nisse/nisse-dev/tests/test_planner.py

import io
from pathlib import Path
from typing import Any

import pytest

from nisse_dev.canonical import (
    AssistantText,
    CanonicalEvent,
    PlanSubmitted,
    StoredEvent,
    ToolCall,
    UserMessage,
)
from nisse_dev.control import AskHuman, Orchestrator, PlanningError, SubmitPlan
from nisse_dev.llm import RoleMap
from nisse_dev.oplog import OpLog
from nisse_dev.planner import PLANNER_TOOLS, LLMPlanningAgent
from nisse_dev.state import StateStore
from nisse_dev.transport import InMemoryTransport

ROLES = RoleMap(
    executor="local/qwen",
    planner="anthropic/claude-opus-5",
    delegate="anthropic/claude-opus-5",
    consultant="anthropic/claude-opus-5",
)


class ScriptedClient:
    """Returns canned canonical events per call, recording what it was asked."""

    def __init__(self, turns: list[list[CanonicalEvent]]) -> None:
        self._turns = turns
        self.calls: list[dict[str, Any]] = []

    def complete(
        self,
        events: list[StoredEvent],
        tools: list[dict[str, Any]] | None,
        model: str,
        system: str | None = None,
    ) -> list[CanonicalEvent]:
        self.calls.append(
            {"events": events, "tools": tools, "model": model, "system": system}
        )
        if not self._turns:
            return []
        return self._turns.pop(0)


def plan_call(content: str, gate: str = "arch") -> ToolCall:
    return ToolCall(
        call_id="call-1",
        tool="submit_plan",
        arguments={"type": gate, "content": content},
    )


@pytest.fixture
def store(tmp_path: Path) -> StateStore:
    return StateStore(tmp_path / "state.db", oplog=OpLog("nisse-dev", io.StringIO()))


def test_submit_plan_tool_call_becomes_submit_plan(store: StateStore) -> None:
    client = ScriptedClient([[plan_call("step 1: create src/app.py")]])
    agent = LLMPlanningAgent(client, store, ROLES)
    action = agent.plan("arch", "demo")
    assert action == SubmitPlan("step 1: create src/app.py")


def test_ask_human_tool_call_becomes_ask_human(store: StateStore) -> None:
    client = ScriptedClient(
        [
            [
                ToolCall(
                    call_id="call-1",
                    tool="ask_human",
                    arguments={"question": "postgres or sqlite?"},
                )
            ]
        ]
    )
    action = LLMPlanningAgent(client, store, ROLES).plan("arch", "demo")
    assert action == AskHuman("postgres or sqlite?")


def test_planner_role_and_tools_are_used(store: StateStore) -> None:
    client = ScriptedClient([[plan_call("plan")]])
    LLMPlanningAgent(client, store, ROLES).plan("arch", "demo")
    call = client.calls[0]
    assert call["model"] == "anthropic/claude-opus-5"  # never the executor
    assert [t["function"]["name"] for t in call["tools"]] == [
        "submit_plan",
        "ask_human",
    ]
    assert "demo" in call["system"] and "arch" in call["system"]


def test_no_proceed_tool_is_offered() -> None:
    names = {tool["function"]["name"] for tool in PLANNER_TOOLS}
    assert names == {"submit_plan", "ask_human"}  # no self-advancement path


def test_model_output_is_appended_to_the_log(store: StateStore) -> None:
    client = ScriptedClient(
        [[AssistantText(text="thinking out loud", model="m"), plan_call("the plan")]]
    )
    LLMPlanningAgent(client, store, ROLES).plan("arch", "demo")
    kinds = [type(e.event).__name__ for e in store.read_events()]
    assert kinds == ["AssistantText", "ToolCall"]


def test_prose_only_turn_retries_once_then_raises(store: StateStore) -> None:
    client = ScriptedClient(
        [
            [AssistantText(text="here's what I think", model="m")],
            [AssistantText(text="still prose", model="m")],
        ]
    )
    with pytest.raises(PlanningError, match="no submit_plan or ask_human"):
        LLMPlanningAgent(client, store, ROLES).plan("arch", "demo")
    assert len(client.calls) == 2
    assert "did not call a tool" in client.calls[1]["system"]


def test_retry_succeeds_after_prose(store: StateStore) -> None:
    client = ScriptedClient(
        [[AssistantText(text="prose", model="m")], [plan_call("the real plan")]]
    )
    action = LLMPlanningAgent(client, store, ROLES).plan("arch", "demo")
    assert action == SubmitPlan("the real plan")


def test_malformed_tool_arguments_are_rejected(store: StateStore) -> None:
    blank = ToolCall(call_id="c", tool="submit_plan", arguments={"content": "   "})
    client = ScriptedClient([[blank], [blank]])
    with pytest.raises(PlanningError):
        LLMPlanningAgent(client, store, ROLES).plan("arch", "demo")


def test_planner_sees_prior_transcript(store: StateStore) -> None:
    store.append_event(UserMessage(text="use demo", source="transport:@don"))
    client = ScriptedClient([[plan_call("plan")]])
    LLMPlanningAgent(client, store, ROLES).plan("arch", "demo")
    seen = [e.event for e in client.calls[0]["events"]]
    assert UserMessage(text="use demo", source="transport:@don") in seen


# --- orchestrator integration (still no network) -------------------------


def build(tmp_path: Path, client: ScriptedClient) -> tuple[Orchestrator, StateStore]:
    store = StateStore(tmp_path / "state.db", oplog=OpLog("nisse-dev", io.StringIO()))
    transport = InMemoryTransport()
    agent = LLMPlanningAgent(client, store, ROLES)
    orch = Orchestrator(store, transport, agent, operators={"don"}, roles=ROLES)
    orch.start()
    return orch, store, transport


def test_use_project_produces_llm_plan_and_transcript(tmp_path: Path) -> None:
    client = ScriptedClient([[plan_call("1. create app.py\n2. add a test")]])
    _, store, transport = build(tmp_path, client)
    transport.receive("don", "use demo")

    request = transport.outbound[-1]
    assert request.kind == "approval_request"
    assert "create app.py" in request.text

    events = [e.event for e in store.read_events()]
    kinds = [type(e).__name__ for e in events]
    assert kinds == ["UserMessage", "ToolCall", "PlanSubmitted", "GateOpened"]
    submitted = next(e for e in events if isinstance(e, PlanSubmitted))
    assert submitted.model == "anthropic/claude-opus-5"  # pinned planner


def test_roles_pinned_at_task_start(tmp_path: Path) -> None:
    client = ScriptedClient([[plan_call("plan")]])
    _, store, transport = build(tmp_path, client)
    transport.receive("don", "use demo")
    state = store.get_state()
    assert state is not None
    assert state.data["roles"]["planner"] == "anthropic/claude-opus-5"


def test_planning_failure_gates_to_await_input(tmp_path: Path) -> None:
    prose = [AssistantText(text="prose", model="m")]
    client = ScriptedClient([prose, prose])
    _, store, transport = build(tmp_path, client)
    transport.receive("don", "use demo")
    state = store.get_state()
    assert state is not None
    assert state.state == "AWAIT_INPUT"
    assert transport.outbound[-1].kind == "question"


def test_rejection_feedback_reaches_the_planner_via_the_log(
    tmp_path: Path,
) -> None:
    client = ScriptedClient([[plan_call("v1")], [plan_call("v2")]])
    _, _store, transport = build(tmp_path, client)
    transport.receive("don", "use demo")
    transport.receive("don", "too vague, name the files")

    replan_context = [e.event for e in client.calls[1]["events"]]
    assert any(
        isinstance(e, UserMessage) and "too vague" in e.text for e in replan_context
    )
    assert "v2" in transport.outbound[-1].text