orchestrator/agent-team/agent_team/graph.py

"""LangGraph graph wiring for the Plane-2 SDLC pipeline (design §3.3, §7.1 P1).

This module is the **P1 skeleton + human gate** wiring (§7.1):

    INTAKE ─► CLARIFY ─► PLAN  (stop at an approved plan — no build yet)

It assembles the durable, resumable LangGraph graph whose state schema is the
foundation's :class:`~agent_team.task_model.PipelineState`. The clarifier raises
a LangGraph ``interrupt()`` carrying a :class:`~agent_team.transport.QuestionSet`
so the task suspends + checkpoints, a question-set is delivered over the chosen
transport (D10), and the task resumes via ``Command(resume=...)`` when Adam's
answer arrives (§3.3, §3.3.1). The planner is the P1 terminal stage: it lands an
approved plan and stops; builders/verifiers are later phases (P3).

What this module owns (Plane-2 P1 graph wiring only):

* :data:`P1_PHASE_SEQUENCE` — the ordered P1 stage list.
* the three pure node functions (:func:`intake_node`, :func:`clarify_node`,
  :func:`plan_node`) operating on :class:`PipelineState`.
* :func:`build_graph` — assemble + compile the ``StateGraph`` over a *caller-
  injected* checkpointer (tests inject an in-memory saver; production injects
  the SQLite saver, which the foundation's :func:`agent_team.db.connect`
  reserves the DB file for).
* :func:`build_sqlite_checkpointer` — the production checkpointer factory, with
  the ``langgraph.checkpoint.sqlite`` import deferred so this module imports
  cleanly even where that optional package is absent (pre-deploy scaffolding).
* :func:`thread_config` / :func:`start_task` / :func:`resume_task` /
  :func:`get_pipeline_state` / :func:`pending_question` — the thin
  ``thread_id``-keyed driver seam the coordinator/responder call.

It imports the committed foundation contracts verbatim and does **not** redefine
them. No provisioning, no scheduling, no live SDK calls: the clarifier's
question authoring is a deterministic stub here (the real Claude clarifier binds
``billing.claude_invoke`` in a later phase), and the human-interaction *ledger*
(``pending_questions``) lives in :mod:`agent_team.db.schema` — this module only
shapes the interrupt payload that drives it.
"""

from __future__ import annotations

import uuid
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import TYPE_CHECKING, Any

from langgraph.graph import END, START, StateGraph
from langgraph.types import Command, interrupt

from agent_team.task_model import (
    Phase,
    PipelineState,
    TaskStatus,
    new_thread_id,
)
from agent_team.transport import QuestionSet

if TYPE_CHECKING:  # pragma: no cover - typing only
    from langgraph.checkpoint.base import BaseCheckpointSaver
    from langgraph.graph.state import CompiledStateGraph

__all__ = [
    "CLARIFY",
    "DEFAULT_CLARIFY_DEADLINE",
    "INTAKE",
    "P1_PHASE_SEQUENCE",
    "PLAN",
    "build_graph",
    "build_sqlite_checkpointer",
    "clarify_node",
    "get_pipeline_state",
    "intake_node",
    "pending_question",
    "plan_node",
    "plan_phase",
    "resume_task",
    "start_task",
    "thread_config",
]

# --- Node names (graph vertices). ------------------------------------------
# Kept as constants so the driver/tests reference the wiring by name rather
# than by string literal.
INTAKE = "intake"
CLARIFY = "clarify"
PLAN = "plan"

# The P1 stage order (§7.1): intake -> clarify -> plan, then stop. Builders and
# verifiers (BUILD/VERIFY) are deliberately NOT wired here — P1 ends at an
# approved plan with no build (§7.1 "Stops at an approved plan, no build yet").
P1_PHASE_SEQUENCE: tuple[Phase, ...] = (Phase.INTAKE, Phase.CLARIFY, Phase.PLAN)

# How long a clarifier question-set stays open before the deadline policy runs
# (§3.3.1 ``deadline_at``). The driver records the concrete ``deadline_at`` on
# the ledger row; this is only the default window the interrupt advertises.
DEFAULT_CLARIFY_DEADLINE = timedelta(hours=24)

# Fixed namespace for deriving a STABLE question_id from (thread_id, turn). The
# clarifier node re-executes from its start on resume (LangGraph replays the
# node, with interrupt() returning the answer the second time), so a fresh
# random id would change between the suspend that delivered/ledgered the
# question and the resume that records it — breaking the §3.3.1 identity
# contract. A uuid5 over (thread_id, turn) is uuid-shaped yet deterministic, so
# the delivered question_id, the ledger key, and the qa_history entry all agree.
_QUESTION_ID_NAMESPACE = uuid.UUID("a7b9c1d2-3e4f-5061-7283-94a5b6c7d8e9")


def _question_id_for(thread_id: str, turn: int) -> str:
    """Return the stable question_id for ``(thread_id, turn)`` (§3.3.1 identity)."""
    return uuid.uuid5(_QUESTION_ID_NAMESPACE, f"{thread_id}:{turn}").hex


def _utc_now_iso() -> str:
    """Return the current UTC time as an ISO-8601 string (ledger-compatible)."""
    return datetime.now(timezone.utc).isoformat()


def _phase_value(phase: Phase) -> str:
    """Return the string value a phase is stored as in :class:`PipelineState`."""
    return phase.value


# --- Nodes. -----------------------------------------------------------------
# Each node is a pure ``PipelineState -> partial PipelineState`` function. They
# write only the keys they change (PipelineState is ``total=False``), so a
# checkpoint transition stays minimal. None of them performs I/O.


def intake_node(state: PipelineState) -> PipelineState:
    """INTAKE stage: stamp the task ACTIVE and advance it into CLARIFY (§3.3).

    A task enters as a new thread record (the driver mints ``thread_id`` and
    seeds INTAKE). This node marks it ``ACTIVE`` and moves the current phase to
    ``CLARIFY`` so the next node runs the human gate. It never blocks.
    """
    return PipelineState(
        status=TaskStatus.ACTIVE.value,
        current_phase=_phase_value(Phase.CLARIFY),
        updated_at=_utc_now_iso(),
    )


def clarify_node(state: PipelineState) -> PipelineState:
    """CLARIFY stage: the human gate (LangGraph ``interrupt()``) (§3.3, §3.3.1).

    Authors a question-set, then suspends the graph with ``interrupt()`` so the
    task checkpoints and waits for Adam's answer. The interrupt payload is the
    :class:`~agent_team.transport.QuestionSet` plus the lifecycle metadata the
    responder/ledger need (``transport``, ``deadline``); the responder posts it
    over the chosen transport and resumes the task via ``Command(resume=...)``.

    On resume, ``interrupt()`` returns Adam's answer; this node appends it to
    ``qa_history`` and advances to ``PLAN``. The P1 skeleton asks exactly one
    question-set (``turn`` 0); the multi-turn "until 98% confident" loop is a
    later phase, and this node's single-turn shape is forward-compatible with it
    (the ``turn`` is read from existing history).

    The actual Claude question authoring (``billing.claude_invoke``) is bound in
    a later phase; here the question-set is a deterministic stub so the wiring
    and the suspend/resume mechanic can be proven without a live model.
    """
    history = list(state.get("qa_history", []))
    turn = len(history)
    thread_id = state.get("thread_id", "")
    transport = state.get("transport", "")

    # Stable across the resume replay of this node (see _question_id_for): the
    # id delivered at suspend == the ledger key == the qa_history entry.
    question_id = _question_id_for(thread_id, turn)
    question_set = QuestionSet(
        thread_id=thread_id,
        question_id=question_id,
        turn=turn,
        questions=_author_questions(state),
        context={"phase": _phase_value(Phase.CLARIFY)},
    )
    deadline = (datetime.now(timezone.utc) + DEFAULT_CLARIFY_DEADLINE).isoformat()

    # Suspend here. The payload mirrors §3.3.1: {thread_id, question_id, turn,
    # question_set, transport, deadline}. On resume, ``answer`` is whatever the
    # responder passed to ``Command(resume=...)``.
    answer = interrupt(
        {
            "thread_id": thread_id,
            "question_id": question_id,
            "turn": turn,
            "question_set": question_set,
            "transport": transport,
            "deadline": deadline,
        }
    )

    history.append({"turn": turn, "question_id": question_id, "answer": answer})
    return PipelineState(
        status=TaskStatus.ACTIVE.value,
        current_phase=_phase_value(Phase.PLAN),
        qa_history=history,
        updated_at=_utc_now_iso(),
    )


def plan_node(state: PipelineState) -> PipelineState:
    """PLAN stage: land an approved plan and stop — the P1 terminus (§7.1).

    Produces the phased plan record and marks the task ``DONE`` for P1 purposes
    (P1 "stops at an approved plan, no build yet"). The real planner is Claude
    (§3.3); here the plan body is a deterministic stub derived from the gathered
    Q&A so the terminal-state wiring is exercised. Builders/verifiers are wired
    in P3.
    """
    plan = plan_phase(state)
    return PipelineState(
        status=TaskStatus.DONE.value,
        current_phase=_phase_value(Phase.DONE),
        plan=plan,
        updated_at=_utc_now_iso(),
    )


def _author_questions(state: PipelineState) -> list[str]:
    """Deterministic stand-in for the Claude clarifier's question authoring.

    The real clarifier gathers repo/memory/handbook context and asks until 98%
    confident (§3.3); the P1 skeleton asks a single fixed question-set so the
    suspend/resume mechanic is what's under test, not the model.
    """
    return ["What problem should this task solve, and what is in scope?"]


def plan_phase(state: PipelineState) -> dict[str, Any]:
    """Build the deterministic P1 plan record from the clarifier Q&A.

    Exposed (and unit-tested) separately from :func:`plan_node` so the plan
    shape can be asserted without driving the whole graph. The real planner
    replaces the body in a later phase.
    """
    return {
        "summary": "Approved P1 plan (skeleton).",
        "phases": ["P1: skeleton + human gate"],
        "qa_turns": len(state.get("qa_history", [])),
        "approved": True,
    }


# --- Graph assembly. --------------------------------------------------------


def build_graph(
    checkpointer: BaseCheckpointSaver | None = None,
) -> CompiledStateGraph:
    """Assemble + compile the P1 pipeline ``StateGraph`` (§3.3, §7.1).

    Wires ``START → intake → clarify → plan → END`` over
    :class:`PipelineState`. The clarifier suspends on ``interrupt()`` for the
    human gate; the planner is the P1 terminus (no build).

    The ``checkpointer`` is **injected**, never constructed here: the design's
    durable store is the SQLite checkpointer (D9), but pre-deploy scaffolding
    must not provision it, and tests inject an in-memory saver. Production wires
    :func:`build_sqlite_checkpointer`. A checkpointer is required for the
    ``interrupt()``/``resume`` mechanic to work, so callers that pass ``None``
    get an uncheckpointed graph that can run straight-through but cannot
    suspend; the driver functions therefore require a checkpointed graph.
    """
    builder: StateGraph = StateGraph(PipelineState)
    builder.add_node(INTAKE, intake_node)
    builder.add_node(CLARIFY, clarify_node)
    builder.add_node(PLAN, plan_node)

    builder.add_edge(START, INTAKE)
    builder.add_edge(INTAKE, CLARIFY)
    builder.add_edge(CLARIFY, PLAN)
    builder.add_edge(PLAN, END)

    if checkpointer is None:
        return builder.compile()
    return builder.compile(checkpointer=checkpointer)


def build_sqlite_checkpointer(db_path: Path | str) -> BaseCheckpointSaver:
    """Construct the production SQLite checkpointer over ``db_path`` (D9, §3.3).

    The import of ``langgraph.checkpoint.sqlite`` is deferred to call time so
    this module imports cleanly in environments where that optional package is
    not installed (pre-deploy scaffolding). The checkpointer creates its own
    tables against the same DB file the foundation's
    :func:`agent_team.db.init_db` reserves for it.

    Raises a clear :class:`RuntimeError` if the optional package is missing, so
    a misconfigured deploy fails loudly rather than silently running
    uncheckpointed.
    """
    try:
        from langgraph.checkpoint.sqlite import SqliteSaver
    except ImportError as exc:  # pragma: no cover - depends on optional dep
        raise RuntimeError(
            "langgraph SQLite checkpointer is unavailable; install the "
            "'langgraph-checkpoint-sqlite' package to use "
            "build_sqlite_checkpointer (D9). Tests inject an in-memory saver."
        ) from exc

    db_path = Path(db_path)
    db_path.parent.mkdir(parents=True, exist_ok=True)
    return SqliteSaver.from_conn_string(str(db_path))


# --- Driver seam (thread_id-keyed). -----------------------------------------
# Thin helpers the coordinator/responder call. They own the mapping between a
# task's ``thread_id`` and the LangGraph ``config``; the durable lifecycle
# ledger lives in agent_team.db, and the transport delivery in agent_team
# .transport. These keep that wiring in one tested place.


def thread_config(thread_id: str) -> dict[str, Any]:
    """Build the LangGraph ``config`` that scopes an invoke to ``thread_id``.

    Every checkpointed invoke/resume for a task must carry the same
    ``{"configurable": {"thread_id": ...}}`` so it reads/writes that task's
    checkpoint and no other (§3.3.1 per-thread isolation).
    """
    return {"configurable": {"thread_id": thread_id}}


def start_task(
    graph: CompiledStateGraph,
    *,
    thread_id: str | None = None,
    transport: str = "",
) -> tuple[str, PipelineState]:
    """Start a new pipeline task and run it up to the first human gate (§3.3).

    Mints a ``thread_id`` (unless one is supplied), seeds the INTAKE state, and
    invokes the graph; it runs through INTAKE into CLARIFY and suspends on the
    clarifier ``interrupt()``. Returns ``(thread_id, state)`` where ``state`` is
    the checkpointed snapshot after the suspend (its ``__interrupt__`` carries
    the pending question-set, surfaced by :func:`pending_question`).

    The graph MUST be compiled with a checkpointer for the suspend to persist;
    an uncheckpointed graph would run straight through without honouring the
    interrupt.
    """
    tid = thread_id or new_thread_id()
    now = _utc_now_iso()
    seed = PipelineState(
        thread_id=tid,
        status=TaskStatus.ACTIVE.value,
        current_phase=_phase_value(Phase.INTAKE),
        qa_history=[],
        transport=transport,
        created_at=now,
        updated_at=now,
    )
    result = graph.invoke(seed, thread_config(tid))
    return tid, result


def resume_task(
    graph: CompiledStateGraph,
    *,
    thread_id: str,
    answer: Any,
) -> PipelineState:
    """Resume a suspended task with Adam's ``answer`` (§3.3, §3.3.1).

    Calls ``graph.invoke(Command(resume=answer), config)`` for ``thread_id``.
    The clarifier's ``interrupt()`` returns ``answer``, the task appends it to
    ``qa_history`` and advances through PLAN to completion. The §3.3.1
    first-answer-wins / turn-guard discipline lives in the responder + ledger;
    this helper is the single-flight resume call the resume worker drives once
    it has won the compare-and-set.
    """
    return graph.invoke(Command(resume=answer), thread_config(thread_id))


def get_pipeline_state(
    graph: CompiledStateGraph,
    *,
    thread_id: str,
) -> PipelineState:
    """Return the live checkpointed :class:`PipelineState` for ``thread_id``.

    Reads the current checkpoint snapshot (post-suspend or post-completion).
    Used by recovery + the manual CLI to inspect a task without resuming it.
    """
    snapshot = graph.get_state(thread_config(thread_id))
    return snapshot.values


def pending_question(
    graph: CompiledStateGraph,
    *,
    thread_id: str,
) -> dict[str, Any] | None:
    """Return the pending interrupt payload for ``thread_id``, or ``None``.

    When a task is suspended on the clarifier human gate, its checkpoint carries
    an interrupt whose value is the §3.3.1 question payload (``thread_id``,
    ``question_id``, ``turn``, ``question_set``, ``transport``, ``deadline``).
    The responder reads this to author the ledger row + transport post. Returns
    ``None`` when the task is not currently waiting on a human answer.
    """
    snapshot = graph.get_state(thread_config(thread_id))
    interrupts = getattr(snapshot, "interrupts", None) or ()
    if not interrupts:
        return None
    return interrupts[0].value