[BREAKING] Python: Add InvokeFunctionTool action for declarative workflows

python/packages/core/agent_framework/_tools.py

@@ -2164,15 +2164,11 @@ async def _get_response() -> ChatResponse:
                         # Error threshold reached: force a final non-tool turn so
                         # function_call_output items are submitted before exit.
                         mutable_options["tool_choice"] = "none"
-                    elif (
-                        max_function_calls is not None
-                        and total_function_calls >= max_function_calls
-                    ):
+                    elif max_function_calls is not None and total_function_calls >= max_function_calls:
                         # Best-effort limit: checked after each batch of parallel calls completes,
                         # so the current batch always runs to completion even if it overshoots.
                         logger.info(
-                            "Maximum function calls reached (%d/%d). "
-                            "Stopping further function calls for this request.",
+                            "Maximum function calls reached (%d/%d). Stopping further function calls for this request.",
                             total_function_calls,
                             max_function_calls,
                         )
@@ -2302,15 +2298,11 @@ async def _stream() -> AsyncIterable[ChatResponseUpdate]:
                     mutable_options["tool_choice"] = "none"
                 elif result["action"] != "continue":
                     return
-                elif (
-                    max_function_calls is not None
-                    and total_function_calls >= max_function_calls
-                ):
+                elif max_function_calls is not None and total_function_calls >= max_function_calls:
                     # Best-effort limit: checked after each batch of parallel calls completes,
                     # so the current batch always runs to completion even if it overshoots.
                     logger.info(
-                        "Maximum function calls reached (%d/%d). "
-                        "Stopping further function calls for this request.",
+                        "Maximum function calls reached (%d/%d). Stopping further function calls for this request.",
                         total_function_calls,
                         max_function_calls,
                     )

python/packages/core/pyproject.toml

@@ -34,7 +34,8 @@ dependencies = [
     # connectors and functions
     "openai>=1.99.0",
     "azure-identity>=1,<2",
-    "azure-ai-projects >= 2.0.0b3",
+    # Pinned to 2.0.0b3 - breaking changes in 2.0.0b4, unpin once upgrades complete
+    "azure-ai-projects == 2.0.0b3",
     "mcp[ws]>=1.24.0,<2",
     "packaging>=24.1",
 ]

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

@@ -34,7 +34,6 @@
     AgentResult,
     ExternalLoopState,
     InvokeAzureAgentExecutor,
-    InvokeToolExecutor,
 )
 from ._executors_basic import (
     BASIC_ACTION_EXECUTORS,
@@ -68,6 +67,17 @@
     RequestExternalInputExecutor,
     WaitForInputExecutor,
 )
+from ._executors_tools import (
+    FUNCTION_TOOL_REGISTRY_KEY,
+    TOOL_ACTION_EXECUTORS,
+    TOOL_APPROVAL_STATE_KEY,
+    BaseToolExecutor,
+    InvokeFunctionToolExecutor,
+    ToolApprovalRequest,
+    ToolApprovalResponse,
+    ToolApprovalState,
+    ToolInvocationResult,
+)
 from ._factory import DeclarativeWorkflowError, WorkflowFactory
 from ._state import WorkflowState
 
@@ -79,13 +89,17 @@
     "CONTROL_FLOW_EXECUTORS",
     "DECLARATIVE_STATE_KEY",
     "EXTERNAL_INPUT_EXECUTORS",
+    "FUNCTION_TOOL_REGISTRY_KEY",
+    "TOOL_ACTION_EXECUTORS",
+    "TOOL_APPROVAL_STATE_KEY",
     "TOOL_REGISTRY_KEY",
     "ActionComplete",
     "ActionTrigger",
     "AgentExternalInputRequest",
     "AgentExternalInputResponse",
     "AgentResult",
     "AppendValueExecutor",
+    "BaseToolExecutor",
     "BreakLoopExecutor",
     "ClearAllVariablesExecutor",
     "ConfirmationExecutor",
@@ -107,7 +121,7 @@
     "ForeachInitExecutor",
     "ForeachNextExecutor",
     "InvokeAzureAgentExecutor",
-    "InvokeToolExecutor",
+    "InvokeFunctionToolExecutor",
     "JoinExecutor",
     "LoopControl",
     "LoopIterationResult",
@@ -119,6 +133,10 @@
     "SetTextVariableExecutor",
     "SetValueExecutor",
     "SetVariableExecutor",
+    "ToolApprovalRequest",
+    "ToolApprovalResponse",
+    "ToolApprovalState",
+    "ToolInvocationResult",
     "WaitForInputExecutor",
     "WorkflowFactory",
     "WorkflowState",

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

@@ -13,6 +13,7 @@
 
 from __future__ import annotations
 
+import logging
 from typing import Any
 
 from agent_framework import (
@@ -38,13 +39,18 @@
     SwitchEvaluatorExecutor,
 )
 from ._executors_external_input import EXTERNAL_INPUT_EXECUTORS
+from ._executors_tools import TOOL_ACTION_EXECUTORS, InvokeFunctionToolExecutor
+
+logger = logging.getLogger(__name__)
+
 
 # Combined mapping of all action kinds to executor classes
 ALL_ACTION_EXECUTORS = {
     **BASIC_ACTION_EXECUTORS,
     **CONTROL_FLOW_EXECUTORS,
     **AGENT_ACTION_EXECUTORS,
     **EXTERNAL_INPUT_EXECUTORS,
+    **TOOL_ACTION_EXECUTORS,
 }
 
 # Action kinds that terminate control flow (no fall-through to successor)
@@ -78,6 +84,7 @@
     "RequestHumanInput": ["variable"],
     "WaitForHumanInput": ["variable"],
     "EmitEvent": ["event"],
+    "InvokeFunctionTool": ["functionName"],
 }
 
 # Alternate field names that satisfy required field requirements
@@ -118,6 +125,7 @@ def __init__(
         yaml_definition: dict[str, Any],
         workflow_id: str | None = None,
         agents: dict[str, Any] | None = None,
+        tools: dict[str, Any] | None = None,
         checkpoint_storage: Any | None = None,
         validate: bool = True,
         max_iterations: int | None = None,
@@ -128,6 +136,7 @@ def __init__(
             yaml_definition: The parsed YAML workflow definition
             workflow_id: Optional ID for the workflow (defaults to name from YAML)
             agents: Registry of agent instances by name (for InvokeAzureAgent actions)
+            tools: Registry of tool/function instances by name (for InvokeFunctionTool actions)
             checkpoint_storage: Optional checkpoint storage for pause/resume support
             validate: Whether to validate the workflow definition before building (default: True)
             max_iterations: Maximum runner supersteps. Falls back to the YAML ``maxTurns``
@@ -138,6 +147,7 @@ def __init__(
         self._executors: dict[str, Any] = {}  # id -> executor
         self._action_index = 0  # Counter for generating unique IDs
         self._agents = agents or {}  # Agent registry for agent executors
+        self._tools = tools or {}  # Tool registry for tool executors
         self._checkpoint_storage = checkpoint_storage
         self._pending_gotos: list[tuple[Any, str]] = []  # (goto_executor, target_id)
         self._validate = validate
@@ -423,8 +433,13 @@ def _create_executor_for_action(
         executor_class = ALL_ACTION_EXECUTORS.get(kind)
 
         if executor_class is None:
-            # Unknown action type - skip with warning
-            # In production, might want to log this
+            # Unknown action type - log warning and skip
+            logger.warning(
+                "Unknown action kind '%s' encountered at index %d - action will be skipped. Available action kinds: %s",
+                kind,
+                self._action_index,
+                list(ALL_ACTION_EXECUTORS.keys()),
+            )
             return None
 
         # Create the executor with ID
@@ -437,10 +452,12 @@ def _create_executor_for_action(
             action_id = f"{parent_id}_{kind}_{self._action_index}" if parent_id else f"{kind}_{self._action_index}"
         self._action_index += 1
 
-        # Pass agents to agent-related executors
+        # Pass agents/tools to specialized executors
         executor: Any
         if kind in ("InvokeAzureAgent",):
             executor = InvokeAzureAgentExecutor(action_def, id=action_id, agents=self._agents)
+        elif kind == "InvokeFunctionTool":
+            executor = InvokeFunctionToolExecutor(action_def, id=action_id, tools=self._tools)
         else:
             executor = executor_class(action_def, id=action_id)
         self._executors[action_id] = executor

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

@@ -1019,75 +1019,7 @@ async def handle_external_input_response(
         await ctx.send_message(ActionComplete())
 
 
-class InvokeToolExecutor(DeclarativeActionExecutor):
-    """Executor that invokes a registered tool/function.
-
-    Tools are simpler than agents - they take input, perform an action,
-    and return a result synchronously (or with a simple async call).
-    """
-
-    @handler
-    async def handle_action(
-        self,
-        trigger: Any,
-        ctx: WorkflowContext[ActionComplete],
-    ) -> None:
-        """Handle the tool invocation."""
-        state = await self._ensure_state_initialized(ctx, trigger)
-
-        tool_name = self._action_def.get("tool") or self._action_def.get("toolName", "")
-        input_expr = self._action_def.get("input")
-        output_property = self._action_def.get("output", {}).get("property") or self._action_def.get("resultProperty")
-        parameters = self._action_def.get("parameters", {})
-
-        # Get tools registry
-        try:
-            tool_registry: dict[str, Any] | None = ctx.state.get(TOOL_REGISTRY_KEY)
-        except KeyError:
-            tool_registry = {}
-
-        tool: Any = tool_registry.get(tool_name) if tool_registry else None
-
-        if tool is None:
-            error_msg = f"Tool '{tool_name}' not found in registry"
-            if output_property:
-                state.set(output_property, {"error": error_msg})
-            await ctx.send_message(ActionComplete())
-            return
-
-        # Build parameters
-        params: dict[str, Any] = {}
-        for param_name, param_expression in parameters.items():
-            params[param_name] = state.eval_if_expression(param_expression)
-
-        # Add main input if specified
-        if input_expr:
-            params["input"] = state.eval_if_expression(input_expr)
-
-        try:
-            # Invoke the tool
-            if callable(tool):
-                from inspect import isawaitable
-
-                result = tool(**params)
-                if isawaitable(result):
-                    result = await result
-
-                # Store result
-                if output_property:
-                    state.set(output_property, result)
-
-        except Exception as e:
-            if output_property:
-                state.set(output_property, {"error": str(e)})
-            await ctx.send_message(ActionComplete())
-            return
-
-        await ctx.send_message(ActionComplete())
-
-
 # Mapping of agent action kinds to executor classes
 AGENT_ACTION_EXECUTORS: dict[str, type[DeclarativeActionExecutor]] = {
     "InvokeAzureAgent": InvokeAzureAgentExecutor,
-    "InvokeTool": InvokeToolExecutor,
 }