test_human_in_the_loop_middleware_preserves_tool_call_order() — langchain Function Reference
Architecture documentation for the test_human_in_the_loop_middleware_preserves_tool_call_order() function in test_human_in_the_loop.py from the langchain codebase.
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Dependency Diagram
graph TD 786b2539_4b34_08fc_7343_1cabbb84823e["test_human_in_the_loop_middleware_preserves_tool_call_order()"] b9ab5ab1_a37b_d0e1_974a_34ca8a76a788["test_human_in_the_loop.py"] 786b2539_4b34_08fc_7343_1cabbb84823e -->|defined in| b9ab5ab1_a37b_d0e1_974a_34ca8a76a788 style 786b2539_4b34_08fc_7343_1cabbb84823e fill:#6366f1,stroke:#818cf8,color:#fff
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Source Code
libs/langchain_v1/tests/unit_tests/agents/middleware/implementations/test_human_in_the_loop.py lines 596–647
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 = AgentState[Any](messages=[HumanMessage(content="Hello"), ai_message])
def mock_approve_all(_: Any) -> dict[str, Any]:
# 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, Runtime())
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"
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Defined In
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Frequently Asked Questions
What does test_human_in_the_loop_middleware_preserves_tool_call_order() do?
test_human_in_the_loop_middleware_preserves_tool_call_order() is a function in the langchain codebase, defined in libs/langchain_v1/tests/unit_tests/agents/middleware/implementations/test_human_in_the_loop.py.
Where is test_human_in_the_loop_middleware_preserves_tool_call_order() defined?
test_human_in_the_loop_middleware_preserves_tool_call_order() is defined in libs/langchain_v1/tests/unit_tests/agents/middleware/implementations/test_human_in_the_loop.py at line 596.
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