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

import logging
import os
from collections.abc import AsyncGenerator
from contextlib import asynccontextmanager
from datetime import UTC

from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware

from app.gateway.auth_middleware import AuthMiddleware
from app.gateway.config import get_gateway_config
from app.gateway.csrf_middleware import CSRFMiddleware
from app.gateway.deps import langgraph_runtime
from app.gateway.routers import (
    agents,
    artifacts,
    assistants_compat,
    auth,
    channels,
    feedback,
    mcp,
    memory,
    models,
    runs,
    skills,
    suggestions,
    thread_runs,
    threads,
    uploads,
)
from deerflow.config.app_config import get_app_config

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
)

logger = logging.getLogger(__name__)


async def _ensure_admin_user(app: FastAPI) -> None:
    """Auto-create the admin user on first boot if no users exist.

    After admin creation, migrate orphan threads from the LangGraph
    store (metadata.owner_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.

    No SQL persistence migration is needed: the four owner_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. "Existing persistence DB + new auth"
    is not a supported upgrade path — fresh install or wipe-and-retry.

    Multi-worker safe: relies on SQLite UNIQUE constraint to resolve
    races during admin creation. Only the worker that successfully
    creates/updates the admin prints the password; losers silently skip.
    """
    import secrets

    from app.gateway.deps import get_local_provider

    provider = get_local_provider()
    user_count = await provider.count_users()

    admin = None
    fresh_admin_created = False

    if user_count == 0:
        password = secrets.token_urlsafe(16)
        try:
            admin = await provider.create_user(email="admin@deerflow.dev", password=password, system_role="admin", needs_setup=True)
            fresh_admin_created = True
        except ValueError:
            return  # Another worker already created the admin.
    else:
        # Admin exists but setup never completed — reset password so operator
        # can always find it in the console without needing the CLI.
        # Multi-worker guard: if admin was created less than 30s ago, another
        # worker just created it and will print the password — skip reset.
        admin = await provider.get_user_by_email("admin@deerflow.dev")
        if admin and admin.needs_setup:
            import time

            age = time.time() - admin.created_at.replace(tzinfo=UTC).timestamp()
            if age >= 30:
                from app.gateway.auth.password import hash_password_async

                password = secrets.token_urlsafe(16)
                admin.password_hash = await hash_password_async(password)
                admin.token_version += 1
                await provider.update_user(admin)

                logger.info("=" * 60)
                logger.info("  Admin account setup incomplete — password reset")
                logger.info("  Email:    %s", admin.email)
                logger.info("  Password: %s", password)
                logger.info("  Change it after login: Settings -> Account")
                logger.info("=" * 60)

    if admin is None:
        return  # Nothing to bind orphans to.

    admin_id = str(admin.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 owner_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)")

    if fresh_admin_created:
        logger.info("=" * 60)
        logger.info("  Admin account created on first boot")
        logger.info("  Email:    %s", admin.email)
        logger.info("  Password: %s", password)  # noqa: F821 — defined in the fresh_admin branch
        logger.info("  Change it after login: Settings -> Account")
        logger.info("=" * 60)


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 owner_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("owner_id"):
            metadata["owner_id"] = admin_user_id
            item.value["metadata"] = metadata
            await store.aput(("threads",), item.key, item.value)
            migrated += 1
    return migrated


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

    # Load config and check necessary environment variables at startup
    try:
        get_app_config()
        logger.info("Configuration loaded successfully")
    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}")

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

        # Ensure admin user exists (auto-create on first boot)
        # Must run AFTER langgraph_runtime so app.state.store is available for thread migration
        await _ensure_admin_user(app)

        # Start IM channel service if any channels are configured
        try:
            from app.channels.service import start_channel_service

            channel_service = await start_channel_service()
            logger.info("Channel service started: %s", channel_service.get_status())
        except Exception:
            logger.exception("No IM channels configured or channel service failed to start")

        yield

        # Stop channel service on shutdown
        try:
            from app.channels.service import stop_channel_service

            await stop_channel_service()
        except Exception:
            logger.exception("Failed to stop channel service")

    logger.info("Shutting down API Gateway")


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

    Returns:
        Configured FastAPI application instance.
    """

    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 requests are handled by nginx reverse proxy.
This gateway provides custom endpoints for models, MCP configuration, skills, and artifacts.
        """,
        version="0.1.0",
        lifespan=lifespan,
        docs_url="/docs",
        redoc_url="/redoc",
        openapi_url="/openapi.json",
        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": "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)

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

    # CORS: when GATEWAY_CORS_ORIGINS is set (dev without nginx), add CORS middleware.
    # In production, nginx handles CORS and no middleware is needed.
    cors_origins_env = os.environ.get("GATEWAY_CORS_ORIGINS", "")
    if cors_origins_env:
        cors_origins = [o.strip() for o in cors_origins_env.split(",") if o.strip()]
        # Validate: wildcard origin with credentials is a security misconfiguration
        for origin in cors_origins:
            if origin == "*":
                logger.error("GATEWAY_CORS_ORIGINS contains wildcard '*' with allow_credentials=True. This is a security misconfiguration — browsers will reject the response. Use explicit scheme://host:port origins instead.")
                cors_origins = [o for o in cors_origins if o != "*"]
                break
        if cors_origins:
            app.add_middleware(
                CORSMiddleware,
                allow_origins=cors_origins,
                allow_credentials=True,
                allow_methods=["*"],
                allow_headers=["*"],
            )

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

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

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

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

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

    # 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)

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

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

    return app


# Create app instance for uvicorn
app = create_app()