/packages/declarative/agent_framework_declarative/_workflows/_actions_agents.py

@@ -327,6 +327,16 @@ async def handle_invoke_azure_agent(ctx: ActionContext) -> AsyncGenerator[Workfl
     max_iterations = 100  # Safety limit
 
     # Start external loop if configured
+    # Build options for kwargs propagation to agent tools
+    run_kwargs = ctx.run_kwargs
+    options: dict[str, Any] | None = None
+    if run_kwargs:
+        # Merge caller-provided options to avoid duplicate keyword argument
+        options = dict(run_kwargs.get("options") or {})
+        options["additional_function_arguments"] = run_kwargs
+        # Exclude 'options' from splat to avoid TypeError on duplicate keyword
+        run_kwargs = {k: v for k, v in run_kwargs.items() if k != "options"}
+
     while True:
         # Invoke the agent
         try:
@@ -337,7 +347,7 @@ async def handle_invoke_azure_agent(ctx: ActionContext) -> AsyncGenerator[Workfl
                     updates: list[Any] = []
                     tool_calls: list[Any] = []
 
-                    async for chunk in agent.run(messages, stream=True):
+                    async for chunk in agent.run(messages, stream=True, options=options, **run_kwargs):
                         updates.append(chunk)
 
                         # Yield streaming events for text chunks
@@ -403,7 +413,7 @@ async def handle_invoke_azure_agent(ctx: ActionContext) -> AsyncGenerator[Workfl
 
                 except TypeError:
                     # Agent doesn't support streaming, fall back to non-streaming
-                    response = await agent.run(messages)
+                    response = await agent.run(messages, options=options, **run_kwargs)
 
                     text = response.text
                     response_messages = response.messages
@@ -570,14 +580,24 @@ async def handle_invoke_prompt_agent(ctx: ActionContext) -> AsyncGenerator[Workf
 
     logger.debug(f"InvokePromptAgent: calling '{agent_name}' with {len(messages)} messages")
 
+    # Build options for kwargs propagation to agent tools
+    prompt_run_kwargs = ctx.run_kwargs
+    prompt_options: dict[str, Any] | None = None
+    if prompt_run_kwargs:
+        # Merge caller-provided options to avoid duplicate keyword argument
+        prompt_options = dict(prompt_run_kwargs.get("options") or {})
+        prompt_options["additional_function_arguments"] = prompt_run_kwargs
+        # Exclude 'options' from splat to avoid TypeError on duplicate keyword
+        prompt_run_kwargs = {k: v for k, v in prompt_run_kwargs.items() if k != "options"}
+
     # Invoke the agent
     try:
         if hasattr(agent, "run"):
             # Try streaming first
             try:
                 updates: list[Any] = []
 
-                async for chunk in agent.run(messages, stream=True):
+                async for chunk in agent.run(messages, stream=True, options=prompt_options, **prompt_run_kwargs):
                     updates.append(chunk)
 
                     if hasattr(chunk, "text") and chunk.text:
@@ -607,7 +627,7 @@ async def handle_invoke_prompt_agent(ctx: ActionContext) -> AsyncGenerator[Workf
 
             except TypeError:
                 # Agent doesn't support streaming, fall back to non-streaming
-                response = await agent.run(messages)
+                response = await agent.run(messages, options=prompt_options, **prompt_run_kwargs)
                 text = response.text
                 response_messages = response.messages
 

python/packages/declarative/agent_framework_declarative/_workflows/_declarative_base.py

@@ -37,7 +37,13 @@
     WorkflowContext,
 )
 from agent_framework._workflows._state import State
-from powerfx import Engine
+
+try:
+    from powerfx import Engine
+except (ImportError, RuntimeError):
+    # ImportError: powerfx package not installed
+    # RuntimeError: .NET runtime not available or misconfigured
+    Engine = None  # type: ignore[assignment, misc]
 
 if sys.version_info >= (3, 11):
     from typing import TypedDict  # type: ignore # pragma: no cover
@@ -339,7 +345,8 @@ def eval(self, expression: str) -> Any:
             undefined variables (matching legacy fallback parser behavior).
 
         Raises:
-            ImportError: If the powerfx package is not installed.
+            RuntimeError: If the powerfx package is not installed and the
+                expression requires PowerFx evaluation.
         """
         if not expression:
             return expression
@@ -363,6 +370,13 @@ def eval(self, expression: str) -> Any:
         # Replace them with their evaluated results before sending to PowerFx
         formula = self._preprocess_custom_functions(formula)
 
+        if Engine is None:
+            raise RuntimeError(
+                f"PowerFx is not available (dotnet runtime not installed). "
+                f"Expression '={formula[:80]}' cannot be evaluated. "
+                f"Install dotnet and the powerfx package for full PowerFx support."
+            )
+
         engine = Engine()
         symbols = self._to_powerfx_symbols()
         try:

python/packages/declarative/agent_framework_declarative/_workflows/_executors_agents.py

@@ -656,10 +656,22 @@ async def _invoke_agent_and_store_results(
         if isinstance(messages_for_agent, list) and messages_for_agent:
             _validate_conversation_history(messages_for_agent, agent_name)
 
+        # Retrieve kwargs passed to workflow.run() so they propagate to agent tools
+        from agent_framework._workflows._const import WORKFLOW_RUN_KWARGS_KEY
+
+        run_kwargs: dict[str, Any] = ctx.get_state(WORKFLOW_RUN_KWARGS_KEY, {})
+        options: dict[str, Any] | None = None
+        if run_kwargs:
+            # Merge caller-provided options to avoid duplicate keyword argument
+            options = dict(run_kwargs.get("options") or {})
+            options["additional_function_arguments"] = run_kwargs
+            # Exclude 'options' from splat to avoid TypeError on duplicate keyword
+            run_kwargs = {k: v for k, v in run_kwargs.items() if k != "options"}
+
         # Use run() method to get properly structured messages (including tool calls and results)
         # This is critical for multi-turn conversations where tool calls must be followed
         # by their results in the message history
-        result: Any = await agent.run(messages_for_agent)
+        result: Any = await agent.run(messages_for_agent, options=options, **run_kwargs)
         if hasattr(result, "text") and result.text:
             accumulated_response = str(result.text)
             if auto_send:

python/packages/declarative/agent_framework_declarative/_workflows/_handlers.py

@@ -10,7 +10,7 @@
 from __future__ import annotations
 
 from collections.abc import AsyncGenerator, Callable
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Any, Protocol, runtime_checkable
 
 from agent_framework import get_logger
@@ -44,6 +44,9 @@ class ActionContext:
     bindings: dict[str, Any]
     """Function bindings for tool calls."""
 
+    run_kwargs: dict[str, Any] = field(default_factory=dict)
+    """Kwargs from workflow.run() to forward to agent invocations."""
+
     @property
     def action_id(self) -> str | None:
         """Get the action's unique identifier."""

python/packages/declarative/tests/test_declarative_loader.py

@@ -1,8 +1,10 @@
 # Copyright (c) Microsoft. All rights reserved.
 
+import builtins
 import sys
 from pathlib import Path
 from typing import Any
+from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 import yaml
@@ -905,3 +907,105 @@ def test_mcp_tool_with_remote_connection_with_endpoint(self):
 
         # Verify project_connection_id is set from connection name
         assert mcp_tool.get("project_connection_id") == "my-oauth-connection"
+
+
+class TestProviderResponseFormat:
+    """response_format from outputSchema must be passed inside default_options."""
+
+    @staticmethod
+    def _make_mock_prompt_agent(*, with_output_schema: bool = False) -> MagicMock:
+        """Create a mock PromptAgent to avoid serialization complexity."""
+        mock_model = MagicMock()
+        mock_model.id = "gpt-4"
+        mock_model.connection = None
+
+        agent = MagicMock()
+        agent.name = "test-agent"
+        agent.description = "test"
+        agent.instructions = "be helpful"
+        agent.model = mock_model
+        agent.tools = None
+
+        if with_output_schema:
+            mock_schema = MagicMock()
+            mock_schema.to_json_schema.return_value = {
+                "type": "object",
+                "properties": {"answer": {"type": "string"}},
+            }
+            agent.outputSchema = mock_schema
+        else:
+            agent.outputSchema = None
+
+        return agent
+
+    @staticmethod
+    def _make_mock_provider() -> tuple[MagicMock, AsyncMock]:
+        """Create a mock provider class and its instance."""
+        mock_agent = MagicMock()
+        mock_provider_instance = AsyncMock()
+        mock_provider_instance.create_agent = AsyncMock(return_value=mock_agent)
+        mock_provider_class = MagicMock(return_value=mock_provider_instance)
+        return mock_provider_class, mock_provider_instance
+
+    @pytest.mark.asyncio
+    async def test_response_format_in_default_options(self):
+        """Provider.create_agent() should receive response_format inside default_options."""
+        from agent_framework_declarative._loader import AgentFactory
+
+        prompt_agent = self._make_mock_prompt_agent(with_output_schema=True)
+        mock_provider_class, mock_provider_instance = self._make_mock_provider()
+
+        mapping = {"package": "some_module", "name": "SomeProvider"}
+        factory = AgentFactory()
+
+        original_import = builtins.__import__
+
+        def mock_import(name, *args, **kwargs):
+            if name == "some_module":
+                mod = MagicMock()
+                mod.SomeProvider = mock_provider_class
+                return mod
+            return original_import(name, *args, **kwargs)
+
+        with (
+            patch.object(builtins, "__import__", side_effect=mock_import),
+            patch.object(factory, "_parse_tools", return_value=None),
+        ):
+            await factory._create_agent_with_provider(prompt_agent, mapping)
+
+        mock_provider_instance.create_agent.assert_called_once()
+        call_kwargs = mock_provider_instance.create_agent.call_args.kwargs
+
+        assert "response_format" not in call_kwargs
+        default_options = call_kwargs.get("default_options")
+        assert default_options is not None
+        assert "response_format" in default_options
+
+    @pytest.mark.asyncio
+    async def test_no_default_options_without_output_schema(self):
+        """When there's no outputSchema, default_options should be None."""
+        from agent_framework_declarative._loader import AgentFactory
+
+        prompt_agent = self._make_mock_prompt_agent(with_output_schema=False)
+        mock_provider_class, mock_provider_instance = self._make_mock_provider()
+
+        mapping = {"package": "some_module", "name": "SomeProvider"}
+        factory = AgentFactory()
+
+        original_import = builtins.__import__
+
+        def mock_import(name, *args, **kwargs):
+            if name == "some_module":
+                mod = MagicMock()
+                mod.SomeProvider = mock_provider_class
+                return mod
+            return original_import(name, *args, **kwargs)
+
+        with (
+            patch.object(builtins, "__import__", side_effect=mock_import),
+            patch.object(factory, "_parse_tools", return_value=None),
+        ):
+            await factory._create_agent_with_provider(prompt_agent, mapping)
+
+        call_kwargs = mock_provider_instance.create_agent.call_args.kwargs
+        assert call_kwargs.get("default_options") is None