"""Tests for the shared agent runner and its integration contracts."""

from __future__ import annotations

import asyncio
import os
import time
from unittest.mock import AsyncMock, MagicMock, patch

import pytest

from nanobot.config.schema import AgentDefaults
from nanobot.agent.tools.base import Tool
from nanobot.agent.tools.registry import ToolRegistry
from nanobot.providers.base import LLMResponse, ToolCallRequest

_MAX_TOOL_RESULT_CHARS = AgentDefaults().max_tool_result_chars


def _make_loop(tmp_path):
    from nanobot.agent.loop import AgentLoop
    from nanobot.bus.queue import MessageBus

    bus = MessageBus()
    provider = MagicMock()
    provider.get_default_model.return_value = "test-model"

    with patch("nanobot.agent.loop.ContextBuilder"), \
         patch("nanobot.agent.loop.SessionManager"), \
         patch("nanobot.agent.loop.SubagentManager") as MockSubMgr:
        MockSubMgr.return_value.cancel_by_session = AsyncMock(return_value=0)
        loop = AgentLoop(bus=bus, provider=provider, workspace=tmp_path)
    return loop


@pytest.mark.asyncio
async def test_runner_preserves_reasoning_fields_and_tool_results():
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    captured_second_call: list[dict] = []
    call_count = {"n": 0}

    async def chat_with_retry(*, messages, **kwargs):
        call_count["n"] += 1
        if call_count["n"] == 1:
            return LLMResponse(
                content="thinking",
                tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={"path": "."})],
                reasoning_content="hidden reasoning",
                thinking_blocks=[{"type": "thinking", "thinking": "step"}],
                usage={"prompt_tokens": 5, "completion_tokens": 3},
            )
        captured_second_call[:] = messages
        return LLMResponse(content="done", tool_calls=[], usage={})

    provider.chat_with_retry = chat_with_retry
    tools = MagicMock()
    tools.get_definitions.return_value = []
    tools.execute = AsyncMock(return_value="tool result")

    runner = AgentRunner(provider)
    result = await runner.run(AgentRunSpec(
        initial_messages=[
            {"role": "system", "content": "system"},
            {"role": "user", "content": "do task"},
        ],
        tools=tools,
        model="test-model",
        max_iterations=3,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
    ))

    assert result.final_content == "done"
    assert result.tools_used == ["list_dir"]
    assert result.tool_events == [
        {"name": "list_dir", "status": "ok", "detail": "tool result"}
    ]

    assistant_messages = [
        msg for msg in captured_second_call
        if msg.get("role") == "assistant" and msg.get("tool_calls")
    ]
    assert len(assistant_messages) == 1
    assert assistant_messages[0]["reasoning_content"] == "hidden reasoning"
    assert assistant_messages[0]["thinking_blocks"] == [{"type": "thinking", "thinking": "step"}]
    assert any(
        msg.get("role") == "tool" and msg.get("content") == "tool result"
        for msg in captured_second_call
    )


@pytest.mark.asyncio
async def test_runner_calls_hooks_in_order():
    from nanobot.agent.hook import AgentHook, AgentHookContext
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    call_count = {"n": 0}
    events: list[tuple] = []

    async def chat_with_retry(**kwargs):
        call_count["n"] += 1
        if call_count["n"] == 1:
            return LLMResponse(
                content="thinking",
                tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={"path": "."})],
            )
        return LLMResponse(content="done", tool_calls=[], usage={})

    provider.chat_with_retry = chat_with_retry
    tools = MagicMock()
    tools.get_definitions.return_value = []
    tools.execute = AsyncMock(return_value="tool result")

    class RecordingHook(AgentHook):
        async def before_iteration(self, context: AgentHookContext) -> None:
            events.append(("before_iteration", context.iteration))

        async def before_execute_tools(self, context: AgentHookContext) -> None:
            events.append((
                "before_execute_tools",
                context.iteration,
                [tc.name for tc in context.tool_calls],
            ))

        async def after_iteration(self, context: AgentHookContext) -> None:
            events.append((
                "after_iteration",
                context.iteration,
                context.final_content,
                list(context.tool_results),
                list(context.tool_events),
                context.stop_reason,
            ))

        def finalize_content(self, context: AgentHookContext, content: str | None) -> str | None:
            events.append(("finalize_content", context.iteration, content))
            return content.upper() if content else content

    runner = AgentRunner(provider)
    result = await runner.run(AgentRunSpec(
        initial_messages=[],
        tools=tools,
        model="test-model",
        max_iterations=3,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
        hook=RecordingHook(),
    ))

    assert result.final_content == "DONE"
    assert events == [
        ("before_iteration", 0),
        ("before_execute_tools", 0, ["list_dir"]),
        (
            "after_iteration",
            0,
            None,
            ["tool result"],
            [{"name": "list_dir", "status": "ok", "detail": "tool result"}],
            None,
        ),
        ("before_iteration", 1),
        ("finalize_content", 1, "done"),
        ("after_iteration", 1, "DONE", [], [], "completed"),
    ]


@pytest.mark.asyncio
async def test_runner_streaming_hook_receives_deltas_and_end_signal():
    from nanobot.agent.hook import AgentHook, AgentHookContext
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    streamed: list[str] = []
    endings: list[bool] = []

    async def chat_stream_with_retry(*, on_content_delta, **kwargs):
        await on_content_delta("he")
        await on_content_delta("llo")
        return LLMResponse(content="hello", tool_calls=[], usage={})

    provider.chat_stream_with_retry = chat_stream_with_retry
    provider.chat_with_retry = AsyncMock()
    tools = MagicMock()
    tools.get_definitions.return_value = []

    class StreamingHook(AgentHook):
        def wants_streaming(self) -> bool:
            return True

        async def on_stream(self, context: AgentHookContext, delta: str) -> None:
            streamed.append(delta)

        async def on_stream_end(self, context: AgentHookContext, *, resuming: bool) -> None:
            endings.append(resuming)

    runner = AgentRunner(provider)
    result = await runner.run(AgentRunSpec(
        initial_messages=[],
        tools=tools,
        model="test-model",
        max_iterations=1,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
        hook=StreamingHook(),
    ))

    assert result.final_content == "hello"
    assert streamed == ["he", "llo"]
    assert endings == [False]
    provider.chat_with_retry.assert_not_awaited()


@pytest.mark.asyncio
async def test_runner_returns_max_iterations_fallback():
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    provider.chat_with_retry = AsyncMock(return_value=LLMResponse(
        content="still working",
        tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={"path": "."})],
    ))
    tools = MagicMock()
    tools.get_definitions.return_value = []
    tools.execute = AsyncMock(return_value="tool result")

    runner = AgentRunner(provider)
    result = await runner.run(AgentRunSpec(
        initial_messages=[],
        tools=tools,
        model="test-model",
        max_iterations=2,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
    ))

    assert result.stop_reason == "max_iterations"
    assert result.final_content == (
        "I reached the maximum number of tool call iterations (2) "
        "without completing the task. You can try breaking the task into smaller steps."
    )
    assert result.messages[-1]["role"] == "assistant"
    assert result.messages[-1]["content"] == result.final_content

@pytest.mark.asyncio
async def test_runner_returns_structured_tool_error():
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    provider.chat_with_retry = AsyncMock(return_value=LLMResponse(
        content="working",
        tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={})],
    ))
    tools = MagicMock()
    tools.get_definitions.return_value = []
    tools.execute = AsyncMock(side_effect=RuntimeError("boom"))

    runner = AgentRunner(provider)

    result = await runner.run(AgentRunSpec(
        initial_messages=[],
        tools=tools,
        model="test-model",
        max_iterations=2,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
        fail_on_tool_error=True,
    ))

    assert result.stop_reason == "tool_error"
    assert result.error == "Error: RuntimeError: boom"
    assert result.tool_events == [
        {"name": "list_dir", "status": "error", "detail": "boom"}
    ]


@pytest.mark.asyncio
async def test_runner_persists_large_tool_results_for_follow_up_calls(tmp_path):
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    captured_second_call: list[dict] = []
    call_count = {"n": 0}

    async def chat_with_retry(*, messages, **kwargs):
        call_count["n"] += 1
        if call_count["n"] == 1:
            return LLMResponse(
                content="working",
                tool_calls=[ToolCallRequest(id="call_big", name="list_dir", arguments={"path": "."})],
                usage={"prompt_tokens": 5, "completion_tokens": 3},
            )
        captured_second_call[:] = messages
        return LLMResponse(content="done", tool_calls=[], usage={})

    provider.chat_with_retry = chat_with_retry
    tools = MagicMock()
    tools.get_definitions.return_value = []
    tools.execute = AsyncMock(return_value="x" * 20_000)

    runner = AgentRunner(provider)
    result = await runner.run(AgentRunSpec(
        initial_messages=[{"role": "user", "content": "do task"}],
        tools=tools,
        model="test-model",
        max_iterations=2,
        workspace=tmp_path,
        session_key="test:runner",
        max_tool_result_chars=2048,
    ))

    assert result.final_content == "done"
    tool_message = next(msg for msg in captured_second_call if msg.get("role") == "tool")
    assert "[tool output persisted]" in tool_message["content"]
    assert "tool-results" in tool_message["content"]
    assert (tmp_path / ".nanobot" / "tool-results" / "test_runner" / "call_big.txt").exists()


def test_persist_tool_result_prunes_old_session_buckets(tmp_path):
    from nanobot.utils.helpers import maybe_persist_tool_result

    root = tmp_path / ".nanobot" / "tool-results"
    old_bucket = root / "old_session"
    recent_bucket = root / "recent_session"
    old_bucket.mkdir(parents=True)
    recent_bucket.mkdir(parents=True)
    (old_bucket / "old.txt").write_text("old", encoding="utf-8")
    (recent_bucket / "recent.txt").write_text("recent", encoding="utf-8")

    stale = time.time() - (8 * 24 * 60 * 60)
    os.utime(old_bucket, (stale, stale))
    os.utime(old_bucket / "old.txt", (stale, stale))

    persisted = maybe_persist_tool_result(
        tmp_path,
        "current:session",
        "call_big",
        "x" * 5000,
        max_chars=64,
    )

    assert "[tool output persisted]" in persisted
    assert not old_bucket.exists()
    assert recent_bucket.exists()
    assert (root / "current_session" / "call_big.txt").exists()


def test_persist_tool_result_leaves_no_temp_files(tmp_path):
    from nanobot.utils.helpers import maybe_persist_tool_result

    root = tmp_path / ".nanobot" / "tool-results"
    maybe_persist_tool_result(
        tmp_path,
        "current:session",
        "call_big",
        "x" * 5000,
        max_chars=64,
    )

    assert (root / "current_session" / "call_big.txt").exists()
    assert list((root / "current_session").glob("*.tmp")) == []


def test_persist_tool_result_logs_cleanup_failures(monkeypatch, tmp_path):
    from nanobot.utils.helpers import maybe_persist_tool_result

    warnings: list[str] = []

    monkeypatch.setattr(
        "nanobot.utils.helpers._cleanup_tool_result_buckets",
        lambda *_args, **_kwargs: (_ for _ in ()).throw(OSError("busy")),
    )
    monkeypatch.setattr(
        "nanobot.utils.helpers.logger.warning",
        lambda message, *args: warnings.append(message.format(*args)),
    )

    persisted = maybe_persist_tool_result(
        tmp_path,
        "current:session",
        "call_big",
        "x" * 5000,
        max_chars=64,
    )

    assert "[tool output persisted]" in persisted
    assert warnings and "Failed to clean stale tool result buckets" in warnings[0]


@pytest.mark.asyncio
async def test_runner_replaces_empty_tool_result_with_marker():
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    captured_second_call: list[dict] = []
    call_count = {"n": 0}

    async def chat_with_retry(*, messages, **kwargs):
        call_count["n"] += 1
        if call_count["n"] == 1:
            return LLMResponse(
                content="working",
                tool_calls=[ToolCallRequest(id="call_1", name="noop", arguments={})],
                usage={},
            )
        captured_second_call[:] = messages
        return LLMResponse(content="done", tool_calls=[], usage={})

    provider.chat_with_retry = chat_with_retry
    tools = MagicMock()
    tools.get_definitions.return_value = []
    tools.execute = AsyncMock(return_value="")

    runner = AgentRunner(provider)
    result = await runner.run(AgentRunSpec(
        initial_messages=[{"role": "user", "content": "do task"}],
        tools=tools,
        model="test-model",
        max_iterations=2,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
    ))

    assert result.final_content == "done"
    tool_message = next(msg for msg in captured_second_call if msg.get("role") == "tool")
    assert tool_message["content"] == "(noop completed with no output)"


@pytest.mark.asyncio
async def test_runner_uses_raw_messages_when_context_governance_fails():
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    captured_messages: list[dict] = []

    async def chat_with_retry(*, messages, **kwargs):
        captured_messages[:] = messages
        return LLMResponse(content="done", tool_calls=[], usage={})

    provider.chat_with_retry = chat_with_retry
    tools = MagicMock()
    tools.get_definitions.return_value = []
    initial_messages = [
        {"role": "system", "content": "system"},
        {"role": "user", "content": "hello"},
    ]

    runner = AgentRunner(provider)
    runner._snip_history = MagicMock(side_effect=RuntimeError("boom"))  # type: ignore[method-assign]
    result = await runner.run(AgentRunSpec(
        initial_messages=initial_messages,
        tools=tools,
        model="test-model",
        max_iterations=1,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
    ))

    assert result.final_content == "done"
    assert captured_messages == initial_messages


@pytest.mark.asyncio
async def test_runner_retries_empty_final_response_with_summary_prompt():
    """Empty responses get 2 silent retries before finalization kicks in."""
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    calls: list[dict] = []

    async def chat_with_retry(*, messages, tools=None, **kwargs):
        calls.append({"messages": messages, "tools": tools})
        if len(calls) <= 2:
            return LLMResponse(
                content=None,
                tool_calls=[],
                usage={"prompt_tokens": 5, "completion_tokens": 1},
            )
        return LLMResponse(
            content="final answer",
            tool_calls=[],
            usage={"prompt_tokens": 3, "completion_tokens": 7},
        )

    provider.chat_with_retry = chat_with_retry
    tools = MagicMock()
    tools.get_definitions.return_value = []

    runner = AgentRunner(provider)
    result = await runner.run(AgentRunSpec(
        initial_messages=[{"role": "user", "content": "do task"}],
        tools=tools,
        model="test-model",
        max_iterations=3,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
    ))

    assert result.final_content == "final answer"
    # 2 silent retries (iterations 0,1) + finalization on iteration 1
    assert len(calls) == 3
    assert calls[0]["tools"] is not None
    assert calls[1]["tools"] is not None
    assert calls[2]["tools"] is None
    assert result.usage["prompt_tokens"] == 13
    assert result.usage["completion_tokens"] == 9


@pytest.mark.asyncio
async def test_runner_uses_specific_message_after_empty_finalization_retry():
    """After silent retries + finalization all return empty, stop_reason is empty_final_response."""
    from nanobot.agent.runner import AgentRunSpec, AgentRunner
    from nanobot.utils.runtime import EMPTY_FINAL_RESPONSE_MESSAGE

    provider = MagicMock()

    async def chat_with_retry(*, messages, **kwargs):
        return LLMResponse(content=None, tool_calls=[], usage={})

    provider.chat_with_retry = chat_with_retry
    tools = MagicMock()
    tools.get_definitions.return_value = []

    runner = AgentRunner(provider)
    result = await runner.run(AgentRunSpec(
        initial_messages=[{"role": "user", "content": "do task"}],
        tools=tools,
        model="test-model",
        max_iterations=3,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
    ))

    assert result.final_content == EMPTY_FINAL_RESPONSE_MESSAGE
    assert result.stop_reason == "empty_final_response"


@pytest.mark.asyncio
async def test_runner_empty_response_does_not_break_tool_chain():
    """An empty intermediate response must not kill an ongoing tool chain.

    Sequence: tool_call → empty → tool_call → final text.
    The runner should recover via silent retry and complete normally.
    """
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    call_count = 0

    async def chat_with_retry(*, messages, tools=None, **kwargs):
        nonlocal call_count
        call_count += 1
        if call_count == 1:
            return LLMResponse(
                content=None,
                tool_calls=[ToolCallRequest(id="tc1", name="read_file", arguments={"path": "a.txt"})],
                usage={"prompt_tokens": 10, "completion_tokens": 5},
            )
        if call_count == 2:
            return LLMResponse(content=None, tool_calls=[], usage={"prompt_tokens": 10, "completion_tokens": 1})
        if call_count == 3:
            return LLMResponse(
                content=None,
                tool_calls=[ToolCallRequest(id="tc2", name="read_file", arguments={"path": "b.txt"})],
                usage={"prompt_tokens": 10, "completion_tokens": 5},
            )
        return LLMResponse(
            content="Here are the results.",
            tool_calls=[],
            usage={"prompt_tokens": 10, "completion_tokens": 10},
        )

    provider.chat_with_retry = chat_with_retry
    provider.chat_stream_with_retry = chat_with_retry

    async def fake_tool(name, args, **kw):
        return "file content"

    tool_registry = MagicMock()
    tool_registry.get_definitions.return_value = [{"type": "function", "function": {"name": "read_file"}}]
    tool_registry.execute = AsyncMock(side_effect=fake_tool)

    runner = AgentRunner(provider)
    result = await runner.run(AgentRunSpec(
        initial_messages=[{"role": "user", "content": "read both files"}],
        tools=tool_registry,
        model="test-model",
        max_iterations=10,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
    ))

    assert result.final_content == "Here are the results."
    assert result.stop_reason == "completed"
    assert call_count == 4
    assert "read_file" in result.tools_used


def test_snip_history_drops_orphaned_tool_results_from_trimmed_slice(monkeypatch):
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    tools = MagicMock()
    tools.get_definitions.return_value = []
    runner = AgentRunner(provider)
    messages = [
        {"role": "system", "content": "system"},
        {"role": "user", "content": "old user"},
        {
            "role": "assistant",
            "content": "tool call",
            "tool_calls": [{"id": "call_1", "type": "function", "function": {"name": "ls", "arguments": "{}"}}],
        },
        {"role": "tool", "tool_call_id": "call_1", "content": "tool output"},
        {"role": "assistant", "content": "after tool"},
    ]
    spec = AgentRunSpec(
        initial_messages=messages,
        tools=tools,
        model="test-model",
        max_iterations=1,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
        context_window_tokens=2000,
        context_block_limit=100,
    )

    monkeypatch.setattr("nanobot.agent.runner.estimate_prompt_tokens_chain", lambda *_args, **_kwargs: (500, None))
    token_sizes = {
        "old user": 120,
        "tool call": 120,
        "tool output": 40,
        "after tool": 40,
        "system": 0,
    }
    monkeypatch.setattr(
        "nanobot.agent.runner.estimate_message_tokens",
        lambda msg: token_sizes.get(str(msg.get("content")), 40),
    )

    trimmed = runner._snip_history(spec, messages)

    assert trimmed == [
        {"role": "system", "content": "system"},
        {"role": "assistant", "content": "after tool"},
    ]


@pytest.mark.asyncio
async def test_runner_keeps_going_when_tool_result_persistence_fails():
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    captured_second_call: list[dict] = []
    call_count = {"n": 0}

    async def chat_with_retry(*, messages, **kwargs):
        call_count["n"] += 1
        if call_count["n"] == 1:
            return LLMResponse(
                content="working",
                tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={"path": "."})],
                usage={"prompt_tokens": 5, "completion_tokens": 3},
            )
        captured_second_call[:] = messages
        return LLMResponse(content="done", tool_calls=[], usage={})

    provider.chat_with_retry = chat_with_retry
    tools = MagicMock()
    tools.get_definitions.return_value = []
    tools.execute = AsyncMock(return_value="tool result")

    runner = AgentRunner(provider)
    with patch("nanobot.agent.runner.maybe_persist_tool_result", side_effect=RuntimeError("disk full")):
        result = await runner.run(AgentRunSpec(
            initial_messages=[{"role": "user", "content": "do task"}],
            tools=tools,
            model="test-model",
            max_iterations=2,
            max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
        ))

    assert result.final_content == "done"
    tool_message = next(msg for msg in captured_second_call if msg.get("role") == "tool")
    assert tool_message["content"] == "tool result"


class _DelayTool(Tool):
    def __init__(self, name: str, *, delay: float, read_only: bool, shared_events: list[str]):
        self._name = name
        self._delay = delay
        self._read_only = read_only
        self._shared_events = shared_events

    @property
    def name(self) -> str:
        return self._name

    @property
    def description(self) -> str:
        return self._name

    @property
    def parameters(self) -> dict:
        return {"type": "object", "properties": {}, "required": []}

    @property
    def read_only(self) -> bool:
        return self._read_only

    async def execute(self, **kwargs):
        self._shared_events.append(f"start:{self._name}")
        await asyncio.sleep(self._delay)
        self._shared_events.append(f"end:{self._name}")
        return self._name


@pytest.mark.asyncio
async def test_runner_batches_read_only_tools_before_exclusive_work():
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    tools = ToolRegistry()
    shared_events: list[str] = []
    read_a = _DelayTool("read_a", delay=0.05, read_only=True, shared_events=shared_events)
    read_b = _DelayTool("read_b", delay=0.05, read_only=True, shared_events=shared_events)
    write_a = _DelayTool("write_a", delay=0.01, read_only=False, shared_events=shared_events)
    tools.register(read_a)
    tools.register(read_b)
    tools.register(write_a)

    runner = AgentRunner(MagicMock())
    await runner._execute_tools(
        AgentRunSpec(
            initial_messages=[],
            tools=tools,
            model="test-model",
            max_iterations=1,
            max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
            concurrent_tools=True,
        ),
        [
            ToolCallRequest(id="ro1", name="read_a", arguments={}),
            ToolCallRequest(id="ro2", name="read_b", arguments={}),
            ToolCallRequest(id="rw1", name="write_a", arguments={}),
        ],
        {},
    )

    assert shared_events[0:2] == ["start:read_a", "start:read_b"]
    assert "end:read_a" in shared_events and "end:read_b" in shared_events
    assert shared_events.index("end:read_a") < shared_events.index("start:write_a")
    assert shared_events.index("end:read_b") < shared_events.index("start:write_a")
    assert shared_events[-2:] == ["start:write_a", "end:write_a"]


@pytest.mark.asyncio
async def test_runner_blocks_repeated_external_fetches():
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    captured_final_call: list[dict] = []
    call_count = {"n": 0}

    async def chat_with_retry(*, messages, **kwargs):
        call_count["n"] += 1
        if call_count["n"] <= 3:
            return LLMResponse(
                content="working",
                tool_calls=[ToolCallRequest(id=f"call_{call_count['n']}", name="web_fetch", arguments={"url": "https://example.com"})],
                usage={},
            )
        captured_final_call[:] = messages
        return LLMResponse(content="done", tool_calls=[], usage={})

    provider.chat_with_retry = chat_with_retry
    tools = MagicMock()
    tools.get_definitions.return_value = []
    tools.execute = AsyncMock(return_value="page content")

    runner = AgentRunner(provider)
    result = await runner.run(AgentRunSpec(
        initial_messages=[{"role": "user", "content": "research task"}],
        tools=tools,
        model="test-model",
        max_iterations=4,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
    ))

    assert result.final_content == "done"
    assert tools.execute.await_count == 2
    blocked_tool_message = [
        msg for msg in captured_final_call
        if msg.get("role") == "tool" and msg.get("tool_call_id") == "call_3"
    ][0]
    assert "repeated external lookup blocked" in blocked_tool_message["content"]


@pytest.mark.asyncio
async def test_loop_max_iterations_message_stays_stable(tmp_path):
    loop = _make_loop(tmp_path)
    loop.provider.chat_with_retry = AsyncMock(return_value=LLMResponse(
        content="working",
        tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={})],
    ))
    loop.tools.get_definitions = MagicMock(return_value=[])
    loop.tools.execute = AsyncMock(return_value="ok")
    loop.max_iterations = 2

    final_content, _, _, _, _ = await loop._run_agent_loop([])

    assert final_content == (
        "I reached the maximum number of tool call iterations (2) "
        "without completing the task. You can try breaking the task into smaller steps."
    )


@pytest.mark.asyncio
async def test_loop_stream_filter_handles_think_only_prefix_without_crashing(tmp_path):
    loop = _make_loop(tmp_path)
    deltas: list[str] = []
    endings: list[bool] = []

    async def chat_stream_with_retry(*, on_content_delta, **kwargs):
        await on_content_delta("<think>hidden")
        await on_content_delta("</think>Hello")
        return LLMResponse(content="<think>hidden</think>Hello", tool_calls=[], usage={})

    loop.provider.chat_stream_with_retry = chat_stream_with_retry

    async def on_stream(delta: str) -> None:
        deltas.append(delta)

    async def on_stream_end(*, resuming: bool = False) -> None:
        endings.append(resuming)

    final_content, _, _, _, _ = await loop._run_agent_loop(
        [],
        on_stream=on_stream,
        on_stream_end=on_stream_end,
    )

    assert final_content == "Hello"
    assert deltas == ["Hello"]
    assert endings == [False]


@pytest.mark.asyncio
async def test_loop_retries_think_only_final_response(tmp_path):
    loop = _make_loop(tmp_path)
    call_count = {"n": 0}

    async def chat_with_retry(**kwargs):
        call_count["n"] += 1
        if call_count["n"] == 1:
            return LLMResponse(content="<think>hidden</think>", tool_calls=[], usage={})
        return LLMResponse(content="Recovered answer", tool_calls=[], usage={})

    loop.provider.chat_with_retry = chat_with_retry

    final_content, _, _, _, _ = await loop._run_agent_loop([])

    assert final_content == "Recovered answer"
    assert call_count["n"] == 2


@pytest.mark.asyncio
async def test_runner_tool_error_sets_final_content():
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()

    async def chat_with_retry(*, messages, **kwargs):
        return LLMResponse(
            content="working",
            tool_calls=[ToolCallRequest(id="call_1", name="read_file", arguments={"path": "x"})],
            usage={},
        )

    provider.chat_with_retry = chat_with_retry
    tools = MagicMock()
    tools.get_definitions.return_value = []
    tools.execute = AsyncMock(side_effect=RuntimeError("boom"))

    runner = AgentRunner(provider)
    result = await runner.run(AgentRunSpec(
        initial_messages=[{"role": "user", "content": "do task"}],
        tools=tools,
        model="test-model",
        max_iterations=1,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
        fail_on_tool_error=True,
    ))

    assert result.final_content == "Error: RuntimeError: boom"
    assert result.stop_reason == "tool_error"


@pytest.mark.asyncio
async def test_subagent_max_iterations_announces_existing_fallback(tmp_path, monkeypatch):
    from nanobot.agent.subagent import SubagentManager
    from nanobot.bus.queue import MessageBus

    bus = MessageBus()
    provider = MagicMock()
    provider.get_default_model.return_value = "test-model"
    provider.chat_with_retry = AsyncMock(return_value=LLMResponse(
        content="working",
        tool_calls=[ToolCallRequest(id="call_1", name="list_dir", arguments={"path": "."})],
    ))
    mgr = SubagentManager(
        provider=provider,
        workspace=tmp_path,
        bus=bus,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
    )
    mgr._announce_result = AsyncMock()

    async def fake_execute(self, **kwargs):
        return "tool result"

    monkeypatch.setattr("nanobot.agent.tools.filesystem.ListDirTool.execute", fake_execute)

    await mgr._run_subagent("sub-1", "do task", "label", {"channel": "test", "chat_id": "c1"})

    mgr._announce_result.assert_awaited_once()
    args = mgr._announce_result.await_args.args
    assert args[3] == "Task completed but no final response was generated."
    assert args[5] == "ok"


@pytest.mark.asyncio
async def test_runner_accumulates_usage_and_preserves_cached_tokens():
    """Runner should accumulate prompt/completion tokens across iterations
    and preserve cached_tokens from provider responses."""
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    call_count = {"n": 0}

    async def chat_with_retry(*, messages, **kwargs):
        call_count["n"] += 1
        if call_count["n"] == 1:
            return LLMResponse(
                content="thinking",
                tool_calls=[ToolCallRequest(id="call_1", name="read_file", arguments={"path": "x"})],
                usage={"prompt_tokens": 100, "completion_tokens": 10, "cached_tokens": 80},
            )
        return LLMResponse(
            content="done",
            tool_calls=[],
            usage={"prompt_tokens": 200, "completion_tokens": 20, "cached_tokens": 150},
        )

    provider.chat_with_retry = chat_with_retry
    tools = MagicMock()
    tools.get_definitions.return_value = []
    tools.execute = AsyncMock(return_value="file content")

    runner = AgentRunner(provider)
    result = await runner.run(AgentRunSpec(
        initial_messages=[{"role": "user", "content": "do task"}],
        tools=tools,
        model="test-model",
        max_iterations=3,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
    ))

    # Usage should be accumulated across iterations
    assert result.usage["prompt_tokens"] == 300  # 100 + 200
    assert result.usage["completion_tokens"] == 30  # 10 + 20
    assert result.usage["cached_tokens"] == 230  # 80 + 150


@pytest.mark.asyncio
async def test_runner_passes_cached_tokens_to_hook_context():
    """Hook context.usage should contain cached_tokens."""
    from nanobot.agent.hook import AgentHook, AgentHookContext
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    captured_usage: list[dict] = []

    class UsageHook(AgentHook):
        async def after_iteration(self, context: AgentHookContext) -> None:
            captured_usage.append(dict(context.usage))

    async def chat_with_retry(**kwargs):
        return LLMResponse(
            content="done",
            tool_calls=[],
            usage={"prompt_tokens": 200, "completion_tokens": 20, "cached_tokens": 150},
        )

    provider.chat_with_retry = chat_with_retry
    tools = MagicMock()
    tools.get_definitions.return_value = []

    runner = AgentRunner(provider)
    await runner.run(AgentRunSpec(
        initial_messages=[],
        tools=tools,
        model="test-model",
        max_iterations=1,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
        hook=UsageHook(),
    ))

    assert len(captured_usage) == 1
    assert captured_usage[0]["cached_tokens"] == 150


# ── Mid-turn injection tests ──────────────────────────────────────────────


@pytest.mark.asyncio
async def test_drain_injections_returns_empty_when_no_callback():
    """No injection_callback → empty list."""
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    runner = AgentRunner(provider)
    tools = MagicMock()
    tools.get_definitions.return_value = []
    spec = AgentRunSpec(
        initial_messages=[], tools=tools, model="m",
        max_iterations=1, max_tool_result_chars=1000,
        injection_callback=None,
    )
    result = await runner._drain_injections(spec)
    assert result == []


@pytest.mark.asyncio
async def test_drain_injections_extracts_content_from_inbound_messages():
    """Should extract .content from InboundMessage objects."""
    from nanobot.agent.runner import AgentRunSpec, AgentRunner, _MAX_INJECTIONS_PER_TURN
    from nanobot.bus.events import InboundMessage

    provider = MagicMock()
    runner = AgentRunner(provider)
    tools = MagicMock()
    tools.get_definitions.return_value = []

    msgs = [
        InboundMessage(channel="cli", sender_id="u", chat_id="c", content="hello"),
        InboundMessage(channel="cli", sender_id="u", chat_id="c", content="world"),
    ]

    async def cb():
        return msgs

    spec = AgentRunSpec(
        initial_messages=[], tools=tools, model="m",
        max_iterations=1, max_tool_result_chars=1000,
        injection_callback=cb,
    )
    result = await runner._drain_injections(spec)
    assert result == ["hello", "world"]


@pytest.mark.asyncio
async def test_drain_injections_caps_at_max_and_logs_warning():
    """When more than _MAX_INJECTIONS_PER_TURN items, only the last N are kept."""
    from nanobot.agent.runner import AgentRunSpec, AgentRunner, _MAX_INJECTIONS_PER_TURN
    from nanobot.bus.events import InboundMessage

    provider = MagicMock()
    runner = AgentRunner(provider)
    tools = MagicMock()
    tools.get_definitions.return_value = []

    msgs = [
        InboundMessage(channel="cli", sender_id="u", chat_id="c", content=f"msg{i}")
        for i in range(_MAX_INJECTIONS_PER_TURN + 3)
    ]

    async def cb():
        return msgs

    spec = AgentRunSpec(
        initial_messages=[], tools=tools, model="m",
        max_iterations=1, max_tool_result_chars=1000,
        injection_callback=cb,
    )
    result = await runner._drain_injections(spec)
    assert len(result) == _MAX_INJECTIONS_PER_TURN
    # Should keep the LAST _MAX_INJECTIONS_PER_TURN items
    assert result[0] == "msg3"
    assert result[-1] == f"msg{_MAX_INJECTIONS_PER_TURN + 2}"


@pytest.mark.asyncio
async def test_drain_injections_skips_empty_content():
    """Messages with blank content should be filtered out."""
    from nanobot.agent.runner import AgentRunSpec, AgentRunner
    from nanobot.bus.events import InboundMessage

    provider = MagicMock()
    runner = AgentRunner(provider)
    tools = MagicMock()
    tools.get_definitions.return_value = []

    msgs = [
        InboundMessage(channel="cli", sender_id="u", chat_id="c", content=""),
        InboundMessage(channel="cli", sender_id="u", chat_id="c", content="   "),
        InboundMessage(channel="cli", sender_id="u", chat_id="c", content="valid"),
    ]

    async def cb():
        return msgs

    spec = AgentRunSpec(
        initial_messages=[], tools=tools, model="m",
        max_iterations=1, max_tool_result_chars=1000,
        injection_callback=cb,
    )
    result = await runner._drain_injections(spec)
    assert result == ["valid"]


@pytest.mark.asyncio
async def test_drain_injections_handles_callback_exception():
    """If the callback raises, return empty list (error is logged)."""
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()
    runner = AgentRunner(provider)
    tools = MagicMock()
    tools.get_definitions.return_value = []

    async def cb():
        raise RuntimeError("boom")

    spec = AgentRunSpec(
        initial_messages=[], tools=tools, model="m",
        max_iterations=1, max_tool_result_chars=1000,
        injection_callback=cb,
    )
    result = await runner._drain_injections(spec)
    assert result == []


@pytest.mark.asyncio
async def test_checkpoint1_injects_after_tool_execution():
    """Follow-up messages are injected after tool execution, before next LLM call."""
    from nanobot.agent.runner import AgentRunSpec, AgentRunner
    from nanobot.bus.events import InboundMessage

    provider = MagicMock()
    call_count = {"n": 0}
    captured_messages = []

    async def chat_with_retry(*, messages, **kwargs):
        call_count["n"] += 1
        captured_messages.append(list(messages))
        if call_count["n"] == 1:
            return LLMResponse(
                content="using tool",
                tool_calls=[ToolCallRequest(id="c1", name="read_file", arguments={"path": "x"})],
                usage={},
            )
        return LLMResponse(content="final answer", tool_calls=[], usage={})

    provider.chat_with_retry = chat_with_retry
    tools = MagicMock()
    tools.get_definitions.return_value = []
    tools.execute = AsyncMock(return_value="file content")

    injection_queue = asyncio.Queue()

    async def inject_cb():
        items = []
        while not injection_queue.empty():
            items.append(await injection_queue.get())
        return items

    # Put a follow-up message in the queue before the run starts
    await injection_queue.put(
        InboundMessage(channel="cli", sender_id="u", chat_id="c", content="follow-up question")
    )

    runner = AgentRunner(provider)
    result = await runner.run(AgentRunSpec(
        initial_messages=[{"role": "user", "content": "hello"}],
        tools=tools,
        model="test-model",
        max_iterations=5,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
        injection_callback=inject_cb,
    ))

    assert result.had_injections is True
    assert result.final_content == "final answer"
    # The second call should have the injected user message
    assert call_count["n"] == 2
    last_messages = captured_messages[-1]
    injected = [m for m in last_messages if m.get("role") == "user" and m.get("content") == "follow-up question"]
    assert len(injected) == 1


@pytest.mark.asyncio
async def test_checkpoint2_injects_after_final_response_with_resuming_stream():
    """After final response, if injections exist, stream_end should get resuming=True."""
    from nanobot.agent.runner import AgentRunSpec, AgentRunner
    from nanobot.agent.hook import AgentHook, AgentHookContext
    from nanobot.bus.events import InboundMessage

    provider = MagicMock()
    call_count = {"n": 0}
    stream_end_calls = []

    class TrackingHook(AgentHook):
        def wants_streaming(self) -> bool:
            return True

        async def on_stream_end(self, context: AgentHookContext, *, resuming: bool) -> None:
            stream_end_calls.append(resuming)

        def finalize_content(self, context: AgentHookContext, content: str | None) -> str | None:
            return content

    async def chat_stream_with_retry(*, messages, on_content_delta=None, **kwargs):
        call_count["n"] += 1
        if call_count["n"] == 1:
            return LLMResponse(content="first answer", tool_calls=[], usage={})
        return LLMResponse(content="second answer", tool_calls=[], usage={})

    provider.chat_stream_with_retry = chat_stream_with_retry
    tools = MagicMock()
    tools.get_definitions.return_value = []

    injection_queue = asyncio.Queue()

    async def inject_cb():
        items = []
        while not injection_queue.empty():
            items.append(await injection_queue.get())
        return items

    # Inject a follow-up that arrives during the first response
    await injection_queue.put(
        InboundMessage(channel="cli", sender_id="u", chat_id="c", content="quick follow-up")
    )

    runner = AgentRunner(provider)
    result = await runner.run(AgentRunSpec(
        initial_messages=[{"role": "user", "content": "hello"}],
        tools=tools,
        model="test-model",
        max_iterations=5,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
        hook=TrackingHook(),
        injection_callback=inject_cb,
    ))

    assert result.had_injections is True
    assert result.final_content == "second answer"
    assert call_count["n"] == 2
    # First stream_end should have resuming=True (because injections found)
    assert stream_end_calls[0] is True
    # Second (final) stream_end should have resuming=False
    assert stream_end_calls[-1] is False


@pytest.mark.asyncio
async def test_injection_cycles_capped_at_max():
    """Injection cycles should be capped at _MAX_INJECTION_CYCLES."""
    from nanobot.agent.runner import AgentRunSpec, AgentRunner, _MAX_INJECTION_CYCLES
    from nanobot.bus.events import InboundMessage

    provider = MagicMock()
    call_count = {"n": 0}

    async def chat_with_retry(*, messages, **kwargs):
        call_count["n"] += 1
        return LLMResponse(content=f"answer-{call_count['n']}", tool_calls=[], usage={})

    provider.chat_with_retry = chat_with_retry
    tools = MagicMock()
    tools.get_definitions.return_value = []

    drain_count = {"n": 0}

    async def inject_cb():
        drain_count["n"] += 1
        # Only inject for the first _MAX_INJECTION_CYCLES drains
        if drain_count["n"] <= _MAX_INJECTION_CYCLES:
            return [InboundMessage(channel="cli", sender_id="u", chat_id="c", content=f"msg-{drain_count['n']}")]
        return []

    runner = AgentRunner(provider)
    result = await runner.run(AgentRunSpec(
        initial_messages=[{"role": "user", "content": "start"}],
        tools=tools,
        model="test-model",
        max_iterations=20,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
        injection_callback=inject_cb,
    ))

    assert result.had_injections is True
    # Should be capped: _MAX_INJECTION_CYCLES injection rounds + 1 final round
    assert call_count["n"] == _MAX_INJECTION_CYCLES + 1


@pytest.mark.asyncio
async def test_no_injections_flag_is_false_by_default():
    """had_injections should be False when no injection callback or no messages."""
    from nanobot.agent.runner import AgentRunSpec, AgentRunner

    provider = MagicMock()

    async def chat_with_retry(**kwargs):
        return LLMResponse(content="done", tool_calls=[], usage={})

    provider.chat_with_retry = chat_with_retry
    tools = MagicMock()
    tools.get_definitions.return_value = []

    runner = AgentRunner(provider)
    result = await runner.run(AgentRunSpec(
        initial_messages=[{"role": "user", "content": "hi"}],
        tools=tools,
        model="test-model",
        max_iterations=1,
        max_tool_result_chars=_MAX_TOOL_RESULT_CHARS,
    ))

    assert result.had_injections is False


@pytest.mark.asyncio
async def test_pending_queue_cleanup_on_dispatch(tmp_path):
    """_pending_queues should be cleaned up after _dispatch completes."""
    loop = _make_loop(tmp_path)

    async def chat_with_retry(**kwargs):
        return LLMResponse(content="done", tool_calls=[], usage={})

    loop.provider.chat_with_retry = chat_with_retry

    from nanobot.bus.events import InboundMessage

    msg = InboundMessage(channel="cli", sender_id="u", chat_id="c", content="hello")
    # The queue should not exist before dispatch
    assert msg.session_key not in loop._pending_queues

    await loop._dispatch(msg)

    # The queue should be cleaned up after dispatch
    assert msg.session_key not in loop._pending_queues


@pytest.mark.asyncio
async def test_followup_routed_to_pending_queue(tmp_path):
    """When a session has an active dispatch, follow-up messages go to pending queue."""
    from nanobot.bus.events import InboundMessage

    loop = _make_loop(tmp_path)

    # Simulate an active dispatch by manually adding a pending queue
    pending = asyncio.Queue(maxsize=20)
    loop._pending_queues["cli:c"] = pending

    msg = InboundMessage(channel="cli", sender_id="u", chat_id="c", content="follow-up")

    # Directly test the routing logic from run() — if session_key is in
    # _pending_queues, the message should be put into the queue.
    assert msg.session_key in loop._pending_queues
    loop._pending_queues[msg.session_key].put_nowait(msg)

    assert not pending.empty()
    queued_msg = pending.get_nowait()
    assert queued_msg.content == "follow-up"


@pytest.mark.asyncio
async def test_dispatch_republishes_leftover_queue_messages(tmp_path):
    """Messages left in the pending queue after _dispatch are re-published to the bus.

    This tests the finally-block cleanup that prevents message loss when
    the runner exits early (e.g., max_iterations, tool_error) with messages
    still in the queue.
    """
    from nanobot.bus.events import InboundMessage

    loop = _make_loop(tmp_path)
    bus = loop.bus

    # Simulate a completed dispatch by manually registering a queue
    # with leftover messages, then running the cleanup logic directly.
    pending = asyncio.Queue(maxsize=20)
    session_key = "cli:c"
    loop._pending_queues[session_key] = pending
    pending.put_nowait(InboundMessage(channel="cli", sender_id="u", chat_id="c", content="leftover-1"))
    pending.put_nowait(InboundMessage(channel="cli", sender_id="u", chat_id="c", content="leftover-2"))

    # Execute the cleanup logic from the finally block
    queue = loop._pending_queues.pop(session_key, None)
    assert queue is not None
    leftover = 0
    while True:
        try:
            item = queue.get_nowait()
        except asyncio.QueueEmpty:
            break
        await bus.publish_inbound(item)
        leftover += 1

    assert leftover == 2

    # Verify the messages are now on the bus
    msgs = []
    while not bus.inbound.empty():
        msgs.append(await asyncio.wait_for(bus.consume_inbound(), timeout=0.5))
    contents = [m.content for m in msgs]
    assert "leftover-1" in contents
    assert "leftover-2" in contents