fix: don't reorder tool calls in HITL middleware

libs/langchain_v1/langchain/agents/middleware/human_in_the_loop.py

@@ -287,36 +287,23 @@ def after_model(self, state: AgentState, runtime: Runtime) -> dict[str, Any] | N
         if not last_ai_msg or not last_ai_msg.tool_calls:
             return None
 
-        # Separate tool calls that need interrupts from those that don't
-        interrupt_tool_calls: list[ToolCall] = []
-        auto_approved_tool_calls = []
-
-        for tool_call in last_ai_msg.tool_calls:
-            interrupt_tool_calls.append(tool_call) if tool_call[
-                "name"
-            ] in self.interrupt_on else auto_approved_tool_calls.append(tool_call)
-
-        # If no interrupts needed, return early
-        if not interrupt_tool_calls:
-            return None
-
-        # Process all tool calls that require interrupts
-        revised_tool_calls: list[ToolCall] = auto_approved_tool_calls.copy()
-        artificial_tool_messages: list[ToolMessage] = []
-
-        # Create action requests and review configs for all tools that need approval
+        # Create action requests and review configs for tools that need approval
         action_requests: list[ActionRequest] = []
         review_configs: list[ReviewConfig] = []
+        interrupt_indices: list[int] = []
 
-        for tool_call in interrupt_tool_calls:
-            config = self.interrupt_on[tool_call["name"]]
+        for idx, tool_call in enumerate(last_ai_msg.tool_calls):
+            if (config := self.interrupt_on.get(tool_call["name"])) is not None:
+                action_request, review_config = self._create_action_and_config(
+                    tool_call, config, state, runtime
+                )
+                action_requests.append(action_request)
+                review_configs.append(review_config)
+                interrupt_indices.append(idx)
 
-            # Create ActionRequest and ReviewConfig using helper method
-            action_request, review_config = self._create_action_and_config(
-                tool_call, config, state, runtime
-            )
-            action_requests.append(action_request)
-            review_configs.append(review_config)
+        # If no interrupts needed, return early
+        if not action_requests:
+            return None
 
         # Create single HITLRequest with all actions and configs
         hitl_request = HITLRequest(
@@ -325,29 +312,38 @@ def after_model(self, state: AgentState, runtime: Runtime) -> dict[str, Any] | N
         )
 
         # Send interrupt and get response
-        hitl_response: HITLResponse = interrupt(hitl_request)
-        decisions = hitl_response["decisions"]
+        decisions = interrupt(hitl_request)["decisions"]
 
         # Validate that the number of decisions matches the number of interrupt tool calls
-        if (decisions_len := len(decisions)) != (
-            interrupt_tool_calls_len := len(interrupt_tool_calls)
-        ):
+        if (decisions_len := len(decisions)) != (interrupt_count := len(interrupt_indices)):
             msg = (
                 f"Number of human decisions ({decisions_len}) does not match "
-                f"number of hanging tool calls ({interrupt_tool_calls_len})."
+                f"number of hanging tool calls ({interrupt_count})."
             )
             raise ValueError(msg)
 
-        # Process each decision using helper method
-        for i, decision in enumerate(decisions):
-            tool_call = interrupt_tool_calls[i]
-            config = self.interrupt_on[tool_call["name"]]
-
-            revised_tool_call, tool_message = self._process_decision(decision, tool_call, config)
-            if revised_tool_call:
-                revised_tool_calls.append(revised_tool_call)
-            if tool_message:
-                artificial_tool_messages.append(tool_message)
+        # Process decisions and rebuild tool calls in original order
+        revised_tool_calls: list[ToolCall] = []
+        artificial_tool_messages: list[ToolMessage] = []
+        decision_idx = 0
+
+        for idx, tool_call in enumerate(last_ai_msg.tool_calls):
+            if idx in interrupt_indices:
+                # This was an interrupt tool call - process the decision
+                config = self.interrupt_on[tool_call["name"]]
+                decision = decisions[decision_idx]
+                decision_idx += 1
+
+                revised_tool_call, tool_message = self._process_decision(
+                    decision, tool_call, config
+                )
+                if revised_tool_call is not None:
+                    revised_tool_calls.append(revised_tool_call)
+                if tool_message:
+                    artificial_tool_messages.append(tool_message)
+            else:
+                # This was auto-approved - keep original
+                revised_tool_calls.append(tool_call)
 
         # Update the AI message to only include approved tool calls
         last_ai_msg.tool_calls = revised_tool_calls

libs/langchain_v1/tests/unit_tests/agents/middleware/implementations/test_human_in_the_loop.py

@@ -573,3 +573,165 @@ def mock_capture_requests(request):
 
         # Check string description
         assert captured_request["action_requests"][1]["description"] == "Static description"
+
+
+def test_human_in_the_loop_middleware_preserves_tool_call_order() -> None:
+    """Test that middleware preserves the original order of tool calls.
+
+    This test verifies that when mixing auto-approved and interrupt tools,
+    the final tool call order matches the original order from the AI message.
+    """
+    middleware = HumanInTheLoopMiddleware(
+        interrupt_on={
+            "tool_b": {"allowed_decisions": ["approve", "edit", "reject"]},
+            "tool_d": {"allowed_decisions": ["approve", "edit", "reject"]},
+        }
+    )
+
+    # Create AI message with interleaved auto-approved and interrupt tools
+    # Order: auto (A) -> interrupt (B) -> auto (C) -> interrupt (D) -> auto (E)
+    ai_message = AIMessage(
+        content="Processing multiple tools",
+        tool_calls=[
+            {"name": "tool_a", "args": {"val": 1}, "id": "id_a"},
+            {"name": "tool_b", "args": {"val": 2}, "id": "id_b"},
+            {"name": "tool_c", "args": {"val": 3}, "id": "id_c"},
+            {"name": "tool_d", "args": {"val": 4}, "id": "id_d"},
+            {"name": "tool_e", "args": {"val": 5}, "id": "id_e"},
+        ],
+    )
+    state = {"messages": [HumanMessage(content="Test"), ai_message]}
+
+    def mock_approve_all(requests):
+        # Approve both interrupt tools (B and D)
+        return {"decisions": [{"type": "approve"}, {"type": "approve"}]}
+
+    with patch(
+        "langchain.agents.middleware.human_in_the_loop.interrupt", side_effect=mock_approve_all
+    ):
+        result = middleware.after_model(state, None)
+        assert result is not None
+        assert "messages" in result
+
+        updated_ai_message = result["messages"][0]
+        assert len(updated_ai_message.tool_calls) == 5
+
+        # Verify original order is preserved: A -> B -> C -> D -> E
+        assert updated_ai_message.tool_calls[0]["name"] == "tool_a"
+        assert updated_ai_message.tool_calls[0]["id"] == "id_a"
+        assert updated_ai_message.tool_calls[1]["name"] == "tool_b"
+        assert updated_ai_message.tool_calls[1]["id"] == "id_b"
+        assert updated_ai_message.tool_calls[2]["name"] == "tool_c"
+        assert updated_ai_message.tool_calls[2]["id"] == "id_c"
+        assert updated_ai_message.tool_calls[3]["name"] == "tool_d"
+        assert updated_ai_message.tool_calls[3]["id"] == "id_d"
+        assert updated_ai_message.tool_calls[4]["name"] == "tool_e"
+        assert updated_ai_message.tool_calls[4]["id"] == "id_e"
+
+
+def test_human_in_the_loop_middleware_preserves_order_with_edits() -> None:
+    """Test that order is preserved when interrupt tools are edited."""
+    middleware = HumanInTheLoopMiddleware(
+        interrupt_on={
+            "tool_b": {"allowed_decisions": ["approve", "edit", "reject"]},
+            "tool_d": {"allowed_decisions": ["approve", "edit", "reject"]},
+        }
+    )
+
+    ai_message = AIMessage(
+        content="Processing multiple tools",
+        tool_calls=[
+            {"name": "tool_a", "args": {"val": 1}, "id": "id_a"},
+            {"name": "tool_b", "args": {"val": 2}, "id": "id_b"},
+            {"name": "tool_c", "args": {"val": 3}, "id": "id_c"},
+            {"name": "tool_d", "args": {"val": 4}, "id": "id_d"},
+        ],
+    )
+    state = {"messages": [HumanMessage(content="Test"), ai_message]}
+
+    def mock_edit_responses(requests):
+        # Edit tool_b, approve tool_d
+        return {
+            "decisions": [
+                {
+                    "type": "edit",
+                    "edited_action": Action(name="tool_b", args={"val": 200}),
+                },
+                {"type": "approve"},
+            ]
+        }
+
+    with patch(
+        "langchain.agents.middleware.human_in_the_loop.interrupt", side_effect=mock_edit_responses
+    ):
+        result = middleware.after_model(state, None)
+        assert result is not None
+
+        updated_ai_message = result["messages"][0]
+        assert len(updated_ai_message.tool_calls) == 4
+
+        # Verify order: A (auto) -> B (edited) -> C (auto) -> D (approved)
+        assert updated_ai_message.tool_calls[0]["name"] == "tool_a"
+        assert updated_ai_message.tool_calls[0]["args"] == {"val": 1}
+        assert updated_ai_message.tool_calls[1]["name"] == "tool_b"
+        assert updated_ai_message.tool_calls[1]["args"] == {"val": 200}  # Edited
+        assert updated_ai_message.tool_calls[1]["id"] == "id_b"  # ID preserved
+        assert updated_ai_message.tool_calls[2]["name"] == "tool_c"
+        assert updated_ai_message.tool_calls[2]["args"] == {"val": 3}
+        assert updated_ai_message.tool_calls[3]["name"] == "tool_d"
+        assert updated_ai_message.tool_calls[3]["args"] == {"val": 4}
+
+
+def test_human_in_the_loop_middleware_preserves_order_with_rejections() -> None:
+    """Test that order is preserved when some interrupt tools are rejected."""
+    middleware = HumanInTheLoopMiddleware(
+        interrupt_on={
+            "tool_b": {"allowed_decisions": ["approve", "edit", "reject"]},
+            "tool_d": {"allowed_decisions": ["approve", "edit", "reject"]},
+        }
+    )
+
+    ai_message = AIMessage(
+        content="Processing multiple tools",
+        tool_calls=[
+            {"name": "tool_a", "args": {"val": 1}, "id": "id_a"},
+            {"name": "tool_b", "args": {"val": 2}, "id": "id_b"},
+            {"name": "tool_c", "args": {"val": 3}, "id": "id_c"},
+            {"name": "tool_d", "args": {"val": 4}, "id": "id_d"},
+            {"name": "tool_e", "args": {"val": 5}, "id": "id_e"},
+        ],
+    )
+    state = {"messages": [HumanMessage(content="Test"), ai_message]}
+
+    def mock_mixed_responses(requests):
+        # Reject tool_b, approve tool_d
+        return {
+            "decisions": [
+                {"type": "reject", "message": "Rejected tool B"},
+                {"type": "approve"},
+            ]
+        }
+
+    with patch(
+        "langchain.agents.middleware.human_in_the_loop.interrupt", side_effect=mock_mixed_responses
+    ):
+        result = middleware.after_model(state, None)
+        assert result is not None
+        assert len(result["messages"]) == 2  # AI message + tool message for rejection
+
+        updated_ai_message = result["messages"][0]
+        # tool_b is still in the list (with rejection handled via tool message)
+        assert len(updated_ai_message.tool_calls) == 5
+
+        # Verify order maintained: A (auto) -> B (rejected) -> C (auto) -> D (approved) -> E (auto)
+        assert updated_ai_message.tool_calls[0]["name"] == "tool_a"
+        assert updated_ai_message.tool_calls[1]["name"] == "tool_b"
+        assert updated_ai_message.tool_calls[2]["name"] == "tool_c"
+        assert updated_ai_message.tool_calls[3]["name"] == "tool_d"
+        assert updated_ai_message.tool_calls[4]["name"] == "tool_e"
+
+        # Check rejection tool message
+        tool_message = result["messages"][1]
+        assert isinstance(tool_message, ToolMessage)
+        assert tool_message.content == "Rejected tool B"
+        assert tool_message.tool_call_id == "id_b"