deer-flow/backend/tests/test_todo_middleware.py

"""Tests for TodoMiddleware context-loss detection."""

import ast
import asyncio
from pathlib import Path
from types import SimpleNamespace
from typing import Any
from unittest.mock import MagicMock

import pytest
from langchain.agents import create_agent
from langchain.agents.middleware import TodoListMiddleware
from langchain.agents.middleware.types import ModelRequest
from langchain.tools import tool
from langchain_core.language_models.fake_chat_models import FakeMessagesListChatModel
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
from langgraph.checkpoint.memory import InMemorySaver
from pydantic import PrivateAttr

from deerflow.agents.middlewares.model_length_finish_reason_middleware import (
    ModelLengthFinishReasonMiddleware,
)
from deerflow.agents.middlewares.safety_finish_reason_middleware import SafetyFinishReasonMiddleware
from deerflow.agents.middlewares.todo_middleware import (
    TODO_REMINDER_MESSAGE_NAME,
    TodoMiddleware,
    _format_todos,
    _has_tool_call_intent_or_error,
    _reminder_in_messages,
    _todos_in_messages,
)
from deerflow.agents.thread_state import ThreadState


def _ai_with_write_todos():
    return AIMessage(content="", tool_calls=[{"name": "write_todos", "id": "tc_1", "args": {}}])


def _reminder_msg():
    return HumanMessage(name=TODO_REMINDER_MESSAGE_NAME, content="reminder")


class _CapturingFakeMessagesListChatModel(FakeMessagesListChatModel):
    _seen_messages: list[list[Any]] = PrivateAttr(default_factory=list)

    @property
    def seen_messages(self) -> list[list[Any]]:
        return self._seen_messages

    def bind_tools(self, tools, *, tool_choice=None, **kwargs):
        return self

    def _generate(self, messages, stop=None, run_manager=None, **kwargs):
        self._seen_messages.append(list(messages))
        return super()._generate(
            messages,
            stop=stop,
            run_manager=run_manager,
            **kwargs,
        )


def _make_runtime():
    runtime = MagicMock()
    runtime.context = {"thread_id": "test-thread", "run_id": "test-run"}
    return runtime


def _make_runtime_for(thread_id: str, run_id: str):
    runtime = _make_runtime()
    runtime.context = {"thread_id": thread_id, "run_id": run_id}
    return runtime


def _make_model_request(messages: list[Any], *, runtime=None) -> ModelRequest:
    return ModelRequest(
        model=object(),
        messages=list(messages),
        state={"messages": list(messages)},
        runtime=runtime,
    )


def _sample_todos():
    return [
        {"status": "completed", "content": "Set up project"},
        {"status": "in_progress", "content": "Write tests"},
        {"status": "pending", "content": "Deploy"},
    ]


class TestTodosInMessages:
    def test_true_when_write_todos_present(self):
        msgs = [HumanMessage(content="hi"), _ai_with_write_todos()]
        assert _todos_in_messages(msgs) is True

    def test_false_when_no_write_todos(self):
        msgs = [
            HumanMessage(content="hi"),
            AIMessage(content="hello", tool_calls=[{"name": "bash", "id": "tc_1", "args": {}}]),
        ]
        assert _todos_in_messages(msgs) is False

    def test_false_for_empty_list(self):
        assert _todos_in_messages([]) is False

    def test_false_for_ai_without_tool_calls(self):
        msgs = [AIMessage(content="hello")]
        assert _todos_in_messages(msgs) is False


class TestReminderInMessages:
    def test_true_when_reminder_present(self):
        msgs = [HumanMessage(content="hi"), _reminder_msg()]
        assert _reminder_in_messages(msgs) is True

    def test_false_when_no_reminder(self):
        msgs = [HumanMessage(content="hi"), AIMessage(content="hello")]
        assert _reminder_in_messages(msgs) is False

    def test_false_for_empty_list(self):
        assert _reminder_in_messages([]) is False

    def test_false_for_human_without_name(self):
        msgs = [HumanMessage(content="todo_reminder")]
        assert _reminder_in_messages(msgs) is False


class TestFormatTodos:
    def test_formats_multiple_items(self):
        todos = _sample_todos()
        result = _format_todos(todos)
        assert "- [completed] Set up project" in result
        assert "- [in_progress] Write tests" in result
        assert "- [pending] Deploy" in result

    def test_empty_list(self):
        assert _format_todos([]) == ""

    def test_missing_fields_use_defaults(self):
        todos = [{"content": "No status"}, {"status": "done"}]
        result = _format_todos(todos)
        assert "- [pending] No status" in result
        assert "- [done] " in result


class TestBeforeModel:
    def test_returns_none_when_no_todos(self):
        mw = TodoMiddleware()
        state = {"messages": [HumanMessage(content="hi")], "todos": []}
        assert mw.before_model(state, _make_runtime()) is None

    def test_returns_none_when_todos_is_none(self):
        mw = TodoMiddleware()
        state = {"messages": [HumanMessage(content="hi")], "todos": None}
        assert mw.before_model(state, _make_runtime()) is None

    def test_returns_none_when_write_todos_still_visible(self):
        mw = TodoMiddleware()
        state = {
            "messages": [_ai_with_write_todos()],
            "todos": _sample_todos(),
        }
        assert mw.before_model(state, _make_runtime()) is None

    def test_returns_none_when_reminder_already_present(self):
        mw = TodoMiddleware()
        state = {
            "messages": [HumanMessage(content="hi"), _reminder_msg()],
            "todos": _sample_todos(),
        }
        assert mw.before_model(state, _make_runtime()) is None

    def test_injects_reminder_when_todos_exist_but_truncated(self):
        mw = TodoMiddleware()
        state = {
            "messages": [HumanMessage(content="hi"), AIMessage(content="sure")],
            "todos": _sample_todos(),
        }
        result = mw.before_model(state, _make_runtime())
        assert result is not None
        msgs = result["messages"]
        assert len(msgs) == 1
        assert isinstance(msgs[0], HumanMessage)
        assert msgs[0].name == TODO_REMINDER_MESSAGE_NAME

    def test_reminder_contains_formatted_todos(self):
        mw = TodoMiddleware()
        state = {
            "messages": [HumanMessage(content="hi")],
            "todos": _sample_todos(),
        }
        result = mw.before_model(state, _make_runtime())
        content = result["messages"][0].content
        assert "Set up project" in content
        assert "Write tests" in content
        assert "Deploy" in content
        assert "system_reminder" in content


class TestAbeforeModel:
    def test_delegates_to_sync(self):
        mw = TodoMiddleware()
        state = {
            "messages": [HumanMessage(content="hi")],
            "todos": _sample_todos(),
        }
        result = asyncio.run(mw.abefore_model(state, _make_runtime()))
        assert result is not None
        assert result["messages"][0].name == TODO_REMINDER_MESSAGE_NAME


def _todo_completion_reminders(messages):
    reminders = []
    for message in messages:
        if isinstance(message, HumanMessage) and message.name == "todo_completion_reminder":
            reminders.append(message)
    return reminders


def _ai_no_tool_calls():
    return AIMessage(content="I'm done!")


def _ai_stopped_by(stop_reason):
    """The response a guard leaves behind: provider stops also mark the message."""
    marker = {"safety_capped": "safety_termination", "model_length_capped": "model_length_termination"}.get(stop_reason)
    return AIMessage(content="I'm done!", additional_kwargs={marker: {"suppressed_tool_call_count": 0}} if marker else {})


def _ai_with_invalid_tool_calls():
    return AIMessage(
        content="",
        tool_calls=[],
        invalid_tool_calls=[
            {
                "type": "invalid_tool_call",
                "id": "write_file:36",
                "name": "write_file",
                "args": "{invalid",
                "error": "Failed to parse tool arguments",
            }
        ],
    )


def _ai_with_raw_provider_tool_calls():
    return AIMessage(
        content="",
        tool_calls=[],
        invalid_tool_calls=[],
        additional_kwargs={
            "tool_calls": [
                {
                    "id": "raw-tool-call",
                    "type": "function",
                    "function": {"name": "write_file", "arguments": '{"path":"report.md"}'},
                }
            ]
        },
    )


def _ai_with_legacy_function_call():
    return AIMessage(
        content="",
        additional_kwargs={"function_call": {"name": "write_file", "arguments": '{"path":"report.md"}'}},
    )


def _ai_with_tool_finish_reason():
    return AIMessage(content="", response_metadata={"finish_reason": "tool_calls"})


def _incomplete_todos():
    return [
        {"status": "completed", "content": "Step 1"},
        {"status": "in_progress", "content": "Step 2"},
        {"status": "pending", "content": "Step 3"},
    ]


def _all_completed_todos():
    return [
        {"status": "completed", "content": "Step 1"},
        {"status": "completed", "content": "Step 2"},
    ]


class TestToolCallIntentOrError:
    def test_false_for_plain_final_answer(self):
        assert _has_tool_call_intent_or_error(_ai_no_tool_calls()) is False

    def test_true_for_structured_tool_calls(self):
        assert _has_tool_call_intent_or_error(_ai_with_write_todos()) is True

    def test_true_for_invalid_tool_calls(self):
        assert _has_tool_call_intent_or_error(_ai_with_invalid_tool_calls()) is True

    def test_true_for_raw_provider_tool_calls(self):
        assert _has_tool_call_intent_or_error(_ai_with_raw_provider_tool_calls()) is True

    def test_true_for_legacy_function_call(self):
        assert _has_tool_call_intent_or_error(_ai_with_legacy_function_call()) is True

    def test_true_for_tool_finish_reason(self):
        assert _has_tool_call_intent_or_error(_ai_with_tool_finish_reason()) is True

    def test_langchain_ai_message_tool_fields_are_explicitly_handled(self):
        # Sentinel for LangChain compatibility: if future AIMessage versions add
        # new top-level tool/function-call fields, this test should fail. When
        # it does, update `_has_tool_call_intent_or_error()` so the completion
        # reminder guard explicitly decides whether each new field means "not a
        # clean final answer"; the helper has a matching comment pointing back
        # to this sentinel.
        tool_related_fields = {name for name in AIMessage.model_fields if "tool" in name.lower() or ("function" in name.lower() and "call" in name.lower())}
        assert tool_related_fields <= {"tool_calls", "invalid_tool_calls"}


class TestAfterModel:
    def test_all_middleware_stop_reason_writes_have_an_explicit_todo_policy(self):
        middleware_dir = Path(__file__).resolve().parents[1] / "packages/harness/deerflow/agents/middlewares"
        stamped_reasons = set()
        for source in sorted(middleware_dir.glob("*.py")):
            tree = ast.parse(source.read_text(encoding="utf-8"))
            constants = {target.id: node.value for node in tree.body if isinstance(node, ast.Assign) for target in node.targets if isinstance(target, ast.Name) and isinstance(node.value, ast.Constant)}
            for node in ast.walk(tree):
                if not isinstance(node, (ast.Assign, ast.AnnAssign)):
                    continue
                targets = node.targets if isinstance(node, ast.Assign) else [node.target]
                for target in targets:
                    if not isinstance(target, ast.Subscript) or not isinstance(target.slice, ast.Constant) or target.slice.value != "stop_reason":
                        continue
                    # This copies a child result into a ledger entry; it does
                    # not stamp the current run's runtime.context.
                    if source.name == "delegation_ledger.py" and isinstance(target.value, ast.Name) and target.value.id == "entry":
                        continue
                    value = constants.get(node.value.id) if isinstance(node.value, ast.Name) else node.value
                    assert isinstance(value, ast.Constant) and isinstance(value.value, str), f"Classify stop_reason writer at {source.name}:{node.lineno}"
                    stamped_reasons.add(value.value)

        # The subagent budget only removes task calls; the lead may still
        # complete its work using other tools. Every other writer is terminal.
        nonterminal_reasons = {"subagent_limit_capped"}
        assert nonterminal_reasons <= stamped_reasons
        policy = TodoMiddleware().release_policy_parameters()
        assert set(policy["completion_reminder_hard_stop_reasons"]) == stamped_reasons - nonterminal_reasons

    @pytest.mark.parametrize("marker", ["safety_termination", "model_length_termination"])
    @pytest.mark.parametrize("context", [None, {}, {"stop_reason": "custom_stop"}])
    def test_termination_marker_suppresses_reminders_without_recognized_context(self, marker, context):
        mw = TodoMiddleware()
        runtime = _make_runtime()
        runtime.context = context
        state = {"messages": [AIMessage(content="partial response", additional_kwargs={marker: {"suppressed_tool_call_count": 0}})], "todos": _incomplete_todos()}

        assert mw.after_model(state, runtime) is None
        assert mw._pending_completion_reminders == {}

    @pytest.mark.parametrize("invoke_async", [False, True], ids=["sync", "async"])
    @pytest.mark.parametrize("stop_reason", ["loop_capped", "token_capped", "safety_capped", "model_length_capped"])
    def test_recognized_stop_reason_suppresses_completion_reminders(self, invoke_async, stop_reason):
        mw = TodoMiddleware()
        runtime = _make_runtime()
        runtime.context["stop_reason"] = stop_reason
        state = {"messages": [_ai_stopped_by(stop_reason)], "todos": _incomplete_todos()}

        result = asyncio.run(mw.aafter_model(state, runtime)) if invoke_async else mw.after_model(state, runtime)

        assert result is None
        assert mw._pending_completion_reminders == {}
        assert mw._completion_reminder_counts == {}
        assert state["todos"] == _incomplete_todos()

    @pytest.mark.parametrize("invoke_async", [False, True], ids=["sync", "async"])
    @pytest.mark.parametrize("stop_reason", ["loop_capped", "token_capped", "safety_capped", "model_length_capped"])
    def test_hard_stop_takes_precedence_over_base_jump(self, monkeypatch, invoke_async, stop_reason):
        base_hook = MagicMock(return_value={"jump_to": "model"})
        monkeypatch.setattr(TodoListMiddleware, "after_model", base_hook)
        mw = TodoMiddleware()
        runtime = _make_runtime()
        state = {"messages": [_ai_no_tool_calls()], "todos": _incomplete_todos()}

        # Preserve normal parent behavior, but never let it override a hard stop.
        def after_model():
            return asyncio.run(mw.aafter_model(state, runtime)) if invoke_async else mw.after_model(state, runtime)

        assert after_model() == {"jump_to": "model"}
        base_hook.assert_called_once()
        base_hook.reset_mock()
        runtime.context["stop_reason"] = stop_reason
        state["messages"] = [_ai_stopped_by(stop_reason)]
        assert after_model() is None
        base_hook.assert_not_called()

    @pytest.mark.parametrize("invoke_async", [False, True], ids=["sync", "async"])
    @pytest.mark.parametrize("stop_reason", ["safety_capped", "model_length_capped"])
    def test_earlier_provider_stop_in_context_keeps_completion_reminders(self, invoke_async, stop_reason):
        # Goal continuation turns share the run's context, which keeps the first
        # cap for the run record. A complete later response is not capped.
        mw = TodoMiddleware()
        runtime = _make_runtime()
        runtime.context["stop_reason"] = stop_reason
        state = {"messages": [_ai_no_tool_calls()], "todos": _incomplete_todos()}

        result = asyncio.run(mw.aafter_model(state, runtime)) if invoke_async else mw.after_model(state, runtime)

        assert result == {"jump_to": "model"}
        assert mw._completion_reminder_count_for_runtime(runtime) == 1

    @pytest.mark.parametrize("stop_reason", [None, "custom_stop", "subagent_limit_capped", [], {}])
    def test_unrecognized_stop_reason_keeps_completion_reminders(self, stop_reason):
        mw = TodoMiddleware()
        runtime = _make_runtime()
        runtime.context["stop_reason"] = stop_reason
        state = {"messages": [_ai_no_tool_calls()], "todos": _incomplete_todos()}

        assert mw.after_model(state, runtime) == {"jump_to": "model"}

    def test_returns_none_when_agent_still_using_tools(self):
        mw = TodoMiddleware()
        state = {
            "messages": [_ai_with_write_todos()],
            "todos": _incomplete_todos(),
        }
        assert mw.after_model(state, _make_runtime()) is None

    def test_returns_none_when_no_todos(self):
        mw = TodoMiddleware()
        state = {
            "messages": [_ai_no_tool_calls()],
            "todos": [],
        }
        assert mw.after_model(state, _make_runtime()) is None

    def test_returns_none_when_todos_is_none(self):
        mw = TodoMiddleware()
        state = {
            "messages": [_ai_no_tool_calls()],
            "todos": None,
        }
        assert mw.after_model(state, _make_runtime()) is None

    def test_returns_none_when_all_completed(self):
        mw = TodoMiddleware()
        state = {
            "messages": [_ai_no_tool_calls()],
            "todos": _all_completed_todos(),
        }
        assert mw.after_model(state, _make_runtime()) is None

    def test_returns_none_when_no_messages(self):
        mw = TodoMiddleware()
        state = {
            "messages": [],
            "todos": _incomplete_todos(),
        }
        assert mw.after_model(state, _make_runtime()) is None

    def test_queues_reminder_and_jumps_to_model_when_incomplete(self):
        mw = TodoMiddleware()
        runtime = _make_runtime()
        state = {
            "messages": [HumanMessage(content="hi"), _ai_no_tool_calls()],
            "todos": _incomplete_todos(),
        }
        result = mw.after_model(state, runtime)
        assert result is not None
        assert result["jump_to"] == "model"
        assert "messages" not in result

        request = _make_model_request(state["messages"], runtime=runtime)
        seen: list[ModelRequest] = []

        def handler(model_request: ModelRequest):
            seen.append(model_request)
            return "response"

        assert mw.wrap_model_call(request, handler) == "response"
        assert len(seen) == 1
        sent = seen[0]
        assert sent.system_message is not None
        assert "write_todos" in sent.system_message.text
        reminder = sent.messages[-1]
        assert isinstance(reminder, HumanMessage)
        assert reminder.name == "todo_completion_reminder"
        assert reminder.additional_kwargs["hide_from_ui"] is True
        assert "Step 2" in reminder.content
        assert "Step 3" in reminder.content

    def test_reminder_lists_only_incomplete_items(self):
        mw = TodoMiddleware()
        runtime = _make_runtime()
        state = {
            "messages": [_ai_no_tool_calls()],
            "todos": _incomplete_todos(),
        }
        result = mw.after_model(state, runtime)
        assert result is not None

        request = _make_model_request(state["messages"], runtime=runtime)
        seen: list[ModelRequest] = []

        def handler(model_request: ModelRequest):
            seen.append(model_request)
            return "response"

        mw.wrap_model_call(request, handler)
        content = seen[0].messages[-1].content
        assert "Step 1" not in content  # completed — should not appear
        assert "Step 2" in content
        assert "Step 3" in content

    def test_allows_exit_after_max_reminders(self):
        mw = TodoMiddleware()
        runtime = _make_runtime()
        state = {
            "messages": [
                _ai_no_tool_calls(),
            ],
            "todos": _incomplete_todos(),
        }
        assert mw.after_model(state, runtime) is not None
        assert mw.after_model(state, runtime) is not None
        assert mw.after_model(state, runtime) is None

    def test_still_sends_reminder_before_cap(self):
        mw = TodoMiddleware()
        runtime = _make_runtime()
        state = {
            "messages": [
                _ai_no_tool_calls(),
            ],
            "todos": _incomplete_todos(),
        }
        assert mw.after_model(state, runtime) is not None
        result = mw.after_model(state, runtime)
        assert result is not None
        assert result["jump_to"] == "model"

    def test_does_not_trigger_for_invalid_tool_calls(self):
        mw = TodoMiddleware()
        state = {
            "messages": [_ai_with_invalid_tool_calls()],
            "todos": _incomplete_todos(),
        }
        assert mw.after_model(state, _make_runtime()) is None

    def test_does_not_trigger_for_raw_provider_tool_calls(self):
        mw = TodoMiddleware()
        state = {
            "messages": [_ai_with_raw_provider_tool_calls()],
            "todos": _incomplete_todos(),
        }
        assert mw.after_model(state, _make_runtime()) is None

    def test_does_not_trigger_for_legacy_function_call(self):
        mw = TodoMiddleware()
        state = {
            "messages": [_ai_with_legacy_function_call()],
            "todos": _incomplete_todos(),
        }
        assert mw.after_model(state, _make_runtime()) is None

    def test_does_not_trigger_for_tool_finish_reason(self):
        mw = TodoMiddleware()
        state = {
            "messages": [_ai_with_tool_finish_reason()],
            "todos": _incomplete_todos(),
        }
        assert mw.after_model(state, _make_runtime()) is None

    def test_does_not_reengage_when_model_length_capped_marker_present(self):
        mw = TodoMiddleware()
        state = {
            "messages": [
                AIMessage(
                    content="nit",
                    tool_calls=[],
                    additional_kwargs={
                        "model_length_termination": {
                            "detector": "openai_compatible_length",
                            "suppressed_tool_call_count": 1,
                            "suppressed_tool_call_names": ["write_file"],
                        }
                    },
                )
            ],
            "todos": _incomplete_todos(),
        }
        assert mw.after_model(state, _make_runtime()) is None

    def test_pure_text_length_cap_without_marker_still_reengages(self):
        mw = TodoMiddleware()
        state = {
            "messages": [
                AIMessage(
                    content="partial answer",
                    response_metadata={"finish_reason": "length"},
                )
            ],
            "todos": _incomplete_todos(),
        }
        result = mw.after_model(state, _make_runtime())
        assert result is not None
        assert result["jump_to"] == "model"


class TestAafterModel:
    def test_delegates_to_sync(self):
        mw = TodoMiddleware()
        runtime = _make_runtime()
        state = {
            "messages": [_ai_no_tool_calls()],
            "todos": _incomplete_todos(),
        }
        result = asyncio.run(mw.aafter_model(state, runtime))
        assert result is not None
        assert result["jump_to"] == "model"
        assert "messages" not in result


class TestWrapModelCall:
    def test_no_pending_reminder_still_injects_todo_system_prompt(self):
        """Regression for bytedance/deer-flow#4714: the base class system-prompt
        injection must survive the override, otherwise the model never learns
        about `write_todos` and no todo list is ever produced."""
        mw = TodoMiddleware()
        runtime = _make_runtime()
        request = _make_model_request([HumanMessage(content="hi")], runtime=runtime)
        seen: list[ModelRequest] = []

        def handler(model_request: ModelRequest):
            seen.append(model_request)
            return "response"

        assert mw.wrap_model_call(request, handler) == "response"
        assert len(seen) == 1
        sent = seen[0]
        assert sent.system_message is not None
        assert "write_todos" in sent.system_message.text
        # No pending reminder — messages must not be augmented.
        assert sent.messages == [HumanMessage(content="hi")]

    def test_pending_reminder_is_injected_once(self):
        mw = TodoMiddleware()
        runtime = _make_runtime()
        state = {
            "messages": [_ai_no_tool_calls()],
            "todos": _incomplete_todos(),
        }
        mw.after_model(state, runtime)

        request = _make_model_request(state["messages"], runtime=runtime)
        seen: list[ModelRequest] = []

        def handler(model_request: ModelRequest):
            seen.append(model_request)
            return "response"

        assert mw.wrap_model_call(request, handler) == "response"
        assert len(seen) == 1
        sent = seen[0]
        assert sent.system_message is not None
        assert "write_todos" in sent.system_message.text
        injected = sent.messages[-1]
        assert isinstance(injected, HumanMessage)
        assert injected.name == "todo_completion_reminder"

        # Second call: the reminder was drained, so only the system prompt is
        # injected and the original messages pass through untouched.
        seen.clear()
        assert mw.wrap_model_call(request, handler) == "response"
        assert len(seen) == 1
        assert seen[0].messages == state["messages"]

    def test_pending_reminder_survives_a_failed_model_call(self):
        """A call that raises is retried by LLMErrorHandlingMiddleware through this wrap; the reminder must still be sent."""
        mw = TodoMiddleware()
        runtime = _make_runtime()
        state = {"messages": [_ai_no_tool_calls()], "todos": _incomplete_todos()}
        mw.after_model(state, runtime)

        request = _make_model_request(state["messages"], runtime=runtime)
        sent: list[list[Any]] = []

        def flaky_handler(model_request: ModelRequest):
            sent.append(model_request.messages)
            if len(sent) == 1:
                raise RuntimeError("503 Service Unavailable")
            return "response"

        with pytest.raises(RuntimeError):
            mw.wrap_model_call(request, flaky_handler)
        assert mw.wrap_model_call(request, flaky_handler) == "response"

        assert [len(_todo_completion_reminders(messages)) for messages in sent] == [1, 1]
        # Restoring is not a new reminder: the retry must not spend the cap.
        assert mw._completion_reminder_count_for_runtime(runtime) == 1
        assert mw._drain_completion_reminders(runtime) == []

    def test_failed_model_call_does_not_restore_a_reminder_for_a_cleared_run(self):
        """If the run's reminder state was dropped while the call was in flight, the failure must not resurrect it."""
        mw = TodoMiddleware()
        runtime = _make_runtime()
        state = {"messages": [_ai_no_tool_calls()], "todos": _incomplete_todos()}
        mw.after_model(state, runtime)

        request = _make_model_request(state["messages"], runtime=runtime)

        def failing_handler(model_request: ModelRequest):
            mw.after_agent({}, runtime)
            raise RuntimeError("503 Service Unavailable")

        with pytest.raises(RuntimeError):
            mw.wrap_model_call(request, failing_handler)

        assert mw._pending_completion_reminders == {}
        assert mw._completion_reminder_counts == {}


@pytest.fixture
def production_chain_config(tmp_path, monkeypatch):
    from deerflow.config.app_config import AppConfig, pop_current_app_config, push_current_app_config
    from deerflow.sandbox.sandbox_provider import shutdown_sandbox_provider

    monkeypatch.setenv("DEER_FLOW_HOME", str(tmp_path))
    # A missing ambient config must not be masked by a developer's checkout.
    monkeypatch.setenv("DEER_FLOW_CONFIG_PATH", str(tmp_path / "missing-config.yaml"))
    monkeypatch.setenv("LANGSMITH_TRACING", "false")
    monkeypatch.setenv("LANGCHAIN_TRACING_V2", "false")
    monkeypatch.setattr("deerflow.config.paths._paths", None)
    monkeypatch.setattr("deerflow.sandbox.sandbox_provider._default_sandbox_provider", None)
    monkeypatch.setattr("deerflow.sandbox.local.local_sandbox_provider._singleton", None)
    app_config = AppConfig.model_validate(
        {
            "models": [{"name": "offline", "use": "langchain_openai:ChatOpenAI", "model": "offline"}],
            "sandbox": {"use": "deerflow.sandbox.local:LocalSandboxProvider", "allow_host_bash": False},
            "skills": {"path": str(tmp_path / "skills")},
            "memory": {"enabled": False},
            "title": {"enabled": False},
            "summarization": {"enabled": False},
        }
    )
    # Sandbox lifecycle hooks resolve get_app_config() at invocation time,
    # independently of the explicit config passed to build_middlewares().
    # The runtime binding propagates through both graph and worker-thread calls.
    push_current_app_config(app_config)
    try:
        yield app_config
    finally:
        try:
            shutdown_sandbox_provider()
        finally:
            pop_current_app_config()


class TestTodoMiddlewareAgentGraphIntegration:
    @pytest.mark.parametrize("invoke_async", [False, True], ids=["sync", "async"])
    @pytest.mark.parametrize("response_kind", ["tool", "blank", "partial_text"])
    def test_production_chain_stops_after_safety_response(self, production_chain_config, invoke_async, response_kind):
        from deerflow.agents.lead_agent.agent import build_middlewares

        executed = []

        @tool
        def record_action(marker: str) -> str:
            """Record a harmless action in memory."""
            executed.append(marker)
            return marker

        filtered = AIMessage(
            id="filtered-response",
            content="Partial report." if response_kind == "partial_text" else "",
            response_metadata={"finish_reason": "content_filter"},
            tool_calls=[{"name": "record_action", "args": {"marker": "filtered"}, "id": "filtered-call"}] if response_kind == "tool" else [],
        )
        model = _CapturingFakeMessagesListChatModel(
            responses=[
                filtered,
                AIMessage(content="", tool_calls=[{"name": "record_action", "args": {"marker": "after-stop"}, "id": "later-call"}]),
                AIMessage(content="final"),
            ]
        )
        middlewares = build_middlewares({"configurable": {"is_plan_mode": True}}, model_name="offline", app_config=production_chain_config, memory_enabled=False, available_skills=set())
        graph = create_agent(model=model, tools=[record_action], middleware=middlewares, state_schema=ThreadState)
        todos = _incomplete_todos()
        context = {"thread_id": "production-safety-thread", "run_id": "production-safety-run"}
        state = {"messages": [("user", "Finish the report.")], "todos": todos}

        result = asyncio.run(graph.ainvoke(state, context=context)) if invoke_async else graph.invoke(state, context=context)

        assert len(model.seen_messages) == 1
        assert executed == []
        assert context["stop_reason"] == "safety_capped"
        final = result["messages"][-1]
        assert final.additional_kwargs["safety_termination"]
        assert final.content
        assert not final.tool_calls
        assert result["todos"] == todos
        if response_kind == "partial_text":
            assert final.content == filtered.content
        todo_mw = next(mw for mw in middlewares if isinstance(mw, TodoMiddleware))
        assert todo_mw._pending_completion_reminders == {}
        assert todo_mw._completion_reminder_counts == {}

    @pytest.mark.parametrize("invoke_async", [False, True], ids=["sync", "async"])
    @pytest.mark.parametrize("guard_kind", ["loop", "token", "safety", "model_length"])
    @pytest.mark.parametrize("pending", [True, False], ids=["unfinished", "completed"])
    def test_run_hard_stop_does_not_reengage_todos(self, invoke_async, guard_kind, pending):
        from deerflow.agents.middlewares.loop_detection_middleware import LoopDetectionMiddleware
        from deerflow.agents.middlewares.token_budget_middleware import TokenBudgetMiddleware
        from deerflow.config.token_budget_config import TokenBudgetConfig

        executed = []

        @tool
        def record_action(marker: str) -> str:
            """Record a harmless action in memory."""
            executed.append(marker)
            return marker

        def action(marker, call_id, tokens=10):
            return AIMessage(
                content="",
                tool_calls=[{"name": "record_action", "args": {"marker": marker}, "id": call_id}],
                usage_metadata={"input_tokens": tokens, "output_tokens": 0, "total_tokens": tokens},
            )

        todos = [{"content": "Record the requested actions", "status": "pending" if pending else "completed"}]
        blocked_action = action("repeated", "blocked", tokens=1000 if guard_kind == "token" else 10)
        if guard_kind in ("safety", "model_length"):
            blocked_action.response_metadata = {"finish_reason": "content_filter" if guard_kind == "safety" else "length"}
        model = _CapturingFakeMessagesListChatModel(
            responses=[
                AIMessage(content="", tool_calls=[{"name": "write_todos", "args": {"todos": todos}, "id": "todos-start"}]),
                action("repeated", "first"),
                blocked_action,
                # A different action makes unintended post-stop execution observable.
                action("after-stop", "after-stop"),
                AIMessage(content="final answer"),
                AIMessage(content="final answer"),
                AIMessage(content="final answer"),
            ]
        )
        todo_mw = TodoMiddleware()
        guards = {
            "loop": LoopDetectionMiddleware(warn_threshold=2, hard_limit=2),
            "token": TokenBudgetMiddleware(TokenBudgetConfig(enabled=True, max_tokens=1000)),
            "safety": SafetyFinishReasonMiddleware(),
            "model_length": ModelLengthFinishReasonMiddleware(),
        }
        guard = guards[guard_kind]
        # Production registers Todo before the guards; after_model runs in reverse.
        graph = create_agent(model=model, tools=[record_action], middleware=[todo_mw, guard], state_schema=ThreadState, checkpointer=InMemorySaver())
        config = {"configurable": {"thread_id": "hard-stop-thread"}, "recursion_limit": 100}
        context = {"thread_id": "hard-stop-thread", "run_id": "hard-stop-run"}
        state = {"messages": [("user", "Record actions and track todos.")]}

        def invoke(state, context):
            return asyncio.run(graph.ainvoke(state, context=context, config=config)) if invoke_async else graph.invoke(state, context=context, config=config)

        result = invoke(state, context)

        assert context["stop_reason"] == f"{guard_kind}_capped"
        assert executed == ["repeated"]
        assert len(model.seen_messages) == 3
        # Pin the real producers to Todo's declared policy, without importing
        # runtime Goal policy (which currently covers only loop/token).
        assert context["stop_reason"] in todo_mw.release_policy_parameters()["completion_reminder_hard_stop_reasons"]
        assert result["todos"] == todos
        assert not result["messages"][-1].tool_calls
        assert not any(_todo_completion_reminders(messages) for messages in model.seen_messages)
        assert todo_mw._pending_completion_reminders == {}
        snapshot = graph.get_state(config)
        assert snapshot.next == ()
        assert snapshot.values["todos"] == todos

        if pending:
            # Restore the same thread from its checkpoint without resupplying Todo.
            # The old cap must not disable the new run or recount historical usage.
            next_context = {"thread_id": "hard-stop-thread", "run_id": "next-run"}
            next_result = invoke({"messages": [("user", "Continue the unfinished task.")]}, next_context)
            assert "stop_reason" not in next_context
            assert executed == ["repeated", "after-stop"]
            assert any(_todo_completion_reminders(messages) for messages in model.seen_messages[3:])
            assert next_result["todos"] == todos

    def test_reuses_thread_state_todos_schema_in_real_agent_graph(self):
        mw = TodoMiddleware()
        model = _CapturingFakeMessagesListChatModel(
            responses=[
                AIMessage(
                    content="",
                    tool_calls=[
                        {
                            "name": "write_todos",
                            "id": "todos-1",
                            "args": {
                                "todos": [
                                    {"content": "Step 1", "status": "pending"},
                                ]
                            },
                        }
                    ],
                ),
                AIMessage(content="final"),
            ],
        )

        graph = create_agent(
            model=model,
            tools=[],
            middleware=[mw],
            state_schema=ThreadState,
        )

        result = graph.invoke(
            {"messages": [("user", "create a todo")]},
            context={"thread_id": "schema-thread", "run_id": "schema-run"},
        )

        assert result["todos"] == [{"content": "Step 1", "status": "pending"}]

        # Regression for bytedance/deer-flow#4714: the model request must carry
        # the `write_todos` system prompt (injected by the base class), otherwise
        # the model never produces a todo list.
        first_model_call = model.seen_messages[0]
        assert isinstance(first_model_call[0], SystemMessage)
        assert "write_todos" in first_model_call[0].text

    def test_completion_reminder_is_transient_in_real_agent_graph(self):
        mw = TodoMiddleware()
        model = _CapturingFakeMessagesListChatModel(
            responses=[
                AIMessage(
                    content="",
                    tool_calls=[
                        {
                            "name": "write_todos",
                            "id": "todos-1",
                            "args": {
                                "todos": [
                                    {"content": "Step 1", "status": "completed"},
                                    {"content": "Step 2", "status": "pending"},
                                ]
                            },
                        }
                    ],
                ),
                AIMessage(content="premature final 1"),
                AIMessage(content="premature final 2"),
                AIMessage(content="premature final 3"),
            ],
        )
        graph = create_agent(model=model, tools=[], middleware=[mw])

        result = graph.invoke(
            {"messages": [("user", "finish all todos")]},
            context={"thread_id": "integration-thread", "run_id": "integration-run"},
        )

        assert len(model.seen_messages) == 4
        reminders_by_call = [_todo_completion_reminders(messages) for messages in model.seen_messages]
        assert reminders_by_call[0] == []
        assert reminders_by_call[1] == []
        assert len(reminders_by_call[2]) == 1
        assert len(reminders_by_call[3]) == 1
        assert "Step 1" not in reminders_by_call[2][0].content
        assert "Step 2" in reminders_by_call[2][0].content

        persisted_reminders = _todo_completion_reminders(result["messages"])
        assert persisted_reminders == []
        assert result["messages"][-1].content == "premature final 3"
        assert result["todos"] == [
            {"content": "Step 1", "status": "completed"},
            {"content": "Step 2", "status": "pending"},
        ]
        assert mw._pending_completion_reminders == {}
        assert mw._completion_reminder_counts == {}

    def test_completion_reminder_survives_a_retried_model_call_in_real_agent_graph(self):
        """LLMErrorHandlingMiddleware retries a failed call by running the inner wraps again; the retry must still carry the reminder."""
        from deerflow.agents.middlewares.llm_error_handling_middleware import LLMErrorHandlingMiddleware
        from deerflow.config.app_config import AppConfig, LlmCallConfig
        from deerflow.config.sandbox_config import SandboxConfig

        class ProviderUnavailable(Exception):
            def __init__(self) -> None:
                super().__init__("503 Service Unavailable")
                self.status_code = 503
                self.response = SimpleNamespace(status_code=503, headers={})

        class FailsOnceOnReminder(_CapturingFakeMessagesListChatModel):
            _failed: bool = PrivateAttr(default=False)

            def _generate(self, messages, stop=None, run_manager=None, **kwargs):
                if not self._failed and _todo_completion_reminders(messages):
                    self._failed = True
                    self._seen_messages.append(list(messages))
                    raise ProviderUnavailable()
                return super()._generate(messages, stop=stop, run_manager=run_manager, **kwargs)

        model = FailsOnceOnReminder(
            responses=[
                AIMessage(
                    content="",
                    tool_calls=[
                        {
                            "name": "write_todos",
                            "id": "todos-1",
                            "args": {"todos": [{"content": "Step 1", "status": "pending"}]},
                        }
                    ],
                ),
                AIMessage(content="premature final 1"),
                AIMessage(content="premature final 2"),
                AIMessage(content="premature final 3"),
            ],
        )
        app_config = AppConfig(
            sandbox=SandboxConfig(use="test"),
            llm_call=LlmCallConfig(retry_max_attempts=3, retry_base_delay_ms=0, retry_cap_delay_ms=0),
        )
        graph = create_agent(
            model=model,
            tools=[],
            middleware=[LLMErrorHandlingMiddleware(app_config=app_config), TodoMiddleware()],
            state_schema=ThreadState,
        )

        result = graph.invoke(
            {"messages": [("user", "finish all todos")]},
            context={"thread_id": "retry-thread", "run_id": "retry-run"},
        )

        # write_todos, the premature exit, the failed attempt and its retry, then
        # the second reminder: the retry did not spend the two-reminder cap.
        assert [len(_todo_completion_reminders(messages)) for messages in model.seen_messages] == [0, 0, 1, 1, 1]
        assert result["messages"][-1].content == "premature final 3"

    @pytest.mark.parametrize("invoke_async", [False, True], ids=["sync", "async"])
    @pytest.mark.parametrize(
        "content,response_metadata,additional_kwargs",
        [
            ("partial answer", {"finish_reason": "length"}, {}),
            ("partial answer", {"stop_reason": "max_tokens"}, {}),
            ([{"type": "text", "text": "partial answer"}], {"finish_reason": "MAX_TOKENS"}, {}),
            ("partial answer", {"status": "incomplete", "incomplete_details": {"reason": "max_output_tokens"}}, {}),
            ("partial answer", {}, {"finish_reason": "length"}),
        ],
        ids=["openai", "anthropic", "gemini-blocks", "responses", "additional-kwargs"],
    )
    def test_text_only_length_cap_does_not_reengage_todos(self, invoke_async, content, response_metadata, additional_kwargs):
        todo_mw = TodoMiddleware()
        capped_message = AIMessage(content=content, response_metadata=response_metadata, additional_kwargs=additional_kwargs)
        model = _CapturingFakeMessagesListChatModel(responses=[capped_message])
        graph = create_agent(
            model=model,
            tools=[],
            middleware=[todo_mw, ModelLengthFinishReasonMiddleware()],
            state_schema=ThreadState,
        )
        todos = [{"content": "Finish the report", "status": "in_progress"}]
        state = {"messages": [("user", "write the report")], "todos": todos}
        context = {"thread_id": "text-cap-thread", "run_id": "text-cap-run"}

        result = asyncio.run(graph.ainvoke(state, context=context)) if invoke_async else graph.invoke(state, context=context)

        assert len(model.seen_messages) == 1
        final_ai = result["messages"][-1]
        assert final_ai.content == content
        assert final_ai.response_metadata == response_metadata
        assert final_ai.additional_kwargs["model_length_termination"]["suppressed_tool_call_count"] == 0
        assert all(final_ai.additional_kwargs[key] == value for key, value in additional_kwargs.items())
        assert "model_length_termination" not in capped_message.additional_kwargs
        assert result["todos"] == todos
        assert todo_mw._pending_completion_reminders == {}
        assert not any(_todo_completion_reminders(messages) for messages in model.seen_messages)

    def test_length_capped_write_file_does_not_reengage_todos(self):
        """Reproduces the incident (thread b1723286): model emits a write_file
        call with finish_reason=length, ModelLength suppresses it, and
        TodoMiddleware must NOT re-engage via jump_to=model."""
        todo_mw = TodoMiddleware()
        model = _CapturingFakeMessagesListChatModel(
            responses=[
                AIMessage(
                    content="nit",
                    tool_calls=[{"name": "write_file", "id": "call_1", "args": {"path": "/mnt/user-data/outputs/report.md", "content": "# truncated report\n| ext4 | jbd2"}}],
                    response_metadata={"finish_reason": "length", "model_name": "deepseek-v4-pro"},
                ),
            ],
        )

        graph = create_agent(
            model=model,
            tools=[],
            middleware=[todo_mw, ModelLengthFinishReasonMiddleware()],
            state_schema=ThreadState,
        )

        result = graph.invoke(
            {"messages": [("user", "write the report")], "todos": [{"content": "Phase 9: Synthesis and final report writing", "status": "in_progress"}]},
            context={"thread_id": "cap-thread", "run_id": "cap-run"},
        )

        assert len(model.seen_messages) == 1
        reminders_by_call = [_todo_completion_reminders(messages) for messages in model.seen_messages]
        assert all(len(r) == 0 for r in reminders_by_call)

        final_ai = result["messages"][-1]
        assert final_ai.additional_kwargs.get("model_length_termination")
        assert "output limit" in str(final_ai.content)
        assert "nit" in str(final_ai.content)
        assert result["todos"] == [{"content": "Phase 9: Synthesis and final report writing", "status": "in_progress"}]
        assert todo_mw._pending_completion_reminders == {}


class TestRunScopedReminderCleanup:
    def test_before_agent_clears_stale_count_without_pending_reminder(self):
        mw = TodoMiddleware()
        stale_runtime = _make_runtime()
        stale_runtime.context = {"thread_id": "test-thread", "run_id": "stale-run"}
        current_runtime = _make_runtime()
        current_runtime.context = {"thread_id": "test-thread", "run_id": "current-run"}
        other_thread_runtime = _make_runtime()
        other_thread_runtime.context = {"thread_id": "other-thread", "run_id": "stale-run"}

        state = {"messages": [_ai_no_tool_calls()], "todos": _incomplete_todos()}
        assert mw.after_model(state, stale_runtime) is not None
        assert mw.after_model(state, other_thread_runtime) is not None

        # Simulate a model call that drained the pending message, followed by an
        # abnormal run end where after_agent did not clear the reminder count.
        assert mw._drain_completion_reminders(stale_runtime)
        assert mw._completion_reminder_count_for_runtime(stale_runtime) == 1

        mw.before_agent({}, current_runtime)

        assert mw._completion_reminder_count_for_runtime(stale_runtime) == 0
        assert mw._completion_reminder_count_for_runtime(other_thread_runtime) == 1

    def test_size_guard_prunes_oldest_count_only_reminder_state(self):
        mw = TodoMiddleware()
        mw._MAX_COMPLETION_REMINDER_KEYS = 2
        first_runtime = _make_runtime_for("thread-a", "run-a")
        second_runtime = _make_runtime_for("thread-b", "run-b")
        third_runtime = _make_runtime_for("thread-c", "run-c")

        state = {"messages": [_ai_no_tool_calls()], "todos": _incomplete_todos()}
        assert mw.after_model(state, first_runtime) is not None

        # Simulate the normal model request path: pending reminder is consumed,
        # but the run count remains until after_agent() or stale cleanup.
        assert mw._drain_completion_reminders(first_runtime)
        assert mw._completion_reminder_count_for_runtime(first_runtime) == 1

        assert mw.after_model(state, second_runtime) is not None
        assert mw.after_model(state, third_runtime) is not None

        assert mw._completion_reminder_count_for_runtime(first_runtime) == 0
        assert mw._completion_reminder_count_for_runtime(second_runtime) == 1
        assert mw._completion_reminder_count_for_runtime(third_runtime) == 1
        assert ("thread-a", "run-a") not in mw._completion_reminder_touch_order

    def test_size_guard_prunes_pending_and_count_state_together(self):
        mw = TodoMiddleware()
        mw._MAX_COMPLETION_REMINDER_KEYS = 1
        stale_runtime = _make_runtime_for("thread-a", "run-a")
        current_runtime = _make_runtime_for("thread-b", "run-b")

        state = {"messages": [_ai_no_tool_calls()], "todos": _incomplete_todos()}
        assert mw.after_model(state, stale_runtime) is not None
        assert mw.after_model(state, current_runtime) is not None

        assert mw._drain_completion_reminders(stale_runtime) == []
        assert mw._completion_reminder_count_for_runtime(stale_runtime) == 0
        assert mw._completion_reminder_count_for_runtime(current_runtime) == 1


class TestAwrapModelCall:
    def test_async_no_pending_reminder_still_injects_todo_system_prompt(self):
        """Mirror of the sync no-reminder test: the async path must also keep the
        base class system-prompt injection while leaving messages untouched when
        there are no pending completion reminders."""
        mw = TodoMiddleware()
        runtime = _make_runtime()
        request = _make_model_request([HumanMessage(content="hi")], runtime=runtime)
        seen: list[ModelRequest] = []

        async def handler(model_request: ModelRequest):
            seen.append(model_request)
            return "response"

        result = asyncio.run(mw.awrap_model_call(request, handler))
        assert result == "response"
        assert len(seen) == 1
        sent = seen[0]
        assert sent.system_message is not None
        assert "write_todos" in sent.system_message.text
        assert sent.messages == [HumanMessage(content="hi")]

    def test_async_pending_reminder_is_injected(self):
        mw = TodoMiddleware()
        runtime = _make_runtime()
        state = {
            "messages": [_ai_no_tool_calls()],
            "todos": _incomplete_todos(),
        }
        mw.after_model(state, runtime)

        request = _make_model_request(state["messages"], runtime=runtime)
        seen: list[ModelRequest] = []

        async def handler(model_request: ModelRequest):
            seen.append(model_request)
            return "response"

        result = asyncio.run(mw.awrap_model_call(request, handler))
        assert result == "response"
        assert len(seen) == 1
        sent = seen[0]
        assert sent.system_message is not None
        assert "write_todos" in sent.system_message.text
        injected = sent.messages[-1]
        assert isinstance(injected, HumanMessage)
        assert injected.name == "todo_completion_reminder"
        assert injected.additional_kwargs["hide_from_ui"] is True

    def test_async_pending_reminder_survives_a_failed_model_call(self):
        mw = TodoMiddleware()
        runtime = _make_runtime()
        state = {"messages": [_ai_no_tool_calls()], "todos": _incomplete_todos()}
        mw.after_model(state, runtime)

        request = _make_model_request(state["messages"], runtime=runtime)
        sent: list[list[Any]] = []

        async def flaky_handler(model_request: ModelRequest):
            sent.append(model_request.messages)
            if len(sent) == 1:
                raise RuntimeError("503 Service Unavailable")
            return "response"

        with pytest.raises(RuntimeError):
            asyncio.run(mw.awrap_model_call(request, flaky_handler))
        assert asyncio.run(mw.awrap_model_call(request, flaky_handler)) == "response"

        assert [len(_todo_completion_reminders(messages)) for messages in sent] == [1, 1]
        assert mw._completion_reminder_count_for_runtime(runtime) == 1


class TestMalformedStoredTodos:
    """State is a boundary: a stored value need not match the write_todos shape.

    ``todos`` is a reducer channel that checkpoint writes and direct integrations
    can Overwrite directly, so the middleware must not assume every entry is a
    mapping. One unknown shape used to raise on every model call of the thread.
    """

    def test_before_model_ignores_entries_it_cannot_render(self):
        mw = TodoMiddleware()
        state = {"messages": [HumanMessage(content="hi")], "todos": ["oops", {"content": "Keep me"}]}
        result = mw.before_model(state, _make_runtime())
        content = result["messages"][0].content
        assert "Keep me" in content
        assert "oops" not in content

    def test_after_model_keeps_guarding_for_the_usable_entries(self):
        mw = TodoMiddleware()
        state = {"messages": [AIMessage(content="final")], "todos": ["oops", {"content": "Keep me", "status": "pending"}]}
        assert mw.after_model(state, _make_runtime()) == {"jump_to": "model"}

    def test_after_model_ignores_malformed_siblings_of_completed_entries(self):
        mw = TodoMiddleware()
        state = {"messages": [AIMessage(content="final")], "todos": [{"content": "Done", "status": "completed"}, "junk"]}
        assert mw.after_model(state, _make_runtime()) is None

    @pytest.mark.parametrize("todos", [{"content": "x"}, "oops", 5])
    def test_todos_that_are_not_a_list_allow_a_clean_exit(self, todos):
        mw = TodoMiddleware()
        assert mw.after_model({"messages": [AIMessage(content="final")], "todos": todos}, _make_runtime()) is None
        assert mw.before_model({"messages": [HumanMessage(content="hi")], "todos": todos}, _make_runtime()) is None