deer-flow/backend/app/gateway/services.py

"""Run lifecycle service layer.

Centralizes the business logic for creating runs, formatting SSE
frames, and consuming stream bridge events.  Router modules
(``thread_runs``, ``runs``) are thin HTTP handlers that delegate here.
"""

from __future__ import annotations

import asyncio
import hashlib
import json
import logging
import re
import threading
import uuid
from collections.abc import AsyncIterator, Mapping
from contextlib import asynccontextmanager
from dataclasses import replace
from types import SimpleNamespace
from typing import Any

from deerflow_extension_api import PROVENANCE_KEYS
from fastapi import HTTPException, Request
from langchain_core.messages import BaseMessage, ChatMessage, HumanMessage, SystemMessage
from langchain_core.messages.utils import convert_to_messages
from langgraph.types import Command

from app.gateway.auth_disabled import AUTH_DISABLED_USER_ID, AUTH_SOURCE_INTERNAL, get_auth_disabled_user, is_auth_disabled
from app.gateway.authz import require_cancel_permission_if, resolve_route_permissions
from app.gateway.deps import get_checkpointer, get_local_provider, get_run_context, get_run_manager, get_stream_bridge
from app.gateway.internal_auth import (
    INTERNAL_OWNER_USER_ID_HEADER_NAME,
    INTERNAL_SYSTEM_ROLE,
    get_internal_user,
    get_trusted_internal_owner_user_id,
)
from app.gateway.knowledge_scope_admission import admit_message_knowledge_scope
from app.gateway.run_models import RunCreateRequest
from app.gateway.run_origin import resolve_request_origin
from app.gateway.utils import sanitize_log_param
from app.mcp_tasks.errors import PermanentNotificationError
from deerflow.agents.human_input import read_human_input_response
from deerflow.agents.interaction_policy import resolve_run_interaction_policy
from deerflow.agents.middlewares.dynamic_context_middleware import _DYNAMIC_CONTEXT_REMINDER_KEY, _REMINDER_DATE_KEY
from deerflow.agents.middlewares.input_sanitization_middleware import frame_untrusted_text
from deerflow.agents.middlewares.message_utils import _SUMMARY_MESSAGE_NAME, is_genuine_user_message
from deerflow.agents.middlewares.skill_usage import SKILL_USAGE_KEY, SKILL_USAGES_KEY
from deerflow.agents.middlewares.tool_output_budget_middleware import TOOL_OUTPUT_BLOB_KEY
from deerflow.agents.middlewares.tool_receipt import TOOL_RECEIPT_KEY, TOOL_RECEIPT_LEDGER_KEY
from deerflow.agents.middlewares.tool_transform_meta import TOOL_TRANSFORMS_KEY
from deerflow.agents.middlewares.view_image_middleware import _IMAGE_CONTEXT_MESSAGE_MARKER_KEY
from deerflow.config.agents_config import load_agent_config
from deerflow.config.app_config import get_app_config
from deerflow.config.database_config import resolve_checkpoint_graph_cache_max
from deerflow.knowledge_scope import KNOWLEDGE_SCOPE_KEY, KNOWLEDGE_SCOPE_RUNTIME_KEY
from deerflow.mcp_scope import (
    THREAD_INCARNATION_METADATA_GUARD_KEY,
    is_valid_thread_incarnation,
)
from deerflow.projects.context import PROJECT_CONTEXT_MESSAGE_MARKER, resolve_project_context
from deerflow.runtime import (
    END_SENTINEL,
    HEARTBEAT_SENTINEL,
    ORPHAN_RECOVERY_STOP_REASON,
    CheckpointStateAccessor,
    ConflictError,
    DisconnectMode,
    RunContext,
    RunManager,
    RunRecord,
    RunStatus,
    StreamBridge,
    StreamGap,
    ThreadOperationKind,
    UnsupportedStrategyError,
    build_state_mutation_graph,
    run_agent,
)
from deerflow.runtime.checkpoint_mode import (
    INTERNAL_CHECKPOINT_MODE_KEY,
    CheckpointModeMismatchError,
    checkpoint_tuple_uses_delta,
    inject_checkpoint_mode,
)
from deerflow.runtime.checkpoint_state import graph_state_schema
from deerflow.runtime.context_keys import PROJECT_CONTEXT_KEY
from deerflow.runtime.events.message_identity import MESSAGE_SEQ_KEY
from deerflow.runtime.goal import goal_thread_lock
from deerflow.runtime.journal import build_checkpoint_history_seed_events
from deerflow.runtime.keyed_lock import KeyedLockTable
from deerflow.runtime.run_origin import DEERFLOW_ORIGIN_KEY, make_origin
from deerflow.runtime.runs.naming import resolve_root_run_name
from deerflow.runtime.secret_context import (
    LegacyRunMetadataSecretError,
    redact_config_secrets,
    validate_run_metadata_secrets,
)
from deerflow.runtime.stream_modes import normalize_stream_modes
from deerflow.runtime.user_context import reset_current_user, set_current_user
from deerflow.sandbox.lease import SANDBOX_SERVER_OWNED_CONTEXT_KEYS
from deerflow.scheduler.runtime import scheduler_tools_enabled
from deerflow.scheduler.schedules import validate_timezone
from deerflow.subagents.status_contract import SUBAGENT_ACCEPTANCE_VERDICT_KEY, SUBAGENT_RECEIPT_VERDICT_KEY, SUBAGENT_TOOL_RECEIPTS_KEY
from deerflow.trace_context import DEERFLOW_TRACE_METADATA_KEY, ensure_trace_context, ensure_trace_id
from deerflow.utils.assembly_io import run_assembly
from deerflow.utils.file_io import await_drained
from deerflow.utils.messages import ORIGINAL_USER_CONTENT_KEY, UNTRUSTED_INPUT_KEY
from deerflow.utils.thread_id import validate_thread_id

logger = logging.getLogger(__name__)


class BusyThreadConflict(HTTPException):
    """A retryable run-manager admission conflict exposed as HTTP 409."""

    def __init__(self, detail: str) -> None:
        super().__init__(status_code=409, detail=detail)


@asynccontextmanager
async def reserve_checkpoint_write(
    request: Request,
    thread_id: str,
    *,
    user_id: str | None = None,
) -> AsyncIterator[None]:
    """Serialize an out-of-run checkpoint writer against all thread operations."""
    run_manager = get_run_manager(request)
    async with goal_thread_lock(thread_id):
        async with run_manager.reserve_thread_operation(
            thread_id,
            kind=ThreadOperationKind.checkpoint_write,
            user_id=user_id,
        ):
            yield


_TERMINAL_RUN_STATUSES = {
    RunStatus.success,
    RunStatus.error,
    RunStatus.timeout,
    RunStatus.interrupted,
}

_THREAD_METADATA_SETUP_TIMEOUT_SECONDS = 5.0

# Message metadata a scheduled launch puts on its prompt: the task, run and the
# user-language parts (instructions, stop condition, notes) the run thread shows
# instead of the launched text. A cross-language contract
# (contracts/scheduled_goal_notes_contract.json "scheduled_origin_key").
SCHEDULED_ORIGIN_KEY = "deerflow_scheduled_origin"

_SERVER_OWNED_MESSAGE_METADATA_KEYS = (
    frozenset(
        {
            # Only the scheduler's internal launch may mark a message as a
            # scheduled run prompt.
            SCHEDULED_ORIGIN_KEY,
            _DYNAMIC_CONTEXT_REMINDER_KEY,
            _REMINDER_DATE_KEY,
            _IMAGE_CONTEXT_MESSAGE_MARKER_KEY,
            KNOWLEDGE_SCOPE_RUNTIME_KEY,
            TOOL_RECEIPT_KEY,
            TOOL_RECEIPT_LEDGER_KEY,
            TOOL_OUTPUT_BLOB_KEY,
            TOOL_TRANSFORMS_KEY,
            SKILL_USAGE_KEY,
            SKILL_USAGES_KEY,
            # Attached when a values frame is serialized, for display ordering only.
            # A replayed message carrying it back would write a thread-scoped seq
            # into the checkpoint, which a fork then re-seeds and reassigns (#4380).
            MESSAGE_SEQ_KEY,
            # The transient project-context request message marker (spec §12):
            # a client-supplied copy must never survive into a run, where the
            # renderer would treat the message as its own.
            PROJECT_CONTEXT_MESSAGE_MARKER,
            SUBAGENT_TOOL_RECEIPTS_KEY,
            SUBAGENT_RECEIPT_VERDICT_KEY,
            SUBAGENT_ACCEPTANCE_VERDICT_KEY,
            UNTRUSTED_INPUT_KEY,
        }
    )
    | PROVENANCE_KEYS
)


# ---------------------------------------------------------------------------
# SSE formatting
# ---------------------------------------------------------------------------


def format_sse(event: str, data: Any, *, event_id: str | None = None) -> str:
    """Format a single SSE frame.

    Field order: ``event:`` -> ``data:`` -> ``id:`` (optional) -> blank line.
    This matches the LangGraph Platform wire format consumed by the
    ``useStream`` React hook and the Python ``langgraph-sdk`` SSE decoder.
    """
    payload = json.dumps(data, default=str, ensure_ascii=False)
    parts = [f"event: {event}", f"data: {payload}"]
    if event_id:
        parts.append(f"id: {event_id}")
    parts.append("")
    parts.append("")
    return "\n".join(parts)


def _run_is_terminal(record: RunRecord) -> bool:
    return record.status in _TERMINAL_RUN_STATUSES


def _consume_task_result(task: asyncio.Task) -> None:
    """Retrieve a detached task's exception without propagating cancellation."""
    if not task.cancelled():
        task.exception()


def _log_thread_metadata_task_result(task: asyncio.Task, *, thread_id: str) -> None:
    """Log detached metadata setup failures while ignoring cancellation."""
    if task.cancelled():
        return
    try:
        task.result()
    except asyncio.CancelledError:
        return
    except Exception:
        logger.warning(
            "Failed to ensure thread_meta for %s after worker detached (non-fatal)",
            sanitize_log_param(thread_id),
            exc_info=True,
        )


async def _ensure_thread_metadata(
    run_ctx: RunContext,
    record: RunRecord,
    *,
    owner_user_id: str | None,
    require_existing_thread: bool = False,
) -> dict[str, Any]:
    """Ensure an admitted run's thread exists without delaying task attachment."""
    thread_store = run_ctx.thread_store
    existing = await thread_store.get(record.thread_id)
    if existing is None and owner_user_id:
        unscoped = await thread_store.get(record.thread_id, user_id=None)
        if unscoped is not None:
            if unscoped.get("user_id") != owner_user_id:
                await thread_store.update_owner(record.thread_id, owner_user_id, user_id=None)
            existing = await thread_store.get(record.thread_id)
    if existing is None:
        if require_existing_thread:
            raise LookupError(f"Thread {record.thread_id} was deleted during run admission")
        from deerflow.persistence.thread_meta import THREAD_PROJECT_METADATA_KEY

        run_metadata = record.metadata or {}
        metadata = {
            key: value
            for key, value in run_metadata.items()
            # Strip the run-scoped trace id (existing) and the reserved
            # membership key: run admission never modifies project membership —
            # the column is written only by POST /api/threads and
            # /threads/{id}/move — so the key must not persist either.
            if key not in (DEERFLOW_TRACE_METADATA_KEY, THREAD_PROJECT_METADATA_KEY)
        }
        # A run that names its thread in the input (a scheduled run) creates it
        # named: the worker copies the title to the thread list only when the
        # run ends, and a server-created thread is listed as soon as it exists.
        run_input = (record.kwargs or {}).get("input")
        title = run_input.get("title") if isinstance(run_input, dict) else None
        existing = await thread_store.create(
            record.thread_id,
            assistant_id=record.assistant_id,
            display_name=title if isinstance(title, str) and title.strip() else None,
            metadata=metadata,
        )
    return existing


async def _terminal_record_stream_missing(bridge: StreamBridge, record: RunRecord) -> bool:
    """True when a terminal run has no retained stream on bridges that can tell."""
    if not _run_is_terminal(record):
        return False
    stream_exists = getattr(bridge, "stream_exists", None)
    if stream_exists is None:
        return False
    try:
        return not bool(await stream_exists(record.run_id))
    except Exception:
        logger.debug(
            "Failed to probe stream existence for terminal run %s",
            sanitize_log_param(record.run_id),
            exc_info=True,
        )
        return False


async def _orphan_recovery_observed_after_heartbeat(
    record: RunRecord,
    run_mgr: RunManager,
) -> bool:
    """Return whether durable orphan recovery is the consumer's liveness edge.

    A normal terminal status is not sufficient: the producer persists status
    before publishing its final error/data frames and END. Orphan recovery is
    different because the producer is known to be gone and the durable
    ``stop_reason`` is written atomically with the terminal status. Only that
    explicit signal may synthesize END after a heartbeat.
    """
    if not record.store_only:
        return False
    refreshed = await run_mgr.get(record.run_id, user_id=record.user_id)
    return refreshed is not None and _run_is_terminal(refreshed) and refreshed.stop_reason == ORPHAN_RECOVERY_STOP_REASON


# ---------------------------------------------------------------------------
# Input / config helpers
# ---------------------------------------------------------------------------


def _skips_input_guardrail(additional_kwargs: dict[str, Any], name: Any) -> bool:
    """Whether these markers would make ``InputSanitizationMiddleware`` skip a message.

    Mirrors ``is_genuine_user_message`` exactly, truthiness included: a
    ``summary`` name, or a truthy ``hide_from_ui`` without a valid human-input
    reply. Keying off key presence instead would mark ``hide_from_ui: False``,
    which never skipped the guardrail and so needs no mark.
    """
    if name == _SUMMARY_MESSAGE_NAME:
        return True
    return bool(additional_kwargs.get("hide_from_ui")) and read_human_input_response(additional_kwargs) is None


def _mark_untrusted_framework_markers(additional_kwargs: dict[str, Any], name: Any) -> dict[str, Any]:
    """Mark a caller's message whose markers would skip the input guardrail.

    ``is_genuine_user_message`` reads ``hide_from_ui`` and a human
    ``name="summary"`` as proof the framework wrote the message, and the guardrail
    skips those — so a caller able to set either one placed raw
    ``<system-reminder>`` text outside the user-input boundary markers, which the
    lead-agent prompt declares trusted internal framework data.

    The markers are deliberately *kept*. ``hide_from_ui`` has a second,
    legitimate role: three frontend senders (quoted conversation context, sidecar
    context, the agent save command) set it purely to keep a context message out
    of the transcript, carry no ``human_input_response``, and are hidden by
    nothing else — removing it would render all three as chat bubbles. Only the
    guardrail-skipping role is a vulnerability, so the two are separated here
    instead: the message stays hidden, and ``requires_input_sanitization`` reads
    this mark and sanitizes it anyway. Marking rather than removing also keeps
    this boundary from silently changing behaviour that reads the marker for
    presentation, persistence, or memory filtering.

    HumanInputCard replies need no mark: a valid ``human_input_response`` already
    makes them genuine, so they are sanitized on that path.
    """
    if not _skips_input_guardrail(additional_kwargs, name):
        return additional_kwargs
    return {**additional_kwargs, UNTRUSTED_INPUT_KEY: True}


def _is_human_message_like(message: Any) -> bool:
    """Whether *message* is the human role ``is_genuine_user_message`` acts on.

    Only that role reads ``name`` as a framework marker, and ``name`` on a
    ToolMessage is the tool's own name — reserving it there would rename tools.

    Matched by ``isinstance``, exactly as the predicate this defends does: a
    ``HumanMessageChunk`` is a ``HumanMessage`` whose ``type`` is not ``"human"``,
    so a ``type``-based check would leave that subclass's marker settable.
    """
    if isinstance(message, BaseMessage):
        return isinstance(message, HumanMessage)
    if isinstance(message, dict):
        return (message.get("type") or message.get("role")) in {"human", "user"}
    return False


def _strip_external_message_metadata(message: Any) -> Any:
    """Remove server-owned metadata from an untrusted input message.

    Also stamps ``untrusted_input`` on a human message whose caller-owned markers
    would skip the input guardrail — see ``_mark_untrusted_framework_markers``.
    The stamp is applied after the strip loop, so a caller cannot preset it.
    """
    if not isinstance(message, BaseMessage):
        return message
    additional_kwargs = dict(message.additional_kwargs)
    additional_kwargs.pop(ORIGINAL_USER_CONTENT_KEY, None)
    for key in _SERVER_OWNED_MESSAGE_METADATA_KEYS:
        additional_kwargs.pop(key, None)
    if _is_human_message_like(message):
        additional_kwargs = _mark_untrusted_framework_markers(additional_kwargs, message.name)
    if additional_kwargs == message.additional_kwargs:
        return message
    return message.model_copy(update={"additional_kwargs": additional_kwargs})


def _strip_external_metadata_from_message_like(item: Any) -> Any:
    """Strip server-owned keys from message-like values outside ``messages``.

    The top-level ``messages`` channel is canonicalized and role-checked by
    ``_normalize_input_messages``. Other middleware-contributed channels may
    still carry either ``BaseMessage`` objects or raw dictionaries, so this
    helper preserves those shapes while stripping metadata and stamping
    ``untrusted_input`` where caller-owned markers would skip the guardrail.
    """
    if isinstance(item, BaseMessage):
        return _strip_external_message_metadata(item)
    if not isinstance(item, dict):
        return item
    # A missing (or non-dict) ``additional_kwargs`` is the most natural request
    # shape, and it still needs the mark: the messages reducer coerces the dict
    # with ``convert_to_messages``, which supplies ``additional_kwargs={}``, so an
    # unmarked ``name="summary"`` would reach the model on the guardrail's
    # genuine-user fallback. Treat it as empty for both steps rather than
    # returning early.
    source_kwargs = item.get("additional_kwargs")
    source_kwargs = source_kwargs if isinstance(source_kwargs, dict) else {}
    additional_kwargs = {key: value for key, value in source_kwargs.items() if key not in _SERVER_OWNED_MESSAGE_METADATA_KEYS and key != ORIGINAL_USER_CONTENT_KEY}
    if _is_human_message_like(item):
        additional_kwargs = _mark_untrusted_framework_markers(additional_kwargs, item.get("name"))
    if additional_kwargs == source_kwargs:
        # Nothing to change — including the ordinary key-omitted message, which
        # must not gain an empty dict just by passing through here.
        return item
    return {**item, "additional_kwargs": additional_kwargs}


#: Server-owned verdict keys on a delegation-ledger entry: runtime-stamped
#: execution evidence (citation verdict PR2, acceptance checklist PR4) that a
#: caller must never supply.
_SERVER_OWNED_DELEGATION_VERDICT_KEYS = frozenset({"receipt_verdict", "acceptance_verdict"})


def _strip_external_delegation_verdict(entry: Any) -> Any:
    """Remove runtime-stamped verdicts from a caller-supplied ledger entry.

    ``receipt_verdict``/``acceptance_verdict`` are server-owned execution
    evidence stamped at task write-back. Ledger entries are plain dicts, not
    messages, so the message-metadata stripper never sees them; without this
    a caller can persist a forged verdict that ``render_delegation_ledger``
    would present as fact.
    """
    if isinstance(entry, dict) and _SERVER_OWNED_DELEGATION_VERDICT_KEYS & entry.keys():
        return {key: value for key, value in entry.items() if key not in _SERVER_OWNED_DELEGATION_VERDICT_KEYS}
    return entry


def _normalize_input_messages(
    value: Any,
    *,
    location: str,
    trusted_internal: bool = False,
) -> list[BaseMessage]:
    """Coerce once, then check the actual role before any checkpoint write.

    Match add_messages' list-or-single convention. Checking raw ``role`` keys
    misses type aliases, (role, content) pairs, constructor envelopes and chunks.
    The normalized objects are also the ones forwarded to the graph: there is
    no second, unchecked interpretation of an accepted wire representation.
    """
    messages = value if isinstance(value, list) else [value]
    converted: list[BaseMessage] = []
    for index, item in enumerate(messages):
        try:
            message = convert_to_messages([item])[0]
        except (ValueError, TypeError, NotImplementedError, KeyError) as exc:
            # LangChain's error may contain the complete caller message.
            raise HTTPException(
                status_code=400,
                detail=f"Invalid message at {location}[{index}]",
            ) from exc
        if not trusted_internal:
            if isinstance(message, SystemMessage) or (isinstance(message, ChatMessage) and message.role.strip().lower() in {"system", "developer"}):
                raise HTTPException(
                    status_code=400,
                    detail=(f"External system/developer messages are not allowed at {location}[{index}]"),
                )
            message = _strip_external_message_metadata(message)
        converted.append(message)
    return converted


def strip_server_owned_state_metadata(values: Mapping[str, Any]) -> dict[str, Any]:
    """Validate and sanitize caller-supplied state values before checkpointing.

    The server-owned ``sandbox``, ``thread_data``, and ``viewed_images`` channels
    are rejected. The ``messages`` channel
    is canonicalized to a list of ``BaseMessage``
    objects, rejects external system/developer roles with HTTP 400, and strips
    server-owned metadata. Other channels keep their existing shapes while
    forged metadata and delegation verdicts are removed. ``normalize_input``
    applies the same message boundary to run input.

    The thread-state mutation route writes values straight into a checkpoint,
    so an authenticated client must not be able to persist forged provenance,
    transform trails, or privileged message roles. Every channel is walked
    because middleware-contributed channels can also carry message-like values.
    """
    server_owned_channels = {"sandbox", "thread_data", "viewed_images"}
    rejected = server_owned_channels.intersection(values)
    if rejected:
        raise HTTPException(
            status_code=400,
            detail=f"External {sorted(rejected)[0]} state is not allowed",
        )

    stripped: dict[str, Any] = {}
    for channel, value in values.items():
        if channel == "messages" and value is not None:
            stripped[channel] = _normalize_input_messages(value, location="values.messages")
        elif channel == "delegations" and isinstance(value, list):
            stripped[channel] = [_strip_external_delegation_verdict(item) for item in value]
        elif isinstance(value, list):
            stripped[channel] = [_strip_external_metadata_from_message_like(item) for item in value]
        else:
            stripped[channel] = _strip_external_metadata_from_message_like(value)
    return stripped


def normalize_input(raw_input: dict[str, Any] | None, *, trusted_internal: bool = False) -> dict[str, Any]:
    """Convert LangGraph Platform input format to LangChain state dict.

    Delegates dict→message coercion to ``langchain_core.messages.utils.convert_to_messages``
    so that ``additional_kwargs`` (e.g. uploaded-file metadata — gh #3132), ``id``,
    ``name``, and history roles (ai/tool) survive unchanged. System/developer
    messages require authenticated internal admission; ordinary API credentials
    (including admin and PAT callers) do not grant system-prompt authority. An earlier
    hand-rolled version only forwarded ``content`` and collapsed every role to
    ``HumanMessage``, which silently stripped frontend-supplied attachments.

    Malformed message dicts (missing ``role``/``type``/``content``, unsupported
    role, etc.) raise ``HTTPException(400)`` with the offending index, instead
    of bubbling up as a 500.  The gateway is a system boundary, so per-entry
    validation errors are the right shape for clients to retry against.

    The ``sandbox``, ``thread_data``, and ``viewed_images`` channels are also
    server-owned. External callers cannot select a provider resource by id or
    supply host image paths; trusted internal run admission may carry restored
    values.

    ``original_user_content``, dynamic-context reminder markers, the transient
    view-image context marker, the execution-only knowledge-scope marker, tool
    receipts, delegated receipt metadata/verdicts, and ``untrusted_input`` are
    server-owned. External callers cannot supply them; trusted internal channel
    calls may preserve metadata they added before invoking this boundary. The
    same applies to the ``delegations`` channel: a caller-supplied ledger entry's
    ``receipt_verdict`` is a forgery and is stripped before the graph runs.

    ``hide_from_ui`` and a human ``summary`` name are the exception: they stay
    caller-owned and are deliberately preserved, because ``hide_from_ui`` is also
    how three frontend senders (quoted conversation context, sidecar context, the
    agent save command) keep a context message out of the transcript, and nothing
    else hides those. What they must not do is tell
    ``is_genuine_user_message`` the framework authored the message, which would
    skip input sanitization — so a caller's message carrying either marker is
    stamped with ``untrusted_input`` instead, and
    ``requires_input_sanitization`` sanitizes it anyway. That key is stripped
    first, so a caller can neither forge nor clear it. HumanInputCard replies need
    no stamp: a valid ``human_input_response`` already makes them genuine.
    """
    if raw_input is None:
        return {}
    if not trusted_internal:
        server_owned_channels = {"sandbox", "thread_data", "viewed_images"}
        rejected = server_owned_channels.intersection(raw_input)
        if rejected:
            raise HTTPException(
                status_code=400,
                detail=f"External {sorted(rejected)[0]} state is not allowed",
            )
    result = raw_input
    messages = raw_input.get("messages")
    if messages is not None:
        converted = _normalize_input_messages(messages, location="input.messages", trusted_internal=trusted_internal)
        result = {**raw_input, "messages": converted}
    if not trusted_internal:
        delegations = result.get("delegations")
        if isinstance(delegations, list):
            cleaned = [_strip_external_delegation_verdict(entry) for entry in delegations]
            if cleaned != delegations:
                result = {**result, "delegations": cleaned}
    return result


def _canonical_run_record_input(
    raw_input: dict[str, Any] | None,
    graph_input: object,
) -> dict[str, Any] | None:
    """Persist the same normalized messages that cross run admission.

    The run record is a client-visible audit surface. Keeping the original raw
    message there would preserve a non-canonical scope even though the graph
    receives the validated form.
    """
    if not isinstance(graph_input, dict):
        return raw_input
    canonical = dict(raw_input or {})
    messages = graph_input.get("messages")
    if isinstance(messages, list):
        canonical["messages"] = [message.model_dump(mode="json") if isinstance(message, BaseMessage) else message for message in messages]
    return canonical


_DEFAULT_ASSISTANT_ID = "lead_agent"


# Whitelist of run-context keys that the langgraph-compat layer forwards from
# ``body.context`` into the run config. ``config["context"]`` exists in
# LangGraph >=0.6, but these values must be written to both ``configurable``
# (for legacy ``_get_runtime_config`` consumers) and ``context`` because
# LangGraph >=1.1.9 no longer makes ``ToolRuntime.context`` fall back to
# ``configurable`` for consumers like ``setup_agent``.
_CONTEXT_CONFIGURABLE_KEYS: frozenset[str] = frozenset(
    {
        "model_name",
        "mode",
        "thinking_enabled",
        "reasoning_effort",
        "is_plan_mode",
        "subagent_enabled",
        "max_concurrent_subagents",
        "max_total_subagents",
        "agent_name",
        "is_bootstrap",
    }
)

# Keys honored only for internally-authenticated callers (the scheduler path).
# ``interaction_mode`` and ``non_interactive`` control clarification availability;
# arbitrary HTTP/IM clients must not be able to force autonomous execution.
_CONTEXT_INTERNAL_CALLER_KEYS: frozenset[str] = frozenset({"interaction_mode", "non_interactive"})

# Server-owned authorization and sandbox lifecycle identity fields. These must
# never be accepted from client-supplied ``body.config.context`` or
# ``body.config.configurable``. They
# are either produced by Gateway auth state, admitted from a separately
# authenticated internal request channel, or reserved for LangGraph Server.
#   ``is_internal``             — derived from ``request.state.auth_source``
#   ``authz_attributes``        — Phase 1A has no Gateway-side producer; cleared.
#   ``channel_user_id``         — accepted only from trusted internal context.
#   ``langgraph_auth_user*``    — populated only by LangGraph Server auth.
#   ``sandbox_*_id``           — created only inside the run/subagent lifecycle.
_SERVER_OWNED_RUNTIME_CONTEXT_KEYS: frozenset[str] = (
    frozenset(
        {
            "is_internal",
            "authz_attributes",
            "channel_user_id",
            "is_subagent",
            "agent_id",
            "__run_loop_detection_recorder",
            "__run_tool_promotion_recorder",
            "__run_tool_progress_recorder",
            "langgraph_auth_user",
            "langgraph_auth_user_id",
            THREAD_INCARNATION_METADATA_GUARD_KEY,
            # Server-owned pinned project snapshot (spec §7.1): resolved once
            # at admission from threads_meta; a client-supplied value must
            # never survive in either run-config section.
            PROJECT_CONTEXT_KEY,
            KNOWLEDGE_SCOPE_KEY,
            KNOWLEDGE_SCOPE_RUNTIME_KEY,
        }
    )
    | SANDBOX_SERVER_OWNED_CONTEXT_KEYS
)

# Keys forwarded from ``body.context`` into ``config['context']`` ONLY (the
# runtime context that becomes ``ToolRuntime.context`` / ``runtime.context``),
# never into ``config['configurable']``. These are read by tools and
# middlewares from ``runtime.context`` and have no reason to live in
# ``configurable`` — and ``configurable`` is persisted in checkpoints, so
# keeping secrets like ``github_token`` out of it avoids writing a
# short-lived installation token into the checkpoint store.
#
#   ``github_token``         — App installation token minted by the GitHub
#                              channel; the bash tool exposes it as
#                              ``GH_TOKEN``/``GITHUB_TOKEN`` so ``gh`` and
#                              ``git`` push as the bot, not the host user.
#   ``disable_clarification`` — set for non-interactive channels (GitHub
#                              webhooks) so ClarificationMiddleware proceeds
#                              instead of dead-ending the run.
#
#   ``channel_name``        — trusted channel identity used by interaction policy.
#
# These are produced server-side by the channel run policies
# (``ChannelManager._apply_channel_policy`` and ``app.gateway.github.run_policy``),
# which reach the Gateway over the internally-authenticated request channel, so
# they are internal-only as well — see :data:`_INTERNAL_ONLY_CONTEXT_KEYS`.
_CONTEXT_RUNTIME_ONLY_KEYS: frozenset[str] = frozenset({"github_token", "disable_clarification", "channel_name"})

# Every run-context key an external client may never supply, in either section.
# The two sets differ only in *where* a legitimate internal caller's value lands
# (both sections vs. ``context`` alone); their trust requirement is identical.
#
# ``disable_clarification`` is not a milder cousin of ``non_interactive``:
# ``ClarificationMiddleware`` answers every clarification — ``risk_confirmation``
# included — with "proceed without asking" instead of interrupting, and
# ``SandboxMiddleware`` reads the two keys as the same non-interactive signal.
# Accepting it from a client therefore reproduces the effect the
# ``non_interactive`` gate exists to prevent. ``github_token`` is a live
# credential that ``bash`` exports as ``GH_TOKEN``/``GITHUB_TOKEN``, and a copy
# smuggled through ``body.config['configurable']`` would be written to the
# checkpoint store.
_INTERNAL_ONLY_CONTEXT_KEYS: frozenset[str] = _CONTEXT_INTERNAL_CALLER_KEYS | _CONTEXT_RUNTIME_ONLY_KEYS | _SERVER_OWNED_RUNTIME_CONTEXT_KEYS


def strip_internal_context_keys(config: dict[str, Any]) -> None:
    """Drop internal-only keys a non-internal caller smuggled into the run config.

    Gating :func:`merge_run_context_overrides` is not enough on its own:
    ``build_run_config`` copies a client-supplied ``body.config['context']`` /
    ``body.config['configurable']`` verbatim, so the same keys must be scrubbed
    from both sections after the config is assembled.
    """
    for section in ("context", "configurable"):
        value = config.get(section)
        if isinstance(value, dict):
            for key in _INTERNAL_ONLY_CONTEXT_KEYS | _SERVER_OWNED_RUNTIME_CONTEXT_KEYS:
                value.pop(key, None)


def merge_run_context_overrides(config: dict[str, Any], context: Mapping[str, Any] | None, *, internal: bool = False) -> None:
    """Merge whitelisted keys from ``body.context`` into both ``config['configurable']``
    and ``config['context']`` so they are visible to legacy configurable readers and
    to LangGraph ``ToolRuntime.context`` consumers (e.g. the ``setup_agent`` tool —
    see issue #2677).

    ``user_id`` is intentionally propagated into ``config['context']`` in addition to
    the whitelisted keys, so non-web callers (e.g. IM channels) that supply identity in
    ``body.context`` keep it on ``ToolRuntime.context``. It is merged with
    ``setdefault`` so a server-authenticated id stamped by
    :func:`inject_authenticated_user_context` always wins over the client-supplied one.

    :data:`_CONTEXT_INTERNAL_CALLER_KEYS` are also forwarded when ``internal``
    is True; for non-internal callers those keys are dropped from client requests
    by :func:`strip_internal_context_keys`.

    A second set of keys (``_CONTEXT_RUNTIME_ONLY_KEYS`` — e.g. ``github_token``,
    ``disable_clarification``) is likewise forwarded only when ``internal`` is True,
    and then into ``config['context']`` only, never ``configurable``. These are
    secrets / runtime flags read by tools and middlewares from ``runtime.context``;
    keeping them out of ``configurable`` avoids persisting a short-lived token in the
    checkpoint store.
    """
    if not context:
        return
    configurable = config.setdefault("configurable", {})
    runtime_context = config.setdefault("context", {})
    keys = _CONTEXT_CONFIGURABLE_KEYS | _CONTEXT_INTERNAL_CALLER_KEYS if internal else _CONTEXT_CONFIGURABLE_KEYS
    for key in keys:
        if key in context:
            if isinstance(configurable, dict):
                configurable.setdefault(key, context[key])
            if isinstance(runtime_context, dict):
                runtime_context.setdefault(key, context[key])
    # Context-only keys (secrets / runtime flags) land in ``config['context']``
    # only — never ``configurable`` (which is persisted in checkpoints) — and only
    # for internal callers, the sole legitimate producers.
    if internal:
        for key in _CONTEXT_RUNTIME_ONLY_KEYS:
            if key in context and isinstance(runtime_context, dict):
                runtime_context.setdefault(key, context[key])
    if "user_id" in context and isinstance(runtime_context, dict):
        runtime_context.setdefault("user_id", context["user_id"])


async def resolve_trusted_internal_owner_for_attribution(request: Request, owner_user_id: str | None) -> Any | None:
    """Resolve the DeerFlow user used only for trusted internal attribution."""

    if not owner_user_id:
        return None
    user = getattr(request.state, "user", None)
    if getattr(user, "system_role", None) != INTERNAL_SYSTEM_ROLE:
        return None
    try:
        return await get_local_provider().get_user(owner_user_id)
    except Exception:
        logger.exception("Failed to resolve trusted internal owner %s", sanitize_log_param(owner_user_id))
        return None


def inject_authenticated_user_context(
    config: dict[str, Any],
    request: Request,
    *,
    internal_owner_user: Any | None = None,
    request_context: Mapping[str, Any] | None = None,
) -> None:
    """Stamp the authenticated user into the run context for background tools.

    Tool execution may happen after the request handler has returned, so tools
    that persist user-scoped files should not rely only on ambient ContextVars.
    The value comes from server-side auth state, never from client context.

    ``request_context.channel_user_id`` is the sole exception: it is honored
    only after ``request.state.auth_source`` proves the caller is internal.
    Values copied through the free-form RunnableConfig are always cleared.
    """

    # --- Server-owned authorization and sandbox lifecycle identity fields ---
    # Clear any client-forged values from both config sections, then write the
    # authoritative is_internal. This runs before ALL early returns so that
    # even user_id-is-None paths get a defined is_internal value.
    runtime_context = config.setdefault("context", {})
    if not isinstance(runtime_context, dict):
        raise TypeError("run context must be a mapping")
    for key in _SERVER_OWNED_RUNTIME_CONTEXT_KEYS:
        runtime_context.pop(key, None)
    configurable = config.get("configurable")
    if isinstance(configurable, dict):
        for key in _SERVER_OWNED_RUNTIME_CONTEXT_KEYS:
            configurable.pop(key, None)
    auth_source = getattr(getattr(request, "state", None), "auth_source", None)
    # ``user_id`` is server-owned for EXTERNAL callers: it now selects which
    # user's credential user-scoped MCP auth injects, so a client-forged value
    # must never survive any early return below — scrub it here and restamp it
    # only from ``request.state.user``. Internal callers (IM channels, the
    # scheduler) are the deliberate exception: they authenticate their own end
    # users and supply that identity in run context (PR #3294), which the
    # internal-role branch below preserves.
    user = getattr(getattr(request, "state", None), "user", None)
    if auth_source != AUTH_SOURCE_INTERNAL and getattr(user, "system_role", None) != INTERNAL_SYSTEM_ROLE:
        runtime_context.pop("user_id", None)
        if isinstance(configurable, dict):
            configurable.pop("user_id", None)
    runtime_context["is_internal"] = auth_source == AUTH_SOURCE_INTERNAL
    if auth_source == AUTH_SOURCE_INTERNAL and request_context is not None:
        channel_user_id = request_context.get("channel_user_id")
        if channel_user_id is not None:
            runtime_context["channel_user_id"] = channel_user_id

    user = getattr(request.state, "user", None)
    user_id = getattr(user, "id", None)
    if user_id is None:
        return

    if getattr(user, "system_role", None) == INTERNAL_SYSTEM_ROLE:
        runtime_context = config.setdefault("context", {})
        if not isinstance(runtime_context, dict):
            return
        if internal_owner_user is None:
            runtime_context.pop("user_role", None)
            runtime_context.pop("oauth_provider", None)
            runtime_context.pop("oauth_id", None)
            return
        owner_user_id = getattr(internal_owner_user, "id", None)
        if owner_user_id is not None:
            runtime_context["user_id"] = str(owner_user_id)
        runtime_context["user_role"] = getattr(internal_owner_user, "system_role", None)
        runtime_context["oauth_provider"] = getattr(internal_owner_user, "oauth_provider", None)
        runtime_context["oauth_id"] = getattr(internal_owner_user, "oauth_id", None)
        return

    runtime_context = config.setdefault("context", {})
    if isinstance(runtime_context, dict):
        runtime_context["user_id"] = str(user_id)
        runtime_context["user_role"] = getattr(user, "system_role", None)
        runtime_context["oauth_provider"] = getattr(user, "oauth_provider", None)
        runtime_context["oauth_id"] = getattr(user, "oauth_id", None)


def resolve_agent_factory(assistant_id: str | None):
    """Resolve the agent factory callable from config.

    Custom agents are implemented as ``lead_agent`` + an ``agent_name``
    injected into ``configurable`` or ``context`` — see
    :func:`build_run_config`.  All ``assistant_id`` values therefore map to the
    same factory; the routing happens inside the assembly when it reads
    ``cfg["agent_name"]``.

    The result is ``assemble_lead_agent``, which returns a
    ``LeadAgentAssembly(graph, descriptor)`` rather than a bare graph, so every
    consumer must unwrap ``.graph``. A third-party factory that still returns a
    bare graph keeps working: the unwrap sites are type-checked, not assumed.
    """
    from deerflow.agents.lead_agent.agent import assemble_lead_agent

    return assemble_lead_agent


# Lead-agent recursion budget bounds. The Gateway must NOT trust a
# client-supplied ``recursion_limit`` verbatim: an arbitrarily large value lets
# a single run execute unbounded LangGraph super-steps (each at least one LLM
# call), enabling runaway API cost / DoS. ``_DEFAULT_RECURSION_LIMIT`` is the
# fallback when app config cannot be loaded; the normal server default and hard
# ceiling are configurable via ``AppConfig.recursion_limit`` and
# ``AppConfig.max_recursion_limit``.
_DEFAULT_RECURSION_LIMIT = 100
_DEFAULT_MAX_RECURSION_LIMIT = 1000


def _resolve_gateway_recursion_limits() -> tuple[int, int]:
    """Resolve the run default and ceiling from one hot-reloaded snapshot."""
    try:
        app_config = get_app_config()
        raw = app_config.recursion_limit
        max_limit = app_config.max_recursion_limit
        if raw > max_limit:
            logger.warning(
                "recursion_limit %d exceeds max_recursion_limit %d; clamped to %d for Gateway runs",
                raw,
                max_limit,
                max_limit,
            )
        return min(raw, max_limit), max_limit
    except Exception:
        logger.warning(
            "failed to load app config; falling back to recursion_limit=%d and max_recursion_limit=%d for Gateway runs",
            _DEFAULT_RECURSION_LIMIT,
            _DEFAULT_MAX_RECURSION_LIMIT,
        )
        return _DEFAULT_RECURSION_LIMIT, _DEFAULT_MAX_RECURSION_LIMIT


def _resolve_max_recursion_limit() -> int:
    """Resolve the clamp ceiling from ``AppConfig.max_recursion_limit``.

    Falls back to ``_DEFAULT_MAX_RECURSION_LIMIT`` when the app config cannot be
    loaded (e.g. no ``config.yaml`` in a bare unit-test environment) so that the
    clamp still applies rather than crashing the run-config assembly.
    """
    try:
        return get_app_config().max_recursion_limit
    except Exception:
        return _DEFAULT_MAX_RECURSION_LIMIT


def _resolve_scheduler_recursion_limit() -> int:
    """Resolve the scheduled-run recursion_limit from ``AppConfig.scheduler``.

    Falls back to ``_DEFAULT_RECURSION_LIMIT`` when the app config cannot be
    loaded so a missing ``config.yaml`` in tests still launches with the server
    default rather than crashing dispatch. The value is clamped to
    ``max_recursion_limit`` here, at dispatch, so an operator value above the
    ceiling never reaches ``build_run_config`` as an unclamped value — which
    would otherwise be misattributed as a "client" overage and emit a
    ``clamped client recursion_limit`` warning on every scheduled run.
    ``build_run_config`` keeps its own clamp for genuine client-supplied
    bodies (defense in depth; its warning then only fires for real clients).

    Both silent paths now emit an operator-visible warning: the pre-clamp
    (operator value above ``max_recursion_limit``) and the config-load failure
    fallback (``_DEFAULT_RECURSION_LIMIT`` returned instead of the operator
    value).
    """
    try:
        raw = get_app_config().scheduler.recursion_limit
        max_limit = _resolve_max_recursion_limit()
        if raw > max_limit:
            logger.warning(
                "scheduler.recursion_limit %d exceeds max_recursion_limit %d; clamped to %d for scheduled runs",
                raw,
                max_limit,
                max_limit,
            )
        return min(raw, max_limit)
    except Exception:
        logger.warning(
            "failed to load app config; falling back to recursion_limit=%d for scheduled runs",
            _DEFAULT_RECURSION_LIMIT,
        )
        return _DEFAULT_RECURSION_LIMIT


def _clamp_recursion_limit(value: Any, max_limit: int, default_limit: int) -> int:
    """Clamp a client-supplied ``recursion_limit`` into a safe server range.

    Non-integer values (including ``bool``, an ``int`` subclass) and non-positive
    values fall back to the configured default; valid positive integers are
    capped at ``max_limit`` (from ``AppConfig.max_recursion_limit``).
    """
    if isinstance(value, bool) or not isinstance(value, int) or value <= 0:
        return default_limit
    return min(value, max_limit)


def build_run_config(
    thread_id: str,
    request_config: dict[str, Any] | None,
    metadata: dict[str, Any] | None,
    *,
    assistant_id: str | None = None,
) -> dict[str, Any]:
    """Build a RunnableConfig dict for the agent.

    When *assistant_id* refers to a custom agent (anything other than
    ``"lead_agent"`` / ``None``), the name is forwarded as ``agent_name`` in
    both ``configurable`` and ``context`` so it is visible to legacy
    configurable readers and to LangGraph ``ToolRuntime.context`` consumers
    (e.g. the ``setup_agent`` tool, which since LangGraph >=1.1.9 no longer
    falls back from ``context`` to ``configurable``).  An explicit
    ``agent_name`` in either container takes precedence over the value
    derived from ``assistant_id``.  ``make_lead_agent`` reads this key to
    load the matching ``agents/<name>/SOUL.md`` and per-agent config —
    without it the agent silently runs as the default lead agent.

    This mirrors the channel manager's ``_resolve_run_params`` logic except for
    the recursion default: Gateway API runs use the configured top-level
    ``recursion_limit``, while IM channel runs retain their own default.
    """
    # Lead-agent recursion budget (LangGraph super-steps for the lead graph
    # only). Independent of subagent depth: a `task()` dispatch runs the whole
    # subagent inside ONE lead tools-node step, and subagents enforce their own
    # limit via `subagents.max_turns`. Do not conflate this budget with the
    # general-purpose subagent's max_turns.
    default_recursion_limit, max_recursion_limit = _resolve_gateway_recursion_limits()
    config: dict[str, Any] = {"recursion_limit": default_recursion_limit}
    if request_config:
        # LangGraph >= 0.6.0 introduced ``context`` as the preferred way to
        # pass thread-level data and rejects requests that include both
        # ``configurable`` and ``context``.  If the caller already sends
        # ``context``, honour it and skip our own ``configurable`` dict.
        if "context" in request_config:
            if "configurable" in request_config:
                logger.warning(
                    "build_run_config: client sent both 'context' and 'configurable'; preferring 'context' (LangGraph >= 0.6.0). thread_id=%s, caller_configurable keys=%s",
                    thread_id,
                    list((request_config.get("configurable") or {}).keys()),
                )
            context_value = request_config["context"]
            if context_value is None:
                context = {}
            elif isinstance(context_value, Mapping):
                # Strip caller-supplied ``__``-prefixed keys: those are the
                # harness's private run-context channels (skill secret-binding
                # sources, the active-secret set, the run journal). A caller must
                # not be able to seed them and forge internal state — e.g. a
                # forged ``__slash_skill_secret_source`` would otherwise bypass the
                # skill enabled/allowlist/declaration gates (#3938). Legitimate
                # caller keys (``secrets``, ``user_id``, model overrides) never use
                # the ``__`` prefix.
                context = {key: value for key, value in context_value.items() if not (isinstance(key, str) and key.startswith("__"))}
            else:
                raise ValueError("request config 'context' must be a mapping or null.")
            context["thread_id"] = thread_id
            config["context"] = context
            # The checkpointer always scopes state by configurable["thread_id"],
            # regardless of whether the caller drives the run via context (e.g.
            # request-scoped secrets, #3861). thread_id comes from the URL path,
            # not caller config, so mirror it here while keeping secret-bearing
            # context keys out of configurable.
            config["configurable"] = {"thread_id": thread_id}
        else:
            configurable = {"thread_id": thread_id}
            configurable.update(request_config.get("configurable") or {})
            configurable["thread_id"] = thread_id
            config["configurable"] = configurable
        for k, v in request_config.items():
            if k not in ("configurable", "context"):
                config[k] = v
        # Never trust a client-supplied recursion_limit verbatim: clamp it to a
        # safe server range so a single run cannot execute unbounded LangGraph
        # super-steps (runaway LLM cost / DoS). Applied after the passthrough so
        # it overrides whatever the client sent.
        if "recursion_limit" in request_config:
            clamped = _clamp_recursion_limit(request_config["recursion_limit"], max_recursion_limit, default_recursion_limit)
            if clamped != request_config["recursion_limit"]:
                logger.warning(
                    "build_run_config: clamped client recursion_limit %r -> %d (max %d). thread_id=%s",
                    request_config["recursion_limit"],
                    clamped,
                    max_recursion_limit,
                    thread_id,
                )
            config["recursion_limit"] = clamped
    else:
        config["configurable"] = {"thread_id": thread_id}

    # Inject custom agent name when the caller specified a non-default assistant.
    # Honour an explicit agent_name in either runtime options container.
    if assistant_id and assistant_id != _DEFAULT_ASSISTANT_ID:
        normalized = assistant_id.strip().lower().replace("_", "-")
        if not normalized or not re.fullmatch(r"[a-z0-9-]+", normalized):
            raise ValueError(f"Invalid assistant_id {assistant_id!r}: must contain only letters, digits, and hyphens after normalization.")
        configurable = config.setdefault("configurable", {})
        runtime_context = config.setdefault("context", {})
        explicit_agent_name: str | None = None
        if isinstance(configurable, dict) and isinstance(configurable.get("agent_name"), str):
            explicit_agent_name = configurable["agent_name"]
        elif isinstance(runtime_context, dict) and isinstance(runtime_context.get("agent_name"), str):
            explicit_agent_name = runtime_context["agent_name"]
        effective_agent_name = explicit_agent_name or normalized
        if isinstance(configurable, dict):
            configurable["agent_name"] = effective_agent_name
        if isinstance(runtime_context, dict):
            runtime_context["agent_name"] = effective_agent_name
        config.setdefault("run_name", resolve_root_run_name(config, normalized))
    for section in ("configurable", "context"):
        external_values = config.get(section)
        if isinstance(external_values, dict):
            external_values.pop(INTERNAL_CHECKPOINT_MODE_KEY, None)

    if metadata:
        # Merged onto a copy: config["metadata"] is the same dict object as the
        # caller's body.config["metadata"] (the passthrough above copies
        # references), and an in-place update would write server-stamped keys
        # -- the trace id -- through into the request body that is persisted
        # and echoed as the run's kwargs.
        existing_metadata = config.get("metadata")
        merged_metadata = dict(existing_metadata) if isinstance(existing_metadata, dict) else {}
        merged_metadata.update(metadata)
        config["metadata"] = merged_metadata
    return config


def build_checkpoint_state_mutation_accessor(
    request: Request,
    *,
    thread_id: str,
    as_node: str,
    checkpoint_id: str | None = None,
    state_schema: Any | None = None,
) -> tuple[CheckpointStateAccessor, dict[str, Any]]:
    """Build a state-only graph whose writer node finishes immediately.

    ``state_schema`` should be the thread's effective schema (from
    :func:`graph_state_schema` on the assistant graph) whenever the write
    carries materialized state; with the base-schema fallback, channels
    contributed by custom middleware are silently discarded.
    """
    mode = getattr(request.app.state, "checkpoint_channel_mode", "full")
    config: dict[str, Any] = {
        "configurable": {
            "thread_id": thread_id,
            "checkpoint_ns": "",
        }
    }
    if checkpoint_id is not None:
        config["configurable"]["checkpoint_id"] = checkpoint_id
    inject_checkpoint_mode(config, mode)

    graph = build_state_mutation_graph(as_node, mode, state_schema)
    accessor = CheckpointStateAccessor.bind(
        graph,
        get_checkpointer(request),
        store=getattr(request.app.state, "store", None),
        mode=mode,
    )
    return accessor, config


# Cache of factory-built accessor graphs. Accessor operations (aget_state /
# aupdate_state) never execute graph nodes or middleware, so per-request
# variations (user, model, skills) cannot affect materialization semantics;
# the compiled graph is stable per (assistant_id, mode, snapshot_frequency,
# app_config). The
# factory and app_config identities are re-validated on every call so patched
# factories take effect immediately and a config.yaml hot-reload (which
# rebuilds the AppConfig object) never serves a stale compiled graph — the
# cached reference keeps the old config alive, so id-reuse cannot produce a
# false hit. Bounded: cleared when too many distinct assistants appear. The
# cap is configurable (database.checkpoint_graph_cache.accessor_graph_max)
# and re-read on every eviction check, so a hot-reload takes effect without
# a restart.
_STATE_ACCESSOR_GRAPH_CACHE_MAX = 64
_state_accessor_graph_cache: dict[tuple[str | None, str, int | None], tuple[Any, Any, Any]] = {}
_state_accessor_graph_cache_lock = threading.Lock()
_state_accessor_graph_build_locks = KeyedLockTable[tuple[str | None, str, int | None]]()


def _accessor_graph_cache_max(app_config: Any) -> int:
    return resolve_checkpoint_graph_cache_max(
        getattr(app_config, "database", None),
        "accessor_graph_max",
        _STATE_ACCESSOR_GRAPH_CACHE_MAX,
    )


def _cached_state_accessor_graph(key: tuple[str | None, str, int | None], agent_factory: Any, app_config: Any) -> Any | None:
    with _state_accessor_graph_cache_lock:
        cached = _state_accessor_graph_cache.get(key)
        if cached is not None and cached[0] is agent_factory and cached[1] is app_config:
            return cached[2]
    return None


def _cache_state_accessor_graph(key: tuple[str | None, str, int | None], agent_factory: Any, app_config: Any, graph: Any) -> None:
    with _state_accessor_graph_cache_lock:
        if len(_state_accessor_graph_cache) >= _accessor_graph_cache_max(app_config):
            _state_accessor_graph_cache.clear()
        _state_accessor_graph_cache[key] = (agent_factory, app_config, graph)


def _build_state_accessor_graph(agent_factory: Any, config: dict[str, Any]) -> Any:
    agent_result = agent_factory(config=config)
    try:
        from deerflow.agents.lead_agent.agent import unwrap_agent_graph

        return unwrap_agent_graph(agent_result)
    except Exception:
        # A custom factory must keep working even if importing the lead
        # assembly type fails.
        return agent_result


def _state_accessor_graph(agent_factory: Any, assistant_id: str | None, mode: str, snapshot_frequency: int | None, config: dict[str, Any]) -> Any:
    app_config = (config.get("context") or {}).get("app_config")
    key = (assistant_id, mode, snapshot_frequency)
    cached = _cached_state_accessor_graph(key, agent_factory, app_config)
    if cached is not None:
        return cached

    # Construction runs on assembly-pool threads, so same-key cold misses are
    # serialized with a thread lock. The re-check under the lock makes
    # overlapping first readers run the factory exactly once; a waiter whose
    # factory or app-config identity changed while it waited still rebuilds,
    # preserving identity-based cache invalidation.
    with _state_accessor_graph_build_locks.hold(key):
        cached = _cached_state_accessor_graph(key, agent_factory, app_config)
        if cached is not None:
            return cached
        graph = _build_state_accessor_graph(agent_factory, config)
        _cache_state_accessor_graph(key, agent_factory, app_config, graph)
        return graph


class _RawCheckpointSnapshot:
    """StateSnapshot-shaped view over a raw checkpoint tuple (full mode only).

    ``next``/``tasks`` are not derivable without the compiled graph and
    degrade to empty; everything the read endpoints serialize (values,
    metadata, config ancestry, created_at) comes straight from the tuple.
    """

    __slots__ = ("checkpoint_exists", "config", "values", "metadata", "parent_config", "created_at", "tasks", "tasks_known", "next")

    def __init__(self, config: dict[str, Any], tup: Any | None) -> None:
        self.checkpoint_exists = tup is not None
        self.config = getattr(tup, "config", None) or config
        checkpoint = getattr(tup, "checkpoint", None) or {}
        self.values = dict(checkpoint.get("channel_values") or {})
        self.metadata = dict(getattr(tup, "metadata", None) or {})
        self.parent_config = getattr(tup, "parent_config", None)
        self.created_at = checkpoint.get("ts") or self.metadata.get("created_at", "")
        self.tasks: tuple = ()
        self.tasks_known = False
        self.next: tuple = ()


class _RawCheckpointReadAccessor:
    """Degraded full-mode read accessor for when the agent factory is down.

    Full-mode checkpoints persist complete ``channel_values``, so reads do not
    need the compiled graph. The fail-closed delta gate still applies: delta
    checkpoints are rejected with :class:`CheckpointModeMismatchError` instead
    of being served as partial state. Writes are unsupported — mutation paths
    keep using the graph-backed accessor.
    """

    def __init__(self, checkpointer: Any, mode: str) -> None:
        self.checkpointer = checkpointer
        self.mode = mode

    @staticmethod
    def _gate(tup: Any) -> None:
        if checkpoint_tuple_uses_delta(tup):
            raise CheckpointModeMismatchError("Thread requires delta mode; materialize and convert its checkpoints before using full mode.")

    async def aget(self, config: dict[str, Any]) -> _RawCheckpointSnapshot:
        tup = await self.checkpointer.aget_tuple(config)
        self._gate(tup)
        return _RawCheckpointSnapshot(config, tup)

    async def ahistory(self, config: dict[str, Any], *, limit: int | None = None) -> list[_RawCheckpointSnapshot]:
        if limit is not None and limit <= 0:
            return []
        result: list[_RawCheckpointSnapshot] = []
        before = None
        walk_config = config
        if config.get("configurable", {}).get("checkpoint_id"):
            # Pregel's get_state_history treats config.checkpoint_id as the
            # inclusive start of the walk, while alist(before=...) is
            # exclusive — fetch the anchor explicitly so the degraded path
            # matches the graph path.
            before = config
            walk_config = {
                **config,
                "configurable": {k: v for k, v in config.get("configurable", {}).items() if k != "checkpoint_id"},
            }
            anchor = await self.checkpointer.aget_tuple(before)
            self._gate(anchor)
            if anchor is not None:
                result.append(_RawCheckpointSnapshot(config, anchor))
        if limit is None or len(result) < limit:
            remaining = None if limit is None else limit - len(result)
            async for tup in self.checkpointer.alist(walk_config, before=before, limit=remaining):
                self._gate(tup)
                result.append(_RawCheckpointSnapshot(config, tup))
                if limit is not None and len(result) >= limit:
                    break
        return result


def build_checkpoint_state_accessor(
    request: Request,
    *,
    thread_id: str,
    assistant_id: str | None = None,
    checkpoint_id: str | None = None,
) -> tuple[CheckpointStateAccessor, dict[str, Any]]:
    """Build the mode-selected lead graph used for materialized checkpoint state."""
    ctx = get_run_context(request)
    config = build_run_config(thread_id, None, None, assistant_id=assistant_id)
    configurable = config.setdefault("configurable", {})
    configurable["checkpoint_ns"] = ""
    if checkpoint_id is not None:
        configurable["checkpoint_id"] = checkpoint_id

    if ctx.app_config is not None:
        config.setdefault("context", {})["app_config"] = ctx.app_config
    inject_checkpoint_mode(config, ctx.checkpoint_channel_mode)

    agent_factory = resolve_agent_factory(assistant_id)
    try:
        graph = _state_accessor_graph(
            agent_factory,
            assistant_id,
            ctx.checkpoint_channel_mode,
            getattr(ctx, "checkpoint_snapshot_frequency", None),
            config,
        )
    except Exception:
        if ctx.checkpoint_channel_mode != "full":
            # Delta materialization needs the graph's channel table; there is
            # no degraded path. Surface the factory failure as-is.
            raise
        # Full-mode checkpoints carry complete channel_values: degrade to raw
        # checkpointer reads so state endpoints survive a broken agent factory
        # (bad model config, MCP server down, misconfigured skill).
        logger.warning(
            "Agent factory unavailable for thread %s; falling back to raw checkpointer reads",
            thread_id,
            exc_info=True,
        )
        return _RawCheckpointReadAccessor(ctx.checkpointer, ctx.checkpoint_channel_mode), config
    accessor = CheckpointStateAccessor.bind(
        graph,
        ctx.checkpointer,
        store=ctx.store,
        mode=ctx.checkpoint_channel_mode,
    )
    return accessor, config


async def abuild_checkpoint_state_accessor(
    request: Request,
    *,
    thread_id: str,
    assistant_id: str | None = None,
    checkpoint_id: str | None = None,
) -> tuple[CheckpointStateAccessor, dict[str, Any]]:
    """Async variant of :func:`build_checkpoint_state_accessor`.

    Identical accessor construction, but the agent-factory assembly — which
    re-enters ``get_available_tools()`` and may block on MCP cache
    initialization — runs off-loop on the dedicated assembly pool so the
    Gateway event loop keeps making progress (issue #5172). Repeat calls hit
    ``_state_accessor_graph_cache`` and only pay the thread hop; overlapping
    cold readers with the same cache key are serialized per key so the
    factory runs exactly once, and a reader whose factory or app-config
    identity changed while it waited rebuilds instead of reusing the
    winner's graph.
    """
    return await run_assembly(
        build_checkpoint_state_accessor,
        request,
        thread_id=thread_id,
        assistant_id=assistant_id,
        checkpoint_id=checkpoint_id,
    )


async def resolve_thread_assistant_id(
    request: Request,
    thread_id: str,
    *,
    fail_closed: bool = False,
) -> str | None:
    """Return the assistant_id recorded in thread metadata, or ``None``.

    Missing records degrade to ``None`` (the default lead agent). Store
    failures do the same for read callers, while mutation callers set
    ``fail_closed`` so they cannot compile a write graph with the wrong schema.
    """
    from app.gateway.deps import get_thread_store

    try:
        thread_store = get_thread_store(request)
        record = await thread_store.get(thread_id)
    except Exception:
        logger.warning("Failed to resolve assistant_id for thread %s", thread_id, exc_info=True)
        if fail_closed:
            raise
        return None
    return record.get("assistant_id") if isinstance(record, dict) else None


async def build_thread_checkpoint_state_accessor(
    request: Request,
    *,
    thread_id: str,
    checkpoint_id: str | None = None,
    fail_closed: bool = False,
) -> tuple[CheckpointStateAccessor, dict[str, Any]]:
    """Single resolution boundary for state endpoints.

    Thread metadata -> assistant_id -> effective assistant graph. Materializing
    with the default lead schema would drop channels contributed by a custom
    ``AgentMiddleware.state_schema`` from the response.
    """
    assistant_id = await resolve_thread_assistant_id(request, thread_id, fail_closed=fail_closed)
    return await abuild_checkpoint_state_accessor(
        request,
        thread_id=thread_id,
        assistant_id=assistant_id,
        checkpoint_id=checkpoint_id,
    )


async def build_thread_checkpoint_state_mutation_accessor(
    request: Request,
    *,
    thread_id: str,
    as_node: str,
    checkpoint_id: str | None = None,
) -> tuple[CheckpointStateAccessor, dict[str, Any]]:
    """Mutation accessor compiled with the thread's effective state schema.

    Derives the schema through :func:`build_thread_checkpoint_state_accessor`
    so writes carrying materialized state do not silently discard
    extension-owned channels.
    """
    read_accessor, _read_config = await build_thread_checkpoint_state_accessor(
        request,
        thread_id=thread_id,
        checkpoint_id=checkpoint_id,
        fail_closed=True,
    )
    state_schema = graph_state_schema(getattr(read_accessor, "graph", None))
    return build_checkpoint_state_mutation_accessor(
        request,
        thread_id=thread_id,
        as_node=as_node,
        checkpoint_id=checkpoint_id,
        state_schema=state_schema,
    )


async def apply_checkpoint_to_run_config(
    config: dict[str, Any],
    *,
    body: Any,
    thread_id: str,
    request: Request,
) -> None:
    """Validate an optional run checkpoint and attach it to RunnableConfig."""
    checkpoint = getattr(body, "checkpoint", None)
    checkpoint_id = getattr(body, "checkpoint_id", None)
    checkpoint_ns = ""
    checkpoint_map = None

    if checkpoint:
        if not isinstance(checkpoint, Mapping):
            raise HTTPException(status_code=400, detail="checkpoint must be an object")
        checkpoint_thread_id = checkpoint.get("thread_id")
        if checkpoint_thread_id is not None and str(checkpoint_thread_id) != thread_id:
            raise HTTPException(status_code=400, detail="checkpoint thread_id does not match request thread_id")
        raw_checkpoint_id = checkpoint.get("checkpoint_id")
        if raw_checkpoint_id:
            checkpoint_id = str(raw_checkpoint_id)
        raw_checkpoint_ns = checkpoint.get("checkpoint_ns")
        if raw_checkpoint_ns is not None:
            checkpoint_ns = str(raw_checkpoint_ns)
        checkpoint_map = checkpoint.get("checkpoint_map")

    if not checkpoint_id:
        return

    read_config: dict[str, Any] = {
        "configurable": {
            "thread_id": thread_id,
            "checkpoint_ns": checkpoint_ns,
            "checkpoint_id": str(checkpoint_id),
        }
    }
    if checkpoint_map is not None:
        read_config["configurable"]["checkpoint_map"] = checkpoint_map

    checkpointer = get_checkpointer(request)
    try:
        checkpoint_tuple = await checkpointer.aget_tuple(read_config)
    except Exception as exc:
        logger.exception("Failed to validate checkpoint %s for thread %s", checkpoint_id, sanitize_log_param(thread_id))
        raise HTTPException(status_code=500, detail="Failed to validate checkpoint") from exc
    if checkpoint_tuple is None:
        raise HTTPException(status_code=404, detail=f"Checkpoint {checkpoint_id} not found")

    configurable = config.setdefault("configurable", {})
    if not isinstance(configurable, dict):
        raise HTTPException(status_code=400, detail="request config configurable must be an object")
    configurable["thread_id"] = thread_id
    configurable["checkpoint_ns"] = checkpoint_ns
    configurable["checkpoint_id"] = str(checkpoint_id)
    if checkpoint_map is not None:
        configurable["checkpoint_map"] = checkpoint_map


async def ensure_checkpoint_history_seeded(
    request: Request,
    *,
    thread_id: str,
    assistant_id: str | None,
) -> None:
    """Backfill an empty run-event feed from an existing checkpoint head.

    No-op unless the feed is empty AND a checkpoint head with messages
    exists — i.e. a legacy checkpoint-only thread facing its first journaled
    run. This is a migration shim: remove it once pre-journal threads are no
    longer a supported upgrade source. The info log on a successful seed is
    the observability hook for that decision — when it stops appearing, the
    shim is dead.
    """
    event_store = request.app.state.run_event_store
    # The emptiness check is deliberately thread-scoped, never user-scoped:
    # seed rows may be stamped with a different principal (NULL for ownerless
    # seeds, or another user on a shared NULL-owner thread), so a user-scoped
    # query would miss them and re-seed a duplicate history per principal.
    # Passing user_id=None also opts out of AUTO resolution explicitly, which
    # would raise when no user contextvar is set (e.g. the scheduler launch
    # path for ownerless internal tasks).
    if await event_store.list_messages(thread_id, limit=1, user_id=None):
        return

    checkpoint_config = {
        "configurable": {
            "thread_id": thread_id,
            "checkpoint_ns": "",
        }
    }
    if await get_checkpointer(request).aget_tuple(checkpoint_config) is None:
        return

    accessor, config = await abuild_checkpoint_state_accessor(
        request,
        thread_id=thread_id,
        assistant_id=assistant_id,
    )
    snapshot = await accessor.aget(config)
    values = getattr(snapshot, "values", None)
    messages = values.get("messages") if isinstance(values, dict) else None
    if not isinstance(messages, list) or not messages:
        return

    events = build_checkpoint_history_seed_events(
        messages,
        thread_id=thread_id,
        run_id_prefix=f"checkpoint-seed-{thread_id}",
    )
    if not events:
        return
    await event_store.put_batch(events)
    logger.info("Seeded %d checkpoint-history events for thread %s", len(events), thread_id)


def _message_identifier(message: Any) -> str | None:
    if isinstance(message, BaseMessage):
        return str(message.id) if message.id else None
    if isinstance(message, Mapping):
        value = message.get("id")
        return str(value) if value else None
    return None


def _message_additional_kwargs(message: Any) -> Mapping[str, Any]:
    if isinstance(message, BaseMessage):
        return message.additional_kwargs
    if isinstance(message, Mapping):
        value = message.get("additional_kwargs")
        return value if isinstance(value, Mapping) else {}
    return {}


def _is_scope_source_human_message(message: Any) -> bool:
    """Return whether a checkpoint message can originate a recovered scope."""
    if isinstance(message, HumanMessage):
        return is_genuine_user_message(message)
    if not isinstance(message, Mapping):
        return False
    if message.get("type") != "human" and message.get("role") not in {"human", "user"}:
        return False
    return not _skips_input_guardrail(dict(_message_additional_kwargs(message)), message.get("name"))


async def _recover_run_knowledge_scope(
    request: Request,
    *,
    thread_id: str,
    target_message_id: str | None,
) -> object | None:
    """Resolve one replay/resume scope from the authoritative latest checkpoint."""
    accessor, config = await build_thread_checkpoint_state_accessor(
        request,
        thread_id=thread_id,
    )
    try:
        snapshot = await accessor.aget(config)
    except Exception as exc:
        logger.exception("Failed to recover knowledge scope for thread %s", sanitize_log_param(thread_id))
        raise HTTPException(status_code=500, detail="Failed to recover knowledge scope") from exc
    values = getattr(snapshot, "values", None)
    messages = values.get("messages") if isinstance(values, Mapping) else None
    if not isinstance(messages, list):
        messages = []

    source: Any | None = None
    if target_message_id:
        target_index = next(
            (index for index, message in enumerate(messages) if _message_identifier(message) == target_message_id),
            None,
        )
        if target_index is not None:
            source = next(
                (message for message in reversed(messages[:target_index]) if _is_scope_source_human_message(message)),
                None,
            )
        else:
            # Interrupted assistant output may never reach a checkpoint. Its
            # source is still the terminal HumanMessage of the latest state.
            source = next(
                (message for message in reversed(messages) if _is_scope_source_human_message(message)),
                None,
            )
        if source is None:
            raise HTTPException(
                status_code=409,
                detail="Could not recover the source HumanMessage knowledge_scope",
            )
    else:
        source = next(
            (message for message in reversed(messages) if _is_scope_source_human_message(message)),
            None,
        )
    if source is None:
        return None
    additional_kwargs = _message_additional_kwargs(source)
    return additional_kwargs.get(KNOWLEDGE_SCOPE_KEY)


def _current_human_message(graph_input: object) -> HumanMessage | None:
    if not isinstance(graph_input, Mapping):
        return None
    messages = graph_input.get("messages")
    if not isinstance(messages, list):
        return None
    return next(
        (message for message in reversed(messages) if isinstance(message, HumanMessage)),
        None,
    )


async def _load_scope_agent_config(
    *,
    assistant_id: str | None,
    user_id: str | None,
) -> Any | None:
    if not assistant_id or assistant_id == _DEFAULT_ASSISTANT_ID:
        return None
    normalized = assistant_id.strip().lower().replace("_", "-")
    try:
        return await asyncio.to_thread(
            load_agent_config,
            normalized,
            user_id=user_id,
        )
    except (FileNotFoundError, ValueError) as exc:
        raise HTTPException(
            status_code=422,
            detail="knowledge_scope assistant configuration could not be resolved",
        ) from exc


async def _validate_scope_thread_binding(
    run_ctx: RunContext,
    *,
    thread_id: str,
    assistant_id: str | None,
) -> None:
    existing = await run_ctx.thread_store.get(thread_id)
    if not isinstance(existing, Mapping):
        return
    bound = existing.get("assistant_id")
    if isinstance(bound, str) and bound and assistant_id and bound != assistant_id:
        raise HTTPException(
            status_code=409,
            detail="Thread assistant does not match knowledge_scope assistant",
        )


# ---------------------------------------------------------------------------
# Run lifecycle
# ---------------------------------------------------------------------------

_SCHEDULER_METADATA_KEYS = frozenset(
    {"scheduled_task_id", "scheduled_task_run_id", "scheduled_trigger", "scheduled_goal_objective", "scheduled_previous_thread_id", "scheduled_context_mode", "scheduled_tool_created", "__scheduler_capability"}
)


_MAX_CLIENT_TIMEZONE_CHARS = 64


async def _client_timezone_from_context(context: Mapping[str, Any] | None) -> str | None:
    """The browser timezone the web client sent in ``body.context``, if valid.

    Read only for the schedule capability (default zone for new tasks). It is
    not a ``_CONTEXT_CONFIGURABLE_KEYS`` entry, so it never reaches the run
    config, the checkpoint or the prompt.
    """
    raw = context.get("client_timezone") if isinstance(context, Mapping) else None
    if raw is None:
        return None
    if not isinstance(raw, str) or not raw or len(raw) > _MAX_CLIENT_TIMEZONE_CHARS:
        logger.debug("Ignoring a malformed client_timezone in the run context")
        return None
    try:
        return await asyncio.to_thread(validate_timezone, raw)
    except (ValueError, OSError):
        logger.debug("Ignoring an unknown client_timezone %s", sanitize_log_param(raw))
        return None


def _admit_scheduler_metadata(value: object, *, trusted: bool) -> dict[str, Any]:
    """Keep scheduler snapshots only on the private scheduler launch path."""
    result = dict(value) if isinstance(value, Mapping) else {}
    if not trusted:
        for key in _SCHEDULER_METADATA_KEYS:
            result.pop(key, None)
    # A serialized value can never recover an execution capability.
    result.pop("__scheduler_capability", None)
    return result


async def start_run(
    body: RunCreateRequest,
    thread_id: str,
    request: Request,
    *,
    idempotency_key: str | None = None,
    require_existing_thread: bool = False,
    scheduled_task_runtime: Mapping[str, Any] | None = None,
) -> RunRecord:
    """Create a RunRecord and launch the background agent task.

    Parameters
    ----------
    body : RunCreateRequest
        The validated request body shared by HTTP and internal launch paths.
    thread_id : str
        Target thread.
    request : Request
        FastAPI request — used to retrieve singletons from ``app.state``.
    require_existing_thread : bool
        Reject a missing thread instead of auto-creating metadata. Internal
        notification runs use this so a deleted chat cannot be resurrected.
    """
    # Cancel-capability gate. interrupt/rollback strategies terminate an already
    # active run — runs:cancel capability, not runs:create — so a create-only
    # PAT must not reach them. Enforced here, the single choke point every
    # run-creation path flows through (HTTP routes and internal launchers
    # alike), so no entry point can bypass it; regenerate launches pass
    # multitask_strategy="reject" and are unaffected. Requests without a
    # stamped auth context (internal/test compositions) skip the gate.
    require_cancel_permission_if(request, body.multitask_strategy != "reject")
    if scheduled_task_runtime is not None and getattr(request.state, "auth_source", None) != AUTH_SOURCE_INTERNAL:
        raise HTTPException(status_code=403, detail="Scheduled runtime authority requires an internal launcher")

    try:
        validate_thread_id(thread_id)
    except ValueError as exc:
        raise HTTPException(status_code=422, detail=str(exc)) from exc

    body_config = getattr(body, "config", None)
    config_metadata = body_config.get("metadata") if isinstance(body_config, dict) else None
    try:
        validate_run_metadata_secrets(getattr(body, "metadata", None))
        validate_run_metadata_secrets(config_metadata)
    except LegacyRunMetadataSecretError as exc:
        raise HTTPException(status_code=422, detail=str(exc)) from exc

    stream_modes = normalize_stream_modes(body.stream_mode)
    bridge = get_stream_bridge(request)
    run_mgr = get_run_manager(request)
    run_ctx = get_run_context(request)

    disconnect = DisconnectMode.cancel if body.on_disconnect == "cancel" else DisconnectMode.continue_

    body_context = getattr(body, "context", None) or {}
    model_name = body_context.get("model_name")
    # Coerce non-string model_name values to str before truncation.
    if model_name is not None and not isinstance(model_name, str):
        model_name = str(model_name)

    # Validate model against the allowlist when a model_name is provided.
    if model_name:
        app_config = get_app_config()
        resolved = app_config.get_model_config(model_name)
        if resolved is None:
            raise HTTPException(
                status_code=400,
                detail=f"Model {model_name!r} is not in the configured model allowlist",
            )

    owner_user_id = get_trusted_internal_owner_user_id(request)
    # Stateless run endpoints carry thread_id in the request *body*, so the
    # @require_permission(owner_check=True) decorator -- which resolves ownership
    # from the path param -- cannot protect them. Enforce thread ownership here,
    # before any run is created, so one user cannot start runs on (or read /wait
    # checkpoint state from) another user's thread. Missing rows (auto-created
    # temp threads) and NULL-owner rows (shared / pre-auth data) stay accessible
    # via check_access; only a thread already owned by another user is rejected
    # with 404, matching thread_runs.py's anti-enumeration behaviour. Internal
    # channel runs act on behalf of the connection owner carried in
    # X-DeerFlow-Owner-User-Id, so they are scoped to that owner instead of
    # bypassing the check -- a leaked internal token must not grant cross-user
    # thread access.
    user = getattr(request.state, "user", None)

    async def thread_access_allowed() -> bool:
        if user is None:
            if not require_existing_thread:
                return True
            return await run_ctx.thread_store.get(thread_id) is not None
        allowed = await run_ctx.thread_store.check_access(
            thread_id,
            str(user.id),
            require_existing=require_existing_thread,
        )
        if not allowed and owner_user_id and getattr(user, "system_role", None) == INTERNAL_SYSTEM_ROLE:
            # Channel workers may also act for the connection owner named in
            # the trusted header (e.g. claiming a legacy default-owned channel
            # thread for its real owner).
            allowed = await run_ctx.thread_store.check_access(
                thread_id,
                owner_user_id,
                require_existing=require_existing_thread,
            )
        return allowed

    if not await thread_access_allowed():
        raise HTTPException(status_code=404, detail=f"Thread {thread_id} not found")

    owner_context_token = set_current_user(SimpleNamespace(id=owner_user_id)) if owner_user_id else None
    try:
        is_internal_caller = getattr(getattr(request, "state", None), "auth_source", None) == AUTH_SOURCE_INTERNAL
        # Validate even when resume takes precedence, so ignored input cannot
        # appear to have been admitted or persist as unchecked run audit data.
        normalized_input = normalize_input(body.input, trusted_internal=is_internal_caller)
        # Cold resolution imports the lead-agent stack. Use the assembly pool
        # so import-lock waiters cannot starve unrelated default-executor work.
        # Keep this before admission so import failures cannot create a run.
        agent_factory = await run_assembly(resolve_agent_factory, body.assistant_id)
        command = getattr(body, "command", None)
        if command and command.get("resume") is not None:
            graph_input = Command(resume=command["resume"])
        else:
            graph_input = normalized_input
        # deerflow_trace_id is server-issued, so the caller's value is replaced
        # here at the trust boundary. body.metadata forks two ways -- through
        # build_run_config into config["metadata"], which the run worker
        # restamps, and through create_or_reject into the run record, which the
        # runs API echoes verbatim. Only the first is covered downstream, so
        # without this the run record is the one surface that persists a forged
        # id, disagreeing with the response header, the logs, and the
        # checkpoint. The caller's own metadata keys are preserved.
        # deerflow_origin is server-owned as well: only a server-side launcher
        # (request.state.run_origin) or the internal channel caller may set it,
        # and both metadata forks drop any client copy. build_run_config merges
        # body.config["metadata"] into the live config, so the second pop below
        # is what keeps a forged value out of the live run config.
        origin = resolve_request_origin(request, body.metadata)
        run_metadata = _admit_scheduler_metadata(body.metadata, trusted=scheduled_task_runtime is not None)
        run_metadata.pop(DEERFLOW_ORIGIN_KEY, None)
        if origin is not None:
            run_metadata[DEERFLOW_ORIGIN_KEY] = origin
        run_metadata[DEERFLOW_TRACE_METADATA_KEY] = ensure_trace_id()

        config = build_run_config(thread_id, body.config, run_metadata, assistant_id=body.assistant_id)
        if isinstance(config.get("metadata"), dict):
            config["metadata"] = _admit_scheduler_metadata(config["metadata"], trusted=scheduled_task_runtime is not None)
            config["metadata"].pop(DEERFLOW_ORIGIN_KEY, None)
            if origin is not None:
                config["metadata"][DEERFLOW_ORIGIN_KEY] = origin
        await apply_checkpoint_to_run_config(config, body=body, thread_id=thread_id, request=request)

        # Merge DeerFlow-specific context overrides into both ``configurable`` and ``context``.
        # The ``context`` field is a custom extension for the langgraph-compat layer
        # that carries agent configuration (model_name, thinking_enabled, etc.).
        # Only agent-relevant keys are forwarded; unknown keys (e.g. thread_id) are ignored.
        merge_run_context_overrides(config, getattr(body, "context", None), internal=is_internal_caller)
        if not is_internal_caller:
            # ``body.config`` is free-form and copied verbatim by
            # ``build_run_config``; scrub internal-only keys smuggled there.
            strip_internal_context_keys(config)

        replay_kind = run_metadata.get("replay_kind")
        target_message_id = run_metadata.get("regenerate_from_message_id")
        scope_graph_input = graph_input if isinstance(graph_input, dict) else {"messages": []}
        current_human_message = _current_human_message(graph_input)
        current_message_has_scope = current_human_message is not None and KNOWLEDGE_SCOPE_KEY in current_human_message.additional_kwargs
        replay_requires_scope_recovery = isinstance(graph_input, Command) or (isinstance(target_message_id, str) and bool(target_message_id) and (replay_kind != "edit" or not current_message_has_scope))
        is_human_input_response = current_human_message is not None and "human_input_response" in current_human_message.additional_kwargs
        # Clarification and edit-replay messages may intentionally replace the
        # source scope. If either client omits its current selector snapshot,
        # inherit the source turn's authoritative scope instead of widening the
        # run to every operator-approved dataset. Other replay paths always use
        # server recovery regardless of client input.
        is_scope_recovery = replay_requires_scope_recovery or (is_human_input_response and not current_message_has_scope)
        recovery_scope = (
            await _recover_run_knowledge_scope(
                request,
                thread_id=thread_id,
                target_message_id=(target_message_id if isinstance(target_message_id, str) else None),
            )
            if is_scope_recovery
            else None
        )
        # Match lead-agent assembly: runtime context overrides configurable.
        # Older API/channel callers may name an agent through context while
        # retaining lead_agent as their routing assistant ID.
        scope_runtime_config = dict(config.get("configurable") or {})
        if isinstance(config.get("context"), dict):
            scope_runtime_config.update(config["context"])
        scope_assistant_id = scope_runtime_config.get("agent_name") or _DEFAULT_ASSISTANT_ID
        # Bootstrap assembly intentionally does not load an agent config: the
        # new agent may not exist yet and setup_agent creates its definition.
        agent_config = (
            await _load_scope_agent_config(
                assistant_id=scope_assistant_id,
                user_id=owner_user_id or (str(user.id) if user is not None else None),
            )
            if not scope_runtime_config.get("is_bootstrap")
            else None
        )
        # Keep the pre-default identity even when the agent is initially
        # unbound: adding a default must not reject an already-accepted retry.
        # The durable input still exposes the original accepted scope.
        request_input = _canonical_run_record_input(body.input, graph_input) if idempotency_key else None
        knowledge_default_request_hash = hashlib.sha256(json.dumps(request_input, sort_keys=True, ensure_ascii=False).encode()).hexdigest() if idempotency_key else None
        accepts_knowledge_default = not is_scope_recovery and not current_message_has_scope and knowledge_default_request_hash is not None
        admitted_knowledge_scope = admit_message_knowledge_scope(
            scope_graph_input,
            assistant_id=scope_assistant_id,
            app_config=run_ctx.app_config or get_app_config(),
            agent_config=agent_config,
            recovery_scope=recovery_scope,
            recovery=is_scope_recovery,
        )
        if admitted_knowledge_scope is not None:
            await _validate_scope_thread_binding(
                run_ctx,
                thread_id=thread_id,
                assistant_id=body.assistant_id,
            )
        run_record_input = _canonical_run_record_input(body.input, graph_input)

        internal_owner_user = await resolve_trusted_internal_owner_for_attribution(request, owner_user_id)
        if internal_owner_user is None and owner_user_id == AUTH_DISABLED_USER_ID and is_auth_disabled():
            internal_owner_user = get_auth_disabled_user()
        inject_authenticated_user_context(
            config,
            request,
            internal_owner_user=internal_owner_user,
            request_context=getattr(body, "context", None),
        )

        if scheduled_task_runtime is not None:
            run_ctx = replace(run_ctx, scheduled_task_runtime=dict(scheduled_task_runtime))
        conversation_references = list(getattr(body, "conversation_references", None) or [])
        if conversation_references:
            from app.gateway.conversation_access import prepare_conversation_reader

            prepared = prepare_conversation_reader(
                conversation_references,
                request=request,
                user_id=owner_user_id or (str(user.id) if user is not None else None),
                run_context=run_ctx,
                run_manager=run_mgr,
                app_config=get_app_config(),
            )
            reader, source_ids = prepared
            run_ctx = replace(run_ctx, conversation_reader=reader)
            if isinstance(graph_input, dict):
                # Keep this endpoint's list-only wire contract even though
                # message admission canonicalizes single-message shorthand.
                raw_messages = (body.input or {}).get("messages")
                if raw_messages is not None and not isinstance(raw_messages, list):
                    raise HTTPException(status_code=422, detail="input.messages must be a list")
                reference_messages = graph_input.get("messages")
                if reference_messages is None:
                    reference_messages = []
                # ``normalize_input`` guarantees a list here. The raw-input
                # check above is the authoritative list-only wire validation.
                # Reference IDs are user-selected data. Keep them out of the
                # system prompt and grant no authority from this persisted hint.
                graph_input = {
                    **graph_input,
                    "messages": [
                        *reference_messages,
                        HumanMessage(
                            content="Read-only conversation references for this run: " + json.dumps(source_ids),
                            additional_kwargs={"hide_from_ui": True},
                        ),
                    ],
                }
        elif scheduled_task_runtime is not None and isinstance(run_metadata.get("scheduled_previous_thread_id"), str):
            from app.gateway.conversation_access import prepare_scheduled_conversation_reader

            owner = internal_owner_user
            if owner is not None and "runs:read" in await resolve_route_permissions(owner, is_internal=False):
                prepared = prepare_scheduled_conversation_reader([run_metadata["scheduled_previous_thread_id"]], owner_user_id=str(owner.id), run_context=run_ctx, run_manager=run_mgr, app_config=run_ctx.app_config or get_app_config())
                if prepared is not None:
                    reader, source_ids = prepared

                    async def scheduled_reader(*args, **kwargs):
                        current_owner = get_auth_disabled_user() if str(owner.id) == AUTH_DISABLED_USER_ID and is_auth_disabled() else await get_local_provider().get_user(str(owner.id))
                        if current_owner is None or "runs:read" not in await resolve_route_permissions(current_owner, is_internal=False):
                            return json.dumps({"status": "unavailable", "messages": [], "next_cursor": None, "has_more": False})
                        return await reader(*args, **kwargs)

                    run_ctx = replace(run_ctx, conversation_reader=scheduled_reader)
                    if isinstance(graph_input, dict):
                        graph_input = {
                            **graph_input,
                            "messages": [*(graph_input.get("messages") or []), HumanMessage(content="Read-only conversation references for this run: " + json.dumps(source_ids), additional_kwargs={"hide_from_ui": True})],
                        }
        # Resolve and pin the thread's project context once per run (spec
        # §7.1): middlewares and tools read only this server-owned snapshot —
        # nothing re-resolves membership mid-run, and admission never writes
        # membership (§10.7). Resolution failure degrades to unassigned with a
        # warning inside the resolver; it never fails the run.
        project_context = await resolve_project_context(
            run_ctx.thread_store,
            getattr(request.app.state, "project_repo", None),
            thread_id,
            getattr(request.app.state, "project_document_repo", None),
        )
        if project_context is not None:
            config["context"][PROJECT_CONTEXT_KEY] = project_context

        async def run_after_metadata(record: RunRecord) -> None:
            metadata_task = asyncio.create_task(
                _ensure_thread_metadata(
                    run_ctx,
                    record,
                    owner_user_id=owner_user_id,
                    require_existing_thread=require_existing_thread,
                )
            )
            abort_task = asyncio.create_task(record.abort_event.wait())
            metadata_failure_logged = False
            metadata_failure: Exception | None = None
            metadata_record: dict[str, Any] | None = None
            try:
                done, _ = await asyncio.wait(
                    (metadata_task, abort_task),
                    timeout=_THREAD_METADATA_SETUP_TIMEOUT_SECONDS,
                    return_when=asyncio.FIRST_COMPLETED,
                )
                if metadata_task in done:
                    try:
                        metadata_record = metadata_task.result()
                    except asyncio.CancelledError:
                        pass
                    except Exception as exc:
                        metadata_failure_logged = True
                        metadata_failure = exc
                        logger.warning(
                            "Failed to ensure thread_meta for %s%s",
                            sanitize_log_param(thread_id),
                            "" if require_existing_thread else " (non-fatal)",
                            exc_info=True,
                        )
                elif abort_task not in done:
                    logger.warning(
                        "Timed out ensuring thread_meta for %s after %.1fs",
                        sanitize_log_param(thread_id),
                        _THREAD_METADATA_SETUP_TIMEOUT_SECONDS,
                    )
                    if require_existing_thread:
                        metadata_failure = TimeoutError("Timed out verifying existing thread metadata")
            finally:
                if metadata_task.done():
                    if metadata_record is None and not metadata_failure_logged:
                        try:
                            metadata_record = metadata_task.result()
                        except asyncio.CancelledError:
                            pass
                        except Exception as exc:
                            metadata_failure_logged = True
                            metadata_failure = exc
                            logger.warning(
                                "Failed to ensure thread_meta for %s%s",
                                sanitize_log_param(thread_id),
                                "" if require_existing_thread else " (non-fatal)",
                                exc_info=True,
                            )
                else:
                    metadata_task.cancel()
                    metadata_task.add_done_callback(
                        lambda task: _log_thread_metadata_task_result(
                            task,
                            thread_id=thread_id,
                        )
                    )
                if not abort_task.done():
                    abort_task.cancel()
                    abort_task.add_done_callback(_consume_task_result)
            if metadata_failure is not None and require_existing_thread:
                await run_mgr.fail_start_if_pending(
                    record.run_id,
                    error=str(metadata_failure),
                )
            # Continue through run_agent even after metadata abort, timeout,
            # or strict verification failure:
            # its startup barrier is the single path that turns pending
            # cancellation into no-agent-construction plus publish_end.
            incarnation_kwargs: dict[str, str | None] = {}
            if metadata_record is None:
                if not record.abort_event.is_set():
                    logger.warning(
                        "Thread metadata for %s is unavailable; MCP access will fail closed",
                        sanitize_log_param(thread_id),
                    )
            else:
                if "incarnation" not in metadata_record:
                    logger.warning(
                        "Thread metadata for %s has no incarnation; MCP access will fail closed",
                        sanitize_log_param(thread_id),
                    )
                else:
                    incarnation = metadata_record["incarnation"]
                    if is_valid_thread_incarnation(incarnation):
                        incarnation_kwargs["thread_incarnation"] = incarnation
                    else:
                        logger.warning(
                            "Thread metadata for %s has an invalid incarnation; MCP access will fail closed",
                            sanitize_log_param(thread_id),
                        )
            from app.gateway.scheduled_task_access import prepare_scheduler_capability

            human_text = ""
            if current_human_message is not None:
                original = current_human_message.additional_kwargs.get("original_user_content", current_human_message.content)
                if isinstance(original, str):
                    human_text = original
                elif isinstance(original, list):
                    human_text = "\n".join(block["text"] for block in original if isinstance(block, dict) and block.get("type") == "text" and isinstance(block.get("text"), str))
            scheduler_capability = None
            if scheduler_tools_enabled(getattr(run_ctx, "app_config", None)):
                client_timezone = await _client_timezone_from_context(body.context)
                try:
                    scheduler_capability = await prepare_scheduler_capability(
                        request,
                        user=internal_owner_user or user,
                        thread_id=thread_id,
                        run_id=record.run_id,
                        assistant_id=scope_assistant_id,
                        original_user_text=human_text,
                        interaction_policy=resolve_run_interaction_policy(config),
                        scheduled_task_runtime=scheduled_task_runtime,
                        client_timezone=client_timezone,
                    )
                except Exception:
                    logger.warning("Scheduler tool capability is unavailable for this run", exc_info=True)
            execution_context = replace(run_ctx, scheduler_capability=scheduler_capability) if scheduler_capability is not None else run_ctx
            await run_agent(
                bridge,
                run_mgr,
                record,
                ctx=execution_context,
                agent_factory=agent_factory,
                graph_input=graph_input,
                config=config,
                stream_modes=stream_modes,
                stream_subgraphs=body.stream_subgraphs,
                interrupt_before=body.interrupt_before,
                interrupt_after=body.interrupt_after,
                knowledge_scope=admitted_knowledge_scope,
                **incarnation_kwargs,
            )

        try:
            async with goal_thread_lock(thread_id):
                await ensure_checkpoint_history_seeded(
                    request,
                    thread_id=thread_id,
                    assistant_id=body.assistant_id,
                )
                # A strict caller may have observed the thread before a
                # concurrent delete removed it while checkpoint preparation
                # yielded. Recheck immediately before durable admission. The
                # delete route holds a durable thread-operation reservation,
                # so after this point either the run or the delete wins; they
                # cannot both succeed across Gateway workers.
                if require_existing_thread and not await thread_access_allowed():
                    raise HTTPException(status_code=404, detail=f"Thread {thread_id} not found")
                record = await run_mgr.create_or_reject(
                    thread_id,
                    body.assistant_id,
                    on_disconnect=disconnect,
                    metadata=run_metadata,
                    # Persist a secret-redacted copy of the config: the run record is
                    # written to runs.kwargs_json and echoed by the run API, so a
                    # request-scoped secret (#3861) must not ride along. The live
                    # config built above keeps the secrets for the actual run.
                    kwargs={
                        "input": run_record_input,
                        **({"knowledge_default_request_hash": knowledge_default_request_hash} if accepts_knowledge_default else {}),
                        "config": redact_config_secrets(body.config),
                        **({"conversation_references": conversation_references} if conversation_references else {}),
                    },
                    multitask_strategy=body.multitask_strategy,
                    model_name=model_name,
                    user_id=owner_user_id,
                    idempotency_key=idempotency_key,
                )

                if record.idempotency_reused:
                    stored = record.kwargs or {}
                    stored_input = stored.get("input")
                    # New runs persist the admitted, canonical message snapshot
                    # so a scope display cannot be rewritten through the run
                    # record. Accept the raw request as well for records written
                    # by older Gateway versions, while comparing canonical
                    # retries to the same representation as the stored record.
                    matches_default_request = knowledge_default_request_hash is not None and stored.get("knowledge_default_request_hash") == knowledge_default_request_hash
                    # Pre-feature unscoped records may already contain normalized
                    # messages, but have no digest. Compare them before injecting
                    # today's default; explicit scopes and recovery do not use
                    # this compatibility path.
                    matches_legacy_default_request = accepts_knowledge_default and "knowledge_default_request_hash" not in stored and stored_input == request_input
                    matches_input = matches_default_request or matches_legacy_default_request or stored_input == body.input or stored_input == run_record_input
                    if not matches_input or record.assistant_id != body.assistant_id or stored.get("conversation_references", []) != conversation_references:
                        raise HTTPException(
                            status_code=409,
                            detail="Idempotency-Key already used with a different request",
                        )
                    return record

                worker = run_after_metadata(record)
                try:
                    # No await is allowed between durable admission and task
                    # attachment. Metadata setup runs inside the attached
                    # worker so a pending cancellation can bypass stalled
                    # thread-store IO and still reach run_agent's startup
                    # barrier / stream finalization.
                    record.task = asyncio.create_task(worker)
                except Exception as exc:
                    worker.close()
                    await run_mgr.fail_start_if_pending(
                        record.run_id,
                        error=f"Failed to attach run worker: {exc}",
                    )
                    raise
        except ConflictError as exc:
            raise BusyThreadConflict(str(exc)) from exc
        except UnsupportedStrategyError as exc:
            raise HTTPException(status_code=501, detail=str(exc)) from exc

        # Title sync is handled by worker.py's finally block which reads the
        # title from the checkpoint and calls thread_store.update_display_name
        # after the run completes.

        return record
    finally:
        if owner_context_token is not None:
            reset_current_user(owner_context_token)


async def launch_scheduled_thread_run(
    *,
    thread_id: str,
    assistant_id: str | None,
    prompt: str,
    request: Request | None = None,
    app: Any | None = None,
    owner_user_id: str | None = None,
    metadata: dict[str, Any] | None = None,
    origin: dict[str, Any] | None = None,
    title: str | None = None,
) -> dict[str, Any]:
    """Start one scheduled occurrence as an internal run.

    The prompt message gets a stable id per occurrence (retried launches and
    reconnect hydration see one message) and, with ``origin``, the
    ``SCHEDULED_ORIGIN_KEY`` metadata the run thread renders. ``title``
    pre-sets a fresh run thread's title, so no title is generated.
    """
    if request is None:
        if app is None:
            raise ValueError("launch_scheduled_thread_run requires request or app")
        request = SimpleNamespace(
            app=app,
            headers=({INTERNAL_OWNER_USER_ID_HEADER_NAME: owner_user_id} if owner_user_id else {}),
            state=SimpleNamespace(
                user=get_internal_user(),
                auth_source=AUTH_SOURCE_INTERNAL,
                # Every trigger (schedule or "Run now") is server-started.
                run_origin=make_origin("schedule"),
            ),
            cookies={},
        )
    scheduled_task_run_id = (metadata or {}).get("scheduled_task_run_id")
    message: dict[str, Any] = {
        "role": "user",
        "content": prompt,
        "id": f"scheduled-{scheduled_task_run_id}" if isinstance(scheduled_task_run_id, str) else f"scheduled-{uuid.uuid4().hex}",
    }
    if origin:
        message["additional_kwargs"] = {SCHEDULED_ORIGIN_KEY: origin}
    graph_input: dict[str, Any] = {"messages": [message]}
    if title:
        graph_input["title"] = title
    body = RunCreateRequest(
        assistant_id=assistant_id,
        input=graph_input,
        command=None,
        metadata=metadata or {},
        config={"recursion_limit": _resolve_scheduler_recursion_limit()},
        # ``user_id`` mirrors what IM channels put in ``body.context`` so
        # runtime-context consumers without a ContextVar fallback (e.g.
        # user-scoped GuardrailMiddleware providers) see the owning user;
        # ``inject_authenticated_user_context`` skips the internal user.
        context=({"non_interactive": True, "user_id": owner_user_id} if owner_user_id else {"non_interactive": True}),
        webhook=None,
        checkpoint_id=None,
        checkpoint=None,
        interrupt_before=None,
        interrupt_after=None,
        stream_mode=None,
        stream_subgraphs=False,
        stream_resumable=None,
        on_disconnect="continue",
        on_completion=None,
        multitask_strategy="reject",
        after_seconds=None,
        if_not_exists="create",
        feedback_keys=None,
    )
    scheduled_task_runtime = None
    task_id = (metadata or {}).get("scheduled_task_id")
    if owner_user_id and isinstance(task_id, str) and isinstance(scheduled_task_run_id, str):
        scheduled_task_runtime = {"task_id": task_id, "occurrence_id": scheduled_task_run_id, "user_id": owner_user_id}
        if (metadata or {}).get("scheduled_context_mode") == "fresh_thread_per_run" and isinstance((metadata or {}).get("scheduled_goal_objective"), str):
            scheduled_task_runtime["goal_objective"] = metadata["scheduled_goal_objective"]
    idempotency_key = f"scheduled-task:{scheduled_task_run_id}" if isinstance(scheduled_task_run_id, str) else None
    # Non-HTTP entry point: the lifespan scheduler calls this with a synthetic
    # request, so TraceMiddleware never runs. The scope is opened per launch,
    # never around the poller loop, or every scheduled run would collapse onto
    # one id. Reached from inside an HTTP request -- a manual trigger, or the
    # scheduler service's own per-occurrence scope -- ensure_trace_context
    # keeps that trace instead of minting a competing one.
    with ensure_trace_context():
        record = await start_run(
            body,
            thread_id,
            request,
            idempotency_key=idempotency_key,
            scheduled_task_runtime=scheduled_task_runtime,
        )
    return {"run_id": record.run_id, "thread_id": record.thread_id}


def _mcp_task_notification_prompt(event: dict[str, Any]) -> str:
    """Build the internal user turn for one immutable MCP task event snapshot."""
    payload = frame_untrusted_text(json.dumps(event, ensure_ascii=False, sort_keys=True, separators=(",", ":"), default=str))
    instruction = (
        "A durable background MCP task has an update that requires the user's attention. "
        "Explain the update clearly and concisely. Do not expose or ask for a remote task ID. "
        "When status is input_required, show the question but explain that this MCP integration "
        "cannot resume the remote task with user input yet. When tracking_degraded is true, explain "
        "that DeerFlow will continue retrying at a lower frequency."
    )
    return f"{instruction}\n\n{payload}"


async def launch_mcp_task_notification_run(
    *,
    app: Any,
    thread_id: str,
    assistant_id: str | None,
    owner_user_id: str,
    task_id: str,
    dispatch_version: int,
    dispatch_attempt: int,
    event: dict[str, Any],
) -> dict[str, Any]:
    """Idempotently launch the Agent run that delivers one task event."""
    request = SimpleNamespace(
        app=app,
        headers={INTERNAL_OWNER_USER_ID_HEADER_NAME: owner_user_id},
        state=SimpleNamespace(user=get_internal_user(), auth_source=AUTH_SOURCE_INTERNAL, run_origin=make_origin("mcp_notification")),
        cookies={},
    )
    body = RunCreateRequest(
        assistant_id=assistant_id,
        input={
            "messages": [
                {
                    "role": "user",
                    "content": _mcp_task_notification_prompt(event),
                    "additional_kwargs": {"hide_from_ui": True},
                }
            ]
        },
        command=None,
        metadata={
            "mcp_task_notification": {
                "task_id": task_id,
                "dispatch_version": dispatch_version,
                "dispatch_attempt": dispatch_attempt,
            }
        },
        config=None,
        context={"non_interactive": True, "user_id": owner_user_id},
        webhook=None,
        checkpoint_id=None,
        checkpoint=None,
        interrupt_before=None,
        interrupt_after=None,
        stream_mode=None,
        stream_subgraphs=False,
        stream_resumable=None,
        on_disconnect="continue",
        on_completion=None,
        multitask_strategy="reject",
        after_seconds=None,
        if_not_exists="create",
        feedback_keys=None,
    )
    idempotency_key = f"mcp-task:{task_id}:{dispatch_version}:{dispatch_attempt}"
    # Non-HTTP entry point, same as launch_scheduled_thread_run above: the MCP
    # task service drives this from its own background loop, so one scope per
    # notification keeps every delivery attempt separately correlatable.
    try:
        with ensure_trace_context():
            record = await start_run(
                body,
                thread_id,
                request,
                idempotency_key=idempotency_key,
                require_existing_thread=True,
            )
    except HTTPException as exc:
        if isinstance(exc, BusyThreadConflict):
            raise ConflictError(str(exc.detail)) from exc
        if exc.status_code in {400, 401, 403, 404, 409, 422, 501}:
            raise PermanentNotificationError(str(exc.detail)) from exc
        raise
    return {"run_id": record.run_id, "thread_id": record.thread_id}


async def sse_consumer(
    bridge: StreamBridge,
    record: RunRecord,
    request: Request,
    run_mgr: RunManager,
    *,
    apply_on_disconnect: bool = True,
    emit_gap_on_missing_stream: bool = False,
):
    """Async generator that yields SSE frames from the bridge.

    The ``finally`` block implements ``on_disconnect`` semantics, but only for
    the stream returned by the *creating* endpoint (``apply_on_disconnect=True``):

    - ``cancel``: abort the background task on client disconnect.
    - ``continue``: let the task run; events are discarded.

    Join/observer streams pass ``apply_on_disconnect=False``: the creator's
    cancel-on-disconnect policy expresses the creator's intent for their own
    connection, and a read-only observer closing a join must not cancel the
    run (a runs:read-only credential would otherwise cancel without
    runs:cancel just by disconnecting).

    ``emit_gap_on_missing_stream`` is a separate creating-retry signal, default
    ``False``. ``create_or_reject`` sets ``record.idempotency_reused`` on the
    shared cached record and never clears it, so this function must not read
    that flag. Thread-scoped ``/runs/stream`` passes True only for this
    request's reuse; default callers (joins, stateless ``/api/runs/stream``,
    tests) keep ``end`` when a terminal record's stream is gone.
    """
    last_event_id = request.headers.get("Last-Event-ID")
    if await _terminal_record_stream_missing(bridge, record):
        if emit_gap_on_missing_stream:
            # Creating-endpoint retry: a bare `end` looks like the run
            # produced nothing. Point the client at durable state instead.
            yield format_sse(
                "gap",
                {
                    "code": "stream_replay_gap",
                    "run_id": record.run_id,
                    "requested_event_id": last_event_id,
                    "earliest_available_event_id": None,
                    "latest_available_event_id": None,
                    "recovery": "reload_durable_state",
                },
            )
            return
        yield format_sse("end", None)
        return

    gap_emitted = False
    terminal_emitted = False
    disconnect_observed = False
    try:
        async for entry in bridge.subscribe(record.run_id, last_event_id=last_event_id):
            if await request.is_disconnected():
                disconnect_observed = True
                break

            if isinstance(entry, StreamGap):
                gap_emitted = True
                yield format_sse(
                    "gap",
                    {
                        "code": "stream_replay_gap",
                        "run_id": record.run_id,
                        "requested_event_id": entry.requested_event_id,
                        "earliest_available_event_id": entry.earliest_available_event_id,
                        "latest_available_event_id": entry.latest_available_event_id,
                        "recovery": "reload_durable_state",
                    },
                )
                return

            if entry is HEARTBEAT_SENTINEL:
                if await _orphan_recovery_observed_after_heartbeat(record, run_mgr):
                    terminal_emitted = True
                    yield format_sse("end", None)
                    return
                yield ": heartbeat\n\n"
                continue

            if entry is END_SENTINEL:
                terminal_emitted = True
                yield format_sse("end", None, event_id=entry.id or None)
                return

            yield format_sse(entry.event, entry.data, event_id=entry.id or None)

        if not disconnect_observed:
            raise RuntimeError("stream bridge subscription ended before a terminal event")
    except (GeneratorExit, asyncio.CancelledError):
        # Starlette closes the response generator or cancels its request task
        # when the creator connection disappears. Ordinary bridge exceptions
        # must not be mistaken for that client-owned lifecycle signal.
        disconnect_observed = True
        raise
    finally:
        # store_only records are cross-worker observation handles. An explicit
        # cancel-then-stream action has already persisted its request before
        # subscribing; a plain join disconnect must not invent a new
        # cancellation request. Only apply on_disconnect to locally-owned runs,
        # and only on the creator's own stream — never on an observer join.
        if disconnect_observed and apply_on_disconnect and not gap_emitted and not terminal_emitted and not record.store_only and record.status in (RunStatus.pending, RunStatus.running):
            if record.on_disconnect == DisconnectMode.cancel:
                await await_drained(run_mgr.cancel(record.run_id))


def serialize_wait_run_status(record: RunRecord) -> dict[str, Any]:
    """Return durable status and the error envelope recognized by the SDK."""
    result: dict[str, Any] = {"status": record.status.value, "error": record.error}
    if record.status == RunStatus.error:
        result["__error__"] = {"error": "RunError", "message": record.error or "Run failed."}
    return result


async def wait_for_run_completion(
    bridge: StreamBridge,
    record: RunRecord,
    request: Request,
    run_mgr: RunManager,
) -> bool:
    """Block until the run publishes ``END_SENTINEL``, honouring on_disconnect.

    Creator-side only, unlike ``sse_consumer``'s observer joins: every caller
    must be the endpoint that created the run or a path reached only after an
    explicit, permission-gated cancel. This helper intentionally keeps
    applying the record's ``on_disconnect`` policy on disconnect — do not
    wire it to observer surfaces.

    The non-streaming ``/wait`` endpoints used to ``await record.task``
    directly with no disconnect handling.  When the client (or an
    intermediate HTTP proxy) timed out during a long tool call such as
    ``pip install``, the handler would swallow ``CancelledError`` and
    serialize whatever checkpoint happened to exist — masking a half-finished
    run as a normal completion (issue #3265).

    This helper consumes the same bridge that ``sse_consumer`` does so the
    wait path shares its disconnect semantics: each wake-up polls
    ``request.is_disconnected()``; on a real disconnect it cancels the
    background run when ``record.on_disconnect`` is ``cancel``.  The bridge's
    heartbeat sentinels guarantee at least one wake-up per
    ``heartbeat_interval`` even when the agent emits no events for a while.

    Returns:
        ``True`` when ``END_SENTINEL`` was observed (run reached a terminal
        state), ``False`` when the loop exited because the client
        disconnected.  Callers must skip checkpoint serialization on
        ``False`` so a partial checkpoint is not returned as a normal
        response.

    Raises:
        RuntimeError: The bridge subscription ended without a terminal event.
        Other bridge failures propagate unchanged.
    """
    completed = False
    disconnect_observed = False
    if await _terminal_record_stream_missing(bridge, record):
        return True

    resume_from_event_id: str | None = None
    try:
        while True:
            gap_seen = False
            async for entry in bridge.subscribe(record.run_id, last_event_id=resume_from_event_id):
                # END_SENTINEL means the run reached a terminal state; honour it
                # even if the client just disconnected so the caller still serializes
                # the real final checkpoint.
                if entry is END_SENTINEL:
                    completed = True
                    return True
                if isinstance(entry, StreamGap):
                    # The wait API only needs terminal completion, not a complete
                    # event replay. Resume at the retained tail rather than
                    # treating a bridge gap as a client disconnect.
                    resume_from_event_id = entry.latest_available_event_id
                    gap_seen = True
                    break
                if entry is HEARTBEAT_SENTINEL and await _orphan_recovery_observed_after_heartbeat(record, run_mgr):
                    completed = True
                    return True
                if await request.is_disconnected():
                    disconnect_observed = True
                    return False
                # Heartbeats and regular events: keep waiting for END_SENTINEL.
            if not gap_seen:
                raise RuntimeError("stream bridge subscription ended before a terminal event")
    except asyncio.CancelledError:
        # Request-task cancellation is the non-streaming equivalent of
        # Starlette closing an SSE response generator.
        disconnect_observed = True
        raise
    finally:
        if disconnect_observed and not completed and record.status in (RunStatus.pending, RunStatus.running):
            if record.on_disconnect == DisconnectMode.cancel:
                await await_drained(run_mgr.cancel(record.run_id))