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

import asyncio
import logging
from collections.abc import AsyncGenerator
from contextlib import asynccontextmanager

from deerflow_extension_api import EXTENSION_PRINCIPAL_RESOLVER_KEY, ExtensionPrincipal
from fastapi import FastAPI, Request, Response
from fastapi.middleware.cors import CORSMiddleware

from app.gateway.auth_disabled import AUTH_SOURCE_INTERNAL, AUTH_SOURCE_PAT, warn_if_auth_disabled_enabled
from app.gateway.auth_middleware import AuthMiddleware
from app.gateway.browser_capability import ensure_browser_runtime_available
from app.gateway.config import get_gateway_config
from app.gateway.csrf_middleware import CORS_EXPOSED_HEADERS, CSRFMiddleware, get_configured_cors_origins
from app.gateway.deps import langgraph_runtime
from app.gateway.health import READINESS_CHECKPOINTER_CONFIG_ATTR, readiness_payload
from app.gateway.routers import (
    agents,
    artifacts,
    assistants_compat,
    auth,
    browser,
    channel_connections,
    channels,
    console,
    features,
    feedback,
    github_webhooks,
    input_polish,
    integrations,
    mcp,
    mcp_tasks,
    memory,
    models,
    projects,
    runs,
    scheduled_tasks,
    skills,
    subagent_batches,
    subagents,
    suggestions,
    thread_runs,
    threads,
    uploads,
    user_preferences,
)
from app.gateway.trace_middleware import TraceMiddleware
from deerflow.config import app_config as deerflow_app_config
from deerflow.logging_config import DEFAULT_LOG_DATE_FORMAT, DEFAULT_LOG_FORMAT, configure_logging
from deerflow.tracing.monocle import setup_monocle_tracing_if_enabled
from deerflow.uploads.manager import cleanup_stale_upload_staging_files

AppConfig = deerflow_app_config.AppConfig
get_app_config = deerflow_app_config.get_app_config

# Default logging; lifespan overrides from config.yaml log_level.
logging.basicConfig(
    level=logging.INFO,
    format=DEFAULT_LOG_FORMAT,
    datefmt=DEFAULT_LOG_DATE_FORMAT,
)

logger = logging.getLogger(__name__)

# Upper bound (seconds) each lifespan shutdown hook is allowed to run.
# Bounds worker exit time so uvicorn's reload supervisor does not keep
# firing signals into a worker that is stuck waiting for shutdown cleanup.
_SHUTDOWN_HOOK_TIMEOUT_SECONDS = 5.0

# The retrieval index is derived state, so shutdown only waits briefly for its
# startup rebuild. The canonical memory flush keeps its full configured budget.
_RETRIEVAL_WARM_SHUTDOWN_TIMEOUT_SECONDS = 1.0


async def _ensure_admin_user(app: FastAPI) -> None:
    """Startup hook: handle first boot and migrate orphan threads otherwise.

    After admin creation, migrate orphan threads from the LangGraph
    store (metadata.user_id unset) to the admin account. This is the
    "no-auth → with-auth" upgrade path: users who ran DeerFlow without
    authentication have existing LangGraph thread data that needs an
    owner assigned.
        First boot (no admin exists):
            - Does NOT create any user accounts automatically.
            - The operator must visit ``/setup`` to create the first admin.

    Subsequent boots (admin already exists):
      - Runs the one-time "no-auth → with-auth" orphan thread migration for
        existing LangGraph thread metadata that has no user_id.

    No SQL persistence migration is needed: the four user_id columns
    (threads_meta, runs, run_events, feedback) only come into existence
    alongside the auth module via create_all, so freshly created tables
    never contain NULL-owner rows.
    """
    from sqlalchemy import select

    from app.gateway.deps import get_local_provider
    from deerflow.persistence.engine import get_session_factory
    from deerflow.persistence.user.model import UserRow

    try:
        provider = get_local_provider()
    except RuntimeError:
        # Auth persistence may not be initialized in some test/boot paths.
        # Skip admin migration work rather than failing gateway startup.
        logger.warning("Auth persistence not ready; skipping admin bootstrap check")
        return

    sf = get_session_factory()
    if sf is None:
        return

    admin_count = await provider.count_admin_users()

    if admin_count == 0:
        logger.info("=" * 60)
        logger.info("  First boot detected — no admin account exists.")
        logger.info("  Visit /setup to complete admin account creation.")
        logger.info("=" * 60)
        return

    # Admin already exists — run orphan thread migration for any
    # LangGraph thread metadata that pre-dates the auth module.
    async with sf() as session:
        stmt = select(UserRow).where(UserRow.system_role == "admin").limit(1)
        row = (await session.execute(stmt)).scalar_one_or_none()

    if row is None:
        return  # Should not happen (admin_count > 0 above), but be safe.

    admin_id = str(row.id)

    # LangGraph store orphan migration — non-fatal.
    # This covers the "no-auth → with-auth" upgrade path for users
    # whose existing LangGraph thread metadata has no user_id set.
    store = getattr(app.state, "store", None)
    if store is not None:
        try:
            migrated = await _migrate_orphaned_threads(store, admin_id)
            if migrated:
                logger.info("Migrated %d orphan LangGraph thread(s) to admin", migrated)
        except Exception:
            logger.exception("LangGraph thread migration failed (non-fatal)")


async def _iter_store_items(store, namespace, *, page_size: int = 500):
    """Paginated async iterator over a LangGraph store namespace.

    Replaces the old hardcoded ``limit=1000`` call with a cursor-style
    loop so that environments with more than one page of orphans do
    not silently lose data. Terminates when a page is empty OR when a
    short page arrives (indicating the last page).
    """
    offset = 0
    while True:
        batch = await store.asearch(namespace, limit=page_size, offset=offset)
        if not batch:
            return
        for item in batch:
            yield item
        if len(batch) < page_size:
            return
        offset += page_size


async def _migrate_orphaned_threads(store, admin_user_id: str) -> int:
    """Migrate LangGraph store threads with no user_id to the given admin.

    Uses cursor pagination so all orphans are migrated regardless of
    count. Returns the number of rows migrated.
    """
    migrated = 0
    async for item in _iter_store_items(store, ("threads",)):
        metadata = item.value.get("metadata", {})
        if not metadata.get("user_id"):
            metadata["user_id"] = admin_user_id
            item.value["metadata"] = metadata
            await store.aput(("threads",), item.key, item.value)
            migrated += 1
    return migrated


async def _warm_memory_retrieval(manager) -> None:
    """Rebuild the derived retrieval index without delaying Gateway readiness."""
    try:
        rebuilt = await asyncio.to_thread(manager.warm_retrieval)
        if rebuilt:
            logger.info("Memory retrieval index rebuilt successfully")
        else:
            logger.warning("Memory retrieval index rebuild failed; scoped searches will retry lazily")
    except Exception:
        logger.warning("Memory retrieval index rebuild skipped", exc_info=True)


@asynccontextmanager
async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
    """Application lifespan handler."""

    # Load config and check necessary environment variables at startup.
    # `startup_config` is a local snapshot used only for one-shot bootstrap
    # work (logging level, langgraph_runtime engines, channels). Request-time
    # config resolution always routes through `get_app_config()` in
    # `app/gateway/deps.py::get_config()` so `config.yaml` edits become
    # visible without a process restart. We deliberately do NOT cache this
    # snapshot on `app.state` to keep that contract enforceable.
    try:
        startup_config = get_app_config()
        from deerflow.config.subagent_batches_config import SubagentBatchesConfig
        from deerflow.config.subagent_runtime_config import SubagentRuntimeConfig
        from deerflow.subagents.capacity import configure_subagent_execution_capacity

        subagent_runtime_config = getattr(startup_config, "subagent_runtime", None)
        if not isinstance(subagent_runtime_config, SubagentRuntimeConfig):
            subagent_runtime_config = SubagentRuntimeConfig()
        subagent_batches_config = getattr(startup_config, "subagent_batches", None)
        if not isinstance(subagent_batches_config, SubagentBatchesConfig):
            subagent_batches_config = SubagentBatchesConfig()
        configure_subagent_execution_capacity(subagent_runtime_config)
        configure_logging(startup_config)
        ensure_browser_runtime_available(startup_config)
        logger.info("Configuration loaded successfully")
        warn_if_auth_disabled_enabled()
    except Exception as e:
        error_msg = f"Failed to load configuration during gateway startup: {e}"
        logger.exception(error_msg)
        raise RuntimeError(error_msg) from e
    config = get_gateway_config()
    logger.info(f"Starting API Gateway on {config.host}:{config.port}")

    from deerflow.skills.projection import ensure_public_skill_projection

    public_projection_ready = await asyncio.to_thread(ensure_public_skill_projection, app_config=startup_config)
    if public_projection_ready:
        logger.info("Ensured the public skill projection; user projections repair lazily on sandbox acquire")

    # Agent observability (Monocle). Off by default; enabled with
    # MONOCLE_TRACING. Initialized here at startup — not at import time — so a
    # plain `import deerflow.agents` never installs a process-global tracer.
    # Unlike LangSmith/Langfuse, whose validation failures abort the agent run,
    # a bad Monocle config only logs: the Gateway keeps serving without tracing.
    try:
        setup_monocle_tracing_if_enabled()
    except Exception:  # observability must never break startup
        logger.exception("Monocle tracing setup failed; continuing without it")

    # Rebuild the derived memory retrieval index in the background. Scoped
    # searches remain correct while this runs because DeerMem lazily rebuilds
    # the requested scope when the full warm-up has not completed yet.
    retrieval_warm_task: asyncio.Task[None] | None = None
    try:
        from deerflow.agents.memory import get_memory_manager

        if startup_config.memory.enabled:
            manager = await asyncio.to_thread(get_memory_manager)
            warm_retrieval = getattr(manager, "warm_retrieval", None)
            if callable(warm_retrieval):
                retrieval_warm_task = asyncio.create_task(
                    _warm_memory_retrieval(manager),
                    name="memory-retrieval-warm-up",
                )
        else:
            logger.info("Memory is disabled; skipping retrieval index rebuild")
    except Exception:
        logger.warning("Memory retrieval index rebuild skipped", exc_info=True)

    # Pre-warm tiktoken encoding cache so the first memory-injection request
    # never blocks on the BPE data download (which hits an OpenAI/Azure URL
    # that may be unreachable in restricted networks — see issue #3402).
    # Warm-up runs via the manager's `warm()` tier-3 hook. DeerMem.warm re-checks
    # token_counting=="char" and returns early, so char-mode backends never touch
    # tiktoken (avoids even the 5s probe in network-restricted deployments - see
    # issue #3429). A backend with nothing to warm (e.g. noop) returns None from
    # the base default -- log "skipping" instead of the misleading "warmed
    # successfully" so the log reflects what actually happened.
    try:
        from deerflow.agents.memory import get_memory_manager

        manager = await asyncio.to_thread(get_memory_manager)
        warmed = await asyncio.wait_for(
            asyncio.to_thread(manager.warm),
            timeout=5,
        )
        if warmed is None:
            logger.info("Memory backend %s has nothing to warm; skipping tiktoken warm-up", type(manager).__name__)
        elif warmed:
            logger.info("tiktoken encoding cache warmed successfully")
        else:
            logger.warning("tiktoken encoding cache warm-up failed; token counting will use character-based fallback until tiktoken loads successfully")
    except TimeoutError:
        logger.warning("tiktoken encoding cache warm-up timed out; token counting will use character-based fallback until tiktoken loads successfully")
    except Exception:
        logger.warning("tiktoken warm-up skipped", exc_info=True)

    try:
        removed_upload_staging_files = await asyncio.to_thread(cleanup_stale_upload_staging_files)
        if removed_upload_staging_files:
            logger.info("Removed %d stale upload staging file(s)", removed_upload_staging_files)
    except Exception:
        logger.warning("Upload staging file cleanup skipped", exc_info=True)

    # Initialize LangGraph runtime components (StreamBridge, RunManager, checkpointer, store)
    async with langgraph_runtime(app, startup_config):
        logger.info("LangGraph runtime initialised")

        # Check admin bootstrap state and migrate orphan threads after admin exists.
        # Must run AFTER langgraph_runtime so app.state.store is available for thread migration
        await _ensure_admin_user(app)

        try:
            from app.gateway.services import launch_scheduled_thread_run
            from app.scheduler import ScheduledTaskService

            if getattr(app.state, "scheduled_task_repo", None) is not None and getattr(app.state, "scheduled_task_run_repo", None) is not None:
                scheduled_task_service = ScheduledTaskService(
                    task_repo=app.state.scheduled_task_repo,
                    task_run_repo=app.state.scheduled_task_run_repo,
                    launch_run=lambda **kwargs: launch_scheduled_thread_run(app=app, **kwargs),
                    poll_interval_seconds=startup_config.scheduler.poll_interval_seconds,
                    lease_seconds=startup_config.scheduler.lease_seconds,
                    max_concurrent_runs=startup_config.scheduler.max_concurrent_runs,
                    queue_timeout_seconds=startup_config.scheduler.queue_timeout_seconds,
                    multi_instance=startup_config.scheduler.multi_instance,
                    run_lease_grace_seconds=startup_config.run_ownership.grace_seconds,
                )
                app.state.scheduled_task_service = scheduled_task_service
                if startup_config.scheduler.enabled:
                    await scheduled_task_service.start()
        except Exception:
            logger.exception("Failed to initialize scheduled task service")
            # If an enabled scheduler rejects start(), keep that rejection as a
            # lifespan failure instead of exposing a half-started service.
            if startup_config.scheduler.enabled:
                raise

        # Start IM channel service only after scheduler recovery succeeds, so a
        # fail-closed scheduler startup cannot strand channel-owned tasks before
        # the lifespan reaches its normal shutdown boundary.
        try:
            from app.channels.service import start_channel_service

            # Closure over `app` (mirrors ScheduledTaskService's `launch_run`
            # above) rather than resolving `app.state.stream_bridge` here
            # directly: `stream_bridge` is a STARTUP_ONLY_FIELDS singleton set
            # once, above, by `langgraph_runtime(app, startup_config)`, so
            # either shape is safe by construction — the closure is just the
            # more defensive/consistent-with-precedent form, and it is what
            # ChannelManager's follow-up-drain watcher (issue #4121 Slice 2)
            # uses to reach the same StreamBridge every other run consumer
            # goes through `get_stream_bridge(request)` for.
            channel_service = await start_channel_service(
                startup_config,
                get_stream_bridge=lambda: getattr(app.state, "stream_bridge", None),
            )
            logger.info("Channel service started: %s", channel_service.get_status())
        except Exception:
            logger.exception("No IM channels configured or channel service failed to start")

        from app.gateway.services import launch_mcp_task_notification_run
        from app.mcp_tasks import McpTaskService
        from deerflow.config.extensions_config import ExtensionsConfig
        from deerflow.config.mcp_tasks_config import McpTasksConfig
        from deerflow.mcp.task_tool_caller import McpTaskToolCaller
        from deerflow.mcp.tasks import (
            ORDINARY_MCP_TASK_DRIVER,
            McpTaskDriverRegistry,
            OrdinaryMcpTaskDriver,
        )
        from deerflow.mcp.tasks.runtime import (
            configured_task_toolset_count,
            set_mcp_task_config_snapshot,
            set_mcp_task_submitter,
            validate_mcp_task_runtime_configuration,
        )

        task_extensions_config = ExtensionsConfig.from_file()
        mcp_tasks_config = getattr(startup_config, "mcp_tasks", McpTasksConfig())
        mcp_task_repo = getattr(app.state, "mcp_task_repo", None)
        app.state.mcp_tasks_available = False
        set_mcp_task_submitter(None)
        set_mcp_task_config_snapshot(task_extensions_config)
        validate_mcp_task_runtime_configuration(
            mcp_tasks_config=mcp_tasks_config,
            extensions_config=task_extensions_config,
            repository_available=mcp_task_repo is not None,
        )
        if mcp_task_repo is not None:
            mcp_task_drivers = McpTaskDriverRegistry()
            if configured_task_toolset_count(task_extensions_config):
                mcp_task_drivers.register(
                    ORDINARY_MCP_TASK_DRIVER,
                    OrdinaryMcpTaskDriver(McpTaskToolCaller(task_extensions_config)),
                )
            mcp_task_service = McpTaskService(
                repository=mcp_task_repo,
                drivers=mcp_task_drivers,
                poll_interval_seconds=mcp_tasks_config.poll_interval_seconds,
                lease_seconds=mcp_tasks_config.lease_seconds,
                max_concurrent_polls=mcp_tasks_config.max_concurrent_polls,
                max_poll_backoff_seconds=mcp_tasks_config.max_poll_backoff_seconds,
                input_required_poll_interval_seconds=mcp_tasks_config.input_required_poll_interval_seconds,
                tracking_degraded_after_errors=mcp_tasks_config.tracking_degraded_after_errors,
                max_result_bytes=mcp_tasks_config.max_result_bytes,
                result_preview_max_chars=mcp_tasks_config.result_preview_max_chars,
                launch_notification=lambda **kwargs: launch_mcp_task_notification_run(app=app, **kwargs),
                get_run=lambda run_id, **kwargs: app.state.run_manager.get(
                    run_id,
                    raise_on_store_error=True,
                    **kwargs,
                ),
            )
            app.state.mcp_task_drivers = mcp_task_drivers
            app.state.mcp_task_service = mcp_task_service
            if mcp_tasks_config.enabled:
                await mcp_task_service.start()
                set_mcp_task_submitter(mcp_task_service)
                app.state.mcp_tasks_available = True

        from app.subagent_batches import SubagentBatchService
        from deerflow.subagents.batch_runtime import set_subagent_batch_submitter

        batch_repo = getattr(app.state, "subagent_batch_repo", None)
        app.state.subagent_batches_available = False
        set_subagent_batch_submitter(None)
        if subagent_batches_config.enabled and batch_repo is None:
            raise RuntimeError("subagent_batches.enabled requires database.backend sqlite or postgres")
        if batch_repo is not None:
            batch_service = SubagentBatchService(
                repository=batch_repo,
                config=subagent_batches_config,
                runtime_config=subagent_runtime_config,
            )
            app.state.subagent_batch_service = batch_service
            if subagent_batches_config.enabled:
                await batch_service.start()
                set_subagent_batch_submitter(batch_service)
                app.state.subagent_batches_available = True

        yield

        try:
            await auth.close_oidc_service()
        except Exception:
            logger.exception("Failed to close OIDC service")

        # Stop channel service on shutdown (bounded to prevent worker hang)
        try:
            from app.channels.service import stop_channel_service

            await asyncio.wait_for(
                stop_channel_service(),
                timeout=_SHUTDOWN_HOOK_TIMEOUT_SECONDS,
            )
        except TimeoutError:
            logger.warning(
                "Channel service shutdown exceeded %.1fs; proceeding with worker exit.",
                _SHUTDOWN_HOOK_TIMEOUT_SECONDS,
            )
        except Exception:
            logger.exception("Failed to stop channel service")

        if getattr(app.state, "scheduled_task_service", None) is not None:
            try:
                await app.state.scheduled_task_service.stop()
            except Exception:
                logger.exception("Failed to stop scheduled task service")

        if getattr(app.state, "mcp_task_service", None) is not None:
            app.state.mcp_tasks_available = False
            try:
                await app.state.mcp_task_service.stop()
            except Exception:
                logger.exception("Failed to stop MCP task service")
            finally:
                from deerflow.mcp.tasks.runtime import set_mcp_task_submitter

                set_mcp_task_submitter(None)
        from deerflow.mcp.tasks.runtime import set_mcp_task_config_snapshot

        set_mcp_task_config_snapshot(None)

        if getattr(app.state, "subagent_batch_service", None) is not None:
            app.state.subagent_batches_available = False
            try:
                await app.state.subagent_batch_service.stop()
            except Exception:
                logger.exception("Failed to stop subagent batch service")
            finally:
                from deerflow.subagents.batch_runtime import set_subagent_batch_submitter

                set_subagent_batch_submitter(None)

        try:
            from deerflow.community.browser_automation import get_browser_session_manager

            closed = await asyncio.wait_for(
                get_browser_session_manager().close_all_sessions(),
                timeout=_SHUTDOWN_HOOK_TIMEOUT_SECONDS,
            )
            if closed:
                logger.info("Closed %d browser session(s)", closed)
        except TimeoutError:
            logger.warning(
                "Browser session shutdown exceeded %.1fs; proceeding with worker exit.",
                _SHUTDOWN_HOOK_TIMEOUT_SECONDS,
            )
        except Exception:
            logger.exception("Failed to close browser sessions")

        # Drain the memory backend's pending-update buffer before the worker
        # exits (best-effort, bounded). IM channels and the scheduler are
        # already stopped above, so no new IM/scheduler updates arrive during
        # the drain; the LangGraph runtime / in-flight HTTP requests can still
        # complete memory enqueues in a narrow window, but anything added after
        # the drain copies the buffer only resets the debounce Timer
        # (best-effort, same as today).
        #
        # No host-level pending/processing guard: ``shutdown_flush``
        # short-circuits on a truly idle buffer (returns True immediately), so
        # calling it unconditionally is cheap and keeps the in-flight-worker
        # race entirely inside the backend (where the buffer lives) -- the host
        # cannot "forget" that case the way a ``pending_count > 0``-only guard
        # would (review #6 on the original PR).
        #
        # K8s caveat: ``shutdown_flush_timeout_seconds`` must fit inside the
        # pod's ``terminationGracePeriodSeconds`` (channel stop + browser
        # session close + the brief retrieval-warm wait + this drain + buffer),
        # set on the gateway Helm deployment -- or K8s SIGKILLs the drain
        # mid-flight and the loss this is fixing is silently re-introduced.
        # The retrieval index is derived from canonical memory files, so its
        # wait is independently capped and never consumes the flush budget.
        retrieval_warm_finished = True
        if retrieval_warm_task is not None and not retrieval_warm_task.done():
            try:
                await asyncio.wait_for(
                    asyncio.shield(retrieval_warm_task),
                    timeout=min(
                        _RETRIEVAL_WARM_SHUTDOWN_TIMEOUT_SECONDS,
                        startup_config.memory.shutdown_flush_timeout_seconds,
                    ),
                )
            except TimeoutError:
                retrieval_warm_finished = False
                logger.warning("Memory retrieval index rebuild is still running; leaving its connection open during shutdown")

        manager = None
        try:
            # Memory shutdown runs on a worker thread and can trigger detached
            # system-model callbacks. Stop accepting those callbacks before
            # flushing, while keeping the registered loop alive for awaited
            # task hooks until langgraph_runtime drains runs and subagents.
            from deerflow.extensions.notify import suspend_extension_system_observations

            suspend_extension_system_observations()
        except Exception:
            logger.debug("Failed to suspend extension system observations (non-fatal)", exc_info=True)

        try:
            app_cfg = get_app_config()
            if app_cfg.memory.enabled:
                from deerflow.agents.memory import get_memory_manager

                manager = await asyncio.to_thread(get_memory_manager)
                flush_timeout = app_cfg.memory.shutdown_flush_timeout_seconds
                completed = await asyncio.to_thread(manager.shutdown_flush, flush_timeout)
                if completed:
                    logger.info("Memory queue flush completed within %.1fs", flush_timeout)
                else:
                    logger.warning(
                        "Memory queue flush did not finish within %.1fs; remaining updates may be lost",
                        flush_timeout,
                    )
        except Exception:
            logger.exception("Failed to flush memory queue on shutdown")
        finally:
            close = getattr(manager, "close", None)
            if callable(close) and retrieval_warm_finished:
                try:
                    await asyncio.to_thread(close)
                except Exception:
                    logger.exception("Failed to close memory backend on shutdown")

    logger.info("Shutting down API Gateway")


def create_app() -> FastAPI:
    """Create and configure the FastAPI application.

    Returns:
        Configured FastAPI application instance.
    """
    config = get_gateway_config()
    docs_url = "/docs" if config.enable_docs else None
    redoc_url = "/redoc" if config.enable_docs else None
    openapi_url = "/openapi.json" if config.enable_docs else None

    app = FastAPI(
        title="DeerFlow API Gateway",
        description="""
## DeerFlow API Gateway

API Gateway for DeerFlow - A LangGraph-based AI agent backend with sandbox execution capabilities.

### Features

- **Models Management**: Query and retrieve available AI models
- **MCP Configuration**: Manage Model Context Protocol (MCP) server configurations
- **Memory Management**: Access and manage global memory data for personalized conversations
- **Skills Management**: Query and manage skills and their enabled status
- **Artifacts**: Access thread artifacts and generated files
- **Health Monitoring**: System health check endpoints

### Architecture

LangGraph-compatible requests are routed through nginx to this gateway.
This gateway provides runtime endpoints for agent runs plus custom endpoints for models, MCP configuration, skills, and artifacts.
        """,
        version="0.1.0",
        lifespan=lifespan,
        docs_url=docs_url,
        redoc_url=redoc_url,
        openapi_url=openapi_url,
        openapi_tags=[
            {
                "name": "models",
                "description": "Operations for querying available AI models and their configurations",
            },
            {
                "name": "mcp",
                "description": "Manage Model Context Protocol (MCP) server configurations",
            },
            {
                "name": "memory",
                "description": "Access and manage global memory data for personalized conversations",
            },
            {
                "name": "skills",
                "description": "Manage skills and their configurations",
            },
            {
                "name": "artifacts",
                "description": "Access and download thread artifacts and generated files",
            },
            {
                "name": "uploads",
                "description": "Upload and manage user files for threads",
            },
            {
                "name": "threads",
                "description": "Manage DeerFlow thread-local filesystem data",
            },
            {
                "name": "agents",
                "description": "Create and manage custom agents with per-agent config and prompts",
            },
            {
                "name": "suggestions",
                "description": "Generate follow-up question suggestions for conversations",
            },
            {
                "name": "input-polish",
                "description": "Polish composer draft input before sending",
            },
            {
                "name": "channels",
                "description": "Manage IM channel integrations (Feishu, Slack, Telegram)",
            },
            {
                "name": "assistants-compat",
                "description": "LangGraph Platform-compatible assistants API (stub)",
            },
            {
                "name": "runs",
                "description": "LangGraph Platform-compatible runs lifecycle (create, stream, cancel)",
            },
            {
                "name": "health",
                "description": "Health check and system status endpoints",
            },
        ],
    )

    # Auth: reject unauthenticated requests to non-public paths (fail-closed safety net)
    app.add_middleware(AuthMiddleware)

    # Give contributed routers a neutral way to ask "is this caller an admin"
    # without importing app.gateway.deps, which would pin them to an
    # unpublished internal layer and defeat independent distribution. The
    # resolver mirrors require_admin_user's primary path (deps.py): it reads
    # request.state.user, which AuthMiddleware stamps before any router runs,
    # rather than the async get_current_user_from_request/get_optional_user_from_request
    # accessors that exist for tests and alternative ASGI compositions. Staying
    # synchronous keeps resolve_principal/require_admin usable from both sync
    # and async route handlers.
    def _resolve_extension_principal(request):
        """Project the host's auth context into the neutral extension shape.

        Deliberately a projection, not a handle: an extension gets the
        questions it may ask (who, is that an admin, and what role they
        hold), not the host's AuthContext, which would pin every extension to
        its internals.
        """
        user = getattr(request.state, "user", None)
        if user is None:
            return None
        system_role = getattr(user, "system_role", None)
        # PAT credentials never carry admin capability (#5041): suppress every
        # admin signal — both ``is_admin`` and the ``admin`` role — so an
        # admin-owned PAT cannot regain admin through extension-side
        # require_admin, mirroring deps.is_admin_user's PAT guard.
        auth_source = getattr(request.state, "auth_source", None)
        is_pat = auth_source == AUTH_SOURCE_PAT
        is_admin = system_role == "admin" and not is_pat
        roles = () if is_pat and system_role == "admin" else (system_role,) if isinstance(system_role, str) and system_role else ()
        return ExtensionPrincipal(
            user_id=str(user.id),
            is_admin=is_admin,
            is_internal=auth_source == AUTH_SOURCE_INTERNAL,
            # The host's only role concept is the single system_role column
            # (e.g. "admin", "user") — there is no multi-role system to
            # project, so a set role becomes the one-element tuple rather
            # than reading a "roles" attribute the user model never had.
            roles=roles,
        )

    setattr(app.state, EXTENSION_PRINCIPAL_RESOLVER_KEY, _resolve_extension_principal)

    # CSRF: Double Submit Cookie pattern for state-changing requests
    app.add_middleware(CSRFMiddleware)

    # CORS: the unified nginx endpoint is same-origin by default. Split-origin
    # browser clients must opt in with this explicit Gateway allowlist so CORS
    # and CSRF origin checks share the same source of truth. They also need the
    # run id the Gateway returns in a non-safelisted response header; without
    # exposing it the SDK never reports a created run, so a new thread keeps its
    # placeholder route and every action gated on an established thread stays
    # hidden until the page is reloaded.
    cors_origins = sorted(get_configured_cors_origins())
    if cors_origins:
        app.add_middleware(
            CORSMiddleware,
            allow_origins=cors_origins,
            allow_credentials=True,
            allow_methods=["*"],
            allow_headers=["*"],
            expose_headers=list(CORS_EXPOSED_HEADERS),
        )

    # Request trace correlation: bind one trace id per Gateway HTTP request
    # and write it to the response start headers. Ungated, so it works without
    # a config.yaml and needs no restart; logging.enhance.enabled only decides
    # whether that id is printed into log records.
    app.add_middleware(TraceMiddleware)

    # Python extensions load once while the Gateway app is constructed. Agent
    # middleware builders consume the same immutable set through the process
    # singleton; app.state exposes it to the Gateway runtime.
    from deerflow.extensions import (
        EMPTY_EXTENSIONS,
        ExtensionLoadError,
        initialize_runtime_diagnostics,
        load_extensions,
        record_runtime_diagnostics,
        set_loaded_extensions,
    )

    # Resolving the configured plugin list is deliberately outside the
    # fail-open guard below: a config.yaml that exists but cannot be parsed or
    # validated is a configuration failure, not an extension failure. Reporting
    # it as the latter would silently drop a `required: true` extension instead
    # of failing the boot. Only an absent config.yaml is tolerated — create_app()
    # runs at import time, and lifespan still performs strict config loading
    # before serving.
    try:
        configured_plugins = get_app_config().plugins
    except FileNotFoundError:
        logger.debug("config.yaml not found while constructing Gateway app; loading no extensions for this app instance")
        configured_plugins = []

    try:
        loaded_extensions, extension_diagnostics = load_extensions(configured_plugins)
    except ExtensionLoadError:
        # `required: true` makes the extension part of the startup contract.
        # Booting without it would silently change configured behaviour.
        raise
    except Exception:
        logger.exception("Extension loading failed; continuing with no extensions")
        loaded_extensions, extension_diagnostics = EMPTY_EXTENSIONS, []
    set_loaded_extensions(loaded_extensions)
    app.state.extensions = loaded_extensions
    app.state.extension_diagnostics = initialize_runtime_diagnostics(extension_diagnostics)

    # Include routers
    # Models API is mounted at /api/models
    app.include_router(models.router)

    # Features API is mounted at /api/features
    app.include_router(features.router)

    # Console API (cross-thread observability) is mounted at /api/console
    app.include_router(console.router)

    # MCP API is mounted at /api/mcp
    app.include_router(mcp.router)

    # Durable MCP tasks are scoped to their owning thread.
    app.include_router(mcp_tasks.router)
    app.include_router(subagent_batches.router)

    # Memory API is mounted at /api/memory
    app.include_router(memory.router)

    # Skills API is mounted at /api/skills
    app.include_router(skills.router)

    # First-party integrations API is mounted at /api/integrations
    app.include_router(integrations.router)

    # Artifacts API is mounted at /api/threads/{thread_id}/artifacts
    app.include_router(artifacts.router)

    # Browser API is mounted at /api/threads/{thread_id}/browser
    app.include_router(browser.router)

    # Uploads API is mounted at /api/threads/{thread_id}/uploads
    app.include_router(uploads.router)

    # Thread cleanup API is mounted at /api/threads/{thread_id}
    app.include_router(threads.router)

    # Scheduled tasks API is mounted at /api/scheduled-tasks
    app.include_router(scheduled_tasks.router)

    # Agents API is mounted at /api/agents
    app.include_router(agents.router)
    # Projects API is mounted at /api/projects
    app.include_router(projects.router)

    # Deployment-level subagent catalog and admin management.
    app.include_router(subagents.router)

    # Suggestions API is mounted at /api/threads/{thread_id}/suggestions
    app.include_router(suggestions.router)

    # Input polishing API is mounted at /api/input-polish
    app.include_router(input_polish.router)

    # User-facing IM channel connection API is mounted at /api/channels
    app.include_router(channel_connections.router)

    # Channels API is mounted at /api/channels
    app.include_router(channels.router)

    # Assistants compatibility API (LangGraph Platform stub)
    app.include_router(assistants_compat.router)

    # Auth API is mounted at /api/v1/auth
    app.include_router(auth.router)
    app.include_router(user_preferences.router)

    # Feedback API is mounted at /api/threads/{thread_id}/runs/{run_id}/feedback
    app.include_router(feedback.router)

    # Thread Runs API (LangGraph Platform-compatible runs lifecycle)
    app.include_router(thread_runs.router)

    # Stateless Runs API (stream/wait without a pre-existing thread)
    app.include_router(runs.router)

    # GitHub webhooks API is mounted at /api/webhooks/github
    # Exempt from auth and CSRF middleware (see auth_middleware._PUBLIC_PATH_PREFIXES
    # and csrf_middleware.should_check_csrf); authenticity is enforced via the
    # X-Hub-Signature-256 HMAC against GITHUB_WEBHOOK_SECRET.
    # Including this router transitively imports app.gateway.github, which
    # registers the GitHub channel's ChannelRunPolicy as an import side-effect.
    #
    # Fail-closed: only mount the route when a webhook secret is configured
    # (or when the explicit DEER_FLOW_ALLOW_UNVERIFIED_GITHUB_WEBHOOKS=1
    # dev opt-in is set). A misconfigured deployment without a secret cannot
    # serve forged deliveries because the URL responds 404 — there is no
    # handler to reach.
    if github_webhooks.is_route_enabled():
        app.include_router(github_webhooks.router)
        logger.info("GitHub webhooks route mounted at /api/webhooks/github")
    else:
        logger.warning("GitHub webhooks route NOT mounted: GITHUB_WEBHOOK_SECRET unset and DEER_FLOW_ALLOW_UNVERIFIED_GITHUB_WEBHOOKS not set. /api/webhooks/github will respond 404. Configure either env var to enable the route.")

    @app.get("/health", tags=["health"])
    async def health_check() -> dict[str, str]:
        """Health check endpoint.

        Returns:
            Service health status information.
        """
        return {"status": "healthy", "service": "deer-flow-gateway"}

    @app.get("/health/ready", tags=["health"])
    async def readiness_check(request: Request, response: Response) -> dict[str, str]:
        """Readiness endpoint: 200 when the persistence backends are reachable.

        Probes the ORM engine behind ``database:`` and the effective LangGraph
        checkpointer/Store backend (legacy ``checkpointer:`` section, otherwise
        derived from ``database:``) concurrently beneath one bounded deadline.
        The checkpointer config comes from the startup snapshot recorded by
        ``langgraph_runtime`` (never hot-reloaded config), so orchestrators can
        gate on the gateway actually being ready rather than merely alive.
        Returns 503 with ``status: degraded`` when either probe fails or the
        startup backend cannot be resolved.
        """
        checkpointer_config = getattr(request.app.state, READINESS_CHECKPOINTER_CONFIG_ATTR, None)
        status_code, payload = await readiness_payload(checkpointer_config)
        response.status_code = status_code
        return payload

    # Extension routes are deliberately last: FastAPI/Starlette dispatches in
    # registration order, so every host route (including conditional routes
    # and /health) keeps precedence. Definite shadows are rejected with an
    # attributed diagnostic while unrelated extension routers still mount.
    from deerflow.extensions.gateway import include_contributed_routers

    record_runtime_diagnostics(include_contributed_routers(app, loaded_extensions))

    return app


# Create app instance for uvicorn
app = create_app()