Merge refactor/config-deerflow-context into release/2.0-rc

Cherry-pick PR #2271's config refactor onto release/2.0-rc.
Used 'git merge -X theirs' to auto-resolve content conflicts in favor of
the PR's design (frozen AppConfig + explicit-parameter passing).

Limitations:
- Release-only changes that overlapped with PR's refactor in 119 files
  are NOT preserved — those files reflect PR's version. Follow-up commits
  on this branch will need to re-apply release-only modifications where
  meaningful.
- See PR #2271 for design rationale.

backend/CLAUDE.md

@@ -127,7 +127,7 @@ from app.gateway.app import app
 from app.channels.service import start_channel_service
 
 # App → Harness (allowed)
-from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
 
 # Harness → App (FORBIDDEN — enforced by test_harness_boundary.py)
 # from app.gateway.routers.uploads import ...  # ← will fail CI
@@ -182,7 +182,16 @@ Setup: Copy `config.example.yaml` to `config.yaml` in the **project root** direc
 
 **Config Versioning**: `config.example.yaml` has a `config_version` field. On startup, `AppConfig.from_file()` compares user version vs example version and emits a warning if outdated. Missing `config_version` = version 0. Run `make config-upgrade` to auto-merge missing fields. When changing the config schema, bump `config_version` in `config.example.yaml`.
 
-**Config Caching**: `get_app_config()` caches the parsed config, but automatically reloads it when the resolved config path changes or the file's mtime increases. This keeps Gateway and LangGraph reads aligned with `config.yaml` edits without requiring a manual process restart.
+**Config Lifecycle**: All config models are `frozen=True` (immutable after construction). `AppConfig.from_file()` is a pure function — no side effects, no process-global state. The resolved `AppConfig` is passed as an explicit parameter down every consumer lane:
+
+- **Gateway**: `app.state.config` populated in lifespan; routers receive it via `Depends(get_config)` from `app/gateway/deps.py`.
+- **Client**: `DeerFlowClient._app_config` captured in the constructor; every method reads `self._app_config`.
+- **Agent run**: wrapped in `DeerFlowContext(app_config=…)` and injected via LangGraph `Runtime[DeerFlowContext].context`. Middleware and tools read `runtime.context.app_config` directly or via `resolve_context(runtime)`.
+- **LangGraph Server bootstrap**: `make_lead_agent` (registered in `langgraph.json`) calls `AppConfig.from_file()` itself — the only place in production that loads from disk at agent-build time.
+
+To update config at runtime (Gateway API mutations for MCP/Skills), write the new file and call `AppConfig.from_file()` to build a fresh snapshot, then swap `app.state.config`. No mtime detection, no auto-reload, no ambient ContextVar lookup (`AppConfig.current()` has been removed).
+
+**DeerFlowContext**: Per-invocation typed context for the agent execution path, injected via LangGraph `Runtime[DeerFlowContext]`. Holds `app_config: AppConfig`, `thread_id: str`, `agent_name: str | None`. Gateway runtime and `DeerFlowClient` construct full `DeerFlowContext` at invoke time; the LangGraph Server boundary builds one inside `make_lead_agent`. Middleware and tools access context through `resolve_context(runtime)` which returns the typed `DeerFlowContext` — legacy dict/None shapes are rejected. Mutable runtime state (`sandbox_id`) flows through `ThreadState.sandbox`, not context.
 
 Configuration priority:
 1. Explicit `config_path` argument

backend/app/channels/feishu.py

@@ -375,7 +375,9 @@ class FeishuChannel(Channel):
         virtual_path = f"{VIRTUAL_PATH_PREFIX}/uploads/{resolved_target.name}"
 
         try:
-            sandbox_provider = get_sandbox_provider()
+            from deerflow.config.app_config import AppConfig
+
+            sandbox_provider = get_sandbox_provider(AppConfig.from_file())
             sandbox_id = sandbox_provider.acquire(thread_id)
             if sandbox_id != "local":
                 sandbox = sandbox_provider.get(sandbox_id)

backend/app/channels/manager.py

@@ -17,8 +17,6 @@ from langgraph_sdk.errors import ConflictError
 from app.channels.commands import KNOWN_CHANNEL_COMMANDS
 from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
 from app.channels.store import ChannelStore
-from app.gateway.csrf_middleware import CSRF_COOKIE_NAME, CSRF_HEADER_NAME, generate_csrf_token
-from app.gateway.internal_auth import create_internal_auth_headers
 from deerflow.runtime.user_context import get_effective_user_id
 
 logger = logging.getLogger(__name__)

backend/app/channels/service.py

@@ -4,13 +4,16 @@ from __future__ import annotations
 
 import logging
 import os
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 from app.channels.base import Channel
 from app.channels.manager import DEFAULT_GATEWAY_URL, DEFAULT_LANGGRAPH_URL, ChannelManager
 from app.channels.message_bus import MessageBus
 from app.channels.store import ChannelStore
 
+if TYPE_CHECKING:
+    from deerflow.config.app_config import AppConfig
+
 logger = logging.getLogger(__name__)
 
 # Channel name → import path for lazy loading
@@ -75,14 +78,11 @@ class ChannelService:
         self._running = False
 
     @classmethod
-    def from_app_config(cls) -> ChannelService:
-        """Create a ChannelService from the application config."""
-        from deerflow.config.app_config import get_app_config
-
-        config = get_app_config()
+    def from_app_config(cls, app_config: AppConfig) -> ChannelService:
+        """Create a ChannelService from an explicit application config."""
         channels_config = {}
         # extra fields are allowed by AppConfig (extra="allow")
-        extra = config.model_extra or {}
+        extra = app_config.model_extra or {}
         if "channels" in extra:
             channels_config = extra["channels"]
         return cls(channels_config=channels_config)
@@ -201,12 +201,12 @@ def get_channel_service() -> ChannelService | None:
     return _channel_service
 
 
-async def start_channel_service() -> ChannelService:
+async def start_channel_service(app_config: AppConfig) -> ChannelService:
     """Create and start the global ChannelService from app config."""
     global _channel_service
     if _channel_service is not None:
         return _channel_service
-    _channel_service = ChannelService.from_app_config()
+    _channel_service = ChannelService.from_app_config(app_config)
     await _channel_service.start()
     return _channel_service
 

backend/app/gateway/app.py

@@ -28,7 +28,7 @@ from app.gateway.routers import (
     threads,
     uploads,
 )
-from deerflow.config.app_config import get_app_config
+from deerflow.config.app_config import AppConfig
 
 # Configure logging
 logging.basicConfig(
@@ -72,18 +72,7 @@ async def _ensure_admin_user(app: FastAPI) -> None:
     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
-
+    provider = get_local_provider()
     admin_count = await provider.count_admin_users()
 
     if admin_count == 0:
@@ -95,6 +84,10 @@ async def _ensure_admin_user(app: FastAPI) -> None:
 
     # Admin already exists — run orphan thread migration for any
     # LangGraph thread metadata that pre-dates the auth module.
+    sf = get_session_factory()
+    if sf is None:
+        return
+
     async with sf() as session:
         stmt = select(UserRow).where(UserRow.system_role == "admin").limit(1)
         row = (await session.execute(stmt)).scalar_one_or_none()
@@ -158,9 +151,11 @@ async def _migrate_orphaned_threads(store, admin_user_id: str) -> int:
 async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
     """Application lifespan handler."""
 
-    # Load config and check necessary environment variables at startup
     try:
-        get_app_config()
+        # ``app.state.config`` is the sole source of truth for
+        # ``Depends(get_config)``. Consumers that want AppConfig must receive
+        # it as an explicit parameter; there is no ambient singleton.
+        app.state.config = AppConfig.from_file()
         logger.info("Configuration loaded successfully")
     except Exception as e:
         error_msg = f"Failed to load configuration during gateway startup: {e}"
@@ -181,7 +176,7 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
         try:
             from app.channels.service import start_channel_service
 
-            channel_service = await start_channel_service()
+            channel_service = await start_channel_service(app.state.config)
             logger.info("Channel service started: %s", channel_service.get_status())
         except Exception:
             logger.exception("No IM channels configured or channel service failed to start")

backend/app/gateway/auth/providers.py

@@ -12,12 +12,12 @@ class AuthProvider(ABC):
 
         Returns User if authentication succeeds, None otherwise.
         """
-        raise NotImplementedError
+        ...
 
     @abstractmethod
     async def get_user(self, user_id: str) -> "User | None":
         """Retrieve user by ID."""
-        raise NotImplementedError
+        ...
 
 
 # Import User at runtime to avoid circular imports

backend/app/gateway/auth/repositories/base.py

@@ -35,7 +35,7 @@ class UserRepository(ABC):
         Raises:
             ValueError: If email already exists
         """
-        raise NotImplementedError
+        ...
 
     @abstractmethod
     async def get_user_by_id(self, user_id: str) -> User | None:
@@ -47,7 +47,7 @@ class UserRepository(ABC):
         Returns:
             User if found, None otherwise
         """
-        raise NotImplementedError
+        ...
 
     @abstractmethod
     async def get_user_by_email(self, email: str) -> User | None:
@@ -59,7 +59,7 @@ class UserRepository(ABC):
         Returns:
             User if found, None otherwise
         """
-        raise NotImplementedError
+        ...
 
     @abstractmethod
     async def update_user(self, user: User) -> User:
@@ -76,17 +76,17 @@ class UserRepository(ABC):
                 a hard failure (not a no-op) so callers cannot mistake a
                 concurrent-delete race for a successful update.
         """
-        raise NotImplementedError
+        ...
 
     @abstractmethod
     async def count_users(self) -> int:
         """Return total number of registered users."""
-        raise NotImplementedError
+        ...
 
     @abstractmethod
     async def count_admin_users(self) -> int:
         """Return number of users with system_role == 'admin'."""
-        raise NotImplementedError
+        ...
 
     @abstractmethod
     async def get_user_by_oauth(self, provider: str, oauth_id: str) -> User | None:
@@ -99,4 +99,4 @@ class UserRepository(ABC):
         Returns:
             User if found, None otherwise
         """
-        raise NotImplementedError
+        ...

backend/app/gateway/auth/reset_admin.py

@@ -25,14 +25,15 @@ from deerflow.persistence.user.model import UserRow
 
 
 async def _run(email: str | None) -> int:
-    from deerflow.config import get_app_config
+    from deerflow.config import AppConfig
     from deerflow.persistence.engine import (
         close_engine,
         get_session_factory,
         init_engine_from_config,
     )
 
-    config = get_app_config()
+    # CLI entry: load config explicitly at the top, pass down through the closure.
+    config = AppConfig.from_file()
     await init_engine_from_config(config.database)
     try:
         sf = get_session_factory()

backend/app/gateway/auth_middleware.py

@@ -18,7 +18,6 @@ from starlette.types import ASGIApp
 
 from app.gateway.auth.errors import AuthErrorCode, AuthErrorResponse
 from app.gateway.authz import _ALL_PERMISSIONS, AuthContext
-from app.gateway.internal_auth import INTERNAL_AUTH_HEADER_NAME, get_internal_user, is_valid_internal_auth_token
 from deerflow.runtime.user_context import reset_current_user, set_current_user
 
 # Paths that never require authentication.
@@ -76,12 +75,8 @@ class AuthMiddleware(BaseHTTPMiddleware):
         if _is_public(request.url.path):
             return await call_next(request)
 
-        internal_user = None
-        if is_valid_internal_auth_token(request.headers.get(INTERNAL_AUTH_HEADER_NAME)):
-            internal_user = get_internal_user()
-
         # Non-public path: require session cookie
-        if internal_user is None and not request.cookies.get("access_token"):
+        if not request.cookies.get("access_token"):
             return JSONResponse(
                 status_code=401,
                 content={
@@ -105,13 +100,10 @@ class AuthMiddleware(BaseHTTPMiddleware):
         # bubble up, so we catch and render it as JSONResponse here.
         from app.gateway.deps import get_current_user_from_request
 
-        if internal_user is not None:
-            user = internal_user
-        else:
-            try:
-                user = await get_current_user_from_request(request)
-            except HTTPException as exc:
-                return JSONResponse(status_code=exc.status_code, content={"detail": exc.detail})
+        try:
+            user = await get_current_user_from_request(request)
+        except HTTPException as exc:
+            return JSONResponse(status_code=exc.status_code, content={"detail": exc.detail})
 
         # Stamp both request.state.user (for the contextvar pattern)
         # and request.state.auth (so @require_permission's "auth is

backend/app/gateway/authz.py

@@ -30,9 +30,7 @@ Inspired by LangGraph Auth system: https://github.com/langchain-ai/langgraph/blo
 from __future__ import annotations
 
 import functools
-import inspect
 from collections.abc import Callable
-from types import SimpleNamespace
 from typing import TYPE_CHECKING, Any, ParamSpec, TypeVar
 
 from fastapi import HTTPException, Request
@@ -119,15 +117,6 @@ _ALL_PERMISSIONS: list[str] = [
 ]
 
 
-def _make_test_request_stub() -> Any:
-    """Create a minimal request-like object for direct unit calls.
-
-    Used when decorated route handlers are invoked without FastAPI's
-    request injection. Includes fields accessed by auth helpers.
-    """
-    return SimpleNamespace(state=SimpleNamespace(), cookies={}, _deerflow_test_bypass_auth=True)
-
-
 async def _authenticate(request: Request) -> AuthContext:
     """Authenticate request and return AuthContext.
 
@@ -165,17 +154,7 @@ def require_auth[**P, T](func: Callable[P, T]) -> Callable[P, T]:
     async def wrapper(*args: Any, **kwargs: Any) -> Any:
         request = kwargs.get("request")
         if request is None:
-            # Unit tests may call decorated handlers directly without a
-            # FastAPI Request object. Inject a minimal request stub when
-            # the wrapped function declares `request`.
-            if "request" in inspect.signature(func).parameters:
-                kwargs["request"] = _make_test_request_stub()
-            else:
-                raise ValueError("require_auth decorator requires 'request' parameter")
-            request = kwargs["request"]
-
-        if getattr(request, "_deerflow_test_bypass_auth", False):
-            return await func(*args, **kwargs)
+            raise ValueError("require_auth decorator requires 'request' parameter")
 
         # Authenticate and set context
         auth_context = await _authenticate(request)
@@ -231,17 +210,7 @@ def require_permission(
         async def wrapper(*args: Any, **kwargs: Any) -> Any:
             request = kwargs.get("request")
             if request is None:
-                # Unit tests may call decorated route handlers directly without
-                # constructing a FastAPI Request object. Inject a minimal stub
-                # when the wrapped function declares `request`.
-                if "request" in inspect.signature(func).parameters:
-                    kwargs["request"] = _make_test_request_stub()
-                else:
-                    return await func(*args, **kwargs)
-                request = kwargs["request"]
-
-            if getattr(request, "_deerflow_test_bypass_auth", False):
-                return await func(*args, **kwargs)
+                raise ValueError("require_permission decorator requires 'request' parameter")
 
             auth: AuthContext = getattr(request.state, "auth", None)
             if auth is None:

backend/app/gateway/deps.py

@@ -10,15 +10,13 @@ from __future__ import annotations
 
 from collections.abc import AsyncGenerator, Callable
 from contextlib import AsyncExitStack, asynccontextmanager
-from typing import TYPE_CHECKING, TypeVar, cast
+from typing import TYPE_CHECKING
 
 from fastapi import FastAPI, HTTPException, Request
 from langgraph.types import Checkpointer
 
-from deerflow.persistence.feedback import FeedbackRepository
-from deerflow.runtime import RunContext, RunManager, StreamBridge
-from deerflow.runtime.events.store.base import RunEventStore
-from deerflow.runtime.runs.store.base import RunStore
+from deerflow.config.app_config import AppConfig
+from deerflow.runtime import RunContext, RunManager
 
 if TYPE_CHECKING:
     from app.gateway.auth.local_provider import LocalAuthProvider
@@ -26,7 +24,17 @@ if TYPE_CHECKING:
     from deerflow.persistence.thread_meta.base import ThreadMetaStore
 
 
-T = TypeVar("T")
+def get_config(request: Request) -> AppConfig:
+    """FastAPI dependency returning the app-scoped ``AppConfig``.
+
+    Reads from ``request.app.state.config`` which is set at startup
+    (``app.py`` lifespan) and swapped on config reload (``routers/mcp.py``,
+    ``routers/skills.py``).
+    """
+    cfg = getattr(request.app.state, "config", None)
+    if cfg is None:
+        raise HTTPException(status_code=503, detail="Configuration not available")
+    return cfg
 
 
 @asynccontextmanager
@@ -38,22 +46,24 @@ async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
         async with langgraph_runtime(app):
             yield
     """
-    from deerflow.config import get_app_config
     from deerflow.persistence.engine import close_engine, get_session_factory, init_engine_from_config
     from deerflow.runtime import make_store, make_stream_bridge
     from deerflow.runtime.checkpointer.async_provider import make_checkpointer
     from deerflow.runtime.events.store import make_run_event_store
 
     async with AsyncExitStack() as stack:
-        app.state.stream_bridge = await stack.enter_async_context(make_stream_bridge())
+        # app.state.config is populated earlier in lifespan(); thread it
+        # explicitly into every provider below.
+        config = app.state.config
+
+        app.state.stream_bridge = await stack.enter_async_context(make_stream_bridge(config))
 
         # Initialize persistence engine BEFORE checkpointer so that
         # auto-create-database logic runs first (postgres backend).
-        config = get_app_config()
         await init_engine_from_config(config.database)
 
-        app.state.checkpointer = await stack.enter_async_context(make_checkpointer())
-        app.state.store = await stack.enter_async_context(make_store())
+        app.state.checkpointer = await stack.enter_async_context(make_checkpointer(config))
+        app.state.store = await stack.enter_async_context(make_store(config))
 
         # Initialize repositories — one get_session_factory() call for all.
         sf = get_session_factory()
@@ -91,25 +101,25 @@ async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
 # ---------------------------------------------------------------------------
 
 
-def _require(attr: str, label: str) -> Callable[[Request], T]:
+def _require(attr: str, label: str):
     """Create a FastAPI dependency that returns ``app.state.<attr>`` or 503."""
 
-    def dep(request: Request) -> T:
+    def dep(request: Request):
         val = getattr(request.app.state, attr, None)
         if val is None:
             raise HTTPException(status_code=503, detail=f"{label} not available")
-        return cast(T, val)
+        return val
 
     dep.__name__ = dep.__qualname__ = f"get_{attr}"
     return dep
 
 
-get_stream_bridge: Callable[[Request], StreamBridge] = _require("stream_bridge", "Stream bridge")
-get_run_manager: Callable[[Request], RunManager] = _require("run_manager", "Run manager")
-get_checkpointer: Callable[[Request], Checkpointer] = _require("checkpointer", "Checkpointer")
-get_run_event_store: Callable[[Request], RunEventStore] = _require("run_event_store", "Run event store")
-get_feedback_repo: Callable[[Request], FeedbackRepository] = _require("feedback_repo", "Feedback")
-get_run_store: Callable[[Request], RunStore] = _require("run_store", "Run store")
+get_stream_bridge = _require("stream_bridge", "Stream bridge")
+get_run_manager = _require("run_manager", "Run manager")
+get_checkpointer = _require("checkpointer", "Checkpointer")
+get_run_event_store = _require("run_event_store", "Run event store")
+get_feedback_repo = _require("feedback_repo", "Feedback")
+get_run_store = _require("run_store", "Run store")
 
 
 def get_store(request: Request):
@@ -128,19 +138,23 @@ def get_thread_store(request: Request) -> ThreadMetaStore:
 def get_run_context(request: Request) -> RunContext:
     """Build a :class:`RunContext` from ``app.state`` singletons.
 
-    Returns a *base* context with infrastructure dependencies.
+    Returns a *base* context with infrastructure dependencies.  Callers that
+    need per-run fields (e.g. ``follow_up_to_run_id``) should use
+    ``dataclasses.replace(ctx, follow_up_to_run_id=...)`` before passing it
+    to :func:`run_agent`.
     """
-    from deerflow.config import get_app_config
-
+    config = get_config(request)
     return RunContext(
         checkpointer=get_checkpointer(request),
         store=get_store(request),
         event_store=get_run_event_store(request),
-        run_events_config=getattr(get_app_config(), "run_events", None),
+        run_events_config=getattr(config, "run_events", None),
         thread_store=get_thread_store(request),
+        app_config=config,
     )
 
 
+
 # ---------------------------------------------------------------------------
 # Auth helpers (used by authz.py and auth middleware)
 # ---------------------------------------------------------------------------

backend/app/gateway/routers/agents.py

@@ -5,11 +5,12 @@ import re
 import shutil
 
 import yaml
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, Depends, HTTPException
 from pydantic import BaseModel, Field
 
-from deerflow.config.agents_api_config import get_agents_api_config
+from app.gateway.deps import get_config
 from deerflow.config.agents_config import AgentConfig, list_custom_agents, load_agent_config, load_agent_soul
+from deerflow.config.app_config import AppConfig
 from deerflow.config.paths import get_paths
 
 logger = logging.getLogger(__name__)
@@ -77,9 +78,9 @@ def _normalize_agent_name(name: str) -> str:
     return name.lower()
 
 
-def _require_agents_api_enabled() -> None:
+def _require_agents_api_enabled(app_config: AppConfig) -> None:
     """Reject access unless the custom-agent management API is explicitly enabled."""
-    if not get_agents_api_config().enabled:
+    if not app_config.agents_api.enabled:
         raise HTTPException(
             status_code=403,
             detail=("Custom-agent management API is disabled. Set agents_api.enabled=true to expose agent and user-profile routes over HTTP."),
@@ -108,13 +109,13 @@ def _agent_config_to_response(agent_cfg: AgentConfig, include_soul: bool = False
     summary="List Custom Agents",
     description="List all custom agents available in the agents directory, including their soul content.",
 )
-async def list_agents() -> AgentsListResponse:
+async def list_agents(app_config: AppConfig = Depends(get_config)) -> AgentsListResponse:
     """List all custom agents.
 
     Returns:
         List of all custom agents with their metadata and soul content.
     """
-    _require_agents_api_enabled()
+    _require_agents_api_enabled(app_config)
 
     try:
         agents = list_custom_agents()
@@ -141,7 +142,7 @@ async def check_agent_name(name: str) -> dict:
     Raises:
         HTTPException: 422 if the name is invalid.
     """
-    _require_agents_api_enabled()
+    _require_agents_api_enabled(app_config)
     _validate_agent_name(name)
     normalized = _normalize_agent_name(name)
     available = not get_paths().agent_dir(normalized).exists()
@@ -154,7 +155,7 @@ async def check_agent_name(name: str) -> dict:
     summary="Get Custom Agent",
     description="Retrieve details and SOUL.md content for a specific custom agent.",
 )
-async def get_agent(name: str) -> AgentResponse:
+async def get_agent(name: str, app_config: AppConfig = Depends(get_config)) -> AgentResponse:
     """Get a specific custom agent by name.
 
     Args:
@@ -166,7 +167,7 @@ async def get_agent(name: str) -> AgentResponse:
     Raises:
         HTTPException: 404 if agent not found.
     """
-    _require_agents_api_enabled()
+    _require_agents_api_enabled(app_config)
     _validate_agent_name(name)
     name = _normalize_agent_name(name)
 
@@ -187,7 +188,7 @@ async def get_agent(name: str) -> AgentResponse:
     summary="Create Custom Agent",
     description="Create a new custom agent with its config and SOUL.md.",
 )
-async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
+async def create_agent_endpoint(request: AgentCreateRequest, app_config: AppConfig = Depends(get_config)) -> AgentResponse:
     """Create a new custom agent.
 
     Args:
@@ -199,7 +200,7 @@ async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
     Raises:
         HTTPException: 409 if agent already exists, 422 if name is invalid.
     """
-    _require_agents_api_enabled()
+    _require_agents_api_enabled(app_config)
     _validate_agent_name(request.name)
     normalized_name = _normalize_agent_name(request.name)
 
@@ -251,7 +252,7 @@ async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
     summary="Update Custom Agent",
     description="Update an existing custom agent's config and/or SOUL.md.",
 )
-async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
+async def update_agent(name: str, request: AgentUpdateRequest, app_config: AppConfig = Depends(get_config)) -> AgentResponse:
     """Update an existing custom agent.
 
     Args:
@@ -264,7 +265,7 @@ async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
     Raises:
         HTTPException: 404 if agent not found.
     """
-    _require_agents_api_enabled()
+    _require_agents_api_enabled(app_config)
     _validate_agent_name(name)
     name = _normalize_agent_name(name)
 
@@ -342,13 +343,13 @@ class UserProfileUpdateRequest(BaseModel):
     summary="Get User Profile",
     description="Read the global USER.md file that is injected into all custom agents.",
 )
-async def get_user_profile() -> UserProfileResponse:
+async def get_user_profile(app_config: AppConfig = Depends(get_config)) -> UserProfileResponse:
     """Return the current USER.md content.
 
     Returns:
         UserProfileResponse with content=None if USER.md does not exist yet.
     """
-    _require_agents_api_enabled()
+    _require_agents_api_enabled(app_config)
 
     try:
         user_md_path = get_paths().user_md_file
@@ -367,7 +368,7 @@ async def get_user_profile() -> UserProfileResponse:
     summary="Update User Profile",
     description="Write the global USER.md file that is injected into all custom agents.",
 )
-async def update_user_profile(request: UserProfileUpdateRequest) -> UserProfileResponse:
+async def update_user_profile(request: UserProfileUpdateRequest, app_config: AppConfig = Depends(get_config)) -> UserProfileResponse:
     """Create or overwrite the global USER.md.
 
     Args:
@@ -376,7 +377,7 @@ async def update_user_profile(request: UserProfileUpdateRequest) -> UserProfileR
     Returns:
         UserProfileResponse with the saved content.
     """
-    _require_agents_api_enabled()
+    _require_agents_api_enabled(app_config)
 
     try:
         paths = get_paths()
@@ -395,7 +396,7 @@ async def update_user_profile(request: UserProfileUpdateRequest) -> UserProfileR
     summary="Delete Custom Agent",
     description="Delete a custom agent and all its files (config, SOUL.md, memory).",
 )
-async def delete_agent(name: str) -> None:
+async def delete_agent(name: str, app_config: AppConfig = Depends(get_config)) -> None:
     """Delete a custom agent.
 
     Args:
@@ -404,7 +405,7 @@ async def delete_agent(name: str) -> None:
     Raises:
         HTTPException: 404 if agent not found.
     """
-    _require_agents_api_enabled()
+    _require_agents_api_enabled(app_config)
     _validate_agent_name(name)
     name = _normalize_agent_name(name)
 

backend/app/gateway/routers/mcp.py

@@ -3,10 +3,12 @@ import logging
 from pathlib import Path
 from typing import Literal
 
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, Depends, HTTPException, Request
 from pydantic import BaseModel, Field
 
-from deerflow.config.extensions_config import ExtensionsConfig, get_extensions_config, reload_extensions_config
+from app.gateway.deps import get_config
+from deerflow.config.app_config import AppConfig
+from deerflow.config.extensions_config import ExtensionsConfig
 
 logger = logging.getLogger(__name__)
 router = APIRouter(prefix="/api", tags=["mcp"])
@@ -69,7 +71,7 @@ class McpConfigUpdateRequest(BaseModel):
     summary="Get MCP Configuration",
     description="Retrieve the current Model Context Protocol (MCP) server configurations.",
 )
-async def get_mcp_configuration() -> McpConfigResponse:
+async def get_mcp_configuration(config: AppConfig = Depends(get_config)) -> McpConfigResponse:
     """Get the current MCP configuration.
 
     Returns:
@@ -90,9 +92,9 @@ async def get_mcp_configuration() -> McpConfigResponse:
         }
         ```
     """
-    config = get_extensions_config()
+    ext = config.extensions
 
-    return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in config.mcp_servers.items()})
+    return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in ext.mcp_servers.items()})
 
 
 @router.put(
@@ -101,7 +103,11 @@ async def get_mcp_configuration() -> McpConfigResponse:
     summary="Update MCP Configuration",
     description="Update Model Context Protocol (MCP) server configurations and save to file.",
 )
-async def update_mcp_configuration(request: McpConfigUpdateRequest) -> McpConfigResponse:
+async def update_mcp_configuration(
+    request: McpConfigUpdateRequest,
+    http_request: Request,
+    config: AppConfig = Depends(get_config),
+) -> McpConfigResponse:
     """Update the MCP configuration.
 
     This will:
@@ -142,13 +148,13 @@ async def update_mcp_configuration(request: McpConfigUpdateRequest) -> McpConfig
             config_path = Path.cwd().parent / "extensions_config.json"
             logger.info(f"No existing extensions config found. Creating new config at: {config_path}")
 
-        # Load current config to preserve skills configuration
-        current_config = get_extensions_config()
+        # Use injected config to preserve skills configuration
+        current_ext = config.extensions
 
         # Convert request to dict format for JSON serialization
         config_data = {
             "mcpServers": {name: server.model_dump() for name, server in request.mcp_servers.items()},
-            "skills": {name: {"enabled": skill.enabled} for name, skill in current_config.skills.items()},
+            "skills": {name: {"enabled": skill.enabled} for name, skill in current_ext.skills.items()},
         }
 
         # Write the configuration to file
@@ -160,9 +166,11 @@ async def update_mcp_configuration(request: McpConfigUpdateRequest) -> McpConfig
         # NOTE: No need to reload/reset cache here - LangGraph Server (separate process)
         # will detect config file changes via mtime and reinitialize MCP tools automatically
 
-        # Reload the configuration and update the global cache
-        reloaded_config = reload_extensions_config()
-        return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in reloaded_config.mcp_servers.items()})
+        # Reload the configuration and swap ``app.state.config`` so subsequent
+        # ``Depends(get_config)`` calls see the refreshed value.
+        reloaded = AppConfig.from_file()
+        http_request.app.state.config = reloaded
+        return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in reloaded.extensions.mcp_servers.items()})
 
     except Exception as e:
         logger.error(f"Failed to update MCP configuration: {e}", exc_info=True)

backend/app/gateway/routers/memory.py

@@ -1,8 +1,9 @@
 """Memory API router for retrieving and managing global memory data."""
 
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, Depends, HTTPException
 from pydantic import BaseModel, Field
 
+from app.gateway.deps import get_config
 from deerflow.agents.memory.updater import (
     clear_memory_data,
     create_memory_fact,
@@ -12,7 +13,7 @@ from deerflow.agents.memory.updater import (
     reload_memory_data,
     update_memory_fact,
 )
-from deerflow.config.memory_config import get_memory_config
+from deerflow.config.app_config import AppConfig
 from deerflow.runtime.user_context import get_effective_user_id
 
 router = APIRouter(prefix="/api", tags=["memory"])
@@ -114,7 +115,7 @@ class MemoryStatusResponse(BaseModel):
     summary="Get Memory Data",
     description="Retrieve the current global memory data including user context, history, and facts.",
 )
-async def get_memory() -> MemoryResponse:
+async def get_memory(app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
     """Get the current global memory data.
 
     Returns:
@@ -148,7 +149,7 @@ async def get_memory() -> MemoryResponse:
         }
         ```
     """
-    memory_data = get_memory_data(user_id=get_effective_user_id())
+    memory_data = get_memory_data(app_config.memory, user_id=get_effective_user_id())
     return MemoryResponse(**memory_data)
 
 
@@ -159,7 +160,7 @@ async def get_memory() -> MemoryResponse:
     summary="Reload Memory Data",
     description="Reload memory data from the storage file, refreshing the in-memory cache.",
 )
-async def reload_memory() -> MemoryResponse:
+async def reload_memory(app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
     """Reload memory data from file.
 
     This forces a reload of the memory data from the storage file,
@@ -168,7 +169,7 @@ async def reload_memory() -> MemoryResponse:
     Returns:
         The reloaded memory data.
     """
-    memory_data = reload_memory_data(user_id=get_effective_user_id())
+    memory_data = reload_memory_data(app_config.memory, user_id=get_effective_user_id())
     return MemoryResponse(**memory_data)
 
 
@@ -179,10 +180,10 @@ async def reload_memory() -> MemoryResponse:
     summary="Clear All Memory Data",
     description="Delete all saved memory data and reset the memory structure to an empty state.",
 )
-async def clear_memory() -> MemoryResponse:
+async def clear_memory(app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
     """Clear all persisted memory data."""
     try:
-        memory_data = clear_memory_data(user_id=get_effective_user_id())
+        memory_data = clear_memory_data(app_config.memory, user_id=get_effective_user_id())
     except OSError as exc:
         raise HTTPException(status_code=500, detail="Failed to clear memory data.") from exc
 
@@ -196,10 +197,11 @@ async def clear_memory() -> MemoryResponse:
     summary="Create Memory Fact",
     description="Create a single saved memory fact manually.",
 )
-async def create_memory_fact_endpoint(request: FactCreateRequest) -> MemoryResponse:
+async def create_memory_fact_endpoint(request: FactCreateRequest, app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
     """Create a single fact manually."""
     try:
         memory_data = create_memory_fact(
+            app_config.memory,
             content=request.content,
             category=request.category,
             confidence=request.confidence,
@@ -220,10 +222,10 @@ async def create_memory_fact_endpoint(request: FactCreateRequest) -> MemoryRespo
     summary="Delete Memory Fact",
     description="Delete a single saved memory fact by its fact id.",
 )
-async def delete_memory_fact_endpoint(fact_id: str) -> MemoryResponse:
+async def delete_memory_fact_endpoint(fact_id: str, app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
     """Delete a single fact from memory by fact id."""
     try:
-        memory_data = delete_memory_fact(fact_id, user_id=get_effective_user_id())
+        memory_data = delete_memory_fact(app_config.memory, fact_id, user_id=get_effective_user_id())
     except KeyError as exc:
         raise HTTPException(status_code=404, detail=f"Memory fact '{fact_id}' not found.") from exc
     except OSError as exc:
@@ -239,10 +241,11 @@ async def delete_memory_fact_endpoint(fact_id: str) -> MemoryResponse:
     summary="Patch Memory Fact",
     description="Partially update a single saved memory fact by its fact id while preserving omitted fields.",
 )
-async def update_memory_fact_endpoint(fact_id: str, request: FactPatchRequest) -> MemoryResponse:
+async def update_memory_fact_endpoint(fact_id: str, request: FactPatchRequest, app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
     """Partially update a single fact manually."""
     try:
         memory_data = update_memory_fact(
+            app_config.memory,
             fact_id=fact_id,
             content=request.content,
             category=request.category,
@@ -266,9 +269,9 @@ async def update_memory_fact_endpoint(fact_id: str, request: FactPatchRequest) -
     summary="Export Memory Data",
     description="Export the current global memory data as JSON for backup or transfer.",
 )
-async def export_memory() -> MemoryResponse:
+async def export_memory(app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
     """Export the current memory data."""
-    memory_data = get_memory_data(user_id=get_effective_user_id())
+    memory_data = get_memory_data(app_config.memory, user_id=get_effective_user_id())
     return MemoryResponse(**memory_data)
 
 
@@ -279,10 +282,10 @@ async def export_memory() -> MemoryResponse:
     summary="Import Memory Data",
     description="Import and overwrite the current global memory data from a JSON payload.",
 )
-async def import_memory(request: MemoryResponse) -> MemoryResponse:
+async def import_memory(request: MemoryResponse, app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
     """Import and persist memory data."""
     try:
-        memory_data = import_memory_data(request.model_dump(), user_id=get_effective_user_id())
+        memory_data = import_memory_data(app_config.memory, request.model_dump(), user_id=get_effective_user_id())
     except OSError as exc:
         raise HTTPException(status_code=500, detail="Failed to import memory data.") from exc
 
@@ -295,7 +298,9 @@ async def import_memory(request: MemoryResponse) -> MemoryResponse:
     summary="Get Memory Configuration",
     description="Retrieve the current memory system configuration.",
 )
-async def get_memory_config_endpoint() -> MemoryConfigResponse:
+async def get_memory_config_endpoint(
+    app_config: AppConfig = Depends(get_config),
+) -> MemoryConfigResponse:
     """Get the memory system configuration.
 
     Returns:
@@ -314,7 +319,7 @@ async def get_memory_config_endpoint() -> MemoryConfigResponse:
         }
         ```
     """
-    config = get_memory_config()
+    config = app_config.memory
     return MemoryConfigResponse(
         enabled=config.enabled,
         storage_path=config.storage_path,
@@ -333,14 +338,16 @@ async def get_memory_config_endpoint() -> MemoryConfigResponse:
     summary="Get Memory Status",
     description="Retrieve both memory configuration and current data in a single request.",
 )
-async def get_memory_status() -> MemoryStatusResponse:
+async def get_memory_status(
+    app_config: AppConfig = Depends(get_config),
+) -> MemoryStatusResponse:
     """Get the memory system status including configuration and data.
 
     Returns:
         Combined memory configuration and current data.
     """
-    config = get_memory_config()
-    memory_data = get_memory_data(user_id=get_effective_user_id())
+    config = app_config.memory
+    memory_data = get_memory_data(config, user_id=get_effective_user_id())
 
     return MemoryStatusResponse(
         config=MemoryConfigResponse(

backend/app/gateway/routers/models.py

@@ -1,7 +1,8 @@
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, Depends, HTTPException
 from pydantic import BaseModel, Field
 
-from deerflow.config import get_app_config
+from app.gateway.deps import get_config
+from deerflow.config.app_config import AppConfig
 
 router = APIRouter(prefix="/api", tags=["models"])
 
@@ -36,7 +37,7 @@ class ModelsListResponse(BaseModel):
     summary="List All Models",
     description="Retrieve a list of all available AI models configured in the system.",
 )
-async def list_models() -> ModelsListResponse:
+async def list_models(config: AppConfig = Depends(get_config)) -> ModelsListResponse:
     """List all available models from configuration.
 
     Returns model information suitable for frontend display,
@@ -72,7 +73,6 @@ async def list_models() -> ModelsListResponse:
         }
         ```
     """
-    config = get_app_config()
     models = [
         ModelResponse(
             name=model.name,
@@ -96,7 +96,7 @@ async def list_models() -> ModelsListResponse:
     summary="Get Model Details",
     description="Retrieve detailed information about a specific AI model by its name.",
 )
-async def get_model(model_name: str) -> ModelResponse:
+async def get_model(model_name: str, config: AppConfig = Depends(get_config)) -> ModelResponse:
     """Get a specific model by name.
 
     Args:
@@ -118,7 +118,6 @@ async def get_model(model_name: str) -> ModelResponse:
         }
         ```
     """
-    config = get_app_config()
     model = config.get_model_config(model_name)
     if model is None:
         raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")

backend/app/gateway/routers/runs.py

@@ -123,8 +123,7 @@ async def run_messages(
     run = await _resolve_run(run_id, request)
     event_store = get_run_event_store(request)
     rows = await event_store.list_messages_by_run(
-        run["thread_id"],
-        run_id,
+        run["thread_id"], run_id,
         limit=limit + 1,
         before_seq=before_seq,
         after_seq=after_seq,

backend/app/gateway/routers/skills.py

@@ -4,12 +4,14 @@ import logging
 import shutil
 from pathlib import Path
 
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, Depends, HTTPException, Request
 from pydantic import BaseModel, Field
 
+from app.gateway.deps import get_config
 from app.gateway.path_utils import resolve_thread_virtual_path
 from deerflow.agents.lead_agent.prompt import refresh_skills_system_prompt_cache_async
-from deerflow.config.extensions_config import ExtensionsConfig, SkillStateConfig, get_extensions_config, reload_extensions_config
+from deerflow.config.app_config import AppConfig
+from deerflow.config.extensions_config import ExtensionsConfig
 from deerflow.skills import Skill, load_skills
 from deerflow.skills.installer import SkillAlreadyExistsError, install_skill_from_archive
 from deerflow.skills.manager import (
@@ -101,9 +103,9 @@ def _skill_to_response(skill: Skill) -> SkillResponse:
     summary="List All Skills",
     description="Retrieve a list of all available skills from both public and custom directories.",
 )
-async def list_skills() -> SkillsListResponse:
+async def list_skills(app_config: AppConfig = Depends(get_config)) -> SkillsListResponse:
     try:
-        skills = load_skills(enabled_only=False)
+        skills = load_skills(app_config, enabled_only=False)
         return SkillsListResponse(skills=[_skill_to_response(skill) for skill in skills])
     except Exception as e:
         logger.error(f"Failed to load skills: {e}", exc_info=True)
@@ -116,11 +118,11 @@ async def list_skills() -> SkillsListResponse:
     summary="Install Skill",
     description="Install a skill from a .skill file (ZIP archive) located in the thread's user-data directory.",
 )
-async def install_skill(request: SkillInstallRequest) -> SkillInstallResponse:
+async def install_skill(request: SkillInstallRequest, app_config: AppConfig = Depends(get_config)) -> SkillInstallResponse:
     try:
         skill_file_path = resolve_thread_virtual_path(request.thread_id, request.path)
         result = install_skill_from_archive(skill_file_path)
-        await refresh_skills_system_prompt_cache_async()
+        await refresh_skills_system_prompt_cache_async(app_config)
         return SkillInstallResponse(**result)
     except FileNotFoundError as e:
         raise HTTPException(status_code=404, detail=str(e))
@@ -136,9 +138,9 @@ async def install_skill(request: SkillInstallRequest) -> SkillInstallResponse:
 
 
 @router.get("/skills/custom", response_model=SkillsListResponse, summary="List Custom Skills")
-async def list_custom_skills() -> SkillsListResponse:
+async def list_custom_skills(app_config: AppConfig = Depends(get_config)) -> SkillsListResponse:
     try:
-        skills = [skill for skill in load_skills(enabled_only=False) if skill.category == "custom"]
+        skills = [skill for skill in load_skills(app_config, enabled_only=False) if skill.category == "custom"]
         return SkillsListResponse(skills=[_skill_to_response(skill) for skill in skills])
     except Exception as e:
         logger.error("Failed to list custom skills: %s", e, exc_info=True)
@@ -146,13 +148,13 @@ async def list_custom_skills() -> SkillsListResponse:
 
 
 @router.get("/skills/custom/{skill_name}", response_model=CustomSkillContentResponse, summary="Get Custom Skill Content")
-async def get_custom_skill(skill_name: str) -> CustomSkillContentResponse:
+async def get_custom_skill(skill_name: str, app_config: AppConfig = Depends(get_config)) -> CustomSkillContentResponse:
     try:
-        skills = load_skills(enabled_only=False)
+        skills = load_skills(app_config, enabled_only=False)
         skill = next((s for s in skills if s.name == skill_name and s.category == "custom"), None)
         if skill is None:
             raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
-        return CustomSkillContentResponse(**_skill_to_response(skill).model_dump(), content=read_custom_skill_content(skill_name))
+        return CustomSkillContentResponse(**_skill_to_response(skill).model_dump(), content=read_custom_skill_content(skill_name, app_config))
     except HTTPException:
         raise
     except Exception as e:
@@ -161,14 +163,18 @@ async def get_custom_skill(skill_name: str) -> CustomSkillContentResponse:
 
 
 @router.put("/skills/custom/{skill_name}", response_model=CustomSkillContentResponse, summary="Edit Custom Skill")
-async def update_custom_skill(skill_name: str, request: CustomSkillUpdateRequest) -> CustomSkillContentResponse:
+async def update_custom_skill(
+    skill_name: str,
+    request: CustomSkillUpdateRequest,
+    app_config: AppConfig = Depends(get_config),
+) -> CustomSkillContentResponse:
     try:
-        ensure_custom_skill_is_editable(skill_name)
+        ensure_custom_skill_is_editable(skill_name, app_config)
         validate_skill_markdown_content(skill_name, request.content)
-        scan = await scan_skill_content(request.content, executable=False, location=f"{skill_name}/SKILL.md")
+        scan = await scan_skill_content(app_config, request.content, executable=False, location=f"{skill_name}/SKILL.md")
         if scan.decision == "block":
             raise HTTPException(status_code=400, detail=f"Security scan blocked the edit: {scan.reason}")
-        skill_file = get_custom_skill_dir(skill_name) / "SKILL.md"
+        skill_file = get_custom_skill_dir(skill_name, app_config) / "SKILL.md"
         prev_content = skill_file.read_text(encoding="utf-8")
         atomic_write(skill_file, request.content)
         append_history(
@@ -182,9 +188,10 @@ async def update_custom_skill(skill_name: str, request: CustomSkillUpdateRequest
                 "new_content": request.content,
                 "scanner": {"decision": scan.decision, "reason": scan.reason},
             },
+            app_config,
         )
-        await refresh_skills_system_prompt_cache_async()
-        return await get_custom_skill(skill_name)
+        await refresh_skills_system_prompt_cache_async(app_config)
+        return await get_custom_skill(skill_name, app_config)
     except HTTPException:
         raise
     except FileNotFoundError as e:
@@ -197,11 +204,11 @@ async def update_custom_skill(skill_name: str, request: CustomSkillUpdateRequest
 
 
 @router.delete("/skills/custom/{skill_name}", summary="Delete Custom Skill")
-async def delete_custom_skill(skill_name: str) -> dict[str, bool]:
+async def delete_custom_skill(skill_name: str, app_config: AppConfig = Depends(get_config)) -> dict[str, bool]:
     try:
-        ensure_custom_skill_is_editable(skill_name)
-        skill_dir = get_custom_skill_dir(skill_name)
-        prev_content = read_custom_skill_content(skill_name)
+        ensure_custom_skill_is_editable(skill_name, app_config)
+        skill_dir = get_custom_skill_dir(skill_name, app_config)
+        prev_content = read_custom_skill_content(skill_name, app_config)
         try:
             append_history(
                 skill_name,
@@ -214,13 +221,14 @@ async def delete_custom_skill(skill_name: str) -> dict[str, bool]:
                     "new_content": None,
                     "scanner": {"decision": "allow", "reason": "Deletion requested."},
                 },
+                app_config,
             )
         except OSError as e:
             if not isinstance(e, PermissionError) and e.errno not in {errno.EACCES, errno.EPERM, errno.EROFS}:
                 raise
             logger.warning("Skipping delete history write for custom skill %s due to readonly/permission failure; continuing with skill directory removal: %s", skill_name, e)
         shutil.rmtree(skill_dir)
-        await refresh_skills_system_prompt_cache_async()
+        await refresh_skills_system_prompt_cache_async(app_config)
         return {"success": True}
     except FileNotFoundError as e:
         raise HTTPException(status_code=404, detail=str(e))
@@ -232,11 +240,11 @@ async def delete_custom_skill(skill_name: str) -> dict[str, bool]:
 
 
 @router.get("/skills/custom/{skill_name}/history", response_model=CustomSkillHistoryResponse, summary="Get Custom Skill History")
-async def get_custom_skill_history(skill_name: str) -> CustomSkillHistoryResponse:
+async def get_custom_skill_history(skill_name: str, app_config: AppConfig = Depends(get_config)) -> CustomSkillHistoryResponse:
     try:
-        if not custom_skill_exists(skill_name) and not get_skill_history_file(skill_name).exists():
+        if not custom_skill_exists(skill_name, app_config) and not get_skill_history_file(skill_name, app_config).exists():
             raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
-        return CustomSkillHistoryResponse(history=read_history(skill_name))
+        return CustomSkillHistoryResponse(history=read_history(skill_name, app_config))
     except HTTPException:
         raise
     except Exception as e:
@@ -245,11 +253,15 @@ async def get_custom_skill_history(skill_name: str) -> CustomSkillHistoryRespons
 
 
 @router.post("/skills/custom/{skill_name}/rollback", response_model=CustomSkillContentResponse, summary="Rollback Custom Skill")
-async def rollback_custom_skill(skill_name: str, request: SkillRollbackRequest) -> CustomSkillContentResponse:
+async def rollback_custom_skill(
+    skill_name: str,
+    request: SkillRollbackRequest,
+    app_config: AppConfig = Depends(get_config),
+) -> CustomSkillContentResponse:
     try:
-        if not custom_skill_exists(skill_name) and not get_skill_history_file(skill_name).exists():
+        if not custom_skill_exists(skill_name, app_config) and not get_skill_history_file(skill_name, app_config).exists():
             raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
-        history = read_history(skill_name)
+        history = read_history(skill_name, app_config)
         if not history:
             raise HTTPException(status_code=400, detail=f"Custom skill '{skill_name}' has no history")
         record = history[request.history_index]
@@ -257,8 +269,8 @@ async def rollback_custom_skill(skill_name: str, request: SkillRollbackRequest)
         if target_content is None:
             raise HTTPException(status_code=400, detail="Selected history entry has no previous content to roll back to")
         validate_skill_markdown_content(skill_name, target_content)
-        scan = await scan_skill_content(target_content, executable=False, location=f"{skill_name}/SKILL.md")
-        skill_file = get_custom_skill_file(skill_name)
+        scan = await scan_skill_content(app_config, target_content, executable=False, location=f"{skill_name}/SKILL.md")
+        skill_file = get_custom_skill_file(skill_name, app_config)
         current_content = skill_file.read_text(encoding="utf-8") if skill_file.exists() else None
         history_entry = {
             "action": "rollback",
@@ -271,12 +283,12 @@ async def rollback_custom_skill(skill_name: str, request: SkillRollbackRequest)
             "scanner": {"decision": scan.decision, "reason": scan.reason},
         }
         if scan.decision == "block":
-            append_history(skill_name, history_entry)
+            append_history(skill_name, history_entry, app_config)
             raise HTTPException(status_code=400, detail=f"Rollback blocked by security scanner: {scan.reason}")
         atomic_write(skill_file, target_content)
-        append_history(skill_name, history_entry)
-        await refresh_skills_system_prompt_cache_async()
-        return await get_custom_skill(skill_name)
+        append_history(skill_name, history_entry, app_config)
+        await refresh_skills_system_prompt_cache_async(app_config)
+        return await get_custom_skill(skill_name, app_config)
     except HTTPException:
         raise
     except IndexError:
@@ -296,9 +308,9 @@ async def rollback_custom_skill(skill_name: str, request: SkillRollbackRequest)
     summary="Get Skill Details",
     description="Retrieve detailed information about a specific skill by its name.",
 )
-async def get_skill(skill_name: str) -> SkillResponse:
+async def get_skill(skill_name: str, app_config: AppConfig = Depends(get_config)) -> SkillResponse:
     try:
-        skills = load_skills(enabled_only=False)
+        skills = load_skills(app_config, enabled_only=False)
         skill = next((s for s in skills if s.name == skill_name), None)
 
         if skill is None:
@@ -318,9 +330,14 @@ async def get_skill(skill_name: str) -> SkillResponse:
     summary="Update Skill",
     description="Update a skill's enabled status by modifying the extensions_config.json file.",
 )
-async def update_skill(skill_name: str, request: SkillUpdateRequest) -> SkillResponse:
+async def update_skill(
+    skill_name: str,
+    request: SkillUpdateRequest,
+    http_request: Request,
+    app_config: AppConfig = Depends(get_config),
+) -> SkillResponse:
     try:
-        skills = load_skills(enabled_only=False)
+        skills = load_skills(app_config, enabled_only=False)
         skill = next((s for s in skills if s.name == skill_name), None)
 
         if skill is None:
@@ -331,22 +348,29 @@ async def update_skill(skill_name: str, request: SkillUpdateRequest) -> SkillRes
             config_path = Path.cwd().parent / "extensions_config.json"
             logger.info(f"No existing extensions config found. Creating new config at: {config_path}")
 
-        extensions_config = get_extensions_config()
-        extensions_config.skills[skill_name] = SkillStateConfig(enabled=request.enabled)
+        # Do not mutate the frozen AppConfig in place. Compose the new skills
+        # state in a fresh dict, write to disk, and reload AppConfig below so
+        # every subsequent Depends(get_config) sees the refreshed snapshot.
+        ext = app_config.extensions
+        updated_skills = {name: {"enabled": skill_config.enabled} for name, skill_config in ext.skills.items()}
+        updated_skills[skill_name] = {"enabled": request.enabled}
 
         config_data = {
-            "mcpServers": {name: server.model_dump() for name, server in extensions_config.mcp_servers.items()},
-            "skills": {name: {"enabled": skill_config.enabled} for name, skill_config in extensions_config.skills.items()},
+            "mcpServers": {name: server.model_dump() for name, server in ext.mcp_servers.items()},
+            "skills": updated_skills,
         }
 
         with open(config_path, "w", encoding="utf-8") as f:
             json.dump(config_data, f, indent=2)
 
         logger.info(f"Skills configuration updated and saved to: {config_path}")
-        reload_extensions_config()
-        await refresh_skills_system_prompt_cache_async()
+        # Reload AppConfig and swap ``app.state.config`` so subsequent
+        # ``Depends(get_config)`` sees the refreshed value.
+        reloaded = AppConfig.from_file()
+        http_request.app.state.config = reloaded
+        await refresh_skills_system_prompt_cache_async(reloaded)
 
-        skills = load_skills(enabled_only=False)
+        skills = load_skills(reloaded, enabled_only=False)
         updated_skill = next((s for s in skills if s.name == skill_name), None)
 
         if updated_skill is None:

backend/app/gateway/routers/suggestions.py

@@ -1,11 +1,13 @@
 import json
 import logging
 
-from fastapi import APIRouter, Request
+from fastapi import APIRouter, Depends, Request
 from langchain_core.messages import HumanMessage, SystemMessage
 from pydantic import BaseModel, Field
 
 from app.gateway.authz import require_permission
+from app.gateway.deps import get_config
+from deerflow.config.app_config import AppConfig
 from deerflow.models import create_chat_model
 
 logger = logging.getLogger(__name__)
@@ -100,7 +102,7 @@ def _format_conversation(messages: list[SuggestionMessage]) -> str:
     description="Generate short follow-up questions a user might ask next, based on recent conversation context.",
 )
 @require_permission("threads", "read", owner_check=True)
-async def generate_suggestions(thread_id: str, body: SuggestionsRequest, request: Request) -> SuggestionsResponse:
+async def generate_suggestions(thread_id: str, body: SuggestionsRequest, request: Request, app_config: AppConfig = Depends(get_config)) -> SuggestionsResponse:
     if not body.messages:
         return SuggestionsResponse(suggestions=[])
 
@@ -122,7 +124,7 @@ async def generate_suggestions(thread_id: str, body: SuggestionsRequest, request
     user_content = f"Conversation Context:\n{conversation}\n\nGenerate {n} follow-up questions"
 
     try:
-        model = create_chat_model(name=body.model_name, thinking_enabled=False)
+        model = create_chat_model(name=body.model_name, thinking_enabled=False, app_config=app_config)
         response = await model.ainvoke([SystemMessage(content=system_instruction), HumanMessage(content=user_content)], config={"run_name": "suggest_agent"})
         raw = _extract_response_text(response.content)
         suggestions = _parse_json_string_list(raw) or []

backend/app/gateway/routers/thread_runs.py

@@ -54,6 +54,7 @@ class RunCreateRequest(BaseModel):
     after_seconds: float | None = Field(default=None, description="Delayed execution")
     if_not_exists: Literal["reject", "create"] = Field(default="create", description="Thread creation policy")
     feedback_keys: list[str] | None = Field(default=None, description="LangSmith feedback keys")
+    follow_up_to_run_id: str | None = Field(default=None, description="Run ID this message follows up on. Auto-detected from latest successful run if not provided.")
 
 
 class RunResponse(BaseModel):
@@ -311,15 +312,11 @@ async def list_thread_messages(
         if i in last_ai_indices:
             run_id = msg["run_id"]
             fb = feedback_map.get(run_id)
-            msg["feedback"] = (
-                {
-                    "feedback_id": fb["feedback_id"],
-                    "rating": fb["rating"],
-                    "comment": fb.get("comment"),
-                }
-                if fb
-                else None
-            )
+            msg["feedback"] = {
+                "feedback_id": fb["feedback_id"],
+                "rating": fb["rating"],
+                "comment": fb.get("comment"),
+            } if fb else None
         else:
             msg["feedback"] = None
 
@@ -342,8 +339,7 @@ async def list_run_messages(
     """
     event_store = get_run_event_store(request)
     rows = await event_store.list_messages_by_run(
-        thread_id,
-        run_id,
+        thread_id, run_id,
         limit=limit + 1,
         before_seq=before_seq,
         after_seq=after_seq,

backend/app/gateway/routers/threads.py

@@ -13,6 +13,7 @@ matching the LangGraph Platform wire format expected by the
 from __future__ import annotations
 
 import logging
+import re
 import time
 import uuid
 from typing import Any

backend/app/gateway/routers/uploads.py

@@ -4,11 +4,12 @@ import logging
 import os
 import stat
 
-from fastapi import APIRouter, File, HTTPException, Request, UploadFile
+from fastapi import APIRouter, Depends, File, HTTPException, Request, UploadFile
 from pydantic import BaseModel
 
 from app.gateway.authz import require_permission
-from deerflow.config.app_config import get_app_config
+from app.gateway.deps import get_config
+from deerflow.config.app_config import AppConfig
 from deerflow.config.paths import get_paths
 from deerflow.runtime.user_context import get_effective_user_id
 from deerflow.sandbox.sandbox_provider import SandboxProvider, get_sandbox_provider
@@ -60,23 +61,22 @@ def _uses_thread_data_mounts(sandbox_provider: SandboxProvider) -> bool:
     return bool(getattr(sandbox_provider, "uses_thread_data_mounts", False))
 
 
-def _get_uploads_config_value(key: str, default: object) -> object:
+def _get_uploads_config_value(app_config: AppConfig, key: str, default: object) -> object:
     """Read a value from the uploads config, supporting dict and attribute access."""
-    cfg = get_app_config()
-    uploads_cfg = getattr(cfg, "uploads", None)
+    uploads_cfg = getattr(app_config, "uploads", None)
     if isinstance(uploads_cfg, dict):
         return uploads_cfg.get(key, default)
     return getattr(uploads_cfg, key, default)
 
 
-def _auto_convert_documents_enabled() -> bool:
+def _auto_convert_documents_enabled(app_config: AppConfig) -> bool:
     """Return whether automatic host-side document conversion is enabled.
 
     The secure default is disabled unless an operator explicitly opts in via
     uploads.auto_convert_documents in config.yaml.
     """
     try:
-        raw = _get_uploads_config_value("auto_convert_documents", False)
+        raw = _get_uploads_config_value(app_config, "auto_convert_documents", False)
         if isinstance(raw, str):
             return raw.strip().lower() in {"1", "true", "yes", "on"}
         return bool(raw)
@@ -85,11 +85,12 @@ def _auto_convert_documents_enabled() -> bool:
 
 
 @router.post("", response_model=UploadResponse)
-@require_permission("threads", "write", owner_check=True, require_existing=False)
+@require_permission("threads", "write", owner_check=True, require_existing=True)
 async def upload_files(
     thread_id: str,
     request: Request,
     files: list[UploadFile] = File(...),
+    app_config: AppConfig = Depends(get_config),
 ) -> UploadResponse:
     """Upload multiple files to a thread's uploads directory."""
     if not files:
@@ -102,13 +103,13 @@ async def upload_files(
     sandbox_uploads = get_paths().sandbox_uploads_dir(thread_id, user_id=get_effective_user_id())
     uploaded_files = []
 
-    sandbox_provider = get_sandbox_provider()
+    sandbox_provider = get_sandbox_provider(app_config)
     sync_to_sandbox = not _uses_thread_data_mounts(sandbox_provider)
     sandbox = None
     if sync_to_sandbox:
         sandbox_id = sandbox_provider.acquire(thread_id)
         sandbox = sandbox_provider.get(sandbox_id)
-    auto_convert_documents = _auto_convert_documents_enabled()
+    auto_convert_documents = _auto_convert_documents_enabled(app_config)
 
     for file in files:
         if not file.filename:

backend/app/gateway/services.py

@@ -8,6 +8,7 @@ frames, and consuming stream bridge events.  Router modules
 from __future__ import annotations
 
 import asyncio
+import dataclasses
 import json
 import logging
 import re
@@ -17,7 +18,7 @@ from typing import Any
 from fastapi import HTTPException, Request
 from langchain_core.messages import HumanMessage
 
-from app.gateway.deps import get_run_context, get_run_manager, get_stream_bridge
+from app.gateway.deps import get_run_context, get_run_manager, get_run_store, get_stream_bridge
 from app.gateway.utils import sanitize_log_param
 from deerflow.runtime import (
     END_SENTINEL,
@@ -211,6 +212,21 @@ async def start_run(
 
     disconnect = DisconnectMode.cancel if body.on_disconnect == "cancel" else DisconnectMode.continue_
 
+    # Resolve follow_up_to_run_id: explicit from request, or auto-detect from latest successful run
+    follow_up_to_run_id = getattr(body, "follow_up_to_run_id", None)
+    if follow_up_to_run_id is None:
+        run_store = get_run_store(request)
+        try:
+            recent_runs = await run_store.list_by_thread(thread_id, limit=1)
+            if recent_runs and recent_runs[0].get("status") == "success":
+                follow_up_to_run_id = recent_runs[0]["run_id"]
+        except Exception:
+            pass  # Don't block run creation
+
+    # Enrich base context with per-run field
+    if follow_up_to_run_id:
+        run_ctx = dataclasses.replace(run_ctx, follow_up_to_run_id=follow_up_to_run_id)
+
     try:
         record = await run_mgr.create_or_reject(
             thread_id,
@@ -219,6 +235,7 @@ async def start_run(
             metadata=body.metadata or {},
             kwargs={"input": body.input, "config": body.config},
             multitask_strategy=body.multitask_strategy,
+            follow_up_to_run_id=follow_up_to_run_id,
         )
     except ConflictError as exc:
         raise HTTPException(status_code=409, detail=str(exc)) from exc

backend/packages/harness/deerflow/agents/lead_agent/agent.py

@@ -3,6 +3,7 @@ import logging
 from langchain.agents import create_agent
 from langchain.agents.middleware import AgentMiddleware
 from langchain_core.runnables import RunnableConfig
+from langgraph.graph.state import CompiledStateGraph
 
 from deerflow.agents.lead_agent.prompt import apply_prompt_template
 from deerflow.agents.memory.summarization_hook import memory_flush_hook
@@ -18,9 +19,8 @@ from deerflow.agents.middlewares.tool_error_handling_middleware import build_lea
 from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddleware
 from deerflow.agents.thread_state import ThreadState
 from deerflow.config.agents_config import load_agent_config, validate_agent_name
-from deerflow.config.app_config import get_app_config
-from deerflow.config.memory_config import get_memory_config
-from deerflow.config.summarization_config import get_summarization_config
+from deerflow.config.app_config import AppConfig
+from deerflow.config.deer_flow_context import DeerFlowContext
 from deerflow.models import create_chat_model
 
 logger = logging.getLogger(__name__)
@@ -35,9 +35,8 @@ def _get_runtime_config(config: RunnableConfig) -> dict:
     return cfg
 
 
-def _resolve_model_name(requested_model_name: str | None = None) -> str:
+def _resolve_model_name(app_config: AppConfig, requested_model_name: str | None = None) -> str:
     """Resolve a runtime model name safely, falling back to default if invalid. Returns None if no models are configured."""
-    app_config = get_app_config()
     default_model_name = app_config.models[0].name if app_config.models else None
     if default_model_name is None:
         raise ValueError("No chat models are configured. Please configure at least one model in config.yaml.")
@@ -50,9 +49,9 @@ def _resolve_model_name(requested_model_name: str | None = None) -> str:
     return default_model_name
 
 
-def _create_summarization_middleware() -> DeerFlowSummarizationMiddleware | None:
+def _create_summarization_middleware(app_config: AppConfig) -> DeerFlowSummarizationMiddleware | None:
     """Create and configure the summarization middleware from config."""
-    config = get_summarization_config()
+    config = app_config.summarization
 
     if not config.enabled:
         return None
@@ -73,9 +72,9 @@ def _create_summarization_middleware() -> DeerFlowSummarizationMiddleware | None
     # as middleware rather than lead_agent (SummarizationMiddleware is a
     # LangChain built-in, so we tag the model at creation time).
     if config.model_name:
-        model = create_chat_model(name=config.model_name, thinking_enabled=False)
+        model = create_chat_model(name=config.model_name, thinking_enabled=False, app_config=app_config)
     else:
-        model = create_chat_model(thinking_enabled=False)
+        model = create_chat_model(thinking_enabled=False, app_config=app_config)
     model = model.with_config(tags=["middleware:summarize"])
 
     # Prepare kwargs
@@ -92,14 +91,14 @@ def _create_summarization_middleware() -> DeerFlowSummarizationMiddleware | None
         kwargs["summary_prompt"] = config.summary_prompt
 
     hooks: list[BeforeSummarizationHook] = []
-    if get_memory_config().enabled:
+    if app_config.memory.enabled:
         hooks.append(memory_flush_hook)
 
     # The logic below relies on two assumptions holding true: this factory is
     # the sole entry point for DeerFlowSummarizationMiddleware, and the runtime
     # config is not expected to change after startup.
     try:
-        skills_container_path = get_app_config().skills.container_path or "/mnt/skills"
+        skills_container_path = app_config.skills.container_path or "/mnt/skills"
     except Exception:
         logger.exception("Failed to resolve skills container path; falling back to default")
         skills_container_path = "/mnt/skills"
@@ -240,10 +239,18 @@ Being proactive with task management demonstrates thoroughness and ensures all r
 # ViewImageMiddleware should be before ClarificationMiddleware to inject image details before LLM
 # ToolErrorHandlingMiddleware should be before ClarificationMiddleware to convert tool exceptions to ToolMessages
 # ClarificationMiddleware should be last to intercept clarification requests after model calls
-def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_name: str | None = None, custom_middlewares: list[AgentMiddleware] | None = None):
+def _build_middlewares(
+    app_config: AppConfig,
+    config: RunnableConfig,
+    *,
+    model_name: str | None,
+    agent_name: str | None = None,
+    custom_middlewares: list[AgentMiddleware] | None = None,
+):
     """Build middleware chain based on runtime configuration.
 
     Args:
+        app_config: Resolved application config.
         config: Runtime configuration containing configurable options like is_plan_mode.
         agent_name: If provided, MemoryMiddleware will use per-agent memory storage.
         custom_middlewares: Optional list of custom middlewares to inject into the chain.
@@ -251,10 +258,10 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
     Returns:
         List of middleware instances.
     """
-    middlewares = build_lead_runtime_middlewares(lazy_init=True)
+    middlewares = build_lead_runtime_middlewares(app_config=app_config, lazy_init=True)
 
     # Add summarization middleware if enabled
-    summarization_middleware = _create_summarization_middleware()
+    summarization_middleware = _create_summarization_middleware(app_config)
     if summarization_middleware is not None:
         middlewares.append(summarization_middleware)
 
@@ -266,7 +273,7 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
         middlewares.append(todo_list_middleware)
 
     # Add TokenUsageMiddleware when token_usage tracking is enabled
-    if get_app_config().token_usage.enabled:
+    if app_config.token_usage.enabled:
         middlewares.append(TokenUsageMiddleware())
 
     # Add TitleMiddleware
@@ -277,7 +284,6 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
 
     # Add ViewImageMiddleware only if the current model supports vision.
     # Use the resolved runtime model_name from make_lead_agent to avoid stale config values.
-    app_config = get_app_config()
     model_config = app_config.get_model_config(model_name) if model_name else None
     if model_config is not None and model_config.supports_vision:
         middlewares.append(ViewImageMiddleware())
@@ -306,11 +312,32 @@ def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_nam
     return middlewares
 
 
-def make_lead_agent(config: RunnableConfig):
+def make_lead_agent(
+    config: RunnableConfig,
+    app_config: AppConfig | None = None,
+) -> CompiledStateGraph:
+    """Build the lead agent from runtime config.
+
+    Args:
+        config: LangGraph ``RunnableConfig`` carrying per-invocation options
+            (``thinking_enabled``, ``model_name``, ``is_plan_mode``, etc.).
+        app_config: Resolved application config. Required for in-process
+            entry points (DeerFlowClient, Gateway Worker). When omitted we
+            are being called via ``langgraph.json`` registration and reload
+            from disk — the LangGraph Server bootstrap path has no other
+            way to thread the value.
+    """
     # Lazy import to avoid circular dependency
     from deerflow.tools import get_available_tools
     from deerflow.tools.builtins import setup_agent
 
+    if app_config is None:
+        # LangGraph Server registers ``make_lead_agent`` via ``langgraph.json``
+        # and hands us only a ``RunnableConfig``. Reload config from disk
+        # here — it's a pure function, equivalent to the process-global the
+        # old code path would have read.
+        app_config = AppConfig.from_file()
+
     cfg = _get_runtime_config(config)
 
     thinking_enabled = cfg.get("thinking_enabled", True)
@@ -327,9 +354,8 @@ def make_lead_agent(config: RunnableConfig):
     agent_model_name = agent_config.model if agent_config and agent_config.model else None
 
     # Final model name resolution: request → agent config → global default, with fallback for unknown names
-    model_name = _resolve_model_name(requested_model_name or agent_model_name)
+    model_name = _resolve_model_name(app_config, requested_model_name or agent_model_name)
 
-    app_config = get_app_config()
     model_config = app_config.get_model_config(model_name)
 
     if model_config is None:
@@ -369,20 +395,22 @@ def make_lead_agent(config: RunnableConfig):
     if is_bootstrap:
         # Special bootstrap agent with minimal prompt for initial custom agent creation flow
         return create_agent(
-            model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled),
-            tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled) + [setup_agent],
-            middleware=_build_middlewares(config, model_name=model_name),
-            system_prompt=apply_prompt_template(subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, available_skills=set(["bootstrap"])),
+            model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, app_config=app_config),
+            tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled, app_config=app_config) + [setup_agent],
+            middleware=_build_middlewares(app_config, config, model_name=model_name),
+            system_prompt=apply_prompt_template(app_config, subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, available_skills=set(["bootstrap"])),
             state_schema=ThreadState,
+            context_schema=DeerFlowContext,
         )
 
     # Default lead agent (unchanged behavior)
     return create_agent(
-        model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort),
-        tools=get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled),
-        middleware=_build_middlewares(config, model_name=model_name, agent_name=agent_name),
+        model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort, app_config=app_config),
+        tools=get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled, app_config=app_config),
+        middleware=_build_middlewares(app_config, config, model_name=model_name, agent_name=agent_name),
         system_prompt=apply_prompt_template(
-            subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, agent_name=agent_name, available_skills=set(agent_config.skills) if agent_config and agent_config.skills is not None else None
+            app_config, subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, agent_name=agent_name, available_skills=set(agent_config.skills) if agent_config and agent_config.skills is not None else None
         ),
         state_schema=ThreadState,
+        context_schema=DeerFlowContext,
     )

backend/packages/harness/deerflow/agents/lead_agent/prompt.py

@@ -5,6 +5,7 @@ from datetime import datetime
 from functools import lru_cache
 
 from deerflow.config.agents_config import load_agent_soul
+from deerflow.config.app_config import AppConfig
 from deerflow.skills import load_skills
 from deerflow.skills.types import Skill
 from deerflow.subagents import get_available_subagent_names
@@ -19,19 +20,20 @@ _enabled_skills_refresh_version = 0
 _enabled_skills_refresh_event = threading.Event()
 
 
-def _load_enabled_skills_sync() -> list[Skill]:
-    return list(load_skills(enabled_only=True))
+def _load_enabled_skills_sync(app_config: AppConfig | None) -> list[Skill]:
+    return list(load_skills(app_config, enabled_only=True))
 
 
-def _start_enabled_skills_refresh_thread() -> None:
+def _start_enabled_skills_refresh_thread(app_config: AppConfig | None) -> None:
     threading.Thread(
         target=_refresh_enabled_skills_cache_worker,
+        args=(app_config,),
         name="deerflow-enabled-skills-loader",
         daemon=True,
     ).start()
 
 
-def _refresh_enabled_skills_cache_worker() -> None:
+def _refresh_enabled_skills_cache_worker(app_config: AppConfig | None) -> None:
     global _enabled_skills_cache, _enabled_skills_refresh_active
 
     while True:
@@ -39,8 +41,8 @@ def _refresh_enabled_skills_cache_worker() -> None:
             target_version = _enabled_skills_refresh_version
 
         try:
-            skills = _load_enabled_skills_sync()
-        except Exception:
+            skills = _load_enabled_skills_sync(app_config)
+        except (OSError, ImportError):
             logger.exception("Failed to load enabled skills for prompt injection")
             skills = []
 
@@ -56,7 +58,7 @@ def _refresh_enabled_skills_cache_worker() -> None:
             _enabled_skills_cache = None
 
 
-def _ensure_enabled_skills_cache() -> threading.Event:
+def _ensure_enabled_skills_cache(app_config: AppConfig | None) -> threading.Event:
     global _enabled_skills_refresh_active
 
     with _enabled_skills_lock:
@@ -68,11 +70,11 @@ def _ensure_enabled_skills_cache() -> threading.Event:
         _enabled_skills_refresh_active = True
         _enabled_skills_refresh_event.clear()
 
-    _start_enabled_skills_refresh_thread()
+    _start_enabled_skills_refresh_thread(app_config)
     return _enabled_skills_refresh_event
 
 
-def _invalidate_enabled_skills_cache() -> threading.Event:
+def _invalidate_enabled_skills_cache(app_config: AppConfig | None) -> threading.Event:
     global _enabled_skills_cache, _enabled_skills_refresh_active, _enabled_skills_refresh_version
 
     _get_cached_skills_prompt_section.cache_clear()
@@ -84,30 +86,30 @@ def _invalidate_enabled_skills_cache() -> threading.Event:
             return _enabled_skills_refresh_event
         _enabled_skills_refresh_active = True
 
-    _start_enabled_skills_refresh_thread()
+    _start_enabled_skills_refresh_thread(app_config)
     return _enabled_skills_refresh_event
 
 
-def prime_enabled_skills_cache() -> None:
-    _ensure_enabled_skills_cache()
+def prime_enabled_skills_cache(app_config: AppConfig | None = None) -> None:
+    _ensure_enabled_skills_cache(app_config)
 
 
-def warm_enabled_skills_cache(timeout_seconds: float = _ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS) -> bool:
-    if _ensure_enabled_skills_cache().wait(timeout=timeout_seconds):
+def warm_enabled_skills_cache(app_config: AppConfig | None = None, timeout_seconds: float = _ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS) -> bool:
+    if _ensure_enabled_skills_cache(app_config).wait(timeout=timeout_seconds):
         return True
 
     logger.warning("Timed out waiting %.1fs for enabled skills cache warm-up", timeout_seconds)
     return False
 
 
-def _get_enabled_skills():
+def _get_enabled_skills(app_config: AppConfig | None = None):
     with _enabled_skills_lock:
         cached = _enabled_skills_cache
 
     if cached is not None:
         return list(cached)
 
-    _ensure_enabled_skills_cache()
+    _ensure_enabled_skills_cache(app_config)
     return []
 
 
@@ -115,12 +117,37 @@ def _skill_mutability_label(category: str) -> str:
     return "[custom, editable]" if category == "custom" else "[built-in]"
 
 
-def clear_skills_system_prompt_cache() -> None:
-    _invalidate_enabled_skills_cache()
+def clear_skills_system_prompt_cache(app_config: AppConfig | None = None) -> None:
+    _invalidate_enabled_skills_cache(app_config)
 
 
-async def refresh_skills_system_prompt_cache_async() -> None:
-    await asyncio.to_thread(_invalidate_enabled_skills_cache().wait)
+async def refresh_skills_system_prompt_cache_async(app_config: AppConfig | None = None) -> None:
+    await asyncio.to_thread(_invalidate_enabled_skills_cache(app_config).wait)
+
+
+def _reset_skills_system_prompt_cache_state() -> None:
+    global _enabled_skills_cache, _enabled_skills_refresh_active, _enabled_skills_refresh_version
+
+    _get_cached_skills_prompt_section.cache_clear()
+    with _enabled_skills_lock:
+        _enabled_skills_cache = None
+        _enabled_skills_refresh_active = False
+        _enabled_skills_refresh_version = 0
+        _enabled_skills_refresh_event.clear()
+
+
+def _refresh_enabled_skills_cache(app_config: AppConfig | None = None) -> None:
+    """Backward-compatible test helper for direct synchronous reload."""
+    try:
+        skills = _load_enabled_skills_sync(app_config)
+    except Exception:
+        logger.exception("Failed to load enabled skills for prompt injection")
+        skills = []
+
+    with _enabled_skills_lock:
+        _enabled_skills_cache = skills
+        _enabled_skills_refresh_active = False
+        _enabled_skills_refresh_event.set()
 
 
 def _build_skill_evolution_section(skill_evolution_enabled: bool) -> str:
@@ -139,7 +166,7 @@ Skip simple one-off tasks.
 """
 
 
-def _build_available_subagents_description(available_names: list[str], bash_available: bool) -> str:
+def _build_available_subagents_description(available_names: list[str], bash_available: bool, app_config: AppConfig) -> str:
     """Dynamically build subagent type descriptions from registry.
 
     Mirrors Codex's pattern where agent_type_description is dynamically generated
@@ -161,7 +188,7 @@ def _build_available_subagents_description(available_names: list[str], bash_avai
         if name in builtin_descriptions:
             lines.append(f"- **{name}**: {builtin_descriptions[name]}")
         else:
-            config = get_subagent_config(name)
+            config = get_subagent_config(name, app_config)
             if config is not None:
                 desc = config.description.split("\n")[0].strip()  # First line only for brevity
                 lines.append(f"- **{name}**: {desc}")
@@ -169,22 +196,23 @@ def _build_available_subagents_description(available_names: list[str], bash_avai
     return "\n".join(lines)
 
 
-def _build_subagent_section(max_concurrent: int) -> str:
+def _build_subagent_section(max_concurrent: int, app_config: AppConfig) -> str:
     """Build the subagent system prompt section with dynamic concurrency limit.
 
     Args:
         max_concurrent: Maximum number of concurrent subagent calls allowed per response.
+        app_config: Application config used to gate bash availability.
 
     Returns:
         Formatted subagent section string.
     """
     n = max_concurrent
-    available_names = get_available_subagent_names()
+    available_names = get_available_subagent_names(app_config)
     bash_available = "bash" in available_names
 
     # Dynamically build subagent type descriptions from registry (aligned with Codex's
     # agent_type_description pattern where all registered roles are listed in the tool spec).
-    available_subagents = _build_available_subagents_description(available_names, bash_available)
+    available_subagents = _build_available_subagents_description(available_names, bash_available, app_config)
     direct_tool_examples = "bash, ls, read_file, web_search, etc." if bash_available else "ls, read_file, web_search, etc."
     direct_execution_example = (
         '# User asks: "Run the tests"\n# Thinking: Cannot decompose into parallel sub-tasks\n# → Execute directly\n\nbash("npm test")  # Direct execution, not task()'
@@ -511,37 +539,34 @@ combined with a FastAPI gateway for REST API access [citation:FastAPI](https://f
 """
 
 
-def _get_memory_context(agent_name: str | None = None) -> str:
+def _get_memory_context(app_config: AppConfig, agent_name: str | None = None) -> str:
     """Get memory context for injection into system prompt.
 
-    Args:
-        agent_name: If provided, loads per-agent memory. If None, loads global memory.
-
-    Returns:
-        Formatted memory context string wrapped in XML tags, or empty string if disabled.
+    Returns an empty string when memory is disabled or the stored memory file
+    cannot be read/parsed. A corrupt memory.json degrades the prompt to
+    no-memory; it never kills the agent.
     """
+    from deerflow.agents.memory import format_memory_for_injection, get_memory_data
+    from deerflow.runtime.user_context import get_effective_user_id
+
+    memory_config = app_config.memory
+    if not memory_config.enabled or not memory_config.injection_enabled:
+        return ""
+
     try:
-        from deerflow.agents.memory import format_memory_for_injection, get_memory_data
-        from deerflow.config.memory_config import get_memory_config
-        from deerflow.runtime.user_context import get_effective_user_id
+        memory_data = get_memory_data(memory_config, agent_name, user_id=get_effective_user_id())
+    except (OSError, ValueError, UnicodeDecodeError):
+        logger.exception("Failed to load memory data for prompt injection")
+        return ""
 
-        config = get_memory_config()
-        if not config.enabled or not config.injection_enabled:
-            return ""
+    memory_content = format_memory_for_injection(memory_data, max_tokens=memory_config.max_injection_tokens)
+    if not memory_content.strip():
+        return ""
 
-        memory_data = get_memory_data(agent_name, user_id=get_effective_user_id())
-        memory_content = format_memory_for_injection(memory_data, max_tokens=config.max_injection_tokens)
-
-        if not memory_content.strip():
-            return ""
-
-        return f"""<memory>
+    return f"""<memory>
 {memory_content}
 </memory>
 """
-    except Exception as e:
-        logger.error("Failed to load memory context: %s", e)
-        return ""
 
 
 @lru_cache(maxsize=32)
@@ -576,19 +601,12 @@ You have access to skills that provide optimized workflows for specific tasks. E
 </skill_system>"""
 
 
-def get_skills_prompt_section(available_skills: set[str] | None = None) -> str:
+def get_skills_prompt_section(app_config: AppConfig, available_skills: set[str] | None = None) -> str:
     """Generate the skills prompt section with available skills list."""
-    skills = _get_enabled_skills()
+    skills = _get_enabled_skills(app_config)
 
-    try:
-        from deerflow.config import get_app_config
-
-        config = get_app_config()
-        container_base_path = config.skills.container_path
-        skill_evolution_enabled = config.skill_evolution.enabled
-    except Exception:
-        container_base_path = "/mnt/skills"
-        skill_evolution_enabled = False
+    container_base_path = app_config.skills.container_path
+    skill_evolution_enabled = app_config.skill_evolution.enabled
 
     if not skills and not skill_evolution_enabled:
         return ""
@@ -612,7 +630,7 @@ def get_agent_soul(agent_name: str | None) -> str:
     return ""
 
 
-def get_deferred_tools_prompt_section() -> str:
+def get_deferred_tools_prompt_section(app_config: AppConfig) -> str:
     """Generate <available-deferred-tools> block for the system prompt.
 
     Lists only deferred tool names so the agent knows what exists
@@ -621,12 +639,7 @@ def get_deferred_tools_prompt_section() -> str:
     """
     from deerflow.tools.builtins.tool_search import get_deferred_registry
 
-    try:
-        from deerflow.config import get_app_config
-
-        if not get_app_config().tool_search.enabled:
-            return ""
-    except Exception:
+    if not app_config.tool_search.enabled:
         return ""
 
     registry = get_deferred_registry()
@@ -637,15 +650,9 @@ def get_deferred_tools_prompt_section() -> str:
     return f"<available-deferred-tools>\n{names}\n</available-deferred-tools>"
 
 
-def _build_acp_section() -> str:
+def _build_acp_section(app_config: AppConfig) -> str:
     """Build the ACP agent prompt section, only if ACP agents are configured."""
-    try:
-        from deerflow.config.acp_config import get_acp_agents
-
-        agents = get_acp_agents()
-        if not agents:
-            return ""
-    except Exception:
+    if not app_config.acp_agents:
         return ""
 
     return (
@@ -657,15 +664,9 @@ def _build_acp_section() -> str:
     )
 
 
-def _build_custom_mounts_section() -> str:
+def _build_custom_mounts_section(app_config: AppConfig) -> str:
     """Build a prompt section for explicitly configured sandbox mounts."""
-    try:
-        from deerflow.config import get_app_config
-
-        mounts = get_app_config().sandbox.mounts or []
-    except Exception:
-        logger.exception("Failed to load configured sandbox mounts for the lead-agent prompt")
-        return ""
+    mounts = app_config.sandbox.mounts or []
 
     if not mounts:
         return ""
@@ -679,13 +680,20 @@ def _build_custom_mounts_section() -> str:
     return f"\n**Custom Mounted Directories:**\n{mounts_list}\n- If the user needs files outside `/mnt/user-data`, use these absolute container paths directly when they match the requested directory"
 
 
-def apply_prompt_template(subagent_enabled: bool = False, max_concurrent_subagents: int = 3, *, agent_name: str | None = None, available_skills: set[str] | None = None) -> str:
+def apply_prompt_template(
+    app_config: AppConfig,
+    subagent_enabled: bool = False,
+    max_concurrent_subagents: int = 3,
+    *,
+    agent_name: str | None = None,
+    available_skills: set[str] | None = None,
+) -> str:
     # Get memory context
-    memory_context = _get_memory_context(agent_name)
+    memory_context = _get_memory_context(app_config, agent_name)
 
     # Include subagent section only if enabled (from runtime parameter)
     n = max_concurrent_subagents
-    subagent_section = _build_subagent_section(n) if subagent_enabled else ""
+    subagent_section = _build_subagent_section(n, app_config) if subagent_enabled else ""
 
     # Add subagent reminder to critical_reminders if enabled
     subagent_reminder = (
@@ -706,14 +714,14 @@ def apply_prompt_template(subagent_enabled: bool = False, max_concurrent_subagen
     )
 
     # Get skills section
-    skills_section = get_skills_prompt_section(available_skills)
+    skills_section = get_skills_prompt_section(app_config, available_skills)
 
     # Get deferred tools section (tool_search)
-    deferred_tools_section = get_deferred_tools_prompt_section()
+    deferred_tools_section = get_deferred_tools_prompt_section(app_config)
 
     # Build ACP agent section only if ACP agents are configured
-    acp_section = _build_acp_section()
-    custom_mounts_section = _build_custom_mounts_section()
+    acp_section = _build_acp_section(app_config)
+    custom_mounts_section = _build_custom_mounts_section(app_config)
     acp_and_mounts_section = "\n".join(section for section in (acp_section, custom_mounts_section) if section)
 
     # Format the prompt with dynamic skills and memory

backend/packages/harness/deerflow/agents/memory/queue.py

@@ -7,11 +7,17 @@ from dataclasses import dataclass, field
 from datetime import UTC, datetime
 from typing import Any
 
-from deerflow.config.memory_config import get_memory_config
+from deerflow.config.app_config import AppConfig
 
 logger = logging.getLogger(__name__)
 
 
+# Module-level config pointer set by the middleware that owns the queue.
+# The queue runs on a background Timer thread where ``Runtime`` and FastAPI
+# request context are not accessible; the enqueuer (which does have runtime
+# context) is responsible for plumbing ``AppConfig`` through ``add()``.
+
+
 @dataclass
 class ConversationContext:
     """Context for a conversation to be processed for memory update."""
@@ -31,10 +37,21 @@ class MemoryUpdateQueue:
     This queue collects conversation contexts and processes them after
     a configurable debounce period. Multiple conversations received within
     the debounce window are batched together.
+
+    The queue captures an ``AppConfig`` reference at construction time and
+    reuses it for the MemoryUpdater it spawns. Callers must construct a
+    fresh queue when the config changes rather than reaching into a global.
     """
 
-    def __init__(self):
-        """Initialize the memory update queue."""
+    def __init__(self, app_config: AppConfig):
+        """Initialize the memory update queue.
+
+        Args:
+            app_config: Application config. The queue reads its own
+                ``memory`` section for debounce timing and hands the full
+                config to :class:`MemoryUpdater`.
+        """
+        self._app_config = app_config
         self._queue: list[ConversationContext] = []
         self._lock = threading.Lock()
         self._timer: threading.Timer | None = None
@@ -49,19 +66,8 @@ class MemoryUpdateQueue:
         correction_detected: bool = False,
         reinforcement_detected: bool = False,
     ) -> None:
-        """Add a conversation to the update queue.
-
-        Args:
-            thread_id: The thread ID.
-            messages: The conversation messages.
-            agent_name: If provided, memory is stored per-agent. If None, uses global memory.
-            user_id: The user ID captured at enqueue time. Stored in ConversationContext so it
-                survives the threading.Timer boundary (ContextVar does not propagate across
-                raw threads).
-            correction_detected: Whether recent turns include an explicit correction signal.
-            reinforcement_detected: Whether recent turns include a positive reinforcement signal.
-        """
-        config = get_memory_config()
+        """Add a conversation to the update queue."""
+        config = self._app_config.memory
         if not config.enabled:
             return
 
@@ -88,7 +94,7 @@ class MemoryUpdateQueue:
         reinforcement_detected: bool = False,
     ) -> None:
         """Add a conversation and start processing immediately in the background."""
-        config = get_memory_config()
+        config = self._app_config.memory
         if not config.enabled:
             return
 
@@ -111,7 +117,7 @@ class MemoryUpdateQueue:
         thread_id: str,
         messages: list[Any],
         agent_name: str | None,
-        user_id: str | None,
+        user_id: str | None = None,
         correction_detected: bool,
         reinforcement_detected: bool,
     ) -> None:
@@ -135,7 +141,7 @@ class MemoryUpdateQueue:
 
     def _reset_timer(self) -> None:
         """Reset the debounce timer."""
-        config = get_memory_config()
+        config = self._app_config.memory
         self._schedule_timer(config.debounce_seconds)
 
         logger.debug("Memory update timer set for %ss", config.debounce_seconds)
@@ -175,7 +181,7 @@ class MemoryUpdateQueue:
         logger.info("Processing %d queued memory updates", len(contexts_to_process))
 
         try:
-            updater = MemoryUpdater()
+            updater = MemoryUpdater(self._app_config)
 
             for context in contexts_to_process:
                 try:
@@ -247,31 +253,35 @@ class MemoryUpdateQueue:
             return self._processing
 
 
-# Global singleton instance
-_memory_queue: MemoryUpdateQueue | None = None
+# Queues keyed by ``id(AppConfig)`` so tests and multi-client setups with
+# distinct configs do not share a debounce queue.
+_memory_queues: dict[int, MemoryUpdateQueue] = {}
 _queue_lock = threading.Lock()
 
 
-def get_memory_queue() -> MemoryUpdateQueue:
-    """Get the global memory update queue singleton.
-
-    Returns:
-        The memory update queue instance.
-    """
-    global _memory_queue
+def get_memory_queue(app_config: AppConfig) -> MemoryUpdateQueue:
+    """Get or create the memory update queue for the given app config."""
+    key = id(app_config)
     with _queue_lock:
-        if _memory_queue is None:
-            _memory_queue = MemoryUpdateQueue()
-        return _memory_queue
+        queue = _memory_queues.get(key)
+        if queue is None:
+            queue = MemoryUpdateQueue(app_config)
+            _memory_queues[key] = queue
+        return queue
 
 
-def reset_memory_queue() -> None:
-    """Reset the global memory queue.
+def reset_memory_queue(app_config: AppConfig | None = None) -> None:
+    """Reset memory queue(s).
 
-    This is useful for testing.
+    Pass an ``app_config`` to reset only its queue, or omit to reset all
+    (useful at test teardown).
     """
-    global _memory_queue
     with _queue_lock:
-        if _memory_queue is not None:
-            _memory_queue.clear()
-        _memory_queue = None
+        if app_config is not None:
+            queue = _memory_queues.pop(id(app_config), None)
+            if queue is not None:
+                queue.clear()
+            return
+        for queue in _memory_queues.values():
+            queue.clear()
+        _memory_queues.clear()

backend/packages/harness/deerflow/agents/memory/storage.py

@@ -10,7 +10,7 @@ from pathlib import Path
 from typing import Any
 
 from deerflow.config.agents_config import AGENT_NAME_PATTERN
-from deerflow.config.memory_config import get_memory_config
+from deerflow.config.memory_config import MemoryConfig
 from deerflow.config.paths import get_paths
 
 logger = logging.getLogger(__name__)
@@ -62,8 +62,15 @@ class MemoryStorage(abc.ABC):
 class FileMemoryStorage(MemoryStorage):
     """File-based memory storage provider."""
 
-    def __init__(self):
-        """Initialize the file memory storage."""
+    def __init__(self, memory_config: MemoryConfig):
+        """Initialize the file memory storage.
+
+        Args:
+            memory_config: Memory configuration (storage_path etc.). Stored on
+                the instance so per-request lookups don't need to reach for
+                ambient state.
+        """
+        self._memory_config = memory_config
         # Per-user/agent memory cache: keyed by (user_id, agent_name) tuple (None = global)
         # Value: (memory_data, file_mtime)
         self._memory_cache: dict[tuple[str | None, str | None], tuple[dict[str, Any], float | None]] = {}
@@ -83,11 +90,11 @@ class FileMemoryStorage(MemoryStorage):
 
     def _get_memory_file_path(self, agent_name: str | None = None, *, user_id: str | None = None) -> Path:
         """Get the path to the memory file."""
+        config = self._memory_config
         if user_id is not None:
             if agent_name is not None:
                 self._validate_agent_name(agent_name)
                 return get_paths().user_agent_memory_file(user_id, agent_name)
-            config = get_memory_config()
             if config.storage_path and Path(config.storage_path).is_absolute():
                 return Path(config.storage_path)
             return get_paths().user_memory_file(user_id)
@@ -95,7 +102,6 @@ class FileMemoryStorage(MemoryStorage):
         if agent_name is not None:
             self._validate_agent_name(agent_name)
             return get_paths().agent_memory_file(agent_name)
-        config = get_memory_config()
         if config.storage_path:
             p = Path(config.storage_path)
             return p if p.is_absolute() else get_paths().base_dir / p
@@ -116,20 +122,16 @@ class FileMemoryStorage(MemoryStorage):
             logger.warning("Failed to load memory file: %s", e)
             return create_empty_memory()
 
-    @staticmethod
-    def _cache_key(agent_name: str | None = None, *, user_id: str | None = None) -> tuple[str | None, str | None]:
-        return (user_id, agent_name)
-
     def load(self, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
         """Load memory data (cached with file modification time check)."""
         file_path = self._get_memory_file_path(agent_name, user_id=user_id)
-        cache_key = self._cache_key(agent_name, user_id=user_id)
 
         try:
             current_mtime = file_path.stat().st_mtime if file_path.exists() else None
         except OSError:
             current_mtime = None
 
+        cache_key = (user_id, agent_name)
         with self._cache_lock:
             cached = self._memory_cache.get(cache_key)
             if cached is not None and cached[1] == current_mtime:
@@ -146,13 +148,13 @@ class FileMemoryStorage(MemoryStorage):
         """Reload memory data from file, forcing cache invalidation."""
         file_path = self._get_memory_file_path(agent_name, user_id=user_id)
         memory_data = self._load_memory_from_file(agent_name, user_id=user_id)
-        cache_key = self._cache_key(agent_name, user_id=user_id)
 
         try:
             mtime = file_path.stat().st_mtime if file_path.exists() else None
         except OSError:
             mtime = None
 
+        cache_key = (user_id, agent_name)
         with self._cache_lock:
             self._memory_cache[cache_key] = (memory_data, mtime)
         return memory_data
@@ -160,7 +162,6 @@ class FileMemoryStorage(MemoryStorage):
     def save(self, memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> bool:
         """Save memory data to file and update cache."""
         file_path = self._get_memory_file_path(agent_name, user_id=user_id)
-        cache_key = self._cache_key(agent_name, user_id=user_id)
 
         try:
             file_path.parent.mkdir(parents=True, exist_ok=True)
@@ -180,6 +181,7 @@ class FileMemoryStorage(MemoryStorage):
             except OSError:
                 mtime = None
 
+            cache_key = (user_id, agent_name)
             with self._cache_lock:
                 self._memory_cache[cache_key] = (memory_data, mtime)
             logger.info("Memory saved to %s", file_path)
@@ -189,23 +191,31 @@ class FileMemoryStorage(MemoryStorage):
             return False
 
 
-_storage_instance: MemoryStorage | None = None
+# Instances keyed by (storage_class_path, id(memory_config)) so tests can
+# construct isolated storages and multi-client setups with different configs
+# don't collide on a single process-wide singleton.
+_storage_instances: dict[tuple[str, int], MemoryStorage] = {}
 _storage_lock = threading.Lock()
 
 
-def get_memory_storage() -> MemoryStorage:
-    """Get the configured memory storage instance."""
-    global _storage_instance
-    if _storage_instance is not None:
-        return _storage_instance
+def get_memory_storage(memory_config: MemoryConfig) -> MemoryStorage:
+    """Get the configured memory storage instance.
+
+    Caches one instance per ``(storage_class, memory_config)`` pair. In
+    single-config deployments this collapses to one instance; in multi-client
+    or test scenarios each config gets its own storage.
+    """
+    key = (memory_config.storage_class, id(memory_config))
+    existing = _storage_instances.get(key)
+    if existing is not None:
+        return existing
 
     with _storage_lock:
-        if _storage_instance is not None:
-            return _storage_instance
-
-        config = get_memory_config()
-        storage_class_path = config.storage_class
+        existing = _storage_instances.get(key)
+        if existing is not None:
+            return existing
 
+        storage_class_path = memory_config.storage_class
         try:
             module_path, class_name = storage_class_path.rsplit(".", 1)
             import importlib
@@ -219,13 +229,14 @@ def get_memory_storage() -> MemoryStorage:
             if not issubclass(storage_class, MemoryStorage):
                 raise TypeError(f"Configured memory storage '{storage_class_path}' is not a subclass of MemoryStorage")
 
-            _storage_instance = storage_class()
+            instance = storage_class(memory_config)
         except Exception as e:
             logger.error(
                 "Failed to load memory storage %s, falling back to FileMemoryStorage: %s",
                 storage_class_path,
                 e,
             )
-            _storage_instance = FileMemoryStorage()
+            instance = FileMemoryStorage(memory_config)
 
-    return _storage_instance
+        _storage_instances[key] = instance
+        return instance

backend/packages/harness/deerflow/agents/memory/summarization_hook.py

@@ -5,12 +5,19 @@ from __future__ import annotations
 from deerflow.agents.memory.message_processing import detect_correction, detect_reinforcement, filter_messages_for_memory
 from deerflow.agents.memory.queue import get_memory_queue
 from deerflow.agents.middlewares.summarization_middleware import SummarizationEvent
-from deerflow.config.memory_config import get_memory_config
+from deerflow.config.app_config import AppConfig
 
 
 def memory_flush_hook(event: SummarizationEvent) -> None:
-    """Flush messages about to be summarized into the memory queue."""
-    if not get_memory_config().enabled or not event.thread_id:
+    """Flush messages about to be summarized into the memory queue.
+
+    Reads ``AppConfig`` from disk on every invocation. This hook is fired by
+    ``SummarizationMiddleware`` which has no ergonomic way to thread an
+    explicit ``app_config`` through; ``AppConfig.from_file()`` is a pure load
+    so the cost is acceptable for this rare pre-summarization callback.
+    """
+    app_config = AppConfig.from_file()
+    if not app_config.memory.enabled or not event.thread_id:
         return
 
     filtered_messages = filter_messages_for_memory(list(event.messages_to_summarize))
@@ -21,7 +28,7 @@ def memory_flush_hook(event: SummarizationEvent) -> None:
 
     correction_detected = detect_correction(filtered_messages)
     reinforcement_detected = not correction_detected and detect_reinforcement(filtered_messages)
-    queue = get_memory_queue()
+    queue = get_memory_queue(app_config)
     queue.add_nowait(
         thread_id=event.thread_id,
         messages=filtered_messages,

backend/packages/harness/deerflow/agents/memory/updater.py

@@ -21,7 +21,8 @@ from deerflow.agents.memory.storage import (
     get_memory_storage,
     utc_now_iso_z,
 )
-from deerflow.config.memory_config import get_memory_config
+from deerflow.config.app_config import AppConfig
+from deerflow.config.memory_config import MemoryConfig
 from deerflow.models import create_chat_model
 
 logger = logging.getLogger(__name__)
@@ -38,45 +39,33 @@ def _create_empty_memory() -> dict[str, Any]:
     return create_empty_memory()
 
 
-def _save_memory_to_file(memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> bool:
-    """Backward-compatible wrapper around the configured memory storage save path."""
-    return get_memory_storage().save(memory_data, agent_name, user_id=user_id)
+def _save_memory_to_file(memory_config: MemoryConfig, memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> bool:
+    """Save via the configured memory storage."""
+    return get_memory_storage(memory_config).save(memory_data, agent_name, user_id=user_id)
 
 
-def get_memory_data(agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
+def get_memory_data(memory_config: MemoryConfig, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
     """Get the current memory data via storage provider."""
-    return get_memory_storage().load(agent_name, user_id=user_id)
+    return get_memory_storage(memory_config).load(agent_name, user_id=user_id)
 
 
-def reload_memory_data(agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
+def reload_memory_data(memory_config: MemoryConfig, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
     """Reload memory data via storage provider."""
-    return get_memory_storage().reload(agent_name, user_id=user_id)
+    return get_memory_storage(memory_config).reload(agent_name, user_id=user_id)
 
 
-def import_memory_data(memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
-    """Persist imported memory data via storage provider.
-
-    Args:
-        memory_data: Full memory payload to persist.
-        agent_name: If provided, imports into per-agent memory.
-        user_id: If provided, scopes memory to a specific user.
-
-    Returns:
-        The saved memory data after storage normalization.
-
-    Raises:
-        OSError: If persisting the imported memory fails.
-    """
-    storage = get_memory_storage()
+def import_memory_data(memory_config: MemoryConfig, memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
+    """Persist imported memory data via storage provider."""
+    storage = get_memory_storage(memory_config)
     if not storage.save(memory_data, agent_name, user_id=user_id):
         raise OSError("Failed to save imported memory data")
     return storage.load(agent_name, user_id=user_id)
 
 
-def clear_memory_data(agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
+def clear_memory_data(memory_config: MemoryConfig, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
     """Clear all stored memory data and persist an empty structure."""
     cleared_memory = create_empty_memory()
-    if not _save_memory_to_file(cleared_memory, agent_name, user_id=user_id):
+    if not _save_memory_to_file(memory_config, cleared_memory, agent_name, user_id=user_id):
         raise OSError("Failed to save cleared memory data")
     return cleared_memory
 
@@ -89,6 +78,7 @@ def _validate_confidence(confidence: float) -> float:
 
 
 def create_memory_fact(
+    memory_config: MemoryConfig,
     content: str,
     category: str = "context",
     confidence: float = 0.5,
@@ -104,7 +94,7 @@ def create_memory_fact(
     normalized_category = category.strip() or "context"
     validated_confidence = _validate_confidence(confidence)
     now = utc_now_iso_z()
-    memory_data = get_memory_data(agent_name, user_id=user_id)
+    memory_data = get_memory_data(memory_config, agent_name, user_id=user_id)
     updated_memory = dict(memory_data)
     facts = list(memory_data.get("facts", []))
     facts.append(
@@ -119,15 +109,15 @@ def create_memory_fact(
     )
     updated_memory["facts"] = facts
 
-    if not _save_memory_to_file(updated_memory, agent_name, user_id=user_id):
+    if not _save_memory_to_file(memory_config, updated_memory, agent_name, user_id=user_id):
         raise OSError("Failed to save memory data after creating fact")
 
     return updated_memory
 
 
-def delete_memory_fact(fact_id: str, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
+def delete_memory_fact(memory_config: MemoryConfig, fact_id: str, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
     """Delete a fact by its id and persist the updated memory data."""
-    memory_data = get_memory_data(agent_name, user_id=user_id)
+    memory_data = get_memory_data(memory_config, agent_name, user_id=user_id)
     facts = memory_data.get("facts", [])
     updated_facts = [fact for fact in facts if fact.get("id") != fact_id]
     if len(updated_facts) == len(facts):
@@ -136,13 +126,14 @@ def delete_memory_fact(fact_id: str, agent_name: str | None = None, *, user_id:
     updated_memory = dict(memory_data)
     updated_memory["facts"] = updated_facts
 
-    if not _save_memory_to_file(updated_memory, agent_name, user_id=user_id):
+    if not _save_memory_to_file(memory_config, updated_memory, agent_name, user_id=user_id):
         raise OSError(f"Failed to save memory data after deleting fact '{fact_id}'")
 
     return updated_memory
 
 
 def update_memory_fact(
+    memory_config: MemoryConfig,
     fact_id: str,
     content: str | None = None,
     category: str | None = None,
@@ -152,7 +143,7 @@ def update_memory_fact(
     user_id: str | None = None,
 ) -> dict[str, Any]:
     """Update an existing fact and persist the updated memory data."""
-    memory_data = get_memory_data(agent_name, user_id=user_id)
+    memory_data = get_memory_data(memory_config, agent_name, user_id=user_id)
     updated_memory = dict(memory_data)
     updated_facts: list[dict[str, Any]] = []
     found = False
@@ -179,7 +170,7 @@ def update_memory_fact(
 
     updated_memory["facts"] = updated_facts
 
-    if not _save_memory_to_file(updated_memory, agent_name, user_id=user_id):
+    if not _save_memory_to_file(memory_config, updated_memory, agent_name, user_id=user_id):
         raise OSError(f"Failed to save memory data after updating fact '{fact_id}'")
 
     return updated_memory
@@ -304,19 +295,25 @@ def _fact_content_key(content: Any) -> str | None:
 class MemoryUpdater:
     """Updates memory using LLM based on conversation context."""
 
-    def __init__(self, model_name: str | None = None):
+    def __init__(self, app_config: AppConfig, model_name: str | None = None):
         """Initialize the memory updater.
 
         Args:
+            app_config: Application config (the updater needs both ``memory``
+                section for behavior and the full config for ``create_chat_model``).
             model_name: Optional model name to use. If None, uses config or default.
         """
+        self._app_config = app_config
         self._model_name = model_name
 
+    @property
+    def _memory_config(self) -> MemoryConfig:
+        return self._app_config.memory
+
     def _get_model(self):
         """Get the model for memory updates."""
-        config = get_memory_config()
-        model_name = self._model_name or config.model_name
-        return create_chat_model(name=model_name, thinking_enabled=False)
+        model_name = self._model_name or self._memory_config.model_name
+        return create_chat_model(name=model_name, thinking_enabled=False, app_config=self._app_config)
 
     def _build_correction_hint(
         self,
@@ -349,13 +346,14 @@ class MemoryUpdater:
         agent_name: str | None,
         correction_detected: bool,
         reinforcement_detected: bool,
+        user_id: str | None = None,
     ) -> tuple[dict[str, Any], str] | None:
         """Load memory and build the update prompt for a conversation."""
-        config = get_memory_config()
+        config = self._memory_config
         if not config.enabled or not messages:
             return None
 
-        current_memory = get_memory_data(agent_name)
+        current_memory = get_memory_data(config, agent_name, user_id=user_id)
         conversation_text = format_conversation_for_update(messages)
         if not conversation_text.strip():
             return None
@@ -377,6 +375,7 @@ class MemoryUpdater:
         response_content: Any,
         thread_id: str | None,
         agent_name: str | None,
+        user_id: str | None = None,
     ) -> bool:
         """Parse the model response, apply updates, and persist memory."""
         response_text = _extract_text(response_content).strip()
@@ -390,7 +389,7 @@ class MemoryUpdater:
         # cannot corrupt the still-cached original object reference.
         updated_memory = self._apply_updates(copy.deepcopy(current_memory), update_data, thread_id)
         updated_memory = _strip_upload_mentions_from_memory(updated_memory)
-        return get_memory_storage().save(updated_memory, agent_name)
+        return get_memory_storage(self._memory_config).save(updated_memory, agent_name, user_id=user_id)
 
     async def aupdate_memory(
         self,
@@ -399,6 +398,7 @@ class MemoryUpdater:
         agent_name: str | None = None,
         correction_detected: bool = False,
         reinforcement_detected: bool = False,
+        user_id: str | None = None,
     ) -> bool:
         """Update memory asynchronously based on conversation messages."""
         try:
@@ -408,6 +408,7 @@ class MemoryUpdater:
                 agent_name=agent_name,
                 correction_detected=correction_detected,
                 reinforcement_detected=reinforcement_detected,
+                user_id=user_id,
             )
             if prepared is None:
                 return False
@@ -421,6 +422,7 @@ class MemoryUpdater:
                 response_content=response.content,
                 thread_id=thread_id,
                 agent_name=agent_name,
+                user_id=user_id,
             )
         except json.JSONDecodeError as e:
             logger.warning("Failed to parse LLM response for memory update: %s", e)
@@ -451,15 +453,78 @@ class MemoryUpdater:
         Returns:
             True if update was successful, False otherwise.
         """
-        return _run_async_update_sync(
-            self.aupdate_memory(
-                messages=messages,
-                thread_id=thread_id,
-                agent_name=agent_name,
-                correction_detected=correction_detected,
-                reinforcement_detected=reinforcement_detected,
+        config = self._memory_config
+        if not config.enabled:
+            return False
+
+        if not messages:
+            return False
+
+        try:
+            # Get current memory
+            current_memory = get_memory_data(config, agent_name, user_id=user_id)
+
+            # Format conversation for prompt
+            conversation_text = format_conversation_for_update(messages)
+
+            if not conversation_text.strip():
+                return False
+
+            # Build prompt
+            correction_hint = ""
+            if correction_detected:
+                correction_hint = (
+                    "IMPORTANT: Explicit correction signals were detected in this conversation. "
+                    "Pay special attention to what the agent got wrong, what the user corrected, "
+                    "and record the correct approach as a fact with category "
+                    '"correction" and confidence >= 0.95 when appropriate.'
+                )
+            if reinforcement_detected:
+                reinforcement_hint = (
+                    "IMPORTANT: Positive reinforcement signals were detected in this conversation. "
+                    "The user explicitly confirmed the agent's approach was correct or helpful. "
+                    "Record the confirmed approach, style, or preference as a fact with category "
+                    '"preference" or "behavior" and confidence >= 0.9 when appropriate.'
+                )
+                correction_hint = (correction_hint + "\n" + reinforcement_hint).strip() if correction_hint else reinforcement_hint
+
+            prompt = MEMORY_UPDATE_PROMPT.format(
+                current_memory=json.dumps(current_memory, indent=2),
+                conversation=conversation_text,
+                correction_hint=correction_hint,
             )
-        )
+
+            # Call LLM
+            model = self._get_model()
+            response = model.invoke(prompt)
+            response_text = _extract_text(response.content).strip()
+
+            # Parse response
+            # Remove markdown code blocks if present
+            if response_text.startswith("```"):
+                lines = response_text.split("\n")
+                response_text = "\n".join(lines[1:-1] if lines[-1] == "```" else lines[1:])
+
+            update_data = json.loads(response_text)
+
+            # Apply updates
+            updated_memory = self._apply_updates(current_memory, update_data, thread_id)
+
+            # Strip file-upload mentions from all summaries before saving.
+            # Uploaded files are session-scoped and won't exist in future sessions,
+            # so recording upload events in long-term memory causes the agent to
+            # try (and fail) to locate those files in subsequent conversations.
+            updated_memory = _strip_upload_mentions_from_memory(updated_memory)
+
+            # Save
+            return get_memory_storage(config).save(updated_memory, agent_name, user_id=user_id)
+
+        except json.JSONDecodeError as e:
+            logger.warning("Failed to parse LLM response for memory update: %s", e)
+            return False
+        except Exception as e:
+            logger.exception("Memory update failed: %s", e)
+            return False
 
     def _apply_updates(
         self,
@@ -477,7 +542,7 @@ class MemoryUpdater:
         Returns:
             Updated memory data.
         """
-        config = get_memory_config()
+        config = self._memory_config
         now = utc_now_iso_z()
 
         # Update user sections

backend/packages/harness/deerflow/agents/middlewares/llm_error_handling_middleware.py

@@ -20,7 +20,7 @@ from langchain.agents.middleware.types import (
 from langchain_core.messages import AIMessage
 from langgraph.errors import GraphBubbleUp
 
-from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
 
 logger = logging.getLogger(__name__)
 
@@ -78,7 +78,7 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
 
         # Load Circuit Breaker configs from app config if available, fall back to defaults
         try:
-            app_config = get_app_config()
+            app_config = AppConfig.from_file()
             self.circuit_failure_threshold = app_config.circuit_breaker.failure_threshold
             self.circuit_recovery_timeout_sec = app_config.circuit_breaker.recovery_timeout_sec
         except (FileNotFoundError, RuntimeError):

backend/packages/harness/deerflow/agents/middlewares/loop_detection_middleware.py

@@ -25,6 +25,8 @@ from langchain.agents.middleware import AgentMiddleware
 from langchain_core.messages import HumanMessage
 from langgraph.runtime import Runtime
 
+from deerflow.config.deer_flow_context import DeerFlowContext
+
 logger = logging.getLogger(__name__)
 
 # Defaults — can be overridden via constructor
@@ -181,12 +183,9 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
         self._tool_freq: dict[str, dict[str, int]] = defaultdict(lambda: defaultdict(int))
         self._tool_freq_warned: dict[str, set[str]] = defaultdict(set)
 
-    def _get_thread_id(self, runtime: Runtime) -> str:
+    def _get_thread_id(self, runtime: Runtime[DeerFlowContext]) -> str:
         """Extract thread_id from runtime context for per-thread tracking."""
-        thread_id = runtime.context.get("thread_id") if runtime.context else None
-        if thread_id:
-            return thread_id
-        return "default"
+        return runtime.context.thread_id or "default"
 
     def _evict_if_needed(self) -> None:
         """Evict least recently used threads if over the limit.
@@ -367,11 +366,11 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
         return None
 
     @override
-    def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
+    def after_model(self, state: AgentState, runtime: Runtime[DeerFlowContext]) -> dict | None:
         return self._apply(state, runtime)
 
     @override
-    async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
+    async def aafter_model(self, state: AgentState, runtime: Runtime[DeerFlowContext]) -> dict | None:
         return self._apply(state, runtime)
 
     def reset(self, thread_id: str | None = None) -> None:

backend/packages/harness/deerflow/agents/middlewares/memory_middleware.py

@@ -5,12 +5,11 @@ from typing import override
 
 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
-from langgraph.config import get_config
 from langgraph.runtime import Runtime
 
 from deerflow.agents.memory.message_processing import detect_correction, detect_reinforcement, filter_messages_for_memory
 from deerflow.agents.memory.queue import get_memory_queue
-from deerflow.config.memory_config import get_memory_config
+from deerflow.config.deer_flow_context import DeerFlowContext
 from deerflow.runtime.user_context import get_effective_user_id
 
 logger = logging.getLogger(__name__)
@@ -44,7 +43,7 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
         self._agent_name = agent_name
 
     @override
-    def after_agent(self, state: MemoryMiddlewareState, runtime: Runtime) -> dict | None:
+    def after_agent(self, state: MemoryMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
         """Queue conversation for memory update after agent completes.
 
         Args:
@@ -54,15 +53,11 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
         Returns:
             None (no state changes needed from this middleware).
         """
-        config = get_memory_config()
-        if not config.enabled:
+        memory_config = runtime.context.app_config.memory
+        if not memory_config.enabled:
             return None
 
-        # Get thread ID from runtime context first, then fall back to LangGraph's configurable metadata
-        thread_id = runtime.context.get("thread_id") if runtime.context else None
-        if thread_id is None:
-            config_data = get_config()
-            thread_id = config_data.get("configurable", {}).get("thread_id")
+        thread_id = runtime.context.thread_id
         if not thread_id:
             logger.debug("No thread_id in context, skipping memory update")
             return None
@@ -91,7 +86,7 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
         # threading.Timer fires on a different thread where ContextVar values are not
         # propagated, so we must store user_id explicitly in ConversationContext.
         user_id = get_effective_user_id()
-        queue = get_memory_queue()
+        queue = get_memory_queue(runtime.context.app_config)
         queue.add(
             thread_id=thread_id,
             messages=filtered_messages,

backend/packages/harness/deerflow/agents/middlewares/thread_data_middleware.py

@@ -4,11 +4,10 @@ from typing import NotRequired, override
 
 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
-from langchain_core.messages import HumanMessage
-from langgraph.config import get_config
 from langgraph.runtime import Runtime
 
 from deerflow.agents.thread_state import ThreadDataState
+from deerflow.config.deer_flow_context import DeerFlowContext
 from deerflow.config.paths import Paths, get_paths
 from deerflow.runtime.user_context import get_effective_user_id
 
@@ -79,14 +78,10 @@ class ThreadDataMiddleware(AgentMiddleware[ThreadDataMiddlewareState]):
         return self._get_thread_paths(thread_id, user_id=user_id)
 
     @override
-    def before_agent(self, state: ThreadDataMiddlewareState, runtime: Runtime) -> dict | None:
-        context = runtime.context or {}
-        thread_id = context.get("thread_id")
-        if thread_id is None:
-            config = get_config()
-            thread_id = config.get("configurable", {}).get("thread_id")
+    def before_agent(self, state: ThreadDataMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
+        thread_id = runtime.context.thread_id
 
-        if thread_id is None:
+        if not thread_id:
             raise ValueError("Thread ID is required in runtime context or config.configurable")
 
         user_id = get_effective_user_id()

backend/packages/harness/deerflow/agents/middlewares/title_middleware.py

@@ -9,7 +9,9 @@ from langchain.agents.middleware import AgentMiddleware
 from langgraph.config import get_config
 from langgraph.runtime import Runtime
 
-from deerflow.config.title_config import get_title_config
+from deerflow.config.app_config import AppConfig
+from deerflow.config.deer_flow_context import DeerFlowContext
+from deerflow.config.title_config import TitleConfig
 from deerflow.models import create_chat_model
 
 logger = logging.getLogger(__name__)
@@ -45,10 +47,9 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
 
         return ""
 
-    def _should_generate_title(self, state: TitleMiddlewareState) -> bool:
+    def _should_generate_title(self, state: TitleMiddlewareState, title_config: TitleConfig) -> bool:
         """Check if we should generate a title for this thread."""
-        config = get_title_config()
-        if not config.enabled:
+        if not title_config.enabled:
             return False
 
         # Check if thread already has a title in state
@@ -67,12 +68,11 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
         # Generate title after first complete exchange
         return len(user_messages) == 1 and len(assistant_messages) >= 1
 
-    def _build_title_prompt(self, state: TitleMiddlewareState) -> tuple[str, str]:
+    def _build_title_prompt(self, state: TitleMiddlewareState, title_config: TitleConfig) -> tuple[str, str]:
         """Extract user/assistant messages and build the title prompt.
 
         Returns (prompt_string, user_msg) so callers can use user_msg as fallback.
         """
-        config = get_title_config()
         messages = state.get("messages", [])
 
         user_msg_content = next((m.content for m in messages if m.type == "human"), "")
@@ -81,8 +81,8 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
         user_msg = self._normalize_content(user_msg_content)
         assistant_msg = self._strip_think_tags(self._normalize_content(assistant_msg_content))
 
-        prompt = config.prompt_template.format(
-            max_words=config.max_words,
+        prompt = title_config.prompt_template.format(
+            max_words=title_config.max_words,
             user_msg=user_msg[:500],
             assistant_msg=assistant_msg[:500],
         )
@@ -92,17 +92,15 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
         """Remove <think>...</think> blocks emitted by reasoning models (e.g. minimax, DeepSeek-R1)."""
         return re.sub(r"<think>[\s\S]*?</think>", "", text, flags=re.IGNORECASE).strip()
 
-    def _parse_title(self, content: object) -> str:
+    def _parse_title(self, content: object, title_config: TitleConfig) -> str:
         """Normalize model output into a clean title string."""
-        config = get_title_config()
         title_content = self._normalize_content(content)
         title_content = self._strip_think_tags(title_content)
         title = title_content.strip().strip('"').strip("'")
-        return title[: config.max_chars] if len(title) > config.max_chars else title
+        return title[: title_config.max_chars] if len(title) > title_config.max_chars else title
 
-    def _fallback_title(self, user_msg: str) -> str:
-        config = get_title_config()
-        fallback_chars = min(config.max_chars, 50)
+    def _fallback_title(self, user_msg: str, title_config: TitleConfig) -> str:
+        fallback_chars = min(title_config.max_chars, 50)
         if len(user_msg) > fallback_chars:
             return user_msg[:fallback_chars].rstrip() + "..."
         return user_msg if user_msg else "New Conversation"
@@ -118,43 +116,42 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
         except Exception:
             parent = {}
         config = {**parent}
-        config["run_name"] = "title_agent"
         config["tags"] = [*(config.get("tags") or []), "middleware:title"]
         return config
 
-    def _generate_title_result(self, state: TitleMiddlewareState) -> dict | None:
+    def _generate_title_result(self, state: TitleMiddlewareState, title_config: TitleConfig) -> dict | None:
         """Generate a local fallback title without blocking on an LLM call."""
-        if not self._should_generate_title(state):
+        if not self._should_generate_title(state, title_config):
             return None
 
-        _, user_msg = self._build_title_prompt(state)
-        return {"title": self._fallback_title(user_msg)}
+        _, user_msg = self._build_title_prompt(state, title_config)
+        return {"title": self._fallback_title(user_msg, title_config)}
 
-    async def _agenerate_title_result(self, state: TitleMiddlewareState) -> dict | None:
+    async def _agenerate_title_result(self, state: TitleMiddlewareState, app_config: AppConfig) -> dict | None:
         """Generate a title asynchronously and fall back locally on failure."""
-        if not self._should_generate_title(state):
+        title_config = app_config.title
+        if not self._should_generate_title(state, title_config):
             return None
 
-        config = get_title_config()
-        prompt, user_msg = self._build_title_prompt(state)
+        prompt, user_msg = self._build_title_prompt(state, title_config)
 
         try:
-            if config.model_name:
-                model = create_chat_model(name=config.model_name, thinking_enabled=False)
+            if title_config.model_name:
+                model = create_chat_model(name=title_config.model_name, thinking_enabled=False, app_config=app_config)
             else:
-                model = create_chat_model(thinking_enabled=False)
+                model = create_chat_model(thinking_enabled=False, app_config=app_config)
             response = await model.ainvoke(prompt, config=self._get_runnable_config())
-            title = self._parse_title(response.content)
+            title = self._parse_title(response.content, title_config)
             if title:
                 return {"title": title}
         except Exception:
             logger.debug("Failed to generate async title; falling back to local title", exc_info=True)
-        return {"title": self._fallback_title(user_msg)}
+        return {"title": self._fallback_title(user_msg, title_config)}
 
     @override
-    def after_model(self, state: TitleMiddlewareState, runtime: Runtime) -> dict | None:
-        return self._generate_title_result(state)
+    def after_model(self, state: TitleMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
+        return self._generate_title_result(state, runtime.context.app_config.title)
 
     @override
-    async def aafter_model(self, state: TitleMiddlewareState, runtime: Runtime) -> dict | None:
-        return await self._agenerate_title_result(state)
+    async def aafter_model(self, state: TitleMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
+        return await self._agenerate_title_result(state, runtime.context.app_config)

backend/packages/harness/deerflow/agents/middlewares/tool_error_handling_middleware.py

@@ -1,8 +1,10 @@
 """Tool error handling middleware and shared runtime middleware builders."""
 
+from __future__ import annotations
+
 import logging
 from collections.abc import Awaitable, Callable
-from typing import override
+from typing import TYPE_CHECKING, override
 
 from langchain.agents import AgentState
 from langchain.agents.middleware import AgentMiddleware
@@ -11,6 +13,9 @@ from langgraph.errors import GraphBubbleUp
 from langgraph.prebuilt.tool_node import ToolCallRequest
 from langgraph.types import Command
 
+if TYPE_CHECKING:
+    from deerflow.config.app_config import AppConfig
+
 logger = logging.getLogger(__name__)
 
 _MISSING_TOOL_CALL_ID = "missing_tool_call_id"
@@ -67,6 +72,7 @@ class ToolErrorHandlingMiddleware(AgentMiddleware[AgentState]):
 
 def _build_runtime_middlewares(
     *,
+    app_config: "AppConfig",
     include_uploads: bool,
     include_dangling_tool_call_patch: bool,
     lazy_init: bool = True,
@@ -94,9 +100,7 @@ def _build_runtime_middlewares(
     middlewares.append(LLMErrorHandlingMiddleware())
 
     # Guardrail middleware (if configured)
-    from deerflow.config.guardrails_config import get_guardrails_config
-
-    guardrails_config = get_guardrails_config()
+    guardrails_config = app_config.guardrails
     if guardrails_config.enabled and guardrails_config.provider:
         import inspect
 
@@ -125,9 +129,10 @@ def _build_runtime_middlewares(
     return middlewares
 
 
-def build_lead_runtime_middlewares(*, lazy_init: bool = True) -> list[AgentMiddleware]:
+def build_lead_runtime_middlewares(*, app_config: "AppConfig", lazy_init: bool = True) -> list[AgentMiddleware]:
     """Middlewares shared by lead agent runtime before lead-only middlewares."""
     return _build_runtime_middlewares(
+        app_config=app_config,
         include_uploads=True,
         include_dangling_tool_call_patch=True,
         lazy_init=lazy_init,

backend/packages/harness/deerflow/agents/middlewares/uploads_middleware.py

@@ -9,6 +9,7 @@ from langchain.agents.middleware import AgentMiddleware
 from langchain_core.messages import HumanMessage
 from langgraph.runtime import Runtime
 
+from deerflow.config.deer_flow_context import DeerFlowContext
 from deerflow.config.paths import Paths, get_paths
 from deerflow.runtime.user_context import get_effective_user_id
 from deerflow.utils.file_conversion import extract_outline
@@ -185,7 +186,7 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
         return files if files else None
 
     @override
-    def before_agent(self, state: UploadsMiddlewareState, runtime: Runtime) -> dict | None:
+    def before_agent(self, state: UploadsMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
         """Inject uploaded files information before agent execution.
 
         New files come from the current message's additional_kwargs.files.
@@ -214,14 +215,7 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
             return None
 
         # Resolve uploads directory for existence checks
-        thread_id = (runtime.context or {}).get("thread_id")
-        if thread_id is None:
-            try:
-                from langgraph.config import get_config
-
-                thread_id = get_config().get("configurable", {}).get("thread_id")
-            except RuntimeError:
-                pass  # get_config() raises outside a runnable context (e.g. unit tests)
+        thread_id = runtime.context.thread_id
         uploads_dir = self._paths.sandbox_uploads_dir(thread_id, user_id=get_effective_user_id()) if thread_id else None
 
         # Get newly uploaded files from the current message's additional_kwargs.files

backend/packages/harness/deerflow/client.py

@@ -36,8 +36,9 @@ from deerflow.agents.lead_agent.agent import _build_middlewares
 from deerflow.agents.lead_agent.prompt import apply_prompt_template
 from deerflow.agents.thread_state import ThreadState
 from deerflow.config.agents_config import AGENT_NAME_PATTERN
-from deerflow.config.app_config import get_app_config, reload_app_config
-from deerflow.config.extensions_config import ExtensionsConfig, SkillStateConfig, get_extensions_config, reload_extensions_config
+from deerflow.config.app_config import AppConfig
+from deerflow.config.deer_flow_context import DeerFlowContext
+from deerflow.config.extensions_config import ExtensionsConfig
 from deerflow.config.paths import get_paths
 from deerflow.models import create_chat_model
 from deerflow.runtime.user_context import get_effective_user_id
@@ -116,6 +117,7 @@ class DeerFlowClient:
         config_path: str | None = None,
         checkpointer=None,
         *,
+        config: AppConfig | None = None,
         model_name: str | None = None,
         thinking_enabled: bool = True,
         subagent_enabled: bool = False,
@@ -130,9 +132,14 @@ class DeerFlowClient:
 
         Args:
             config_path: Path to config.yaml. Uses default resolution if None.
+                Ignored when ``config`` is provided.
             checkpointer: LangGraph checkpointer instance for state persistence.
                 Required for multi-turn conversations on the same thread_id.
                 Without a checkpointer, each call is stateless.
+            config: Optional pre-constructed AppConfig. When provided, it takes
+                precedence over ``config_path`` and no file is read. Enables
+                multi-client isolation: two clients with different configs can
+                coexist in the same process without touching process-global state.
             model_name: Override the default model name from config.
             thinking_enabled: Enable model's extended thinking.
             subagent_enabled: Enable subagent delegation.
@@ -141,9 +148,18 @@ class DeerFlowClient:
             available_skills: Optional set of skill names to make available. If None (default), all scanned skills are available.
             middlewares: Optional list of custom middlewares to inject into the agent.
         """
-        if config_path is not None:
-            reload_app_config(config_path)
-        self._app_config = get_app_config()
+        # Constructor-captured config: the client owns its AppConfig for its lifetime.
+        # Multiple clients with different configs do not contend.
+        #
+        # Priority: explicit ``config=`` > explicit ``config_path=`` > ``AppConfig.from_file()``
+        # with default path resolution. There is no ambient global fallback; if
+        # config.yaml cannot be located, ``from_file`` raises loudly.
+        if config is not None:
+            self._app_config = config
+        elif config_path is not None:
+            self._app_config = AppConfig.from_file(config_path)
+        else:
+            self._app_config = AppConfig.from_file()
 
         if agent_name is not None and not AGENT_NAME_PATTERN.match(agent_name):
             raise ValueError(f"Invalid agent name '{agent_name}'. Must match pattern: {AGENT_NAME_PATTERN.pattern}")
@@ -171,6 +187,15 @@ class DeerFlowClient:
         self._agent = None
         self._agent_config_key = None
 
+    def _reload_config(self) -> None:
+        """Reload config from file and refresh the cached reference.
+
+        Only the client's own ``_app_config`` is rebuilt. Other clients
+        and the process-global are untouched, so multi-client coexistence
+        survives reload.
+        """
+        self._app_config = AppConfig.from_file()
+
     # ------------------------------------------------------------------
     # Internal helpers
     # ------------------------------------------------------------------
@@ -228,10 +253,11 @@ class DeerFlowClient:
         max_concurrent_subagents = cfg.get("max_concurrent_subagents", 3)
 
         kwargs: dict[str, Any] = {
-            "model": create_chat_model(name=model_name, thinking_enabled=thinking_enabled),
+            "model": create_chat_model(name=model_name, thinking_enabled=thinking_enabled, app_config=self._app_config),
             "tools": self._get_tools(model_name=model_name, subagent_enabled=subagent_enabled),
-            "middleware": _build_middlewares(config, model_name=model_name, agent_name=self._agent_name, custom_middlewares=self._middlewares),
+            "middleware": _build_middlewares(self._app_config, config, model_name=model_name, agent_name=self._agent_name, custom_middlewares=self._middlewares),
             "system_prompt": apply_prompt_template(
+                self._app_config,
                 subagent_enabled=subagent_enabled,
                 max_concurrent_subagents=max_concurrent_subagents,
                 agent_name=self._agent_name,
@@ -243,7 +269,7 @@ class DeerFlowClient:
         if checkpointer is None:
             from deerflow.runtime.checkpointer import get_checkpointer
 
-            checkpointer = get_checkpointer()
+            checkpointer = get_checkpointer(self._app_config)
         if checkpointer is not None:
             kwargs["checkpointer"] = checkpointer
 
@@ -251,12 +277,11 @@ class DeerFlowClient:
         self._agent_config_key = key
         logger.info("Agent created: agent_name=%s, model=%s, thinking=%s", self._agent_name, model_name, thinking_enabled)
 
-    @staticmethod
-    def _get_tools(*, model_name: str | None, subagent_enabled: bool):
+    def _get_tools(self, *, model_name: str | None, subagent_enabled: bool):
         """Lazy import to avoid circular dependency at module level."""
         from deerflow.tools import get_available_tools
 
-        return get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled)
+        return get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled, app_config=self._app_config)
 
     @staticmethod
     def _serialize_tool_calls(tool_calls) -> list[dict]:
@@ -377,7 +402,7 @@ class DeerFlowClient:
         if checkpointer is None:
             from deerflow.runtime.checkpointer.provider import get_checkpointer
 
-            checkpointer = get_checkpointer()
+            checkpointer = get_checkpointer(self._app_config)
 
         thread_info_map = {}
 
@@ -432,7 +457,7 @@ class DeerFlowClient:
         if checkpointer is None:
             from deerflow.runtime.checkpointer.provider import get_checkpointer
 
-            checkpointer = get_checkpointer()
+            checkpointer = get_checkpointer(self._app_config)
 
         config = {"configurable": {"thread_id": thread_id}}
         checkpoints = []
@@ -552,9 +577,7 @@ class DeerFlowClient:
         self._ensure_agent(config)
 
         state: dict[str, Any] = {"messages": [HumanMessage(content=message)]}
-        context = {"thread_id": thread_id}
-        if self._agent_name:
-            context["agent_name"] = self._agent_name
+        context = DeerFlowContext(app_config=self._app_config, thread_id=thread_id, agent_name=self._agent_name)
 
         seen_ids: set[str] = set()
         # Cross-mode handoff: ids already streamed via LangGraph ``messages``
@@ -763,7 +786,7 @@ class DeerFlowClient:
                     "category": s.category,
                     "enabled": s.enabled,
                 }
-                for s in load_skills(enabled_only=enabled_only)
+                for s in load_skills(self._app_config, enabled_only=enabled_only)
             ]
         }
 
@@ -775,19 +798,19 @@ class DeerFlowClient:
         """
         from deerflow.agents.memory.updater import get_memory_data
 
-        return get_memory_data(user_id=get_effective_user_id())
+        return get_memory_data(self._app_config.memory, user_id=get_effective_user_id())
 
     def export_memory(self) -> dict:
         """Export current memory data for backup or transfer."""
         from deerflow.agents.memory.updater import get_memory_data
 
-        return get_memory_data(user_id=get_effective_user_id())
+        return get_memory_data(self._app_config.memory, user_id=get_effective_user_id())
 
     def import_memory(self, memory_data: dict) -> dict:
         """Import and persist full memory data."""
         from deerflow.agents.memory.updater import import_memory_data
 
-        return import_memory_data(memory_data, user_id=get_effective_user_id())
+        return import_memory_data(self._app_config.memory, memory_data, user_id=get_effective_user_id())
 
     def get_model(self, name: str) -> dict | None:
         """Get a specific model's configuration by name.
@@ -822,8 +845,8 @@ class DeerFlowClient:
             Dict with "mcp_servers" key mapping server name to config,
             matching the Gateway API ``McpConfigResponse`` schema.
         """
-        config = get_extensions_config()
-        return {"mcp_servers": {name: server.model_dump() for name, server in config.mcp_servers.items()}}
+        ext = self._app_config.extensions
+        return {"mcp_servers": {name: server.model_dump() for name, server in ext.mcp_servers.items()}}
 
     def update_mcp_config(self, mcp_servers: dict[str, dict]) -> dict:
         """Update MCP server configurations.
@@ -845,18 +868,19 @@ class DeerFlowClient:
         if config_path is None:
             raise FileNotFoundError("Cannot locate extensions_config.json. Set DEER_FLOW_EXTENSIONS_CONFIG_PATH or ensure it exists in the project root.")
 
-        current_config = get_extensions_config()
+        current_ext = self._app_config.extensions
 
         config_data = {
             "mcpServers": mcp_servers,
-            "skills": {name: {"enabled": skill.enabled} for name, skill in current_config.skills.items()},
+            "skills": {name: {"enabled": skill.enabled} for name, skill in current_ext.skills.items()},
         }
 
         self._atomic_write_json(config_path, config_data)
 
         self._agent = None
         self._agent_config_key = None
-        reloaded = reload_extensions_config()
+        self._reload_config()
+        reloaded = self._app_config.extensions
         return {"mcp_servers": {name: server.model_dump() for name, server in reloaded.mcp_servers.items()}}
 
     # ------------------------------------------------------------------
@@ -874,7 +898,7 @@ class DeerFlowClient:
         """
         from deerflow.skills.loader import load_skills
 
-        skill = next((s for s in load_skills(enabled_only=False) if s.name == name), None)
+        skill = next((s for s in load_skills(self._app_config, enabled_only=False) if s.name == name), None)
         if skill is None:
             return None
         return {
@@ -901,7 +925,7 @@ class DeerFlowClient:
         """
         from deerflow.skills.loader import load_skills
 
-        skills = load_skills(enabled_only=False)
+        skills = load_skills(self._app_config, enabled_only=False)
         skill = next((s for s in skills if s.name == name), None)
         if skill is None:
             raise ValueError(f"Skill '{name}' not found")
@@ -910,21 +934,25 @@ class DeerFlowClient:
         if config_path is None:
             raise FileNotFoundError("Cannot locate extensions_config.json. Set DEER_FLOW_EXTENSIONS_CONFIG_PATH or ensure it exists in the project root.")
 
-        extensions_config = get_extensions_config()
-        extensions_config.skills[name] = SkillStateConfig(enabled=enabled)
+        # Do not mutate self._app_config (frozen value). Compose the new
+        # skills state in a fresh dict, write it to disk, and let _reload_config()
+        # below rebuild AppConfig from the updated file.
+        ext = self._app_config.extensions
+        new_skills = {n: {"enabled": sc.enabled} for n, sc in ext.skills.items()}
+        new_skills[name] = {"enabled": enabled}
 
         config_data = {
-            "mcpServers": {n: s.model_dump() for n, s in extensions_config.mcp_servers.items()},
-            "skills": {n: {"enabled": sc.enabled} for n, sc in extensions_config.skills.items()},
+            "mcpServers": {n: s.model_dump() for n, s in ext.mcp_servers.items()},
+            "skills": new_skills,
         }
 
         self._atomic_write_json(config_path, config_data)
 
         self._agent = None
         self._agent_config_key = None
-        reload_extensions_config()
+        self._reload_config()
 
-        updated = next((s for s in load_skills(enabled_only=False) if s.name == name), None)
+        updated = next((s for s in load_skills(self._app_config, enabled_only=False) if s.name == name), None)
         if updated is None:
             raise RuntimeError(f"Skill '{name}' disappeared after update")
         return {
@@ -962,25 +990,25 @@ class DeerFlowClient:
         """
         from deerflow.agents.memory.updater import reload_memory_data
 
-        return reload_memory_data(user_id=get_effective_user_id())
+        return reload_memory_data(self._app_config.memory, user_id=get_effective_user_id())
 
     def clear_memory(self) -> dict:
         """Clear all persisted memory data."""
         from deerflow.agents.memory.updater import clear_memory_data
 
-        return clear_memory_data(user_id=get_effective_user_id())
+        return clear_memory_data(self._app_config.memory, user_id=get_effective_user_id())
 
     def create_memory_fact(self, content: str, category: str = "context", confidence: float = 0.5) -> dict:
         """Create a single fact manually."""
         from deerflow.agents.memory.updater import create_memory_fact
 
-        return create_memory_fact(content=content, category=category, confidence=confidence)
+        return create_memory_fact(self._app_config.memory, content=content, category=category, confidence=confidence)
 
     def delete_memory_fact(self, fact_id: str) -> dict:
         """Delete a single fact from memory by fact id."""
         from deerflow.agents.memory.updater import delete_memory_fact
 
-        return delete_memory_fact(fact_id)
+        return delete_memory_fact(self._app_config.memory, fact_id)
 
     def update_memory_fact(
         self,
@@ -993,6 +1021,7 @@ class DeerFlowClient:
         from deerflow.agents.memory.updater import update_memory_fact
 
         return update_memory_fact(
+            self._app_config.memory,
             fact_id=fact_id,
             content=content,
             category=category,
@@ -1005,9 +1034,7 @@ class DeerFlowClient:
         Returns:
             Memory config dict.
         """
-        from deerflow.config.memory_config import get_memory_config
-
-        config = get_memory_config()
+        config = self._app_config.memory
         return {
             "enabled": config.enabled,
             "storage_path": config.storage_path,

backend/packages/harness/deerflow/community/aio_sandbox/aio_sandbox_provider.py

@@ -25,7 +25,7 @@ except ImportError:  # pragma: no cover - Windows fallback
     fcntl = None  # type: ignore[assignment]
     import msvcrt
 
-from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
 from deerflow.config.paths import VIRTUAL_PATH_PREFIX, get_paths
 from deerflow.runtime.user_context import get_effective_user_id
 from deerflow.sandbox.sandbox import Sandbox
@@ -90,7 +90,8 @@ class AioSandboxProvider(SandboxProvider):
           API_KEY: $MY_API_KEY
     """
 
-    def __init__(self):
+    def __init__(self, app_config: "AppConfig"):
+        self._app_config = app_config
         self._lock = threading.Lock()
         self._sandboxes: dict[str, AioSandbox] = {}  # sandbox_id -> AioSandbox instance
         self._sandbox_infos: dict[str, SandboxInfo] = {}  # sandbox_id -> SandboxInfo (for destroy)
@@ -159,8 +160,7 @@ class AioSandboxProvider(SandboxProvider):
 
     def _load_config(self) -> dict:
         """Load sandbox configuration from app config."""
-        config = get_app_config()
-        sandbox_config = config.sandbox
+        sandbox_config = self._app_config.sandbox
 
         idle_timeout = getattr(sandbox_config, "idle_timeout", None)
         replicas = getattr(sandbox_config, "replicas", None)
@@ -283,17 +283,15 @@ class AioSandboxProvider(SandboxProvider):
             (paths.host_acp_workspace_dir(thread_id, user_id=user_id), "/mnt/acp-workspace", True),
         ]
 
-    @staticmethod
-    def _get_skills_mount() -> tuple[str, str, bool] | None:
+    def _get_skills_mount(self) -> tuple[str, str, bool] | None:
         """Get the skills directory mount configuration.
 
         Mount source uses DEER_FLOW_HOST_SKILLS_PATH when running inside Docker (DooD)
         so the host Docker daemon can resolve the path.
         """
         try:
-            config = get_app_config()
-            skills_path = config.skills.get_skills_path()
-            container_path = config.skills.container_path
+            skills_path = self._app_config.skills.get_skills_path()
+            container_path = self._app_config.skills.container_path
 
             if skills_path.exists():
                 # When running inside Docker with DooD, use host-side skills path.

backend/packages/harness/deerflow/community/ddg_search/tools.py

@@ -5,9 +5,9 @@ Web Search Tool - Search the web using DuckDuckGo (no API key required).
 import json
 import logging
 
-from langchain.tools import tool
+from langchain.tools import ToolRuntime, tool
 
-from deerflow.config import get_app_config
+from deerflow.config.deer_flow_context import resolve_context
 
 logger = logging.getLogger(__name__)
 
@@ -55,6 +55,7 @@ def _search_text(
 @tool("web_search", parse_docstring=True)
 def web_search_tool(
     query: str,
+    runtime: ToolRuntime,
     max_results: int = 5,
 ) -> str:
     """Search the web for information. Use this tool to find current information, news, articles, and facts from the internet.
@@ -63,11 +64,11 @@ def web_search_tool(
         query: Search keywords describing what you want to find. Be specific for better results.
         max_results: Maximum number of results to return. Default is 5.
     """
-    config = get_app_config().get_tool_config("web_search")
+    tool_config = resolve_context(runtime).app_config.get_tool_config("web_search")
 
     # Override max_results from config if set
-    if config is not None and "max_results" in config.model_extra:
-        max_results = config.model_extra.get("max_results", max_results)
+    if tool_config is not None and "max_results" in tool_config.model_extra:
+        max_results = tool_config.model_extra.get("max_results", max_results)
 
     results = _search_text(
         query=query,

backend/packages/harness/deerflow/community/exa/tools.py

@@ -1,37 +1,39 @@
 import json
 
 from exa_py import Exa
-from langchain.tools import tool
+from langchain.tools import ToolRuntime, tool
 
-from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
+from deerflow.config.deer_flow_context import resolve_context
 
 
-def _get_exa_client(tool_name: str = "web_search") -> Exa:
-    config = get_app_config().get_tool_config(tool_name)
+def _get_exa_client(app_config: AppConfig, tool_name: str = "web_search") -> Exa:
+    tool_config = app_config.get_tool_config(tool_name)
     api_key = None
-    if config is not None and "api_key" in config.model_extra:
-        api_key = config.model_extra.get("api_key")
+    if tool_config is not None and "api_key" in tool_config.model_extra:
+        api_key = tool_config.model_extra.get("api_key")
     return Exa(api_key=api_key)
 
 
 @tool("web_search", parse_docstring=True)
-def web_search_tool(query: str) -> str:
+def web_search_tool(query: str, runtime: ToolRuntime) -> str:
     """Search the web.
 
     Args:
         query: The query to search for.
     """
     try:
-        config = get_app_config().get_tool_config("web_search")
+        app_config = resolve_context(runtime).app_config
+        tool_config = app_config.get_tool_config("web_search")
         max_results = 5
         search_type = "auto"
         contents_max_characters = 1000
-        if config is not None:
-            max_results = config.model_extra.get("max_results", max_results)
-            search_type = config.model_extra.get("search_type", search_type)
-            contents_max_characters = config.model_extra.get("contents_max_characters", contents_max_characters)
+        if tool_config is not None:
+            max_results = tool_config.model_extra.get("max_results", max_results)
+            search_type = tool_config.model_extra.get("search_type", search_type)
+            contents_max_characters = tool_config.model_extra.get("contents_max_characters", contents_max_characters)
 
-        client = _get_exa_client()
+        client = _get_exa_client(app_config)
         res = client.search(
             query,
             type=search_type,
@@ -54,7 +56,7 @@ def web_search_tool(query: str) -> str:
 
 
 @tool("web_fetch", parse_docstring=True)
-def web_fetch_tool(url: str) -> str:
+def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
     """Fetch the contents of a web page at a given URL.
     Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
     This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
@@ -65,7 +67,7 @@ def web_fetch_tool(url: str) -> str:
         url: The URL to fetch the contents of.
     """
     try:
-        client = _get_exa_client("web_fetch")
+        client = _get_exa_client(resolve_context(runtime).app_config, "web_fetch")
         res = client.get_contents([url], text={"max_characters": 4096})
 
         if res.results:

backend/packages/harness/deerflow/community/firecrawl/tools.py

@@ -1,33 +1,35 @@
 import json
 
 from firecrawl import FirecrawlApp
-from langchain.tools import tool
+from langchain.tools import ToolRuntime, tool
 
-from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
+from deerflow.config.deer_flow_context import resolve_context
 
 
-def _get_firecrawl_client(tool_name: str = "web_search") -> FirecrawlApp:
-    config = get_app_config().get_tool_config(tool_name)
+def _get_firecrawl_client(app_config: AppConfig, tool_name: str = "web_search") -> FirecrawlApp:
+    tool_config = app_config.get_tool_config(tool_name)
     api_key = None
-    if config is not None and "api_key" in config.model_extra:
-        api_key = config.model_extra.get("api_key")
+    if tool_config is not None and "api_key" in tool_config.model_extra:
+        api_key = tool_config.model_extra.get("api_key")
     return FirecrawlApp(api_key=api_key)  # type: ignore[arg-type]
 
 
 @tool("web_search", parse_docstring=True)
-def web_search_tool(query: str) -> str:
+def web_search_tool(query: str, runtime: ToolRuntime) -> str:
     """Search the web.
 
     Args:
         query: The query to search for.
     """
     try:
-        config = get_app_config().get_tool_config("web_search")
+        app_config = resolve_context(runtime).app_config
+        tool_config = app_config.get_tool_config("web_search")
         max_results = 5
-        if config is not None:
-            max_results = config.model_extra.get("max_results", max_results)
+        if tool_config is not None:
+            max_results = tool_config.model_extra.get("max_results", max_results)
 
-        client = _get_firecrawl_client("web_search")
+        client = _get_firecrawl_client(app_config, "web_search")
         result = client.search(query, limit=max_results)
 
         # result.web contains list of SearchResultWeb objects
@@ -47,7 +49,7 @@ def web_search_tool(query: str) -> str:
 
 
 @tool("web_fetch", parse_docstring=True)
-def web_fetch_tool(url: str) -> str:
+def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
     """Fetch the contents of a web page at a given URL.
     Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
     This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
@@ -58,7 +60,8 @@ def web_fetch_tool(url: str) -> str:
         url: The URL to fetch the contents of.
     """
     try:
-        client = _get_firecrawl_client("web_fetch")
+        app_config = resolve_context(runtime).app_config
+        client = _get_firecrawl_client(app_config, "web_fetch")
         result = client.scrape(url, formats=["markdown"])
 
         markdown_content = result.markdown or ""

backend/packages/harness/deerflow/community/image_search/tools.py

@@ -5,9 +5,9 @@ Image Search Tool - Search images using DuckDuckGo for reference in image genera
 import json
 import logging
 
-from langchain.tools import tool
+from langchain.tools import ToolRuntime, tool
 
-from deerflow.config import get_app_config
+from deerflow.config.deer_flow_context import resolve_context
 
 logger = logging.getLogger(__name__)
 
@@ -77,6 +77,7 @@ def _search_images(
 @tool("image_search", parse_docstring=True)
 def image_search_tool(
     query: str,
+    runtime: ToolRuntime,
     max_results: int = 5,
     size: str | None = None,
     type_image: str | None = None,
@@ -99,11 +100,11 @@ def image_search_tool(
         type_image: Image type filter. Options: "photo", "clipart", "gif", "transparent", "line". Use "photo" for realistic references.
         layout: Layout filter. Options: "Square", "Tall", "Wide". Choose based on your generation needs.
     """
-    config = get_app_config().get_tool_config("image_search")
+    tool_config = resolve_context(runtime).app_config.get_tool_config("image_search")
 
     # Override max_results from config if set
-    if config is not None and "max_results" in config.model_extra:
-        max_results = config.model_extra.get("max_results", max_results)
+    if tool_config is not None and "max_results" in tool_config.model_extra:
+        max_results = tool_config.model_extra.get("max_results", max_results)
 
     results = _search_images(
         query=query,

backend/packages/harness/deerflow/community/infoquest/tools.py

@@ -1,6 +1,7 @@
-from langchain.tools import tool
+from langchain.tools import ToolRuntime, tool
 
-from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
+from deerflow.config.deer_flow_context import resolve_context
 from deerflow.utils.readability import ReadabilityExtractor
 
 from .infoquest_client import InfoQuestClient
@@ -8,13 +9,13 @@ from .infoquest_client import InfoQuestClient
 readability_extractor = ReadabilityExtractor()
 
 
-def _get_infoquest_client() -> InfoQuestClient:
-    search_config = get_app_config().get_tool_config("web_search")
+def _get_infoquest_client(app_config: AppConfig) -> InfoQuestClient:
+    search_config = app_config.get_tool_config("web_search")
     search_time_range = -1
     if search_config is not None and "search_time_range" in search_config.model_extra:
         search_time_range = search_config.model_extra.get("search_time_range")
 
-    fetch_config = get_app_config().get_tool_config("web_fetch")
+    fetch_config = app_config.get_tool_config("web_fetch")
     fetch_time = -1
     if fetch_config is not None and "fetch_time" in fetch_config.model_extra:
         fetch_time = fetch_config.model_extra.get("fetch_time")
@@ -25,7 +26,7 @@ def _get_infoquest_client() -> InfoQuestClient:
     if fetch_config is not None and "navigation_timeout" in fetch_config.model_extra:
         navigation_timeout = fetch_config.model_extra.get("navigation_timeout")
 
-    image_search_config = get_app_config().get_tool_config("image_search")
+    image_search_config = app_config.get_tool_config("image_search")
     image_search_time_range = -1
     if image_search_config is not None and "image_search_time_range" in image_search_config.model_extra:
         image_search_time_range = image_search_config.model_extra.get("image_search_time_range")
@@ -44,19 +45,18 @@ def _get_infoquest_client() -> InfoQuestClient:
 
 
 @tool("web_search", parse_docstring=True)
-def web_search_tool(query: str) -> str:
+def web_search_tool(query: str, runtime: ToolRuntime) -> str:
     """Search the web.
 
     Args:
         query: The query to search for.
     """
-
-    client = _get_infoquest_client()
+    client = _get_infoquest_client(resolve_context(runtime).app_config)
     return client.web_search(query)
 
 
 @tool("web_fetch", parse_docstring=True)
-def web_fetch_tool(url: str) -> str:
+def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
     """Fetch the contents of a web page at a given URL.
     Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
     This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
@@ -66,7 +66,7 @@ def web_fetch_tool(url: str) -> str:
     Args:
         url: The URL to fetch the contents of.
     """
-    client = _get_infoquest_client()
+    client = _get_infoquest_client(resolve_context(runtime).app_config)
     result = client.fetch(url)
     if result.startswith("Error: "):
         return result
@@ -75,7 +75,7 @@ def web_fetch_tool(url: str) -> str:
 
 
 @tool("image_search", parse_docstring=True)
-def image_search_tool(query: str) -> str:
+def image_search_tool(query: str, runtime: ToolRuntime) -> str:
     """Search for images online. Use this tool BEFORE image generation to find reference images for characters, portraits, objects, scenes, or any content requiring visual accuracy.
 
     **When to use:**
@@ -89,5 +89,5 @@ def image_search_tool(query: str) -> str:
     Args:
         query: The query to search for images.
     """
-    client = _get_infoquest_client()
+    client = _get_infoquest_client(resolve_context(runtime).app_config)
     return client.image_search(query)

backend/packages/harness/deerflow/community/jina_ai/tools.py

@@ -1,16 +1,16 @@
 import asyncio
 
-from langchain.tools import tool
+from langchain.tools import ToolRuntime, tool
 
 from deerflow.community.jina_ai.jina_client import JinaClient
-from deerflow.config import get_app_config
+from deerflow.config.deer_flow_context import resolve_context
 from deerflow.utils.readability import ReadabilityExtractor
 
 readability_extractor = ReadabilityExtractor()
 
 
 @tool("web_fetch", parse_docstring=True)
-async def web_fetch_tool(url: str) -> str:
+async def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
     """Fetch the contents of a web page at a given URL.
     Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
     This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
@@ -22,9 +22,9 @@ async def web_fetch_tool(url: str) -> str:
     """
     jina_client = JinaClient()
     timeout = 10
-    config = get_app_config().get_tool_config("web_fetch")
-    if config is not None and "timeout" in config.model_extra:
-        timeout = config.model_extra.get("timeout")
+    tool_config = resolve_context(runtime).app_config.get_tool_config("web_fetch")
+    if tool_config is not None and "timeout" in tool_config.model_extra:
+        timeout = tool_config.model_extra.get("timeout")
     html_content = await jina_client.crawl(url, return_format="html", timeout=timeout)
     if isinstance(html_content, str) and html_content.startswith("Error:"):
         return html_content

backend/packages/harness/deerflow/community/tavily/tools.py

@@ -1,32 +1,34 @@
 import json
 
-from langchain.tools import tool
+from langchain.tools import ToolRuntime, tool
 from tavily import TavilyClient
 
-from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
+from deerflow.config.deer_flow_context import resolve_context
 
 
-def _get_tavily_client() -> TavilyClient:
-    config = get_app_config().get_tool_config("web_search")
+def _get_tavily_client(app_config: AppConfig) -> TavilyClient:
+    tool_config = app_config.get_tool_config("web_search")
     api_key = None
-    if config is not None and "api_key" in config.model_extra:
-        api_key = config.model_extra.get("api_key")
+    if tool_config is not None and "api_key" in tool_config.model_extra:
+        api_key = tool_config.model_extra.get("api_key")
     return TavilyClient(api_key=api_key)
 
 
 @tool("web_search", parse_docstring=True)
-def web_search_tool(query: str) -> str:
+def web_search_tool(query: str, runtime: ToolRuntime) -> str:
     """Search the web.
 
     Args:
         query: The query to search for.
     """
-    config = get_app_config().get_tool_config("web_search")
+    app_config = resolve_context(runtime).app_config
+    tool_config = app_config.get_tool_config("web_search")
     max_results = 5
-    if config is not None and "max_results" in config.model_extra:
-        max_results = config.model_extra.get("max_results")
+    if tool_config is not None and "max_results" in tool_config.model_extra:
+        max_results = tool_config.model_extra.get("max_results")
 
-    client = _get_tavily_client()
+    client = _get_tavily_client(app_config)
     res = client.search(query, max_results=max_results)
     normalized_results = [
         {
@@ -41,7 +43,7 @@ def web_search_tool(query: str) -> str:
 
 
 @tool("web_fetch", parse_docstring=True)
-def web_fetch_tool(url: str) -> str:
+def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
     """Fetch the contents of a web page at a given URL.
     Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
     This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
@@ -51,7 +53,8 @@ def web_fetch_tool(url: str) -> str:
     Args:
         url: The URL to fetch the contents of.
     """
-    client = _get_tavily_client()
+    app_config = resolve_context(runtime).app_config
+    client = _get_tavily_client(app_config)
     res = client.extract([url])
     if "failed_results" in res and len(res["failed_results"]) > 0:
         return f"Error: {res['failed_results'][0]['error']}"

backend/packages/harness/deerflow/config/__init__.py

@@ -1,6 +1,6 @@
-from .app_config import get_app_config
-from .extensions_config import ExtensionsConfig, get_extensions_config
-from .memory_config import MemoryConfig, get_memory_config
+from .app_config import AppConfig
+from .extensions_config import ExtensionsConfig
+from .memory_config import MemoryConfig
 from .paths import Paths, get_paths
 from .skill_evolution_config import SkillEvolutionConfig
 from .skills_config import SkillsConfig
@@ -13,18 +13,16 @@ from .tracing_config import (
 )
 
 __all__ = [
-    "get_app_config",
-    "SkillEvolutionConfig",
-    "Paths",
-    "get_paths",
-    "SkillsConfig",
+    "AppConfig",
     "ExtensionsConfig",
-    "get_extensions_config",
     "MemoryConfig",
-    "get_memory_config",
-    "get_tracing_config",
-    "get_explicitly_enabled_tracing_providers",
+    "Paths",
+    "SkillEvolutionConfig",
+    "SkillsConfig",
     "get_enabled_tracing_providers",
+    "get_explicitly_enabled_tracing_providers",
+    "get_paths",
+    "get_tracing_config",
     "is_tracing_enabled",
     "validate_enabled_tracing_providers",
 ]

backend/packages/harness/deerflow/config/acp_config.py

@@ -1,16 +1,13 @@
 """ACP (Agent Client Protocol) agent configuration loaded from config.yaml."""
 
-import logging
-from collections.abc import Mapping
-
-from pydantic import BaseModel, Field
-
-logger = logging.getLogger(__name__)
+from pydantic import BaseModel, ConfigDict, Field
 
 
 class ACPAgentConfig(BaseModel):
     """Configuration for a single ACP-compatible agent."""
 
+    model_config = ConfigDict(frozen=True)
+
     command: str = Field(description="Command to launch the ACP agent subprocess")
     args: list[str] = Field(default_factory=list, description="Additional command arguments")
     env: dict[str, str] = Field(default_factory=dict, description="Environment variables to inject into the agent subprocess. Values starting with $ are resolved from host environment variables.")
@@ -24,28 +21,3 @@ class ACPAgentConfig(BaseModel):
             "are denied — the agent must be configured to operate without requesting permissions."
         ),
     )
-
-
-_acp_agents: dict[str, ACPAgentConfig] = {}
-
-
-def get_acp_agents() -> dict[str, ACPAgentConfig]:
-    """Get the currently configured ACP agents.
-
-    Returns:
-        Mapping of agent name -> ACPAgentConfig.  Empty dict if no ACP agents are configured.
-    """
-    return _acp_agents
-
-
-def load_acp_config_from_dict(config_dict: Mapping[str, Mapping[str, object]] | None) -> None:
-    """Load ACP agent configuration from a dictionary (typically from config.yaml).
-
-    Args:
-        config_dict: Mapping of agent name -> config fields.
-    """
-    global _acp_agents
-    if config_dict is None:
-        config_dict = {}
-    _acp_agents = {name: ACPAgentConfig(**cfg) for name, cfg in config_dict.items()}
-    logger.info("ACP config loaded: %d agent(s): %s", len(_acp_agents), list(_acp_agents.keys()))

backend/packages/harness/deerflow/config/agents_api_config.py

@@ -1,32 +1,14 @@
 """Configuration for the custom agents management API."""
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 
 class AgentsApiConfig(BaseModel):
     """Configuration for custom-agent and user-profile management routes."""
 
+    model_config = ConfigDict(frozen=True)
+
     enabled: bool = Field(
         default=False,
         description=("Whether to expose the custom-agent management API over HTTP. When disabled, the gateway rejects read/write access to custom agent SOUL.md, config, and USER.md prompt-management routes."),
     )
-
-
-_agents_api_config: AgentsApiConfig = AgentsApiConfig()
-
-
-def get_agents_api_config() -> AgentsApiConfig:
-    """Get the current agents API configuration."""
-    return _agents_api_config
-
-
-def set_agents_api_config(config: AgentsApiConfig) -> None:
-    """Set the agents API configuration."""
-    global _agents_api_config
-    _agents_api_config = config
-
-
-def load_agents_api_config_from_dict(config_dict: dict) -> None:
-    """Load agents API configuration from a dictionary."""
-    global _agents_api_config
-    _agents_api_config = AgentsApiConfig(**config_dict)

backend/packages/harness/deerflow/config/agents_config.py

@@ -5,7 +5,7 @@ import re
 from typing import Any
 
 import yaml
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict
 
 from deerflow.config.paths import get_paths
 
@@ -29,6 +29,8 @@ def validate_agent_name(name: str | None) -> str | None:
 class AgentConfig(BaseModel):
     """Configuration for a custom agent."""
 
+    model_config = ConfigDict(frozen=True)
+
     name: str
     description: str = ""
     model: str | None = None

backend/packages/harness/deerflow/config/app_config.py

@@ -1,6 +1,7 @@
+from __future__ import annotations
+
 import logging
 import os
-from contextvars import ContextVar
 from pathlib import Path
 from typing import Any, Self
 
@@ -8,25 +9,25 @@ import yaml
 from dotenv import load_dotenv
 from pydantic import BaseModel, ConfigDict, Field
 
-from deerflow.config.acp_config import load_acp_config_from_dict
-from deerflow.config.agents_api_config import AgentsApiConfig, load_agents_api_config_from_dict
-from deerflow.config.checkpointer_config import CheckpointerConfig, load_checkpointer_config_from_dict
+from deerflow.config.acp_config import ACPAgentConfig
+from deerflow.config.agents_api_config import AgentsApiConfig
+from deerflow.config.checkpointer_config import CheckpointerConfig
 from deerflow.config.database_config import DatabaseConfig
 from deerflow.config.extensions_config import ExtensionsConfig
-from deerflow.config.guardrails_config import GuardrailsConfig, load_guardrails_config_from_dict
-from deerflow.config.memory_config import MemoryConfig, load_memory_config_from_dict
+from deerflow.config.guardrails_config import GuardrailsConfig
+from deerflow.config.memory_config import MemoryConfig
 from deerflow.config.model_config import ModelConfig
 from deerflow.config.run_events_config import RunEventsConfig
 from deerflow.config.sandbox_config import SandboxConfig
 from deerflow.config.skill_evolution_config import SkillEvolutionConfig
 from deerflow.config.skills_config import SkillsConfig
-from deerflow.config.stream_bridge_config import StreamBridgeConfig, load_stream_bridge_config_from_dict
-from deerflow.config.subagents_config import SubagentsAppConfig, load_subagents_config_from_dict
-from deerflow.config.summarization_config import SummarizationConfig, load_summarization_config_from_dict
-from deerflow.config.title_config import TitleConfig, load_title_config_from_dict
+from deerflow.config.stream_bridge_config import StreamBridgeConfig
+from deerflow.config.subagents_config import SubagentsAppConfig
+from deerflow.config.summarization_config import SummarizationConfig
+from deerflow.config.title_config import TitleConfig
 from deerflow.config.token_usage_config import TokenUsageConfig
 from deerflow.config.tool_config import ToolConfig, ToolGroupConfig
-from deerflow.config.tool_search_config import ToolSearchConfig, load_tool_search_config_from_dict
+from deerflow.config.tool_search_config import ToolSearchConfig
 
 load_dotenv()
 
@@ -73,11 +74,12 @@ class AppConfig(BaseModel):
     subagents: SubagentsAppConfig = Field(default_factory=SubagentsAppConfig, description="Subagent runtime configuration")
     guardrails: GuardrailsConfig = Field(default_factory=GuardrailsConfig, description="Guardrail middleware configuration")
     circuit_breaker: CircuitBreakerConfig = Field(default_factory=CircuitBreakerConfig, description="LLM circuit breaker configuration")
-    model_config = ConfigDict(extra="allow", frozen=False)
     database: DatabaseConfig = Field(default_factory=DatabaseConfig, description="Unified database backend configuration")
     run_events: RunEventsConfig = Field(default_factory=RunEventsConfig, description="Run event storage configuration")
+    model_config = ConfigDict(extra="allow", frozen=True)
     checkpointer: CheckpointerConfig | None = Field(default=None, description="Checkpointer configuration")
     stream_bridge: StreamBridgeConfig | None = Field(default=None, description="Stream bridge configuration")
+    acp_agents: dict[str, ACPAgentConfig] = Field(default_factory=dict, description="ACP agent configurations keyed by agent name")
 
     @classmethod
     def resolve_config_path(cls, config_path: str | None = None) -> Path:
@@ -126,49 +128,6 @@ class AppConfig(BaseModel):
         config_data = cls.resolve_env_variables(config_data)
         cls._apply_database_defaults(config_data)
 
-        # Load title config if present
-        if "title" in config_data:
-            load_title_config_from_dict(config_data["title"])
-
-        # Load summarization config if present
-        if "summarization" in config_data:
-            load_summarization_config_from_dict(config_data["summarization"])
-
-        # Load memory config if present
-        if "memory" in config_data:
-            load_memory_config_from_dict(config_data["memory"])
-
-        # Always refresh agents API config so removed config sections reset
-        # singleton-backed state to its default/disabled values on reload.
-        load_agents_api_config_from_dict(config_data.get("agents_api") or {})
-
-        # Load subagents config if present
-        if "subagents" in config_data:
-            load_subagents_config_from_dict(config_data["subagents"])
-
-        # Load tool_search config if present
-        if "tool_search" in config_data:
-            load_tool_search_config_from_dict(config_data["tool_search"])
-
-        # Load guardrails config if present
-        if "guardrails" in config_data:
-            load_guardrails_config_from_dict(config_data["guardrails"])
-
-        # Load circuit_breaker config if present
-        if "circuit_breaker" in config_data:
-            config_data["circuit_breaker"] = config_data["circuit_breaker"]
-
-        # Load checkpointer config if present
-        if "checkpointer" in config_data:
-            load_checkpointer_config_from_dict(config_data["checkpointer"])
-
-        # Load stream bridge config if present
-        if "stream_bridge" in config_data:
-            load_stream_bridge_config_from_dict(config_data["stream_bridge"])
-
-        # Always refresh ACP agent config so removed entries do not linger across reloads.
-        load_acp_config_from_dict(config_data.get("acp_agents", {}))
-
         # Load extensions config separately (it's in a different file)
         extensions_config = ExtensionsConfig.from_file()
         config_data["extensions"] = extensions_config.model_dump()
@@ -291,130 +250,8 @@ class AppConfig(BaseModel):
         """
         return next((group for group in self.tool_groups if group.name == name), None)
 
-
-_app_config: AppConfig | None = None
-_app_config_path: Path | None = None
-_app_config_mtime: float | None = None
-_app_config_is_custom = False
-_current_app_config: ContextVar[AppConfig | None] = ContextVar("deerflow_current_app_config", default=None)
-_current_app_config_stack: ContextVar[tuple[AppConfig | None, ...]] = ContextVar("deerflow_current_app_config_stack", default=())
-
-
-def _get_config_mtime(config_path: Path) -> float | None:
-    """Get the modification time of a config file if it exists."""
-    try:
-        return config_path.stat().st_mtime
-    except OSError:
-        return None
-
-
-def _load_and_cache_app_config(config_path: str | None = None) -> AppConfig:
-    """Load config from disk and refresh cache metadata."""
-    global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
-
-    resolved_path = AppConfig.resolve_config_path(config_path)
-    _app_config = AppConfig.from_file(str(resolved_path))
-    _app_config_path = resolved_path
-    _app_config_mtime = _get_config_mtime(resolved_path)
-    _app_config_is_custom = False
-    return _app_config
-
-
-def get_app_config() -> AppConfig:
-    """Get the DeerFlow config instance.
-
-    Returns a cached singleton instance and automatically reloads it when the
-    underlying config file path or modification time changes. Use
-    `reload_app_config()` to force a reload, or `reset_app_config()` to clear
-    the cache.
-    """
-    global _app_config, _app_config_path, _app_config_mtime
-
-    runtime_override = _current_app_config.get()
-    if runtime_override is not None:
-        return runtime_override
-
-    if _app_config is not None and _app_config_is_custom:
-        return _app_config
-
-    resolved_path = AppConfig.resolve_config_path()
-    current_mtime = _get_config_mtime(resolved_path)
-
-    should_reload = _app_config is None or _app_config_path != resolved_path or _app_config_mtime != current_mtime
-    if should_reload:
-        if _app_config_path == resolved_path and _app_config_mtime is not None and current_mtime is not None and _app_config_mtime != current_mtime:
-            logger.info(
-                "Config file has been modified (mtime: %s -> %s), reloading AppConfig",
-                _app_config_mtime,
-                current_mtime,
-            )
-        _load_and_cache_app_config(str(resolved_path))
-    return _app_config
-
-
-def reload_app_config(config_path: str | None = None) -> AppConfig:
-    """Reload the config from file and update the cached instance.
-
-    This is useful when the config file has been modified and you want
-    to pick up the changes without restarting the application.
-
-    Args:
-        config_path: Optional path to config file. If not provided,
-                     uses the default resolution strategy.
-
-    Returns:
-        The newly loaded AppConfig instance.
-    """
-    return _load_and_cache_app_config(config_path)
-
-
-def reset_app_config() -> None:
-    """Reset the cached config instance.
-
-    This clears the singleton cache, causing the next call to
-    `get_app_config()` to reload from file. Useful for testing
-    or when switching between different configurations.
-    """
-    global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
-    _app_config = None
-    _app_config_path = None
-    _app_config_mtime = None
-    _app_config_is_custom = False
-
-
-def set_app_config(config: AppConfig) -> None:
-    """Set a custom config instance.
-
-    This allows injecting a custom or mock config for testing purposes.
-
-    Args:
-        config: The AppConfig instance to use.
-    """
-    global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
-    _app_config = config
-    _app_config_path = None
-    _app_config_mtime = None
-    _app_config_is_custom = True
-
-
-def peek_current_app_config() -> AppConfig | None:
-    """Return the runtime-scoped AppConfig override, if one is active."""
-    return _current_app_config.get()
-
-
-def push_current_app_config(config: AppConfig) -> None:
-    """Push a runtime-scoped AppConfig override for the current execution context."""
-    stack = _current_app_config_stack.get()
-    _current_app_config_stack.set(stack + (_current_app_config.get(),))
-    _current_app_config.set(config)
-
-
-def pop_current_app_config() -> None:
-    """Pop the latest runtime-scoped AppConfig override for the current execution context."""
-    stack = _current_app_config_stack.get()
-    if not stack:
-        _current_app_config.set(None)
-        return
-    previous = stack[-1]
-    _current_app_config_stack.set(stack[:-1])
-    _current_app_config.set(previous)
+    # AppConfig is a pure value object: construct with ``from_file()``, pass around.
+    # Composition roots that hold the resolved instance:
+    #   - Gateway:   ``app.state.config`` via ``Depends(get_config)``
+    #   - Client:    ``DeerFlowClient._app_config``
+    #   - Agent run: ``Runtime[DeerFlowContext].context.app_config``

backend/packages/harness/deerflow/config/checkpointer_config.py

@@ -2,7 +2,7 @@
 
 from typing import Literal
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 CheckpointerType = Literal["memory", "sqlite", "postgres"]
 
@@ -10,6 +10,8 @@ CheckpointerType = Literal["memory", "sqlite", "postgres"]
 class CheckpointerConfig(BaseModel):
     """Configuration for LangGraph state persistence checkpointer."""
 
+    model_config = ConfigDict(frozen=True)
+
     type: CheckpointerType = Field(
         description="Checkpointer backend type. "
         "'memory' is in-process only (lost on restart). "
@@ -23,24 +25,3 @@ class CheckpointerConfig(BaseModel):
         "For sqlite, use a file path like '.deer-flow/checkpoints.db' or ':memory:' for in-memory. "
         "For postgres, use a DSN like 'postgresql://user:pass@localhost:5432/db'.",
     )
-
-
-# Global configuration instance — None means no checkpointer is configured.
-_checkpointer_config: CheckpointerConfig | None = None
-
-
-def get_checkpointer_config() -> CheckpointerConfig | None:
-    """Get the current checkpointer configuration, or None if not configured."""
-    return _checkpointer_config
-
-
-def set_checkpointer_config(config: CheckpointerConfig | None) -> None:
-    """Set the checkpointer configuration."""
-    global _checkpointer_config
-    _checkpointer_config = config
-
-
-def load_checkpointer_config_from_dict(config_dict: dict) -> None:
-    """Load checkpointer configuration from a dictionary."""
-    global _checkpointer_config
-    _checkpointer_config = CheckpointerConfig(**config_dict)

backend/packages/harness/deerflow/config/database_config.py

@@ -34,10 +34,11 @@ from __future__ import annotations
 import os
 from typing import Literal
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 
 class DatabaseConfig(BaseModel):
+    model_config = ConfigDict(frozen=True)
     backend: Literal["memory", "sqlite", "postgres"] = Field(
         default="memory",
         description=("Storage backend for both checkpointer and application data. 'memory' for development (no persistence across restarts), 'sqlite' for single-node deployment, 'postgres' for production multi-node deployment."),

backend/packages/harness/deerflow/config/deer_flow_context.py

@@ -0,0 +1,55 @@
+"""Per-invocation context for DeerFlow agent execution.
+
+Injected via LangGraph Runtime. Middleware and tools access this
+via Runtime[DeerFlowContext] parameters, through resolve_context().
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from deerflow.config.app_config import AppConfig
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass(frozen=True)
+class DeerFlowContext:
+    """Typed, immutable, per-invocation context injected via LangGraph Runtime.
+
+    Fields are all known at run start and never change during execution.
+    Mutable runtime state (e.g. sandbox_id) flows through ThreadState, not here.
+    """
+
+    app_config: AppConfig
+    thread_id: str
+    agent_name: str | None = None
+
+
+def resolve_context(runtime: Any) -> DeerFlowContext:
+    """Return the typed DeerFlowContext that the runtime carries.
+
+    Gateway mode (``DeerFlowClient``, ``run_agent``) always attaches a typed
+    ``DeerFlowContext`` via ``agent.astream(context=...)``; the LangGraph
+    Server path uses ``langgraph.json`` registration where the top-level
+    ``make_lead_agent`` loads ``AppConfig`` from disk itself, so we still
+    arrive here with a typed context.
+
+    Only the dict/None shapes that legacy tests used to exercise would fall
+    through this function; we now reject them loudly instead of papering
+    over the missing context with an ambient ``AppConfig`` lookup.
+    """
+    ctx = getattr(runtime, "context", None)
+    if isinstance(ctx, DeerFlowContext):
+        return ctx
+
+    raise RuntimeError(
+        "resolve_context: runtime.context is not a DeerFlowContext "
+        "(got type %s). Every entry point must attach one at invoke time — "
+        "Gateway/Client via agent.astream(context=DeerFlowContext(...)), "
+        "LangGraph Server via the make_lead_agent boundary that loads "
+        "AppConfig.from_file()." % type(ctx).__name__
+    )

backend/packages/harness/deerflow/config/extensions_config.py

@@ -11,6 +11,8 @@ from pydantic import BaseModel, ConfigDict, Field
 class McpOAuthConfig(BaseModel):
     """OAuth configuration for an MCP server (HTTP/SSE transports)."""
 
+    model_config = ConfigDict(extra="allow", frozen=True)
+
     enabled: bool = Field(default=True, description="Whether OAuth token injection is enabled")
     token_url: str = Field(description="OAuth token endpoint URL")
     grant_type: Literal["client_credentials", "refresh_token"] = Field(
@@ -28,12 +30,13 @@ class McpOAuthConfig(BaseModel):
     default_token_type: str = Field(default="Bearer", description="Default token type when missing in token response")
     refresh_skew_seconds: int = Field(default=60, description="Refresh token this many seconds before expiry")
     extra_token_params: dict[str, str] = Field(default_factory=dict, description="Additional form params sent to token endpoint")
-    model_config = ConfigDict(extra="allow")
 
 
 class McpServerConfig(BaseModel):
     """Configuration for a single MCP server."""
 
+    model_config = ConfigDict(extra="allow", frozen=True)
+
     enabled: bool = Field(default=True, description="Whether this MCP server is enabled")
     type: str = Field(default="stdio", description="Transport type: 'stdio', 'sse', or 'http'")
     command: str | None = Field(default=None, description="Command to execute to start the MCP server (for stdio type)")
@@ -43,12 +46,13 @@ class McpServerConfig(BaseModel):
     headers: dict[str, str] = Field(default_factory=dict, description="HTTP headers to send (for sse or http type)")
     oauth: McpOAuthConfig | None = Field(default=None, description="OAuth configuration (for sse or http type)")
     description: str = Field(default="", description="Human-readable description of what this MCP server provides")
-    model_config = ConfigDict(extra="allow")
 
 
 class SkillStateConfig(BaseModel):
     """Configuration for a single skill's state."""
 
+    model_config = ConfigDict(frozen=True)
+
     enabled: bool = Field(default=True, description="Whether this skill is enabled")
 
 
@@ -64,7 +68,7 @@ class ExtensionsConfig(BaseModel):
         default_factory=dict,
         description="Map of skill name to state configuration",
     )
-    model_config = ConfigDict(extra="allow", populate_by_name=True)
+    model_config = ConfigDict(extra="allow", frozen=True, populate_by_name=True)
 
     @classmethod
     def resolve_config_path(cls, config_path: str | None = None) -> Path | None:
@@ -195,62 +199,3 @@ class ExtensionsConfig(BaseModel):
             # Default to enable for public & custom skill
             return skill_category in ("public", "custom")
         return skill_config.enabled
-
-
-_extensions_config: ExtensionsConfig | None = None
-
-
-def get_extensions_config() -> ExtensionsConfig:
-    """Get the extensions config instance.
-
-    Returns a cached singleton instance. Use `reload_extensions_config()` to reload
-    from file, or `reset_extensions_config()` to clear the cache.
-
-    Returns:
-        The cached ExtensionsConfig instance.
-    """
-    global _extensions_config
-    if _extensions_config is None:
-        _extensions_config = ExtensionsConfig.from_file()
-    return _extensions_config
-
-
-def reload_extensions_config(config_path: str | None = None) -> ExtensionsConfig:
-    """Reload the extensions config from file and update the cached instance.
-
-    This is useful when the config file has been modified and you want
-    to pick up the changes without restarting the application.
-
-    Args:
-        config_path: Optional path to extensions config file. If not provided,
-                     uses the default resolution strategy.
-
-    Returns:
-        The newly loaded ExtensionsConfig instance.
-    """
-    global _extensions_config
-    _extensions_config = ExtensionsConfig.from_file(config_path)
-    return _extensions_config
-
-
-def reset_extensions_config() -> None:
-    """Reset the cached extensions config instance.
-
-    This clears the singleton cache, causing the next call to
-    `get_extensions_config()` to reload from file. Useful for testing
-    or when switching between different configurations.
-    """
-    global _extensions_config
-    _extensions_config = None
-
-
-def set_extensions_config(config: ExtensionsConfig) -> None:
-    """Set a custom extensions config instance.
-
-    This allows injecting a custom or mock config for testing purposes.
-
-    Args:
-        config: The ExtensionsConfig instance to use.
-    """
-    global _extensions_config
-    _extensions_config = config

backend/packages/harness/deerflow/config/guardrails_config.py

@@ -1,11 +1,13 @@
 """Configuration for pre-tool-call authorization."""
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 
 class GuardrailProviderConfig(BaseModel):
     """Configuration for a guardrail provider."""
 
+    model_config = ConfigDict(frozen=True)
+
     use: str = Field(description="Class path (e.g. 'deerflow.guardrails.builtin:AllowlistProvider')")
     config: dict = Field(default_factory=dict, description="Provider-specific settings passed as kwargs")
 
@@ -18,31 +20,9 @@ class GuardrailsConfig(BaseModel):
     agent's passport reference, and returns an allow/deny decision.
     """
 
+    model_config = ConfigDict(frozen=True)
+
     enabled: bool = Field(default=False, description="Enable guardrail middleware")
     fail_closed: bool = Field(default=True, description="Block tool calls if provider errors")
     passport: str | None = Field(default=None, description="OAP passport path or hosted agent ID")
     provider: GuardrailProviderConfig | None = Field(default=None, description="Guardrail provider configuration")
-
-
-_guardrails_config: GuardrailsConfig | None = None
-
-
-def get_guardrails_config() -> GuardrailsConfig:
-    """Get the guardrails config, returning defaults if not loaded."""
-    global _guardrails_config
-    if _guardrails_config is None:
-        _guardrails_config = GuardrailsConfig()
-    return _guardrails_config
-
-
-def load_guardrails_config_from_dict(data: dict) -> GuardrailsConfig:
-    """Load guardrails config from a dict (called during AppConfig loading)."""
-    global _guardrails_config
-    _guardrails_config = GuardrailsConfig.model_validate(data)
-    return _guardrails_config
-
-
-def reset_guardrails_config() -> None:
-    """Reset the cached config instance. Used in tests to prevent singleton leaks."""
-    global _guardrails_config
-    _guardrails_config = None

backend/packages/harness/deerflow/config/memory_config.py

@@ -1,11 +1,13 @@
 """Configuration for memory mechanism."""
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 
 class MemoryConfig(BaseModel):
     """Configuration for global memory mechanism."""
 
+    model_config = ConfigDict(frozen=True)
+
     enabled: bool = Field(
         default=True,
         description="Whether to enable memory mechanism",
@@ -60,24 +62,3 @@ class MemoryConfig(BaseModel):
         le=8000,
         description="Maximum tokens to use for memory injection",
     )
-
-
-# Global configuration instance
-_memory_config: MemoryConfig = MemoryConfig()
-
-
-def get_memory_config() -> MemoryConfig:
-    """Get the current memory configuration."""
-    return _memory_config
-
-
-def set_memory_config(config: MemoryConfig) -> None:
-    """Set the memory configuration."""
-    global _memory_config
-    _memory_config = config
-
-
-def load_memory_config_from_dict(config_dict: dict) -> None:
-    """Load memory configuration from a dictionary."""
-    global _memory_config
-    _memory_config = MemoryConfig(**config_dict)

backend/packages/harness/deerflow/config/model_config.py

@@ -12,7 +12,7 @@ class ModelConfig(BaseModel):
         description="Class path of the model provider(e.g. langchain_openai.ChatOpenAI)",
     )
     model: str = Field(..., description="Model name")
-    model_config = ConfigDict(extra="allow")
+    model_config = ConfigDict(extra="allow", frozen=True)
     use_responses_api: bool | None = Field(
         default=None,
         description="Whether to route OpenAI ChatOpenAI calls through the /v1/responses API",

backend/packages/harness/deerflow/config/run_events_config.py

@@ -15,10 +15,11 @@ from __future__ import annotations
 
 from typing import Literal
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 
 class RunEventsConfig(BaseModel):
+    model_config = ConfigDict(frozen=True)
     backend: Literal["memory", "db", "jsonl"] = Field(
         default="memory",
         description="Storage backend for run events. 'memory' for development (no persistence), 'db' for production (SQL queries), 'jsonl' for lightweight single-node persistence.",

backend/packages/harness/deerflow/config/sandbox_config.py

@@ -4,6 +4,8 @@ from pydantic import BaseModel, ConfigDict, Field
 class VolumeMountConfig(BaseModel):
     """Configuration for a volume mount."""
 
+    model_config = ConfigDict(frozen=True)
+
     host_path: str = Field(..., description="Path on the host machine")
     container_path: str = Field(..., description="Path inside the container")
     read_only: bool = Field(default=False, description="Whether the mount is read-only")
@@ -80,4 +82,4 @@ class SandboxConfig(BaseModel):
         description="Maximum characters to keep from ls tool output. Output exceeding this limit is head-truncated. Set to 0 to disable truncation.",
     )
 
-    model_config = ConfigDict(extra="allow")
+    model_config = ConfigDict(extra="allow", frozen=True)

backend/packages/harness/deerflow/config/skill_evolution_config.py

@@ -1,9 +1,11 @@
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 
 class SkillEvolutionConfig(BaseModel):
     """Configuration for agent-managed skill evolution."""
 
+    model_config = ConfigDict(frozen=True)
+
     enabled: bool = Field(
         default=False,
         description="Whether the agent can create and modify skills under skills/custom.",

backend/packages/harness/deerflow/config/skills_config.py

@@ -1,6 +1,6 @@
 from pathlib import Path
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 
 def _default_repo_root() -> Path:
@@ -11,6 +11,8 @@ def _default_repo_root() -> Path:
 class SkillsConfig(BaseModel):
     """Configuration for skills system"""
 
+    model_config = ConfigDict(frozen=True)
+
     path: str | None = Field(
         default=None,
         description="Path to skills directory. If not specified, defaults to ../skills relative to backend directory",

backend/packages/harness/deerflow/config/stream_bridge_config.py

@@ -2,7 +2,7 @@
 
 from typing import Literal
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 StreamBridgeType = Literal["memory", "redis"]
 
@@ -10,6 +10,8 @@ StreamBridgeType = Literal["memory", "redis"]
 class StreamBridgeConfig(BaseModel):
     """Configuration for the stream bridge that connects agent workers to SSE endpoints."""
 
+    model_config = ConfigDict(frozen=True)
+
     type: StreamBridgeType = Field(
         default="memory",
         description="Stream bridge backend type. 'memory' uses in-process asyncio.Queue (single-process only). 'redis' uses Redis Streams (planned for Phase 2, not yet implemented).",
@@ -22,25 +24,3 @@ class StreamBridgeConfig(BaseModel):
         default=256,
         description="Maximum number of events buffered per run in the memory bridge.",
     )
-
-
-# Global configuration instance — None means no stream bridge is configured
-# (falls back to memory with defaults).
-_stream_bridge_config: StreamBridgeConfig | None = None
-
-
-def get_stream_bridge_config() -> StreamBridgeConfig | None:
-    """Get the current stream bridge configuration, or None if not configured."""
-    return _stream_bridge_config
-
-
-def set_stream_bridge_config(config: StreamBridgeConfig | None) -> None:
-    """Set the stream bridge configuration."""
-    global _stream_bridge_config
-    _stream_bridge_config = config
-
-
-def load_stream_bridge_config_from_dict(config_dict: dict) -> None:
-    """Load stream bridge configuration from a dictionary."""
-    global _stream_bridge_config
-    _stream_bridge_config = StreamBridgeConfig(**config_dict)

backend/packages/harness/deerflow/config/subagents_config.py

@@ -1,15 +1,13 @@
 """Configuration for the subagent system loaded from config.yaml."""
 
-import logging
-
-from pydantic import BaseModel, Field
-
-logger = logging.getLogger(__name__)
+from pydantic import BaseModel, ConfigDict, Field
 
 
 class SubagentOverrideConfig(BaseModel):
     """Per-agent configuration overrides."""
 
+    model_config = ConfigDict(frozen=True)
+
     timeout_seconds: int | None = Field(
         default=None,
         ge=1,
@@ -71,6 +69,8 @@ class CustomSubagentConfig(BaseModel):
 class SubagentsAppConfig(BaseModel):
     """Configuration for the subagent system."""
 
+    model_config = ConfigDict(frozen=True)
+
     timeout_seconds: int = Field(
         default=900,
         ge=1,
@@ -140,48 +140,3 @@ class SubagentsAppConfig(BaseModel):
         if override is not None and override.skills is not None:
             return override.skills
         return None
-
-
-_subagents_config: SubagentsAppConfig = SubagentsAppConfig()
-
-
-def get_subagents_app_config() -> SubagentsAppConfig:
-    """Get the current subagents configuration."""
-    return _subagents_config
-
-
-def load_subagents_config_from_dict(config_dict: dict) -> None:
-    """Load subagents configuration from a dictionary."""
-    global _subagents_config
-    _subagents_config = SubagentsAppConfig(**config_dict)
-
-    overrides_summary = {}
-    for name, override in _subagents_config.agents.items():
-        parts = []
-        if override.timeout_seconds is not None:
-            parts.append(f"timeout={override.timeout_seconds}s")
-        if override.max_turns is not None:
-            parts.append(f"max_turns={override.max_turns}")
-        if override.model is not None:
-            parts.append(f"model={override.model}")
-        if override.skills is not None:
-            parts.append(f"skills={override.skills}")
-        if parts:
-            overrides_summary[name] = ", ".join(parts)
-
-    custom_agents_names = list(_subagents_config.custom_agents.keys())
-
-    if overrides_summary or custom_agents_names:
-        logger.info(
-            "Subagents config loaded: default timeout=%ss, default max_turns=%s, per-agent overrides=%s, custom_agents=%s",
-            _subagents_config.timeout_seconds,
-            _subagents_config.max_turns,
-            overrides_summary or "none",
-            custom_agents_names or "none",
-        )
-    else:
-        logger.info(
-            "Subagents config loaded: default timeout=%ss, default max_turns=%s, no per-agent overrides",
-            _subagents_config.timeout_seconds,
-            _subagents_config.max_turns,
-        )

backend/packages/harness/deerflow/config/summarization_config.py

@@ -2,7 +2,7 @@
 
 from typing import Literal
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 ContextSizeType = Literal["fraction", "tokens", "messages"]
 
@@ -10,6 +10,8 @@ ContextSizeType = Literal["fraction", "tokens", "messages"]
 class ContextSize(BaseModel):
     """Context size specification for trigger or keep parameters."""
 
+    model_config = ConfigDict(frozen=True)
+
     type: ContextSizeType = Field(description="Type of context size specification")
     value: int | float = Field(description="Value for the context size specification")
 
@@ -21,6 +23,8 @@ class ContextSize(BaseModel):
 class SummarizationConfig(BaseModel):
     """Configuration for automatic conversation summarization."""
 
+    model_config = ConfigDict(frozen=True)
+
     enabled: bool = Field(
         default=False,
         description="Whether to enable automatic conversation summarization",
@@ -70,24 +74,3 @@ class SummarizationConfig(BaseModel):
         default_factory=lambda: ["read_file", "read", "view", "cat"],
         description="Tool names treated as skill file reads when preserving recently-loaded skills across summarization.",
     )
-
-
-# Global configuration instance
-_summarization_config: SummarizationConfig = SummarizationConfig()
-
-
-def get_summarization_config() -> SummarizationConfig:
-    """Get the current summarization configuration."""
-    return _summarization_config
-
-
-def set_summarization_config(config: SummarizationConfig) -> None:
-    """Set the summarization configuration."""
-    global _summarization_config
-    _summarization_config = config
-
-
-def load_summarization_config_from_dict(config_dict: dict) -> None:
-    """Load summarization configuration from a dictionary."""
-    global _summarization_config
-    _summarization_config = SummarizationConfig(**config_dict)

backend/packages/harness/deerflow/config/title_config.py

@@ -1,11 +1,13 @@
 """Configuration for automatic thread title generation."""
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 
 class TitleConfig(BaseModel):
     """Configuration for automatic thread title generation."""
 
+    model_config = ConfigDict(frozen=True)
+
     enabled: bool = Field(
         default=True,
         description="Whether to enable automatic title generation",
@@ -30,24 +32,3 @@ class TitleConfig(BaseModel):
         default=("Generate a concise title (max {max_words} words) for this conversation.\nUser: {user_msg}\nAssistant: {assistant_msg}\n\nReturn ONLY the title, no quotes, no explanation."),
         description="Prompt template for title generation",
     )
-
-
-# Global configuration instance
-_title_config: TitleConfig = TitleConfig()
-
-
-def get_title_config() -> TitleConfig:
-    """Get the current title configuration."""
-    return _title_config
-
-
-def set_title_config(config: TitleConfig) -> None:
-    """Set the title configuration."""
-    global _title_config
-    _title_config = config
-
-
-def load_title_config_from_dict(config_dict: dict) -> None:
-    """Load title configuration from a dictionary."""
-    global _title_config
-    _title_config = TitleConfig(**config_dict)

backend/packages/harness/deerflow/config/token_usage_config.py

@@ -1,7 +1,9 @@
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 
 class TokenUsageConfig(BaseModel):
     """Configuration for token usage tracking."""
 
+    model_config = ConfigDict(frozen=True)
+
     enabled: bool = Field(default=False, description="Enable token usage tracking middleware")

backend/packages/harness/deerflow/config/tool_config.py

@@ -5,7 +5,7 @@ class ToolGroupConfig(BaseModel):
     """Config section for a tool group"""
 
     name: str = Field(..., description="Unique name for the tool group")
-    model_config = ConfigDict(extra="allow")
+    model_config = ConfigDict(extra="allow", frozen=True)
 
 
 class ToolConfig(BaseModel):
@@ -17,4 +17,4 @@ class ToolConfig(BaseModel):
         ...,
         description="Variable name of the tool provider(e.g. deerflow.sandbox.tools:bash_tool)",
     )
-    model_config = ConfigDict(extra="allow")
+    model_config = ConfigDict(extra="allow", frozen=True)

backend/packages/harness/deerflow/config/tool_search_config.py

@@ -1,6 +1,6 @@
 """Configuration for deferred tool loading via tool_search."""
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 
 class ToolSearchConfig(BaseModel):
@@ -11,25 +11,9 @@ class ToolSearchConfig(BaseModel):
     via the tool_search tool at runtime.
     """
 
+    model_config = ConfigDict(frozen=True)
+
     enabled: bool = Field(
         default=False,
         description="Defer tools and enable tool_search",
     )
-
-
-_tool_search_config: ToolSearchConfig | None = None
-
-
-def get_tool_search_config() -> ToolSearchConfig:
-    """Get the tool search config, loading from AppConfig if needed."""
-    global _tool_search_config
-    if _tool_search_config is None:
-        _tool_search_config = ToolSearchConfig()
-    return _tool_search_config
-
-
-def load_tool_search_config_from_dict(data: dict) -> ToolSearchConfig:
-    """Load tool search config from a dict (called during AppConfig loading)."""
-    global _tool_search_config
-    _tool_search_config = ToolSearchConfig.model_validate(data)
-    return _tool_search_config

backend/packages/harness/deerflow/config/tracing_config.py

@@ -1,7 +1,7 @@
 import os
 import threading
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 _config_lock = threading.Lock()
 
@@ -9,6 +9,8 @@ _config_lock = threading.Lock()
 class LangSmithTracingConfig(BaseModel):
     """Configuration for LangSmith tracing."""
 
+    model_config = ConfigDict(frozen=True)
+
     enabled: bool = Field(...)
     api_key: str | None = Field(...)
     project: str = Field(...)
@@ -26,6 +28,8 @@ class LangSmithTracingConfig(BaseModel):
 class LangfuseTracingConfig(BaseModel):
     """Configuration for Langfuse tracing."""
 
+    model_config = ConfigDict(frozen=True)
+
     enabled: bool = Field(...)
     public_key: str | None = Field(...)
     secret_key: str | None = Field(...)
@@ -50,6 +54,8 @@ class LangfuseTracingConfig(BaseModel):
 class TracingConfig(BaseModel):
     """Tracing configuration for supported providers."""
 
+    model_config = ConfigDict(frozen=True)
+
     langsmith: LangSmithTracingConfig = Field(...)
     langfuse: LangfuseTracingConfig = Field(...)
 

backend/packages/harness/deerflow/models/factory.py

@@ -2,7 +2,7 @@ import logging
 
 from langchain.chat_models import BaseChatModel
 
-from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
 from deerflow.reflection import resolve_class
 from deerflow.tracing import build_tracing_callbacks
 
@@ -46,16 +46,23 @@ def _enable_stream_usage_by_default(model_use_path: str, model_settings_from_con
         model_settings_from_config["stream_usage"] = True
 
 
-def create_chat_model(name: str | None = None, thinking_enabled: bool = False, **kwargs) -> BaseChatModel:
+def create_chat_model(
+    name: str | None = None,
+    thinking_enabled: bool = False,
+    *,
+    app_config: "AppConfig",
+    **kwargs,
+) -> BaseChatModel:
     """Create a chat model instance from the config.
 
     Args:
         name: The name of the model to create. If None, the first model in the config will be used.
+        app_config: Application config — required.
 
     Returns:
         A chat model instance.
     """
-    config = get_app_config()
+    config = app_config
     if name is None:
         name = config.models[0].name
     model_config = config.get_model_config(name)

backend/packages/harness/deerflow/persistence/feedback/model.py

@@ -13,7 +13,9 @@ from deerflow.persistence.base import Base
 class FeedbackRow(Base):
     __tablename__ = "feedback"
 
-    __table_args__ = (UniqueConstraint("thread_id", "run_id", "user_id", name="uq_feedback_thread_run_user"),)
+    __table_args__ = (
+        UniqueConstraint("thread_id", "run_id", "user_id", name="uq_feedback_thread_run_user"),
+    )
 
     feedback_id: Mapped[str] = mapped_column(String(64), primary_key=True)
     run_id: Mapped[str] = mapped_column(String(64), nullable=False, index=True)

backend/packages/harness/deerflow/persistence/migrations/env.py

@@ -18,9 +18,7 @@ from deerflow.persistence.base import Base
 
 # Import all models so metadata is populated.
 try:
-    import deerflow.persistence.models as models  # register ORM models with Base.metadata
-
-    _ = models
+    import deerflow.persistence.models  # noqa: F401 — register ORM models with Base.metadata
 except ImportError:
     # Models not available — migration will work with existing metadata only.
     logging.getLogger(__name__).warning("Could not import deerflow.persistence.models; Alembic may not detect all tables")

backend/packages/harness/deerflow/runtime/checkpointer/async_provider.py

@@ -24,7 +24,7 @@ from collections.abc import AsyncIterator
 
 from langgraph.types import Checkpointer
 
-from deerflow.config.app_config import get_app_config
+from deerflow.config.app_config import AppConfig
 from deerflow.runtime.checkpointer.provider import (
     POSTGRES_CONN_REQUIRED,
     POSTGRES_INSTALL,
@@ -123,11 +123,11 @@ async def _async_checkpointer_from_database(db_config) -> AsyncIterator[Checkpoi
 
 
 @contextlib.asynccontextmanager
-async def make_checkpointer() -> AsyncIterator[Checkpointer]:
+async def make_checkpointer(app_config: AppConfig) -> AsyncIterator[Checkpointer]:
     """Async context manager that yields a checkpointer for the caller's lifetime.
     Resources are opened on enter and closed on exit -- no global state::
 
-        async with make_checkpointer() as checkpointer:
+        async with make_checkpointer(app_config) as checkpointer:
             app.state.checkpointer = checkpointer
 
     Yields an ``InMemorySaver`` when no checkpointer is configured in *config.yaml*.
@@ -138,16 +138,14 @@ async def make_checkpointer() -> AsyncIterator[Checkpointer]:
     3. Default InMemorySaver
     """
 
-    config = get_app_config()
-
     # Legacy: standalone checkpointer config takes precedence
-    if config.checkpointer is not None:
-        async with _async_checkpointer(config.checkpointer) as saver:
+    if app_config.checkpointer is not None:
+        async with _async_checkpointer(app_config.checkpointer) as saver:
             yield saver
             return
 
     # Unified database config
-    db_config = getattr(config, "database", None)
+    db_config = getattr(app_config, "database", None)
     if db_config is not None and db_config.backend != "memory":
         async with _async_checkpointer_from_database(db_config) as saver:
             yield saver

backend/packages/harness/deerflow/runtime/checkpointer/provider.py

@@ -25,7 +25,7 @@ from collections.abc import Iterator
 
 from langgraph.types import Checkpointer
 
-from deerflow.config.app_config import get_app_config
+from deerflow.config.app_config import AppConfig
 from deerflow.config.checkpointer_config import CheckpointerConfig
 from deerflow.runtime.store._sqlite_utils import ensure_sqlite_parent_dir, resolve_sqlite_conn_str
 
@@ -100,10 +100,13 @@ _checkpointer: Checkpointer | None = None
 _checkpointer_ctx = None  # open context manager keeping the connection alive
 
 
-def get_checkpointer() -> Checkpointer:
+def get_checkpointer(app_config: AppConfig) -> Checkpointer:
     """Return the global sync checkpointer singleton, creating it on first call.
 
-    Returns an ``InMemorySaver`` when no checkpointer is configured in *config.yaml*.
+    Returns an ``InMemorySaver`` only when ``checkpointer`` is explicitly
+    absent from config.yaml. Any other failure (missing config, invalid
+    backend, connection error) propagates — silent degradation to in-memory
+    would drop persistent-run state on process restart.
 
     Raises:
         ImportError: If the required package for the configured backend is not installed.
@@ -114,25 +117,7 @@ def get_checkpointer() -> Checkpointer:
     if _checkpointer is not None:
         return _checkpointer
 
-    # Ensure app config is loaded before checking checkpointer config
-    # This prevents returning InMemorySaver when config.yaml actually has a checkpointer section
-    # but hasn't been loaded yet
-    from deerflow.config.app_config import _app_config
-    from deerflow.config.checkpointer_config import get_checkpointer_config
-
-    config = get_checkpointer_config()
-
-    if config is None and _app_config is None:
-        # Only load app config lazily when neither the app config nor an explicit
-        # checkpointer config has been initialized yet. This keeps tests that
-        # intentionally set the global checkpointer config isolated from any
-        # ambient config.yaml on disk.
-        try:
-            get_app_config()
-        except FileNotFoundError:
-            # In test environments without config.yaml, this is expected.
-            pass
-        config = get_checkpointer_config()
+    config = app_config.checkpointer
     if config is None:
         from langgraph.checkpoint.memory import InMemorySaver
 
@@ -168,25 +153,23 @@ def reset_checkpointer() -> None:
 
 
 @contextlib.contextmanager
-def checkpointer_context() -> Iterator[Checkpointer]:
+def checkpointer_context(app_config: AppConfig) -> Iterator[Checkpointer]:
     """Sync context manager that yields a checkpointer and cleans up on exit.
 
     Unlike :func:`get_checkpointer`, this does **not** cache the instance —
     each ``with`` block creates and destroys its own connection.  Use it in
     CLI scripts or tests where you want deterministic cleanup::
 
-        with checkpointer_context() as cp:
+        with checkpointer_context(app_config) as cp:
             graph.invoke(input, config={"configurable": {"thread_id": "1"}})
 
     Yields an ``InMemorySaver`` when no checkpointer is configured in *config.yaml*.
     """
-
-    config = get_app_config()
-    if config.checkpointer is None:
+    if app_config.checkpointer is None:
         from langgraph.checkpoint.memory import InMemorySaver
 
         yield InMemorySaver()
         return
 
-    with _sync_checkpointer_cm(config.checkpointer) as saver:
+    with _sync_checkpointer_cm(app_config.checkpointer) as saver:
         yield saver

backend/packages/harness/deerflow/runtime/journal.py

@@ -6,10 +6,7 @@ handles token usage accumulation.
 
 Key design decisions:
 - on_llm_new_token is NOT implemented -- only complete messages via on_llm_end
-- on_chat_model_start captures structured prompts as llm_request (OpenAI format) and
-  extracts the first human message for run.input, because it is more reliable than
-  on_chain_start (fires on every node) — messages here are fully structured.
-- on_chain_start with parent_run_id=None emits a run.start trace marking root invocation.
+- on_chat_model_start captures structured prompts as llm_request (OpenAI format)
 - on_llm_end emits llm_response in OpenAI Chat Completions format
 - Token usage accumulated in memory, written to RunRow on run completion
 - Caller identification via tags injection (lead_agent / subagent:{name} / middleware:{name})
@@ -21,12 +18,10 @@ import asyncio
 import logging
 import time
 from datetime import UTC, datetime
-from typing import TYPE_CHECKING, Any, cast
+from typing import TYPE_CHECKING, Any
 from uuid import UUID
 
 from langchain_core.callbacks import BaseCallbackHandler
-from langchain_core.messages import AnyMessage, BaseMessage, HumanMessage, ToolMessage
-from langgraph.types import Command
 
 if TYPE_CHECKING:
     from deerflow.runtime.events.store.base import RunEventStore
@@ -77,39 +72,34 @@ class RunJournal(BaseCallbackHandler):
         # LLM request/response tracking
         self._llm_call_index = 0
         self._cached_prompts: dict[str, list[dict]] = {}  # langchain run_id -> OpenAI messages
+        self._cached_models: dict[str, str] = {}  # langchain run_id -> model name
+
+        # Tool call ID cache
+        self._tool_call_ids: dict[str, str] = {}  # langchain run_id -> tool_call_id
 
     # -- Lifecycle callbacks --
 
-    def on_chain_start(
-        self,
-        serialized: dict[str, Any],
-        inputs: dict[str, Any],
-        *,
-        run_id: UUID,
-        parent_run_id: UUID | None = None,
-        tags: list[str] | None = None,
-        metadata: dict[str, Any] | None = None,
-        **kwargs: Any,
-    ) -> None:
-        caller = self._identify_caller(tags)
-        if parent_run_id is None:
-            # Root graph invocation — emit a single trace event for the run start.
-            chain_name = (serialized or {}).get("name", "unknown")
-            self._put(
-                event_type="run.start",
-                category="trace",
-                content={"chain": chain_name},
-                metadata={"caller": caller, **(metadata or {})},
-            )
+    def on_chain_start(self, serialized: dict, inputs: Any, *, run_id: UUID, **kwargs: Any) -> None:
+        if kwargs.get("parent_run_id") is not None:
+            return
+        self._put(
+            event_type="run_start",
+            category="lifecycle",
+            metadata={"input_preview": str(inputs)[:500]},
+        )
 
     def on_chain_end(self, outputs: Any, *, run_id: UUID, **kwargs: Any) -> None:
-        self._put(event_type="run.end", category="outputs", content=outputs, metadata={"status": "success"})
+        if kwargs.get("parent_run_id") is not None:
+            return
+        self._put(event_type="run_end", category="lifecycle", metadata={"status": "success"})
         self._flush_sync()
 
     def on_chain_error(self, error: BaseException, *, run_id: UUID, **kwargs: Any) -> None:
+        if kwargs.get("parent_run_id") is not None:
+            return
         self._put(
-            event_type="run.error",
-            category="error",
+            event_type="run_error",
+            category="lifecycle",
             content=str(error),
             metadata={"error_type": type(error).__name__},
         )
@@ -117,132 +107,266 @@ class RunJournal(BaseCallbackHandler):
 
     # -- LLM callbacks --
 
-    def on_chat_model_start(
-        self,
-        serialized: dict,
-        messages: list[list[BaseMessage]],
-        *,
-        run_id: UUID,
-        tags: list[str] | None = None,
-        **kwargs: Any,
-    ) -> None:
-        """Capture structured prompt messages for llm_request event.
+    def on_chat_model_start(self, serialized: dict, messages: list[list], *, run_id: UUID, **kwargs: Any) -> None:
+        """Capture structured prompt messages for llm_request event."""
+        from deerflow.runtime.converters import langchain_messages_to_openai
 
-        This is also the canonical place to extract the first human message:
-        messages are fully structured here, it fires only on real LLM calls,
-        and the content is never compressed by checkpoint trimming.
-        """
         rid = str(run_id)
         self._llm_start_times[rid] = time.monotonic()
         self._llm_call_index += 1
-        # Mark this run_id as seen so on_llm_end knows not to increment again.
-        self._cached_prompts[rid] = []
 
-        logger.info(f"on_chat_model_start {run_id}: tags={tags} serialized={serialized} messages={messages}")
+        model_name = serialized.get("name", "")
+        self._cached_models[rid] = model_name
 
-        # Capture the first human message sent to any LLM in this run.
-        if not self._first_human_msg and not messages:
-            for batch in messages.reversed():
-                for m in batch.reversed():
-                    if isinstance(m, HumanMessage) and m.name != "summary":
-                        caller = self._identify_caller(tags)
-                        self.set_first_human_message(m.text)
-                        self._put(
-                            event_type="llm.human.input",
-                            category="message",
-                            content=m.model_dump(),
-                            metadata={"caller": caller},
-                        )
-                        break
-                if self._first_human_msg:
-                    break
+        # Convert the first message list (LangChain passes list-of-lists)
+        prompt_msgs = messages[0] if messages else []
+        openai_msgs = langchain_messages_to_openai(prompt_msgs)
+        self._cached_prompts[rid] = openai_msgs
 
-    def on_llm_start(self, serialized: dict, prompts: list[str], *, run_id: UUID, parent_run_id: UUID | None = None, tags: list[str] | None = None, metadata: dict[str, Any] | None = None, **kwargs: Any) -> None:
+        caller = self._identify_caller(kwargs)
+        self._put(
+            event_type="llm_request",
+            category="trace",
+            content={"model": model_name, "messages": openai_msgs},
+            metadata={"caller": caller, "llm_call_index": self._llm_call_index},
+        )
+
+    def on_llm_start(self, serialized: dict, prompts: list[str], *, run_id: UUID, **kwargs: Any) -> None:
         # Fallback: on_chat_model_start is preferred. This just tracks latency.
         self._llm_start_times[str(run_id)] = time.monotonic()
 
-    def on_llm_end(self, response, *, run_id, parent_run_id, tags, **kwargs) -> None:
-        messages: list[AnyMessage] = []
-        logger.info(f"on_llm_end {run_id}: response: {tags} {kwargs}")
-        for generation in response.generations:
-            for gen in generation:
-                if hasattr(gen, "message"):
-                    messages.append(gen.message)
-                else:
-                    logger.warning(f"on_llm_end {run_id}: generation has no message attribute: {gen}")
+    def on_llm_end(self, response: Any, *, run_id: UUID, **kwargs: Any) -> None:
+        from deerflow.runtime.converters import langchain_to_openai_completion
 
-        for message in messages:
-            caller = self._identify_caller(tags)
+        try:
+            message = response.generations[0][0].message
+        except (IndexError, AttributeError):
+            logger.debug("on_llm_end: could not extract message from response")
+            return
 
-            # Latency
-            rid = str(run_id)
-            start = self._llm_start_times.pop(rid, None)
-            latency_ms = int((time.monotonic() - start) * 1000) if start else None
+        caller = self._identify_caller(kwargs)
 
-            # Token usage from message
-            usage = getattr(message, "usage_metadata", None)
-            usage_dict = dict(usage) if usage else {}
+        # Latency
+        rid = str(run_id)
+        start = self._llm_start_times.pop(rid, None)
+        latency_ms = int((time.monotonic() - start) * 1000) if start else None
 
-            # Resolve call index
+        # Token usage from message
+        usage = getattr(message, "usage_metadata", None)
+        usage_dict = dict(usage) if usage else {}
+
+        # Resolve call index
+        call_index = self._llm_call_index
+        if rid not in self._cached_prompts:
+            # Fallback: on_chat_model_start was not called
+            self._llm_call_index += 1
             call_index = self._llm_call_index
-            if rid not in self._cached_prompts:
-                # Fallback: on_chat_model_start was not called
-                self._llm_call_index += 1
-                call_index = self._llm_call_index
 
-            # Trace event: llm_response (OpenAI completion format)
-            self._put(
-                event_type="llm.ai.response",
-                category="message",
-                content=message.model_dump(),
-                metadata={
-                    "caller": caller,
-                    "usage": usage_dict,
-                    "latency_ms": latency_ms,
-                    "llm_call_index": call_index,
-                },
-            )
+        # Clean up caches
+        self._cached_prompts.pop(rid, None)
+        self._cached_models.pop(rid, None)
 
-            # Token accumulation
-            if self._track_tokens:
-                input_tk = usage_dict.get("input_tokens", 0) or 0
-                output_tk = usage_dict.get("output_tokens", 0) or 0
-                total_tk = usage_dict.get("total_tokens", 0) or 0
-                if total_tk == 0:
-                    total_tk = input_tk + output_tk
-                if total_tk > 0:
-                    self._total_input_tokens += input_tk
-                    self._total_output_tokens += output_tk
-                    self._total_tokens += total_tk
-                    self._llm_call_count += 1
+        # Trace event: llm_response (OpenAI completion format)
+        content = getattr(message, "content", "")
+        self._put(
+            event_type="llm_response",
+            category="trace",
+            content=langchain_to_openai_completion(message),
+            metadata={
+                "caller": caller,
+                "usage": usage_dict,
+                "latency_ms": latency_ms,
+                "llm_call_index": call_index,
+            },
+        )
+
+        # Message events: only lead_agent gets message-category events.
+        # Content uses message.model_dump() to align with checkpoint format.
+        tool_calls = getattr(message, "tool_calls", None) or []
+        if caller == "lead_agent":
+            resp_meta = getattr(message, "response_metadata", None) or {}
+            model_name = resp_meta.get("model_name") if isinstance(resp_meta, dict) else None
+            if tool_calls:
+                # ai_tool_call: agent decided to use tools
+                self._put(
+                    event_type="ai_tool_call",
+                    category="message",
+                    content=message.model_dump(),
+                    metadata={"model_name": model_name, "finish_reason": "tool_calls"},
+                )
+            elif isinstance(content, str) and content:
+                # ai_message: final text reply
+                self._put(
+                    event_type="ai_message",
+                    category="message",
+                    content=message.model_dump(),
+                    metadata={"model_name": model_name, "finish_reason": "stop"},
+                )
+                self._last_ai_msg = content
+                self._msg_count += 1
+
+        # Token accumulation
+        if self._track_tokens:
+            input_tk = usage_dict.get("input_tokens", 0) or 0
+            output_tk = usage_dict.get("output_tokens", 0) or 0
+            total_tk = usage_dict.get("total_tokens", 0) or 0
+            if total_tk == 0:
+                total_tk = input_tk + output_tk
+            if total_tk > 0:
+                self._total_input_tokens += input_tk
+                self._total_output_tokens += output_tk
+                self._total_tokens += total_tk
+                self._llm_call_count += 1
+                if caller.startswith("subagent:"):
+                    self._subagent_tokens += total_tk
+                elif caller.startswith("middleware:"):
+                    self._middleware_tokens += total_tk
+                else:
+                    self._lead_agent_tokens += total_tk
 
     def on_llm_error(self, error: BaseException, *, run_id: UUID, **kwargs: Any) -> None:
         self._llm_start_times.pop(str(run_id), None)
-        self._put(event_type="llm.error", category="trace", content=str(error))
+        self._put(event_type="llm_error", category="trace", content=str(error))
 
-    def on_tool_start(self, serialized, input_str, *, run_id, parent_run_id=None, tags=None, metadata=None, inputs=None, **kwargs):
-        """Handle tool start event, cache tool call ID for later correlation"""
-        tool_call_id = str(run_id)
-        logger.info(f"Tool start for node {run_id}, tool_call_id={tool_call_id}, tags={tags}, metadata={metadata}")
+    # -- Tool callbacks --
 
-    def on_tool_end(self, output, *, run_id, parent_run_id=None, **kwargs):
-        """Handle tool end event, append message and clear node data"""
-        try:
-            if isinstance(output, ToolMessage):
-                msg = cast(ToolMessage, output)
-                self._put(event_type="llm.tool.result", category="message", content=msg.model_dump())
-            elif isinstance(output, Command):
-                cmd = cast(Command, output)
-                messages = cmd.update.get("messages", [])
-                for message in messages:
-                    if isinstance(message, BaseMessage):
-                        self._put(event_type="llm.tool.result", category="message", content=message.model_dump())
-                    else:
-                        logger.warning(f"on_tool_end {run_id}: command update message is not BaseMessage: {type(message)}")
-            else:
-                logger.warning(f"on_tool_end {run_id}: output is not ToolMessage: {type(output)}")
-        finally:
-            logger.info(f"Tool end for node {run_id}")
+    def on_tool_start(self, serialized: dict, input_str: str, *, run_id: UUID, **kwargs: Any) -> None:
+        tool_call_id = kwargs.get("tool_call_id")
+        if tool_call_id:
+            self._tool_call_ids[str(run_id)] = tool_call_id
+        self._put(
+            event_type="tool_start",
+            category="trace",
+            metadata={
+                "tool_name": serialized.get("name", ""),
+                "tool_call_id": tool_call_id,
+                "args": str(input_str)[:2000],
+            },
+        )
+
+    def on_tool_end(self, output: Any, *, run_id: UUID, **kwargs: Any) -> None:
+        from langchain_core.messages import ToolMessage
+        from langgraph.types import Command
+
+        # Tools that update graph state return a ``Command`` (e.g.
+        # ``present_files``). LangGraph later unwraps the inner ToolMessage
+        # into checkpoint state, so to stay checkpoint-aligned we must
+        # extract it here rather than storing ``str(Command(...))``.
+        if isinstance(output, Command):
+            update = getattr(output, "update", None) or {}
+            inner_msgs = update.get("messages") if isinstance(update, dict) else None
+            if isinstance(inner_msgs, list):
+                inner_tool_msg = next((m for m in inner_msgs if isinstance(m, ToolMessage)), None)
+                if inner_tool_msg is not None:
+                    output = inner_tool_msg
+
+        # Extract fields from ToolMessage object when LangChain provides one.
+        # LangChain's _format_output wraps tool results into a ToolMessage
+        # with tool_call_id, name, status, and artifact — more complete than
+        # what kwargs alone provides.
+        if isinstance(output, ToolMessage):
+            tool_call_id = output.tool_call_id or kwargs.get("tool_call_id") or self._tool_call_ids.pop(str(run_id), None)
+            tool_name = output.name or kwargs.get("name", "")
+            status = getattr(output, "status", "success") or "success"
+            content_str = output.content if isinstance(output.content, str) else str(output.content)
+            # Use model_dump() for checkpoint-aligned message content.
+            # Override tool_call_id if it was resolved from cache.
+            msg_content = output.model_dump()
+            if msg_content.get("tool_call_id") != tool_call_id:
+                msg_content["tool_call_id"] = tool_call_id
+        else:
+            tool_call_id = kwargs.get("tool_call_id") or self._tool_call_ids.pop(str(run_id), None)
+            tool_name = kwargs.get("name", "")
+            status = "success"
+            content_str = str(output)
+            # Construct checkpoint-aligned dict when output is a plain string.
+            msg_content = ToolMessage(
+                content=content_str,
+                tool_call_id=tool_call_id or "",
+                name=tool_name,
+                status=status,
+            ).model_dump()
+
+        # Trace event (always)
+        self._put(
+            event_type="tool_end",
+            category="trace",
+            content=content_str,
+            metadata={
+                "tool_name": tool_name,
+                "tool_call_id": tool_call_id,
+                "status": status,
+            },
+        )
+
+        # Message event: tool_result (checkpoint-aligned model_dump format)
+        self._put(
+            event_type="tool_result",
+            category="message",
+            content=msg_content,
+            metadata={"tool_name": tool_name, "status": status},
+        )
+
+    def on_tool_error(self, error: BaseException, *, run_id: UUID, **kwargs: Any) -> None:
+        from langchain_core.messages import ToolMessage
+
+        tool_call_id = kwargs.get("tool_call_id") or self._tool_call_ids.pop(str(run_id), None)
+        tool_name = kwargs.get("name", "")
+
+        # Trace event
+        self._put(
+            event_type="tool_error",
+            category="trace",
+            content=str(error),
+            metadata={
+                "tool_name": tool_name,
+                "tool_call_id": tool_call_id,
+            },
+        )
+
+        # Message event: tool_result with error status (checkpoint-aligned)
+        msg_content = ToolMessage(
+            content=str(error),
+            tool_call_id=tool_call_id or "",
+            name=tool_name,
+            status="error",
+        ).model_dump()
+        self._put(
+            event_type="tool_result",
+            category="message",
+            content=msg_content,
+            metadata={"tool_name": tool_name, "status": "error"},
+        )
+
+    # -- Custom event callback --
+
+    def on_custom_event(self, name: str, data: Any, *, run_id: UUID, **kwargs: Any) -> None:
+        from deerflow.runtime.serialization import serialize_lc_object
+
+        if name == "summarization":
+            data_dict = data if isinstance(data, dict) else {}
+            self._put(
+                event_type="summarization",
+                category="trace",
+                content=data_dict.get("summary", ""),
+                metadata={
+                    "replaced_message_ids": data_dict.get("replaced_message_ids", []),
+                    "replaced_count": data_dict.get("replaced_count", 0),
+                },
+            )
+            self._put(
+                event_type="middleware:summarize",
+                category="middleware",
+                content={"role": "system", "content": data_dict.get("summary", "")},
+                metadata={"replaced_count": data_dict.get("replaced_count", 0)},
+            )
+        else:
+            event_data = serialize_lc_object(data) if not isinstance(data, dict) else data
+            self._put(
+                event_type=name,
+                category="trace",
+                metadata=event_data if isinstance(event_data, dict) else {"data": event_data},
+            )
 
     # -- Internal methods --
 
@@ -307,9 +431,8 @@ class RunJournal(BaseCallbackHandler):
         if exc:
             logger.warning("Journal flush task failed: %s", exc)
 
-    def _identify_caller(self, tags: list[str] | None, **kwargs) -> str:
-        _tags = tags or kwargs.get("tags", [])
-        for tag in _tags:
+    def _identify_caller(self, kwargs: dict) -> str:
+        for tag in kwargs.get("tags") or []:
             if isinstance(tag, str) and (tag.startswith("subagent:") or tag.startswith("middleware:") or tag == "lead_agent"):
                 return tag
         # Default to lead_agent: the main agent graph does not inject

backend/packages/harness/deerflow/runtime/runs/manager.py

@@ -54,7 +54,7 @@ class RunManager:
         self._lock = asyncio.Lock()
         self._store = store
 
-    async def _persist_to_store(self, record: RunRecord) -> None:
+    async def _persist_to_store(self, record: RunRecord, *, follow_up_to_run_id: str | None = None) -> None:
         """Best-effort persist run record to backing store."""
         if self._store is None:
             return
@@ -68,6 +68,7 @@ class RunManager:
                 metadata=record.metadata or {},
                 kwargs=record.kwargs or {},
                 created_at=record.created_at,
+                follow_up_to_run_id=follow_up_to_run_id,
             )
         except Exception:
             logger.warning("Failed to persist run %s to store", record.run_id, exc_info=True)
@@ -89,6 +90,7 @@ class RunManager:
         metadata: dict | None = None,
         kwargs: dict | None = None,
         multitask_strategy: str = "reject",
+        follow_up_to_run_id: str | None = None,
     ) -> RunRecord:
         """Create a new pending run and register it."""
         run_id = str(uuid.uuid4())
@@ -107,7 +109,7 @@ class RunManager:
         )
         async with self._lock:
             self._runs[run_id] = record
-        await self._persist_to_store(record)
+        await self._persist_to_store(record, follow_up_to_run_id=follow_up_to_run_id)
         logger.info("Run created: run_id=%s thread_id=%s", run_id, thread_id)
         return record
 
@@ -174,6 +176,7 @@ class RunManager:
         metadata: dict | None = None,
         kwargs: dict | None = None,
         multitask_strategy: str = "reject",
+        follow_up_to_run_id: str | None = None,
     ) -> RunRecord:
         """Atomically check for inflight runs and create a new one.
 
@@ -227,7 +230,7 @@ class RunManager:
             )
             self._runs[run_id] = record
 
-        await self._persist_to_store(record)
+        await self._persist_to_store(record, follow_up_to_run_id=follow_up_to_run_id)
         logger.info("Run created: run_id=%s thread_id=%s", run_id, thread_id)
         return record
 

backend/packages/harness/deerflow/runtime/runs/store/base.py

@@ -29,6 +29,7 @@ class RunStore(abc.ABC):
         kwargs: dict[str, Any] | None = None,
         error: str | None = None,
         created_at: str | None = None,
+        follow_up_to_run_id: str | None = None,
     ) -> None:
         pass
 

backend/packages/harness/deerflow/runtime/runs/store/memory.py

@@ -28,6 +28,7 @@ class MemoryRunStore(RunStore):
         kwargs=None,
         error=None,
         created_at=None,
+        follow_up_to_run_id=None,
     ):
         now = datetime.now(UTC).isoformat()
         self._runs[run_id] = {
@@ -40,6 +41,7 @@ class MemoryRunStore(RunStore):
             "metadata": metadata or {},
             "kwargs": kwargs or {},
             "error": error,
+            "follow_up_to_run_id": follow_up_to_run_id,
             "created_at": created_at or now,
             "updated_at": now,
         }

backend/packages/harness/deerflow/runtime/runs/worker.py

@@ -25,6 +25,8 @@ from typing import TYPE_CHECKING, Any, Literal
 if TYPE_CHECKING:
     from langchain_core.messages import HumanMessage
 
+from deerflow.config.app_config import AppConfig
+from deerflow.config.deer_flow_context import DeerFlowContext
 from deerflow.runtime.serialization import serialize
 from deerflow.runtime.stream_bridge import StreamBridge
 
@@ -51,6 +53,8 @@ class RunContext:
     event_store: Any | None = field(default=None)
     run_events_config: Any | None = field(default=None)
     thread_store: Any | None = field(default=None)
+    follow_up_to_run_id: str | None = field(default=None)
+    app_config: AppConfig | None = field(default=None)
 
 
 async def run_agent(
@@ -75,6 +79,7 @@ async def run_agent(
     event_store = ctx.event_store
     run_events_config = ctx.run_events_config
     thread_store = ctx.thread_store
+    follow_up_to_run_id = ctx.follow_up_to_run_id
 
     run_id = record.run_id
     thread_id = record.thread_id
@@ -111,6 +116,22 @@ async def run_agent(
                 track_token_usage=getattr(run_events_config, "track_token_usage", True),
             )
 
+            human_msg = _extract_human_message(graph_input)
+            if human_msg is not None:
+                msg_metadata = {}
+                if follow_up_to_run_id:
+                    msg_metadata["follow_up_to_run_id"] = follow_up_to_run_id
+                await event_store.put(
+                    thread_id=thread_id,
+                    run_id=run_id,
+                    event_type="human_message",
+                    category="message",
+                    content=human_msg.model_dump(),
+                    metadata=msg_metadata or None,
+                )
+                content = human_msg.content
+                journal.set_first_human_message(content if isinstance(content, str) else str(content))
+
         # 1. Mark running
         await run_manager.set_status(run_id, RunStatus.running)
 
@@ -144,18 +165,21 @@ async def run_agent(
 
         # 3. Build the agent
         from langchain_core.runnables import RunnableConfig
-        from langgraph.runtime import Runtime
 
-        # Inject runtime context so middlewares can access thread_id
-        # (langgraph-cli does this automatically; we must do it manually)
-        runtime = Runtime(context={"thread_id": thread_id, "run_id": run_id}, store=store)
-        # If the caller already set a ``context`` key (LangGraph >= 0.6.0
-        # prefers it over ``configurable`` for thread-level data), make
-        # sure ``thread_id`` is available there too.
-        if "context" in config and isinstance(config["context"], dict):
-            config["context"].setdefault("thread_id", thread_id)
-            config["context"].setdefault("run_id", run_id)
-        config.setdefault("configurable", {})["__pregel_runtime"] = runtime
+        # Construct typed context for the agent run.
+        # LangGraph's astream(context=...) injects this into Runtime.context
+        # so middleware/tools can access it via resolve_context().
+        if ctx.app_config is None:
+            raise RuntimeError("RunContext.app_config is required — Gateway must populate it via get_run_context")
+        deer_flow_context = DeerFlowContext(
+            app_config=ctx.app_config,
+            thread_id=thread_id,
+        )
+
+        # Inject RunJournal as a LangChain callback handler.
+        # on_llm_end captures token usage; on_chain_start/end captures lifecycle.
+        if journal is not None:
+            config.setdefault("callbacks", []).append(journal)
 
         # Inject RunJournal as a LangChain callback handler.
         # on_llm_end captures token usage; on_chain_start/end captures lifecycle.
@@ -207,7 +231,7 @@ async def run_agent(
         if len(lg_modes) == 1 and not stream_subgraphs:
             # Single mode, no subgraphs: astream yields raw chunks
             single_mode = lg_modes[0]
-            async for chunk in agent.astream(graph_input, config=runnable_config, stream_mode=single_mode):
+            async for chunk in agent.astream(graph_input, config=runnable_config, context=deer_flow_context, stream_mode=single_mode):
                 if record.abort_event.is_set():
                     logger.info("Run %s abort requested — stopping", run_id)
                     break
@@ -218,6 +242,7 @@ async def run_agent(
             async for item in agent.astream(
                 graph_input,
                 config=runnable_config,
+                context=deer_flow_context,
                 stream_mode=lg_modes,
                 subgraphs=stream_subgraphs,
             ):

backend/packages/harness/deerflow/runtime/store/async_provider.py

@@ -23,7 +23,7 @@ from collections.abc import AsyncIterator
 
 from langgraph.store.base import BaseStore
 
-from deerflow.config.app_config import get_app_config
+from deerflow.config.app_config import AppConfig
 from deerflow.runtime.store.provider import POSTGRES_CONN_REQUIRED, POSTGRES_STORE_INSTALL, SQLITE_STORE_INSTALL, ensure_sqlite_parent_dir, resolve_sqlite_conn_str
 
 logger = logging.getLogger(__name__)
@@ -86,7 +86,7 @@ async def _async_store(config) -> AsyncIterator[BaseStore]:
 
 
 @contextlib.asynccontextmanager
-async def make_store() -> AsyncIterator[BaseStore]:
+async def make_store(app_config: AppConfig) -> AsyncIterator[BaseStore]:
     """Async context manager that yields a Store whose backend matches the
     configured checkpointer.
 
@@ -94,20 +94,18 @@ async def make_store() -> AsyncIterator[BaseStore]:
     :func:`deerflow.runtime.checkpointer.async_provider.make_checkpointer` so
     that both singletons always use the same persistence technology::
 
-        async with make_store() as store:
+        async with make_store(app_config) as store:
             app.state.store = store
 
     Yields an :class:`~langgraph.store.memory.InMemoryStore` when no
     ``checkpointer`` section is configured (emits a WARNING in that case).
     """
-    config = get_app_config()
-
-    if config.checkpointer is None:
+    if app_config.checkpointer is None:
         from langgraph.store.memory import InMemoryStore
 
         logger.warning("No 'checkpointer' section in config.yaml — using InMemoryStore for the store. Thread list will be lost on server restart. Configure a sqlite or postgres backend for persistence.")
         yield InMemoryStore()
         return
 
-    async with _async_store(config.checkpointer) as store:
+    async with _async_store(app_config.checkpointer) as store:
         yield store

backend/packages/harness/deerflow/runtime/store/provider.py

@@ -26,7 +26,7 @@ from collections.abc import Iterator
 
 from langgraph.store.base import BaseStore
 
-from deerflow.config.app_config import get_app_config
+from deerflow.config.app_config import AppConfig
 from deerflow.runtime.store._sqlite_utils import ensure_sqlite_parent_dir, resolve_sqlite_conn_str
 
 logger = logging.getLogger(__name__)
@@ -100,7 +100,7 @@ _store: BaseStore | None = None
 _store_ctx = None  # open context manager keeping the connection alive
 
 
-def get_store() -> BaseStore:
+def get_store(app_config: AppConfig) -> BaseStore:
     """Return the global sync Store singleton, creating it on first call.
 
     Returns an :class:`~langgraph.store.memory.InMemoryStore` when no
@@ -115,19 +115,10 @@ def get_store() -> BaseStore:
     if _store is not None:
         return _store
 
-    # Lazily load app config, mirroring the checkpointer singleton pattern so
-    # that tests that set the global checkpointer config explicitly remain isolated.
-    from deerflow.config.app_config import _app_config
-    from deerflow.config.checkpointer_config import get_checkpointer_config
-
-    config = get_checkpointer_config()
-
-    if config is None and _app_config is None:
-        try:
-            get_app_config()
-        except FileNotFoundError:
-            pass
-        config = get_checkpointer_config()
+    # See matching comment in checkpointer/provider.py: a missing config.yaml
+    # is a deployment error, not a cue to silently pick InMemoryStore. Only
+    # the explicit "no checkpointer section" path falls through to memory.
+    config = app_config.checkpointer
 
     if config is None:
         from langgraph.store.memory import InMemoryStore
@@ -163,26 +154,25 @@ def reset_store() -> None:
 
 
 @contextlib.contextmanager
-def store_context() -> Iterator[BaseStore]:
+def store_context(app_config: AppConfig) -> Iterator[BaseStore]:
     """Sync context manager that yields a Store and cleans up on exit.
 
     Unlike :func:`get_store`, this does **not** cache the instance — each
     ``with`` block creates and destroys its own connection.  Use it in CLI
     scripts or tests where you want deterministic cleanup::
 
-        with store_context() as store:
+        with store_context(app_config) as store:
             store.put(("threads",), thread_id, {...})
 
     Yields an :class:`~langgraph.store.memory.InMemoryStore` when no
     checkpointer is configured in *config.yaml*.
     """
-    config = get_app_config()
-    if config.checkpointer is None:
+    if app_config.checkpointer is None:
         from langgraph.store.memory import InMemoryStore
 
         logger.warning("No 'checkpointer' section in config.yaml — using InMemoryStore for the store. Thread list will be lost on server restart. Configure a sqlite or postgres backend for persistence.")
         yield InMemoryStore()
         return
 
-    with _sync_store_cm(config.checkpointer) as store:
+    with _sync_store_cm(app_config.checkpointer) as store:
         yield store

backend/packages/harness/deerflow/runtime/stream_bridge/async_provider.py

@@ -17,7 +17,7 @@ import contextlib
 import logging
 from collections.abc import AsyncIterator
 
-from deerflow.config.stream_bridge_config import get_stream_bridge_config
+from deerflow.config.app_config import AppConfig
 
 from .base import StreamBridge
 
@@ -25,14 +25,13 @@ logger = logging.getLogger(__name__)
 
 
 @contextlib.asynccontextmanager
-async def make_stream_bridge(config=None) -> AsyncIterator[StreamBridge]:
+async def make_stream_bridge(app_config: AppConfig) -> AsyncIterator[StreamBridge]:
     """Async context manager that yields a :class:`StreamBridge`.
 
-    Falls back to :class:`MemoryStreamBridge` when no configuration is
-    provided and nothing is set globally.
+    Falls back to :class:`MemoryStreamBridge` when no ``stream_bridge``
+    section is configured.
     """
-    if config is None:
-        config = get_stream_bridge_config()
+    config = app_config.stream_bridge
 
     if config is None or config.type == "memory":
         from deerflow.runtime.stream_bridge.memory import MemoryStreamBridge

backend/packages/harness/deerflow/sandbox/local/local_sandbox_provider.py

@@ -1,10 +1,14 @@
 import logging
 from pathlib import Path
+from typing import TYPE_CHECKING
 
 from deerflow.sandbox.local.local_sandbox import LocalSandbox, PathMapping
 from deerflow.sandbox.sandbox import Sandbox
 from deerflow.sandbox.sandbox_provider import SandboxProvider
 
+if TYPE_CHECKING:
+    from deerflow.config.app_config import AppConfig
+
 logger = logging.getLogger(__name__)
 
 _singleton: LocalSandbox | None = None
@@ -13,8 +17,9 @@ _singleton: LocalSandbox | None = None
 class LocalSandboxProvider(SandboxProvider):
     uses_thread_data_mounts = True
 
-    def __init__(self):
+    def __init__(self, app_config: "AppConfig"):
         """Initialize the local sandbox provider with path mappings."""
+        self._app_config = app_config
         self._path_mappings = self._setup_path_mappings()
 
     def _setup_path_mappings(self) -> list[PathMapping]:
@@ -31,9 +36,7 @@ class LocalSandboxProvider(SandboxProvider):
 
         # Map skills container path to local skills directory
         try:
-            from deerflow.config import get_app_config
-
-            config = get_app_config()
+            config = self._app_config
             skills_path = config.skills.get_skills_path()
             container_path = config.skills.container_path
 

backend/packages/harness/deerflow/sandbox/middleware.py

@@ -6,6 +6,7 @@ from langchain.agents.middleware import AgentMiddleware
 from langgraph.runtime import Runtime
 
 from deerflow.agents.thread_state import SandboxState, ThreadDataState
+from deerflow.config.deer_flow_context import DeerFlowContext
 from deerflow.sandbox import get_sandbox_provider
 
 logger = logging.getLogger(__name__)
@@ -42,41 +43,35 @@ class SandboxMiddleware(AgentMiddleware[SandboxMiddlewareState]):
         super().__init__()
         self._lazy_init = lazy_init
 
-    def _acquire_sandbox(self, thread_id: str) -> str:
-        provider = get_sandbox_provider()
+    def _acquire_sandbox(self, thread_id: str, runtime: Runtime[DeerFlowContext]) -> str:
+        provider = get_sandbox_provider(runtime.context.app_config)
         sandbox_id = provider.acquire(thread_id)
         logger.info(f"Acquiring sandbox {sandbox_id}")
         return sandbox_id
 
     @override
-    def before_agent(self, state: SandboxMiddlewareState, runtime: Runtime) -> dict | None:
+    def before_agent(self, state: SandboxMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
         # Skip acquisition if lazy_init is enabled
         if self._lazy_init:
             return super().before_agent(state, runtime)
 
         # Eager initialization (original behavior)
         if "sandbox" not in state or state["sandbox"] is None:
-            thread_id = (runtime.context or {}).get("thread_id")
-            if thread_id is None:
+            thread_id = runtime.context.thread_id
+            if not thread_id:
                 return super().before_agent(state, runtime)
-            sandbox_id = self._acquire_sandbox(thread_id)
+            sandbox_id = self._acquire_sandbox(thread_id, runtime)
             logger.info(f"Assigned sandbox {sandbox_id} to thread {thread_id}")
             return {"sandbox": {"sandbox_id": sandbox_id}}
         return super().before_agent(state, runtime)
 
     @override
-    def after_agent(self, state: SandboxMiddlewareState, runtime: Runtime) -> dict | None:
+    def after_agent(self, state: SandboxMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
         sandbox = state.get("sandbox")
         if sandbox is not None:
             sandbox_id = sandbox["sandbox_id"]
             logger.info(f"Releasing sandbox {sandbox_id}")
-            get_sandbox_provider().release(sandbox_id)
-            return None
-
-        if (runtime.context or {}).get("sandbox_id") is not None:
-            sandbox_id = runtime.context.get("sandbox_id")
-            logger.info(f"Releasing sandbox {sandbox_id} from context")
-            get_sandbox_provider().release(sandbox_id)
+            get_sandbox_provider(runtime.context.app_config).release(sandbox_id)
             return None
 
         # No sandbox to release

backend/packages/harness/deerflow/sandbox/sandbox_provider.py

@@ -1,6 +1,6 @@
 from abc import ABC, abstractmethod
 
-from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
 from deerflow.reflection import resolve_class
 from deerflow.sandbox.sandbox import Sandbox
 
@@ -41,23 +41,38 @@ class SandboxProvider(ABC):
 _default_sandbox_provider: SandboxProvider | None = None
 
 
-def get_sandbox_provider(**kwargs) -> SandboxProvider:
+def get_sandbox_provider(app_config: AppConfig, **kwargs) -> SandboxProvider:
     """Get the sandbox provider singleton.
 
     Returns a cached singleton instance. Use `reset_sandbox_provider()` to clear
     the cache, or `shutdown_sandbox_provider()` to properly shutdown and clear.
 
+    Args:
+        app_config: Application config used the first time the singleton is built.
+                    Ignored on subsequent calls — the cached instance is returned
+                    regardless of the config passed.
+
     Returns:
         A sandbox provider instance.
     """
     global _default_sandbox_provider
     if _default_sandbox_provider is None:
-        config = get_app_config()
-        cls = resolve_class(config.sandbox.use, SandboxProvider)
-        _default_sandbox_provider = cls(**kwargs)
+        cls = resolve_class(app_config.sandbox.use, SandboxProvider)
+        _default_sandbox_provider = cls(app_config=app_config, **kwargs) if _accepts_app_config(cls) else cls(**kwargs)
     return _default_sandbox_provider
 
 
+def _accepts_app_config(cls: type) -> bool:
+    """Return True when the provider's __init__ accepts an ``app_config`` kwarg."""
+    import inspect
+
+    try:
+        sig = inspect.signature(cls.__init__)
+    except (TypeError, ValueError):
+        return False
+    return "app_config" in sig.parameters
+
+
 def reset_sandbox_provider() -> None:
     """Reset the sandbox provider singleton.
 

backend/packages/harness/deerflow/sandbox/security.py

@@ -1,6 +1,6 @@
 """Security helpers for sandbox capability gating."""
 
-from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
 
 _LOCAL_SANDBOX_PROVIDER_MARKERS = (
     "deerflow.sandbox.local:LocalSandboxProvider",
@@ -20,11 +20,8 @@ LOCAL_BASH_SUBAGENT_DISABLED_MESSAGE = (
 )
 
 
-def uses_local_sandbox_provider(config=None) -> bool:
+def uses_local_sandbox_provider(config: AppConfig) -> bool:
     """Return True when the active sandbox provider is the host-local provider."""
-    if config is None:
-        config = get_app_config()
-
     sandbox_cfg = getattr(config, "sandbox", None)
     sandbox_use = getattr(sandbox_cfg, "use", "")
     if sandbox_use in _LOCAL_SANDBOX_PROVIDER_MARKERS:
@@ -32,11 +29,8 @@ def uses_local_sandbox_provider(config=None) -> bool:
     return sandbox_use.endswith(":LocalSandboxProvider") and "deerflow.sandbox.local" in sandbox_use
 
 
-def is_host_bash_allowed(config=None) -> bool:
+def is_host_bash_allowed(config: AppConfig) -> bool:
     """Return whether host bash execution is explicitly allowed."""
-    if config is None:
-        config = get_app_config()
-
     sandbox_cfg = getattr(config, "sandbox", None)
     if sandbox_cfg is None:
         return False

backend/packages/harness/deerflow/sandbox/tools.py

@@ -7,7 +7,8 @@ from langchain.tools import ToolRuntime, tool
 from langgraph.typing import ContextT
 
 from deerflow.agents.thread_state import ThreadDataState, ThreadState
-from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
+from deerflow.config.deer_flow_context import resolve_context
 from deerflow.config.paths import VIRTUAL_PATH_PREFIX
 from deerflow.sandbox.exceptions import (
     SandboxError,
@@ -39,62 +40,43 @@ _DEFAULT_GREP_MAX_RESULTS = 100
 _MAX_GREP_MAX_RESULTS = 500
 
 
-def _get_skills_container_path() -> str:
-    """Get the skills container path from config, with fallback to default.
-
-    Result is cached after the first successful config load.  If config loading
-    fails the default is returned *without* caching so that a later call can
-    pick up the real value once the config is available.
-    """
-    cached = getattr(_get_skills_container_path, "_cached", None)
-    if cached is not None:
-        return cached
-    try:
-        from deerflow.config import get_app_config
-
-        value = get_app_config().skills.container_path
-        _get_skills_container_path._cached = value  # type: ignore[attr-defined]
-        return value
-    except Exception:
+def _get_skills_container_path(app_config: AppConfig) -> str:
+    """Get the skills container path from config, with fallback to default."""
+    skills_cfg = getattr(app_config, "skills", None)
+    if skills_cfg is None:
         return _DEFAULT_SKILLS_CONTAINER_PATH
+    return skills_cfg.container_path
 
 
-def _get_skills_host_path() -> str | None:
+def _get_skills_host_path(app_config: AppConfig) -> str | None:
     """Get the skills host filesystem path from config.
 
-    Returns None if the skills directory does not exist or config cannot be
-    loaded.  Only successful lookups are cached; failures are retried on the
-    next call so that a transiently unavailable skills directory does not
-    permanently disable skills access.
+    Returns None if the skills directory does not exist or is not configured.
     """
-    cached = getattr(_get_skills_host_path, "_cached", None)
-    if cached is not None:
-        return cached
+    skills_cfg = getattr(app_config, "skills", None)
+    if skills_cfg is None:
+        return None
     try:
-        from deerflow.config import get_app_config
-
-        config = get_app_config()
-        skills_path = config.skills.get_skills_path()
-        if skills_path.exists():
-            value = str(skills_path)
-            _get_skills_host_path._cached = value  # type: ignore[attr-defined]
-            return value
+        skills_path = skills_cfg.get_skills_path()
     except Exception:
-        pass
+        return None
+    if skills_path.exists():
+        return str(skills_path)
     return None
 
 
-def _is_skills_path(path: str) -> bool:
+def _is_skills_path(path: str, app_config: AppConfig) -> bool:
     """Check if a path is under the skills container path."""
-    skills_prefix = _get_skills_container_path()
+    skills_prefix = _get_skills_container_path(app_config)
     return path == skills_prefix or path.startswith(f"{skills_prefix}/")
 
 
-def _resolve_skills_path(path: str) -> str:
+def _resolve_skills_path(path: str, app_config: AppConfig) -> str:
     """Resolve a virtual skills path to a host filesystem path.
 
     Args:
         path: Virtual skills path (e.g. /mnt/skills/public/bootstrap/SKILL.md)
+        app_config: Resolved application config.
 
     Returns:
         Resolved host path.
@@ -102,8 +84,8 @@ def _resolve_skills_path(path: str) -> str:
     Raises:
         FileNotFoundError: If skills directory is not configured or doesn't exist.
     """
-    skills_container = _get_skills_container_path()
-    skills_host = _get_skills_host_path()
+    skills_container = _get_skills_container_path(app_config)
+    skills_host = _get_skills_host_path(app_config)
     if skills_host is None:
         raise FileNotFoundError(f"Skills directory not available for path: {path}")
 
@@ -119,48 +101,31 @@ def _is_acp_workspace_path(path: str) -> bool:
     return path == _ACP_WORKSPACE_VIRTUAL_PATH or path.startswith(f"{_ACP_WORKSPACE_VIRTUAL_PATH}/")
 
 
-def _get_custom_mounts():
+def _get_custom_mounts(app_config: AppConfig):
     """Get custom volume mounts from sandbox config.
 
-    Result is cached after the first successful config load.  If config loading
-    fails an empty list is returned *without* caching so that a later call can
-    pick up the real value once the config is available.
+    Only includes mounts whose host_path exists, consistent with
+    ``LocalSandboxProvider._setup_path_mappings()`` which also filters by
+    ``host_path.exists()``.
     """
-    cached = getattr(_get_custom_mounts, "_cached", None)
-    if cached is not None:
-        return cached
-    try:
-        from pathlib import Path
-
-        from deerflow.config import get_app_config
-
-        config = get_app_config()
-        mounts = []
-        if config.sandbox and config.sandbox.mounts:
-            # Only include mounts whose host_path exists, consistent with
-            # LocalSandboxProvider._setup_path_mappings() which also filters
-            # by host_path.exists().
-            mounts = [m for m in config.sandbox.mounts if Path(m.host_path).exists()]
-        _get_custom_mounts._cached = mounts  # type: ignore[attr-defined]
-        return mounts
-    except Exception:
-        # If config loading fails, return an empty list without caching so that
-        # a later call can retry once the config is available.
+    sandbox_cfg = getattr(app_config, "sandbox", None)
+    if sandbox_cfg is None or not sandbox_cfg.mounts:
         return []
+    return [m for m in sandbox_cfg.mounts if Path(m.host_path).exists()]
 
 
-def _is_custom_mount_path(path: str) -> bool:
+def _is_custom_mount_path(path: str, app_config: AppConfig) -> bool:
     """Check if path is under a custom mount container_path."""
-    for mount in _get_custom_mounts():
+    for mount in _get_custom_mounts(app_config):
         if path == mount.container_path or path.startswith(f"{mount.container_path}/"):
             return True
     return False
 
 
-def _get_custom_mount_for_path(path: str):
+def _get_custom_mount_for_path(path: str, app_config: AppConfig):
     """Get the mount config matching this path (longest prefix first)."""
     best = None
-    for mount in _get_custom_mounts():
+    for mount in _get_custom_mounts(app_config):
         if path == mount.container_path or path.startswith(f"{mount.container_path}/"):
             if best is None or len(mount.container_path) > len(best.container_path):
                 best = mount
@@ -271,44 +236,40 @@ def _resolve_acp_workspace_path(path: str, thread_id: str | None = None) -> str:
     return str(resolved_path)
 
 
-def _get_mcp_allowed_paths() -> list[str]:
+def _get_mcp_allowed_paths(app_config: AppConfig) -> list[str]:
     """Get the list of allowed paths from MCP config for file system server."""
-    allowed_paths = []
-    try:
-        from deerflow.config.extensions_config import get_extensions_config
+    allowed_paths: list[str] = []
+    extensions_config = getattr(app_config, "extensions", None)
+    if extensions_config is None:
+        return allowed_paths
 
-        extensions_config = get_extensions_config()
+    for _, server in extensions_config.mcp_servers.items():
+        if not server.enabled:
+            continue
 
-        for _, server in extensions_config.mcp_servers.items():
-            if not server.enabled:
-                continue
-
-            # Only check the filesystem server
-            args = server.args or []
-            # Check if args has server-filesystem package
-            has_filesystem = any("server-filesystem" in arg for arg in args)
-            if not has_filesystem:
-                continue
-            # Unpack the allowed file system paths in config
-            for arg in args:
-                if not arg.startswith("-") and arg.startswith("/"):
-                    allowed_paths.append(arg.rstrip("/") + "/")
-
-    except Exception:
-        pass
+        # Only check the filesystem server
+        args = server.args or []
+        # Check if args has server-filesystem package
+        has_filesystem = any("server-filesystem" in arg for arg in args)
+        if not has_filesystem:
+            continue
+        # Unpack the allowed file system paths in config
+        for arg in args:
+            if not arg.startswith("-") and arg.startswith("/"):
+                allowed_paths.append(arg.rstrip("/") + "/")
 
     return allowed_paths
 
 
-def _get_tool_config_int(name: str, key: str, default: int) -> int:
+def _get_tool_config_int(app_config: AppConfig, name: str, key: str, default: int) -> int:
     try:
-        tool_config = get_app_config().get_tool_config(name)
-        if tool_config is not None and key in tool_config.model_extra:
-            value = tool_config.model_extra.get(key)
-            if isinstance(value, int):
-                return value
+        tool_config = app_config.get_tool_config(name)
     except Exception:
-        pass
+        return default
+    if tool_config is not None and key in tool_config.model_extra:
+        value = tool_config.model_extra.get(key)
+        if isinstance(value, int):
+            return value
     return default
 
 
@@ -318,23 +279,23 @@ def _clamp_max_results(value: int, *, default: int, upper_bound: int) -> int:
     return min(value, upper_bound)
 
 
-def _resolve_max_results(name: str, requested: int, *, default: int, upper_bound: int) -> int:
+def _resolve_max_results(app_config: AppConfig, name: str, requested: int, *, default: int, upper_bound: int) -> int:
     requested_max_results = _clamp_max_results(requested, default=default, upper_bound=upper_bound)
     configured_max_results = _clamp_max_results(
-        _get_tool_config_int(name, "max_results", default),
+        _get_tool_config_int(app_config, name, "max_results", default),
         default=default,
         upper_bound=upper_bound,
     )
     return min(requested_max_results, configured_max_results)
 
 
-def _resolve_local_read_path(path: str, thread_data: ThreadDataState) -> str:
-    validate_local_tool_path(path, thread_data, read_only=True)
-    if _is_skills_path(path):
-        return _resolve_skills_path(path)
+def _resolve_local_read_path(path: str, thread_data: ThreadDataState, app_config: AppConfig) -> str:
+    validate_local_tool_path(path, thread_data, app_config, read_only=True)
+    if _is_skills_path(path, app_config):
+        return _resolve_skills_path(path, app_config)
     if _is_acp_workspace_path(path):
         return _resolve_acp_workspace_path(path, _extract_thread_id_from_thread_data(thread_data))
-    return _resolve_and_validate_user_data_path(path, thread_data)
+    return _resolve_and_validate_user_data_path(path, thread_data, app_config)
 
 
 def _format_glob_results(root_path: str, matches: list[str], truncated: bool) -> str:
@@ -380,7 +341,11 @@ def _join_path_preserving_style(base: str, relative: str) -> str:
     return f"{stripped_base}{separator}{normalized_relative}"
 
 
-def _sanitize_error(error: Exception, runtime: "ToolRuntime[ContextT, ThreadState] | None" = None) -> str:
+def _sanitize_error(
+    error: Exception,
+    runtime: "ToolRuntime[ContextT, ThreadState] | None" = None,
+    app_config: AppConfig | None = None,
+) -> str:
     """Sanitize an error message to avoid leaking host filesystem paths.
 
     In local-sandbox mode, resolved host paths in the error string are masked
@@ -389,8 +354,12 @@ def _sanitize_error(error: Exception, runtime: "ToolRuntime[ContextT, ThreadStat
     """
     msg = f"{type(error).__name__}: {error}"
     if runtime is not None and is_local_sandbox(runtime):
-        thread_data = get_thread_data(runtime)
-        msg = mask_local_paths_in_output(msg, thread_data)
+        if app_config is None:
+            ctx = getattr(runtime, "context", None)
+            app_config = getattr(ctx, "app_config", None)
+        if app_config is not None:
+            thread_data = get_thread_data(runtime)
+            msg = mask_local_paths_in_output(msg, thread_data, app_config)
     return msg
 
 
@@ -460,7 +429,7 @@ def _thread_actual_to_virtual_mappings(thread_data: ThreadDataState) -> dict[str
     return {actual: virtual for virtual, actual in _thread_virtual_to_actual_mappings(thread_data).items()}
 
 
-def mask_local_paths_in_output(output: str, thread_data: ThreadDataState | None) -> str:
+def mask_local_paths_in_output(output: str, thread_data: ThreadDataState | None, app_config: AppConfig) -> str:
     """Mask host absolute paths from local sandbox output using virtual paths.
 
     Handles user-data paths (per-thread), skills paths, and ACP workspace paths (global).
@@ -468,8 +437,8 @@ def mask_local_paths_in_output(output: str, thread_data: ThreadDataState | None)
     result = output
 
     # Mask skills host paths
-    skills_host = _get_skills_host_path()
-    skills_container = _get_skills_container_path()
+    skills_host = _get_skills_host_path(app_config)
+    skills_container = _get_skills_container_path(app_config)
     if skills_host:
         raw_base = str(Path(skills_host))
         resolved_base = str(Path(skills_host).resolve())
@@ -543,7 +512,13 @@ def _reject_path_traversal(path: str) -> None:
             raise PermissionError("Access denied: path traversal detected")
 
 
-def validate_local_tool_path(path: str, thread_data: ThreadDataState | None, *, read_only: bool = False) -> None:
+def validate_local_tool_path(
+    path: str,
+    thread_data: ThreadDataState | None,
+    app_config: AppConfig,
+    *,
+    read_only: bool = False,
+) -> None:
     """Validate that a virtual path is allowed for local-sandbox access.
 
     This function is a security gate — it checks whether *path* may be
@@ -572,7 +547,7 @@ def validate_local_tool_path(path: str, thread_data: ThreadDataState | None, *,
     _reject_path_traversal(path)
 
     # Skills paths — read-only access only
-    if _is_skills_path(path):
+    if _is_skills_path(path, app_config):
         if not read_only:
             raise PermissionError(f"Write access to skills path is not allowed: {path}")
         return
@@ -588,13 +563,13 @@ def validate_local_tool_path(path: str, thread_data: ThreadDataState | None, *,
         return
 
     # Custom mount paths — respect read_only config
-    if _is_custom_mount_path(path):
-        mount = _get_custom_mount_for_path(path)
+    if _is_custom_mount_path(path, app_config):
+        mount = _get_custom_mount_for_path(path, app_config)
         if mount and mount.read_only and not read_only:
             raise PermissionError(f"Write access to read-only mount is not allowed: {path}")
         return
 
-    raise PermissionError(f"Only paths under {VIRTUAL_PATH_PREFIX}/, {_get_skills_container_path()}/, {_ACP_WORKSPACE_VIRTUAL_PATH}/, or configured mount paths are allowed")
+    raise PermissionError(f"Only paths under {VIRTUAL_PATH_PREFIX}/, {_get_skills_container_path(app_config)}/, {_ACP_WORKSPACE_VIRTUAL_PATH}/, or configured mount paths are allowed")
 
 
 def _validate_resolved_user_data_path(resolved: Path, thread_data: ThreadDataState) -> None:
@@ -625,18 +600,23 @@ def _validate_resolved_user_data_path(resolved: Path, thread_data: ThreadDataSta
     raise PermissionError("Access denied: path traversal detected")
 
 
-def _resolve_and_validate_user_data_path(path: str, thread_data: ThreadDataState) -> str:
+def _resolve_and_validate_user_data_path(path: str, thread_data: ThreadDataState, app_config: AppConfig) -> str:
     """Resolve a /mnt/user-data virtual path and validate it stays in bounds.
 
     Returns the resolved host path string.
+
+    ``app_config`` is accepted for signature symmetry with the other resolver
+    helpers; the user-data resolution path itself is fully derivable from
+    ``thread_data``.
     """
+    _ = app_config  # noqa: F841 — kept for interface symmetry with sibling resolvers
     resolved_str = replace_virtual_path(path, thread_data)
     resolved = Path(resolved_str).resolve()
     _validate_resolved_user_data_path(resolved, thread_data)
     return str(resolved)
 
 
-def validate_local_bash_command_paths(command: str, thread_data: ThreadDataState | None) -> None:
+def validate_local_bash_command_paths(command: str, thread_data: ThreadDataState | None, app_config: AppConfig) -> None:
     """Validate absolute paths in local-sandbox bash commands.
 
     This validation is only a best-effort guard for the explicit
@@ -660,7 +640,7 @@ def validate_local_bash_command_paths(command: str, thread_data: ThreadDataState
         raise PermissionError(f"Unsafe file:// URL in command: {file_url_match.group()}. Use paths under {VIRTUAL_PATH_PREFIX}")
 
     unsafe_paths: list[str] = []
-    allowed_paths = _get_mcp_allowed_paths()
+    allowed_paths = _get_mcp_allowed_paths(app_config)
 
     for absolute_path in _ABSOLUTE_PATH_PATTERN.findall(command):
         # Check for MCP filesystem server allowed paths
@@ -673,7 +653,7 @@ def validate_local_bash_command_paths(command: str, thread_data: ThreadDataState
             continue
 
         # Allow skills container path (resolved by tools.py before passing to sandbox)
-        if _is_skills_path(absolute_path):
+        if _is_skills_path(absolute_path, app_config):
             _reject_path_traversal(absolute_path)
             continue
 
@@ -683,7 +663,7 @@ def validate_local_bash_command_paths(command: str, thread_data: ThreadDataState
             continue
 
         # Allow custom mount container paths
-        if _is_custom_mount_path(absolute_path):
+        if _is_custom_mount_path(absolute_path, app_config):
             _reject_path_traversal(absolute_path)
             continue
 
@@ -697,12 +677,13 @@ def validate_local_bash_command_paths(command: str, thread_data: ThreadDataState
         raise PermissionError(f"Unsafe absolute paths in command: {unsafe}. Use paths under {VIRTUAL_PATH_PREFIX}")
 
 
-def replace_virtual_paths_in_command(command: str, thread_data: ThreadDataState | None) -> str:
+def replace_virtual_paths_in_command(command: str, thread_data: ThreadDataState | None, app_config: AppConfig) -> str:
     """Replace all virtual paths (/mnt/user-data, /mnt/skills, /mnt/acp-workspace) in a command string.
 
     Args:
         command: The command string that may contain virtual paths.
         thread_data: The thread data containing actual paths.
+        app_config: Resolved application config.
 
     Returns:
         The command with all virtual paths replaced.
@@ -710,13 +691,13 @@ def replace_virtual_paths_in_command(command: str, thread_data: ThreadDataState
     result = command
 
     # Replace skills paths
-    skills_container = _get_skills_container_path()
-    skills_host = _get_skills_host_path()
+    skills_container = _get_skills_container_path(app_config)
+    skills_host = _get_skills_host_path(app_config)
     if skills_host and skills_container in result:
         skills_pattern = re.compile(rf"{re.escape(skills_container)}(/[^\s\"';&|<>()]*)?")
 
         def replace_skills_match(match: re.Match) -> str:
-            return _resolve_skills_path(match.group(0))
+            return _resolve_skills_path(match.group(0), app_config)
 
         result = skills_pattern.sub(replace_skills_match, result)
 
@@ -806,12 +787,10 @@ def sandbox_from_runtime(runtime: ToolRuntime[ContextT, ThreadState] | None = No
     sandbox_id = sandbox_state.get("sandbox_id")
     if sandbox_id is None:
         raise SandboxRuntimeError("Sandbox ID not found in state")
-    sandbox = get_sandbox_provider().get(sandbox_id)
+    sandbox = get_sandbox_provider(resolve_context(runtime).app_config).get(sandbox_id)
     if sandbox is None:
         raise SandboxNotFoundError(f"Sandbox with ID '{sandbox_id}' not found", sandbox_id=sandbox_id)
 
-    if runtime.context is not None:
-        runtime.context["sandbox_id"] = sandbox_id  # Ensure sandbox_id is in context for downstream use
     return sandbox
 
 
@@ -839,26 +818,24 @@ def ensure_sandbox_initialized(runtime: ToolRuntime[ContextT, ThreadState] | Non
     if runtime.state is None:
         raise SandboxRuntimeError("Tool runtime state not available")
 
+    app_config = runtime.context.app_config
+
     # Check if sandbox already exists in state
     sandbox_state = runtime.state.get("sandbox")
     if sandbox_state is not None:
         sandbox_id = sandbox_state.get("sandbox_id")
         if sandbox_id is not None:
-            sandbox = get_sandbox_provider().get(sandbox_id)
+            sandbox = get_sandbox_provider(app_config).get(sandbox_id)
             if sandbox is not None:
-                if runtime.context is not None:
-                    runtime.context["sandbox_id"] = sandbox_id  # Ensure sandbox_id is in context for releasing in after_agent
                 return sandbox
             # Sandbox was released, fall through to acquire new one
 
     # Lazy acquisition: get thread_id and acquire sandbox
-    thread_id = runtime.context.get("thread_id") if runtime.context else None
-    if thread_id is None:
-        thread_id = runtime.config.get("configurable", {}).get("thread_id") if runtime.config else None
-    if thread_id is None:
+    thread_id = runtime.context.thread_id
+    if not thread_id:
         raise SandboxRuntimeError("Thread ID not available in runtime context")
 
-    provider = get_sandbox_provider()
+    provider = get_sandbox_provider(app_config)
     sandbox_id = provider.acquire(thread_id)
 
     # Update runtime state - this persists across tool calls
@@ -869,8 +846,6 @@ def ensure_sandbox_initialized(runtime: ToolRuntime[ContextT, ThreadState] | Non
     if sandbox is None:
         raise SandboxNotFoundError("Sandbox not found after acquisition", sandbox_id=sandbox_id)
 
-    if runtime.context is not None:
-        runtime.context["sandbox_id"] = sandbox_id  # Ensure sandbox_id is in context for releasing in after_agent
     return sandbox
 
 
@@ -1000,40 +975,29 @@ def bash_tool(runtime: ToolRuntime[ContextT, ThreadState], description: str, com
         description: Explain why you are running this command in short words. ALWAYS PROVIDE THIS PARAMETER FIRST.
         command: The bash command to execute. Always use absolute paths for files and directories.
     """
+    app_config = resolve_context(runtime).app_config
     try:
         sandbox = ensure_sandbox_initialized(runtime)
+        sandbox_cfg = app_config.sandbox
+        max_chars = sandbox_cfg.bash_output_max_chars if sandbox_cfg else 20000
         if is_local_sandbox(runtime):
-            if not is_host_bash_allowed():
+            if not is_host_bash_allowed(app_config):
                 return f"Error: {LOCAL_HOST_BASH_DISABLED_MESSAGE}"
             ensure_thread_directories_exist(runtime)
             thread_data = get_thread_data(runtime)
-            validate_local_bash_command_paths(command, thread_data)
-            command = replace_virtual_paths_in_command(command, thread_data)
+            validate_local_bash_command_paths(command, thread_data, app_config)
+            command = replace_virtual_paths_in_command(command, thread_data, app_config)
             command = _apply_cwd_prefix(command, thread_data)
             output = sandbox.execute_command(command)
-            try:
-                from deerflow.config.app_config import get_app_config
-
-                sandbox_cfg = get_app_config().sandbox
-                max_chars = sandbox_cfg.bash_output_max_chars if sandbox_cfg else 20000
-            except Exception:
-                max_chars = 20000
-            return _truncate_bash_output(mask_local_paths_in_output(output, thread_data), max_chars)
+            return _truncate_bash_output(mask_local_paths_in_output(output, thread_data, app_config), max_chars)
         ensure_thread_directories_exist(runtime)
-        try:
-            from deerflow.config.app_config import get_app_config
-
-            sandbox_cfg = get_app_config().sandbox
-            max_chars = sandbox_cfg.bash_output_max_chars if sandbox_cfg else 20000
-        except Exception:
-            max_chars = 20000
         return _truncate_bash_output(sandbox.execute_command(command), max_chars)
     except SandboxError as e:
         return f"Error: {e}"
     except PermissionError as e:
         return f"Error: {e}"
     except Exception as e:
-        return f"Error: Unexpected error executing command: {_sanitize_error(e, runtime)}"
+        return f"Error: Unexpected error executing command: {_sanitize_error(e, runtime, app_config)}"
 
 
 @tool("ls", parse_docstring=True)
@@ -1044,6 +1008,7 @@ def ls_tool(runtime: ToolRuntime[ContextT, ThreadState], description: str, path:
         description: Explain why you are listing this directory in short words. ALWAYS PROVIDE THIS PARAMETER FIRST.
         path: The **absolute** path to the directory to list.
     """
+    app_config = resolve_context(runtime).app_config
     try:
         sandbox = ensure_sandbox_initialized(runtime)
         ensure_thread_directories_exist(runtime)
@@ -1051,13 +1016,13 @@ def ls_tool(runtime: ToolRuntime[ContextT, ThreadState], description: str, path:
         thread_data = None
         if is_local_sandbox(runtime):
             thread_data = get_thread_data(runtime)
-            validate_local_tool_path(path, thread_data, read_only=True)
-            if _is_skills_path(path):
-                path = _resolve_skills_path(path)
+            validate_local_tool_path(path, thread_data, app_config, read_only=True)
+            if _is_skills_path(path, app_config):
+                path = _resolve_skills_path(path, app_config)
             elif _is_acp_workspace_path(path):
                 path = _resolve_acp_workspace_path(path, _extract_thread_id_from_thread_data(thread_data))
-            elif not _is_custom_mount_path(path):
-                path = _resolve_and_validate_user_data_path(path, thread_data)
+            elif not _is_custom_mount_path(path, app_config):
+                path = _resolve_and_validate_user_data_path(path, thread_data, app_config)
             # Custom mount paths are resolved by LocalSandbox._resolve_path()
         children = sandbox.list_dir(path)
         if not children:
@@ -1065,13 +1030,8 @@ def ls_tool(runtime: ToolRuntime[ContextT, ThreadState], description: str, path:
         output = "\n".join(children)
         if thread_data is not None:
             output = mask_local_paths_in_output(output, thread_data)
-        try:
-            from deerflow.config.app_config import get_app_config
-
-            sandbox_cfg = get_app_config().sandbox
-            max_chars = sandbox_cfg.ls_output_max_chars if sandbox_cfg else 20000
-        except Exception:
-            max_chars = 20000
+        sandbox_cfg = app_config.sandbox
+        max_chars = sandbox_cfg.ls_output_max_chars if sandbox_cfg else 20000
         return _truncate_ls_output(output, max_chars)
     except SandboxError as e:
         return f"Error: {e}"
@@ -1080,7 +1040,7 @@ def ls_tool(runtime: ToolRuntime[ContextT, ThreadState], description: str, path:
     except PermissionError:
         return f"Error: Permission denied: {requested_path}"
     except Exception as e:
-        return f"Error: Unexpected error listing directory: {_sanitize_error(e, runtime)}"
+        return f"Error: Unexpected error listing directory: {_sanitize_error(e, runtime, app_config)}"
 
 
 @tool("glob", parse_docstring=True)
@@ -1101,11 +1061,13 @@ def glob_tool(
         include_dirs: Whether matching directories should also be returned. Default is False.
         max_results: Maximum number of paths to return. Default is 200.
     """
+    app_config = resolve_context(runtime).app_config
     try:
         sandbox = ensure_sandbox_initialized(runtime)
         ensure_thread_directories_exist(runtime)
         requested_path = path
         effective_max_results = _resolve_max_results(
+            app_config,
             "glob",
             max_results,
             default=_DEFAULT_GLOB_MAX_RESULTS,
@@ -1116,10 +1078,10 @@ def glob_tool(
             thread_data = get_thread_data(runtime)
             if thread_data is None:
                 raise SandboxRuntimeError("Thread data not available for local sandbox")
-            path = _resolve_local_read_path(path, thread_data)
+            path = _resolve_local_read_path(path, thread_data, app_config)
         matches, truncated = sandbox.glob(path, pattern, include_dirs=include_dirs, max_results=effective_max_results)
         if thread_data is not None:
-            matches = [mask_local_paths_in_output(match, thread_data) for match in matches]
+            matches = [mask_local_paths_in_output(match, thread_data, app_config) for match in matches]
         return _format_glob_results(requested_path, matches, truncated)
     except SandboxError as e:
         return f"Error: {e}"
@@ -1130,7 +1092,7 @@ def glob_tool(
     except PermissionError:
         return f"Error: Permission denied: {requested_path}"
     except Exception as e:
-        return f"Error: Unexpected error searching paths: {_sanitize_error(e, runtime)}"
+        return f"Error: Unexpected error searching paths: {_sanitize_error(e, runtime, app_config)}"
 
 
 @tool("grep", parse_docstring=True)
@@ -1155,11 +1117,13 @@ def grep_tool(
         case_sensitive: Whether matching is case-sensitive. Default is False.
         max_results: Maximum number of matching lines to return. Default is 100.
     """
+    app_config = resolve_context(runtime).app_config
     try:
         sandbox = ensure_sandbox_initialized(runtime)
         ensure_thread_directories_exist(runtime)
         requested_path = path
         effective_max_results = _resolve_max_results(
+            app_config,
             "grep",
             max_results,
             default=_DEFAULT_GREP_MAX_RESULTS,
@@ -1170,7 +1134,7 @@ def grep_tool(
             thread_data = get_thread_data(runtime)
             if thread_data is None:
                 raise SandboxRuntimeError("Thread data not available for local sandbox")
-            path = _resolve_local_read_path(path, thread_data)
+            path = _resolve_local_read_path(path, thread_data, app_config)
         matches, truncated = sandbox.grep(
             path,
             pattern,
@@ -1182,7 +1146,7 @@ def grep_tool(
         if thread_data is not None:
             matches = [
                 GrepMatch(
-                    path=mask_local_paths_in_output(match.path, thread_data),
+                    path=mask_local_paths_in_output(match.path, thread_data, app_config),
                     line_number=match.line_number,
                     line=match.line,
                 )
@@ -1200,7 +1164,7 @@ def grep_tool(
     except PermissionError:
         return f"Error: Permission denied: {requested_path}"
     except Exception as e:
-        return f"Error: Unexpected error searching file contents: {_sanitize_error(e, runtime)}"
+        return f"Error: Unexpected error searching file contents: {_sanitize_error(e, runtime, app_config)}"
 
 
 @tool("read_file", parse_docstring=True)
@@ -1219,32 +1183,28 @@ def read_file_tool(
         start_line: Optional starting line number (1-indexed, inclusive). Use with end_line to read a specific range.
         end_line: Optional ending line number (1-indexed, inclusive). Use with start_line to read a specific range.
     """
+    app_config = resolve_context(runtime).app_config
     try:
         sandbox = ensure_sandbox_initialized(runtime)
         ensure_thread_directories_exist(runtime)
         requested_path = path
         if is_local_sandbox(runtime):
             thread_data = get_thread_data(runtime)
-            validate_local_tool_path(path, thread_data, read_only=True)
-            if _is_skills_path(path):
-                path = _resolve_skills_path(path)
+            validate_local_tool_path(path, thread_data, app_config, read_only=True)
+            if _is_skills_path(path, app_config):
+                path = _resolve_skills_path(path, app_config)
             elif _is_acp_workspace_path(path):
                 path = _resolve_acp_workspace_path(path, _extract_thread_id_from_thread_data(thread_data))
-            elif not _is_custom_mount_path(path):
-                path = _resolve_and_validate_user_data_path(path, thread_data)
+            elif not _is_custom_mount_path(path, app_config):
+                path = _resolve_and_validate_user_data_path(path, thread_data, app_config)
             # Custom mount paths are resolved by LocalSandbox._resolve_path()
         content = sandbox.read_file(path)
         if not content:
             return "(empty)"
         if start_line is not None and end_line is not None:
             content = "\n".join(content.splitlines()[start_line - 1 : end_line])
-        try:
-            from deerflow.config.app_config import get_app_config
-
-            sandbox_cfg = get_app_config().sandbox
-            max_chars = sandbox_cfg.read_file_output_max_chars if sandbox_cfg else 50000
-        except Exception:
-            max_chars = 50000
+        sandbox_cfg = app_config.sandbox
+        max_chars = sandbox_cfg.read_file_output_max_chars if sandbox_cfg else 50000
         return _truncate_read_file_output(content, max_chars)
     except SandboxError as e:
         return f"Error: {e}"
@@ -1255,7 +1215,7 @@ def read_file_tool(
     except IsADirectoryError:
         return f"Error: Path is a directory, not a file: {requested_path}"
     except Exception as e:
-        return f"Error: Unexpected error reading file: {_sanitize_error(e, runtime)}"
+        return f"Error: Unexpected error reading file: {_sanitize_error(e, runtime, app_config)}"
 
 
 @tool("write_file", parse_docstring=True)
@@ -1273,15 +1233,16 @@ def write_file_tool(
         path: The **absolute** path to the file to write to. ALWAYS PROVIDE THIS PARAMETER SECOND.
         content: The content to write to the file. ALWAYS PROVIDE THIS PARAMETER THIRD.
     """
+    app_config = resolve_context(runtime).app_config
     try:
         sandbox = ensure_sandbox_initialized(runtime)
         ensure_thread_directories_exist(runtime)
         requested_path = path
         if is_local_sandbox(runtime):
             thread_data = get_thread_data(runtime)
-            validate_local_tool_path(path, thread_data)
-            if not _is_custom_mount_path(path):
-                path = _resolve_and_validate_user_data_path(path, thread_data)
+            validate_local_tool_path(path, thread_data, app_config)
+            if not _is_custom_mount_path(path, app_config):
+                path = _resolve_and_validate_user_data_path(path, thread_data, app_config)
             # Custom mount paths are resolved by LocalSandbox._resolve_path()
         with get_file_operation_lock(sandbox, path):
             sandbox.write_file(path, content, append)
@@ -1293,9 +1254,9 @@ def write_file_tool(
     except IsADirectoryError:
         return f"Error: Path is a directory, not a file: {requested_path}"
     except OSError as e:
-        return f"Error: Failed to write file '{requested_path}': {_sanitize_error(e, runtime)}"
+        return f"Error: Failed to write file '{requested_path}': {_sanitize_error(e, runtime, app_config)}"
     except Exception as e:
-        return f"Error: Unexpected error writing file: {_sanitize_error(e, runtime)}"
+        return f"Error: Unexpected error writing file: {_sanitize_error(e, runtime, app_config)}"
 
 
 @tool("str_replace", parse_docstring=True)
@@ -1317,15 +1278,16 @@ def str_replace_tool(
         new_str: The new substring. ALWAYS PROVIDE THIS PARAMETER FOURTH.
         replace_all: Whether to replace all occurrences of the substring. If False, only the first occurrence will be replaced. Default is False.
     """
+    app_config = resolve_context(runtime).app_config
     try:
         sandbox = ensure_sandbox_initialized(runtime)
         ensure_thread_directories_exist(runtime)
         requested_path = path
         if is_local_sandbox(runtime):
             thread_data = get_thread_data(runtime)
-            validate_local_tool_path(path, thread_data)
-            if not _is_custom_mount_path(path):
-                path = _resolve_and_validate_user_data_path(path, thread_data)
+            validate_local_tool_path(path, thread_data, app_config)
+            if not _is_custom_mount_path(path, app_config):
+                path = _resolve_and_validate_user_data_path(path, thread_data, app_config)
             # Custom mount paths are resolved by LocalSandbox._resolve_path()
         with get_file_operation_lock(sandbox, path):
             content = sandbox.read_file(path)
@@ -1346,4 +1308,4 @@ def str_replace_tool(
     except PermissionError:
         return f"Error: Permission denied accessing file: {requested_path}"
     except Exception as e:
-        return f"Error: Unexpected error replacing string: {_sanitize_error(e, runtime)}"
+        return f"Error: Unexpected error replacing string: {_sanitize_error(e, runtime, app_config)}"

backend/packages/harness/deerflow/skills/loader.py

@@ -1,10 +1,14 @@
 import logging
 import os
 from pathlib import Path
+from typing import TYPE_CHECKING
 
 from .parser import parse_skill_file
 from .types import Skill
 
+if TYPE_CHECKING:
+    from deerflow.config.app_config import AppConfig
+
 logger = logging.getLogger(__name__)
 
 
@@ -22,7 +26,12 @@ def get_skills_root_path() -> Path:
     return skills_dir
 
 
-def load_skills(skills_path: Path | None = None, use_config: bool = True, enabled_only: bool = False) -> list[Skill]:
+def load_skills(
+    app_config: "AppConfig | None" = None,
+    *,
+    skills_path: Path | None = None,
+    enabled_only: bool = False,
+) -> list[Skill]:
     """
     Load all skills from the skills directory.
 
@@ -30,25 +39,19 @@ def load_skills(skills_path: Path | None = None, use_config: bool = True, enable
     to extract metadata. The enabled state is determined by the skills_state_config.json file.
 
     Args:
-        skills_path: Optional custom path to skills directory.
-                     If not provided and use_config is True, uses path from config.
-                     Otherwise defaults to deer-flow/skills
-        use_config: Whether to load skills path from config (default: True)
+        app_config: Application config used to resolve the configured skills
+                    directory. Ignored when ``skills_path`` is supplied.
+        skills_path: Explicit override for the skills directory. When both
+                     ``skills_path`` and ``app_config`` are omitted the
+                     default repository layout is used (``deer-flow/skills``).
         enabled_only: If True, only return enabled skills (default: False)
 
     Returns:
         List of Skill objects, sorted by name
     """
     if skills_path is None:
-        if use_config:
-            try:
-                from deerflow.config import get_app_config
-
-                config = get_app_config()
-                skills_path = config.skills.get_skills_path()
-            except Exception:
-                # Fallback to default if config fails
-                skills_path = get_skills_root_path()
+        if app_config is not None:
+            skills_path = app_config.skills.get_skills_path()
         else:
             skills_path = get_skills_root_path()
 

backend/packages/harness/deerflow/skills/manager.py

@@ -9,7 +9,7 @@ from datetime import UTC, datetime
 from pathlib import Path
 from typing import Any
 
-from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
 from deerflow.skills.loader import load_skills
 from deerflow.skills.validation import _validate_skill_frontmatter
 
@@ -20,16 +20,17 @@ ALLOWED_SUPPORT_SUBDIRS = {"references", "templates", "scripts", "assets"}
 _SKILL_NAME_PATTERN = re.compile(r"^[a-z0-9]+(?:-[a-z0-9]+)*$")
 
 
-def get_skills_root_dir() -> Path:
-    return get_app_config().skills.get_skills_path()
+def get_skills_root_dir(app_config: AppConfig) -> Path:
+    """Return the configured skills root."""
+    return app_config.skills.get_skills_path()
 
 
-def get_public_skills_dir() -> Path:
-    return get_skills_root_dir() / "public"
+def get_public_skills_dir(app_config: AppConfig) -> Path:
+    return get_skills_root_dir(app_config) / "public"
 
 
-def get_custom_skills_dir() -> Path:
-    path = get_skills_root_dir() / "custom"
+def get_custom_skills_dir(app_config: AppConfig) -> Path:
+    path = get_skills_root_dir(app_config) / "custom"
     path.mkdir(parents=True, exist_ok=True)
     return path
 
@@ -43,46 +44,46 @@ def validate_skill_name(name: str) -> str:
     return normalized
 
 
-def get_custom_skill_dir(name: str) -> Path:
-    return get_custom_skills_dir() / validate_skill_name(name)
+def get_custom_skill_dir(name: str, app_config: AppConfig) -> Path:
+    return get_custom_skills_dir(app_config) / validate_skill_name(name)
 
 
-def get_custom_skill_file(name: str) -> Path:
-    return get_custom_skill_dir(name) / SKILL_FILE_NAME
+def get_custom_skill_file(name: str, app_config: AppConfig) -> Path:
+    return get_custom_skill_dir(name, app_config) / SKILL_FILE_NAME
 
 
-def get_custom_skill_history_dir() -> Path:
-    path = get_custom_skills_dir() / HISTORY_DIR_NAME
+def get_custom_skill_history_dir(app_config: AppConfig) -> Path:
+    path = get_custom_skills_dir(app_config) / HISTORY_DIR_NAME
     path.mkdir(parents=True, exist_ok=True)
     return path
 
 
-def get_skill_history_file(name: str) -> Path:
-    return get_custom_skill_history_dir() / f"{validate_skill_name(name)}.jsonl"
+def get_skill_history_file(name: str, app_config: AppConfig) -> Path:
+    return get_custom_skill_history_dir(app_config) / f"{validate_skill_name(name)}.jsonl"
 
 
-def get_public_skill_dir(name: str) -> Path:
-    return get_public_skills_dir() / validate_skill_name(name)
+def get_public_skill_dir(name: str, app_config: AppConfig) -> Path:
+    return get_public_skills_dir(app_config) / validate_skill_name(name)
 
 
-def custom_skill_exists(name: str) -> bool:
-    return get_custom_skill_file(name).exists()
+def custom_skill_exists(name: str, app_config: AppConfig) -> bool:
+    return get_custom_skill_file(name, app_config).exists()
 
 
-def public_skill_exists(name: str) -> bool:
-    return (get_public_skill_dir(name) / SKILL_FILE_NAME).exists()
+def public_skill_exists(name: str, app_config: AppConfig) -> bool:
+    return (get_public_skill_dir(name, app_config) / SKILL_FILE_NAME).exists()
 
 
-def ensure_custom_skill_is_editable(name: str) -> None:
-    if custom_skill_exists(name):
+def ensure_custom_skill_is_editable(name: str, app_config: AppConfig) -> None:
+    if custom_skill_exists(name, app_config):
         return
-    if public_skill_exists(name):
+    if public_skill_exists(name, app_config):
         raise ValueError(f"'{name}' is a built-in skill. To customise it, create a new skill with the same name under skills/custom/.")
     raise FileNotFoundError(f"Custom skill '{name}' not found.")
 
 
-def ensure_safe_support_path(name: str, relative_path: str) -> Path:
-    skill_dir = get_custom_skill_dir(name).resolve()
+def ensure_safe_support_path(name: str, relative_path: str, app_config: AppConfig) -> Path:
+    skill_dir = get_custom_skill_dir(name, app_config).resolve()
     if not relative_path or relative_path.endswith("/"):
         raise ValueError("Supporting file path must include a filename.")
     relative = Path(relative_path)
@@ -124,8 +125,8 @@ def atomic_write(path: Path, content: str) -> None:
     tmp_path.replace(path)
 
 
-def append_history(name: str, record: dict[str, Any]) -> None:
-    history_path = get_skill_history_file(name)
+def append_history(name: str, record: dict[str, Any], app_config: AppConfig) -> None:
+    history_path = get_skill_history_file(name, app_config)
     history_path.parent.mkdir(parents=True, exist_ok=True)
     payload = {
         "ts": datetime.now(UTC).isoformat(),
@@ -136,8 +137,8 @@ def append_history(name: str, record: dict[str, Any]) -> None:
         f.write("\n")
 
 
-def read_history(name: str) -> list[dict[str, Any]]:
-    history_path = get_skill_history_file(name)
+def read_history(name: str, app_config: AppConfig) -> list[dict[str, Any]]:
+    history_path = get_skill_history_file(name, app_config)
     if not history_path.exists():
         return []
     records: list[dict[str, Any]] = []
@@ -148,12 +149,12 @@ def read_history(name: str) -> list[dict[str, Any]]:
     return records
 
 
-def list_custom_skills() -> list:
-    return [skill for skill in load_skills(enabled_only=False) if skill.category == "custom"]
+def list_custom_skills(app_config: AppConfig) -> list:
+    return [skill for skill in load_skills(app_config, enabled_only=False) if skill.category == "custom"]
 
 
-def read_custom_skill_content(name: str) -> str:
-    skill_file = get_custom_skill_file(name)
+def read_custom_skill_content(name: str, app_config: AppConfig) -> str:
+    skill_file = get_custom_skill_file(name, app_config)
     if not skill_file.exists():
         raise FileNotFoundError(f"Custom skill '{name}' not found.")
     return skill_file.read_text(encoding="utf-8")

backend/packages/harness/deerflow/skills/security_scanner.py

@@ -7,7 +7,7 @@ import logging
 import re
 from dataclasses import dataclass
 
-from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
 from deerflow.models import create_chat_model
 
 logger = logging.getLogger(__name__)
@@ -35,7 +35,7 @@ def _extract_json_object(raw: str) -> dict | None:
         return None
 
 
-async def scan_skill_content(content: str, *, executable: bool = False, location: str = "SKILL.md") -> ScanResult:
+async def scan_skill_content(app_config: AppConfig, content: str, *, executable: bool = False, location: str = "SKILL.md") -> ScanResult:
     """Screen skill content before it is written to disk."""
     rubric = (
         "You are a security reviewer for AI agent skills. "
@@ -47,9 +47,12 @@ async def scan_skill_content(content: str, *, executable: bool = False, location
     prompt = f"Location: {location}\nExecutable: {str(executable).lower()}\n\nReview this content:\n-----\n{content}\n-----"
 
     try:
-        config = get_app_config()
-        model_name = config.skill_evolution.moderation_model_name
-        model = create_chat_model(name=model_name, thinking_enabled=False) if model_name else create_chat_model(thinking_enabled=False)
+        model_name = app_config.skill_evolution.moderation_model_name
+        model = (
+            create_chat_model(name=model_name, thinking_enabled=False, app_config=app_config)
+            if model_name
+            else create_chat_model(thinking_enabled=False, app_config=app_config)
+        )
         response = await model.ainvoke(
             [
                 {"role": "system", "content": rubric},

backend/packages/harness/deerflow/subagents/executor.py

@@ -17,6 +17,7 @@ from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
 from langchain_core.runnables import RunnableConfig
 
 from deerflow.agents.thread_state import SandboxState, ThreadDataState, ThreadState
+from deerflow.config.app_config import AppConfig
 from deerflow.models import create_chat_model
 from deerflow.subagents.config import SubagentConfig
 
@@ -132,24 +133,16 @@ class SubagentExecutor:
         self,
         config: SubagentConfig,
         tools: list[BaseTool],
+        app_config: AppConfig,
         parent_model: str | None = None,
         sandbox_state: SandboxState | None = None,
         thread_data: ThreadDataState | None = None,
         thread_id: str | None = None,
         trace_id: str | None = None,
     ):
-        """Initialize the executor.
-
-        Args:
-            config: Subagent configuration.
-            tools: List of all available tools (will be filtered).
-            parent_model: The parent agent's model name for inheritance.
-            sandbox_state: Sandbox state from parent agent.
-            thread_data: Thread data from parent agent.
-            thread_id: Thread ID for sandbox operations.
-            trace_id: Trace ID from parent for distributed tracing.
-        """
+        """Initialize the executor."""
         self.config = config
+        self.app_config = app_config
         self.parent_model = parent_model
         self.sandbox_state = sandbox_state
         self.thread_data = thread_data
@@ -169,7 +162,7 @@ class SubagentExecutor:
     def _create_agent(self):
         """Create the agent instance."""
         model_name = _get_model_name(self.config, self.parent_model)
-        model = create_chat_model(name=model_name, thinking_enabled=False)
+        model = create_chat_model(name=model_name, thinking_enabled=False, app_config=self.app_config)
 
         from deerflow.agents.middlewares.tool_error_handling_middleware import build_subagent_runtime_middlewares
 

backend/packages/harness/deerflow/subagents/registry.py

@@ -3,6 +3,7 @@
 import logging
 from dataclasses import replace
 
+from deerflow.config.app_config import AppConfig
 from deerflow.sandbox.security import is_host_bash_allowed
 from deerflow.subagents.builtins import BUILTIN_SUBAGENTS
 from deerflow.subagents.config import SubagentConfig
@@ -10,19 +11,17 @@ from deerflow.subagents.config import SubagentConfig
 logger = logging.getLogger(__name__)
 
 
-def _build_custom_subagent_config(name: str) -> SubagentConfig | None:
+def _build_custom_subagent_config(name: str, app_config: AppConfig) -> SubagentConfig | None:
     """Build a SubagentConfig from config.yaml custom_agents section.
 
     Args:
         name: The name of the custom subagent.
+        app_config: The resolved application config.
 
     Returns:
         SubagentConfig if found in custom_agents, None otherwise.
     """
-    from deerflow.config.subagents_config import get_subagents_app_config
-
-    app_config = get_subagents_app_config()
-    custom = app_config.custom_agents.get(name)
+    custom = app_config.subagents.custom_agents.get(name)
     if custom is None:
         return None
 
@@ -39,67 +38,44 @@ def _build_custom_subagent_config(name: str) -> SubagentConfig | None:
     )
 
 
-def get_subagent_config(name: str) -> SubagentConfig | None:
+def get_subagent_config(name: str, app_config: AppConfig) -> SubagentConfig | None:
     """Get a subagent configuration by name, with config.yaml overrides applied.
 
     Resolution order (mirrors Codex's config layering):
     1. Built-in subagents (general-purpose, bash)
     2. Custom subagents from config.yaml custom_agents section
     3. Per-agent overrides from config.yaml agents section (timeout, max_turns, model, skills)
-
-    Args:
-        name: The name of the subagent.
-
-    Returns:
-        SubagentConfig if found (with any config.yaml overrides applied), None otherwise.
     """
-    # Step 1: Look up built-in, then fall back to custom_agents
     config = BUILTIN_SUBAGENTS.get(name)
     if config is None:
-        config = _build_custom_subagent_config(name)
+        config = _build_custom_subagent_config(name, app_config)
     if config is None:
         return None
 
-    # Step 2: Apply per-agent overrides from config.yaml agents section.
-    # Only explicit per-agent overrides are applied here. Global defaults
-    # (timeout_seconds, max_turns at the top level) apply to built-in agents
-    # but must NOT override custom agents' own values — custom agents define
-    # their own defaults in the custom_agents section.
-    # Lazy import to avoid circular deps.
-    from deerflow.config.subagents_config import get_subagents_app_config
+    sub_config = app_config.subagents
+    overrides: dict = {}
 
-    app_config = get_subagents_app_config()
-    is_builtin = name in BUILTIN_SUBAGENTS
-    agent_override = app_config.agents.get(name)
+    # Timeout: subagents config supplies effective per-agent override or global default.
+    effective_timeout = sub_config.get_timeout_for(name)
+    if effective_timeout != config.timeout_seconds:
+        logger.debug("Subagent '%s': timeout overridden (%ss -> %ss)", name, config.timeout_seconds, effective_timeout)
+        overrides["timeout_seconds"] = effective_timeout
 
-    overrides = {}
-
-    # Timeout: per-agent override > global default (builtins only) > config's own value
-    if agent_override is not None and agent_override.timeout_seconds is not None:
-        if agent_override.timeout_seconds != config.timeout_seconds:
-            logger.debug("Subagent '%s': timeout overridden (%ss -> %ss)", name, config.timeout_seconds, agent_override.timeout_seconds)
-            overrides["timeout_seconds"] = agent_override.timeout_seconds
-    elif is_builtin and app_config.timeout_seconds != config.timeout_seconds:
-        logger.debug("Subagent '%s': timeout from global default (%ss -> %ss)", name, config.timeout_seconds, app_config.timeout_seconds)
-        overrides["timeout_seconds"] = app_config.timeout_seconds
-
-    # Max turns: per-agent override > global default (builtins only) > config's own value
-    if agent_override is not None and agent_override.max_turns is not None:
-        if agent_override.max_turns != config.max_turns:
-            logger.debug("Subagent '%s': max_turns overridden (%s -> %s)", name, config.max_turns, agent_override.max_turns)
-            overrides["max_turns"] = agent_override.max_turns
-    elif is_builtin and app_config.max_turns is not None and app_config.max_turns != config.max_turns:
-        logger.debug("Subagent '%s': max_turns from global default (%s -> %s)", name, config.max_turns, app_config.max_turns)
-        overrides["max_turns"] = app_config.max_turns
+    # Max turns: subagents config supplies effective per-agent override or global default
+    # (falls back to ``config.max_turns`` when no override is configured).
+    effective_max_turns = sub_config.get_max_turns_for(name, config.max_turns)
+    if effective_max_turns != config.max_turns:
+        logger.debug("Subagent '%s': max_turns overridden (%s -> %s)", name, config.max_turns, effective_max_turns)
+        overrides["max_turns"] = effective_max_turns
 
     # Model: per-agent override only (no global default for model)
-    effective_model = app_config.get_model_for(name)
+    effective_model = sub_config.get_model_for(name)
     if effective_model is not None and effective_model != config.model:
         logger.debug("Subagent '%s': model overridden (%s -> %s)", name, config.model, effective_model)
         overrides["model"] = effective_model
 
     # Skills: per-agent override only (no global default for skills)
-    effective_skills = app_config.get_skills_for(name)
+    effective_skills = sub_config.get_skills_for(name)
     if effective_skills is not None and effective_skills != config.skills:
         logger.debug("Subagent '%s': skills overridden (%s -> %s)", name, config.skills, effective_skills)
         overrides["skills"] = effective_skills
@@ -110,21 +86,21 @@ def get_subagent_config(name: str) -> SubagentConfig | None:
     return config
 
 
-def list_subagents() -> list[SubagentConfig]:
+def list_subagents(app_config: AppConfig) -> list[SubagentConfig]:
     """List all available subagent configurations (with config.yaml overrides applied).
 
     Returns:
         List of all registered SubagentConfig instances (built-in + custom).
     """
-    configs = []
-    for name in get_subagent_names():
-        config = get_subagent_config(name)
+    configs: list[SubagentConfig] = []
+    for name in get_subagent_names(app_config):
+        config = get_subagent_config(name, app_config)
         if config is not None:
             configs.append(config)
     return configs
 
 
-def get_subagent_names() -> list[str]:
+def get_subagent_names(app_config: AppConfig) -> list[str]:
     """Get all available subagent names (built-in + custom).
 
     Returns:
@@ -132,26 +108,22 @@ def get_subagent_names() -> list[str]:
     """
     names = list(BUILTIN_SUBAGENTS.keys())
 
-    # Merge custom_agents from config.yaml
-    from deerflow.config.subagents_config import get_subagents_app_config
-
-    app_config = get_subagents_app_config()
-    for custom_name in app_config.custom_agents:
+    for custom_name in app_config.subagents.custom_agents:
         if custom_name not in names:
             names.append(custom_name)
 
     return names
 
 
-def get_available_subagent_names() -> list[str]:
+def get_available_subagent_names(app_config: AppConfig) -> list[str]:
     """Get subagent names that should be exposed to the active runtime.
 
     Returns:
         List of subagent names visible to the current sandbox configuration.
     """
-    names = get_subagent_names()
+    names = get_subagent_names(app_config)
     try:
-        host_bash_allowed = is_host_bash_allowed()
+        host_bash_allowed = is_host_bash_allowed(app_config)
     except Exception:
         logger.debug("Could not determine host bash availability; exposing all subagents")
         return names

backend/packages/harness/deerflow/tools/builtins/present_file_tool.py

@@ -52,7 +52,7 @@ def _normalize_presented_filepath(
     if runtime.state is None:
         raise ValueError("Thread runtime state is not available")
 
-    thread_id = _get_thread_id(runtime)
+    thread_id = runtime.context.thread_id
     if not thread_id:
         raise ValueError("Thread ID is not available in runtime context or runtime config")
 
@@ -66,10 +66,7 @@ def _normalize_presented_filepath(
     virtual_prefix = VIRTUAL_PATH_PREFIX.lstrip("/")
 
     if stripped == virtual_prefix or stripped.startswith(virtual_prefix + "/"):
-        try:
-            actual_path = get_paths().resolve_virtual_path(thread_id, filepath, user_id=get_effective_user_id())
-        except TypeError:
-            actual_path = get_paths().resolve_virtual_path(thread_id, filepath)
+        actual_path = get_paths().resolve_virtual_path(thread_id, filepath, user_id=get_effective_user_id())
     else:
         actual_path = Path(filepath).expanduser().resolve()
 

backend/packages/harness/deerflow/tools/builtins/setup_agent_tool.py

@@ -27,7 +27,7 @@ def setup_agent(
         skills: Optional list of skill names this agent should use. None means use all enabled skills, empty list means no skills.
     """
 
-    agent_name: str | None = runtime.context.get("agent_name") if runtime.context else None
+    agent_name: str | None = runtime.context.agent_name
     agent_dir = None
     is_new_dir = False
 

backend/packages/harness/deerflow/tools/builtins/task_tool.py

@@ -11,6 +11,7 @@ from langgraph.config import get_stream_writer
 from langgraph.typing import ContextT
 
 from deerflow.agents.thread_state import ThreadState
+from deerflow.config.deer_flow_context import resolve_context
 from deerflow.sandbox.security import LOCAL_BASH_SUBAGENT_DISABLED_MESSAGE, is_host_bash_allowed
 from deerflow.subagents import SubagentExecutor, get_available_subagent_names, get_subagent_config
 from deerflow.subagents.executor import SubagentStatus, cleanup_background_task, get_background_task_result, request_cancel_background_task
@@ -74,14 +75,15 @@ async def task_tool(
         subagent_type: The type of subagent to use. ALWAYS PROVIDE THIS PARAMETER THIRD.
         max_turns: Optional maximum number of agent turns. Defaults to subagent's configured max.
     """
-    available_subagent_names = get_available_subagent_names()
+    ctx = resolve_context(runtime)
+    available_subagent_names = get_available_subagent_names(ctx.app_config)
 
     # Get subagent configuration
-    config = get_subagent_config(subagent_type)
+    config = get_subagent_config(subagent_type, ctx.app_config)
     if config is None:
         available = ", ".join(available_subagent_names)
         return f"Error: Unknown subagent type '{subagent_type}'. Available: {available}"
-    if subagent_type == "bash" and not is_host_bash_allowed():
+    if subagent_type == "bash" and not is_host_bash_allowed(ctx.app_config):
         return f"Error: {LOCAL_BASH_SUBAGENT_DISABLED_MESSAGE}"
 
     # Build config overrides
@@ -105,9 +107,7 @@ async def task_tool(
     if runtime is not None:
         sandbox_state = runtime.state.get("sandbox")
         thread_data = runtime.state.get("thread_data")
-        thread_id = runtime.context.get("thread_id") if runtime.context else None
-        if thread_id is None:
-            thread_id = runtime.config.get("configurable", {}).get("thread_id")
+        thread_id = runtime.context.thread_id
 
         # Try to get parent model from configurable
         metadata = runtime.config.get("metadata", {})
@@ -131,12 +131,13 @@ async def task_tool(
     parent_tool_groups = metadata.get("tool_groups")
 
     # Subagents should not have subagent tools enabled (prevent recursive nesting)
-    tools = get_available_tools(model_name=parent_model, groups=parent_tool_groups, subagent_enabled=False)
+    tools = get_available_tools(model_name=parent_model, groups=parent_tool_groups, subagent_enabled=False, app_config=ctx.app_config)
 
     # Create executor
     executor = SubagentExecutor(
         config=config,
         tools=tools,
+        app_config=ctx.app_config,
         parent_model=parent_model,
         sandbox_state=sandbox_state,
         thread_data=thread_data,

backend/packages/harness/deerflow/tools/skill_manage_tool.py

@@ -5,7 +5,7 @@ from __future__ import annotations
 import asyncio
 import logging
 import shutil
-from typing import Any
+from typing import TYPE_CHECKING, Any
 from weakref import WeakValueDictionary
 
 from langchain.tools import ToolRuntime, tool
@@ -13,6 +13,9 @@ from langgraph.typing import ContextT
 
 from deerflow.agents.lead_agent.prompt import refresh_skills_system_prompt_cache_async
 from deerflow.agents.thread_state import ThreadState
+
+if TYPE_CHECKING:
+    from deerflow.config.app_config import AppConfig
 from deerflow.mcp.tools import _make_sync_tool_wrapper
 from deerflow.skills.manager import (
     append_history,
@@ -45,9 +48,7 @@ def _get_lock(name: str) -> asyncio.Lock:
 def _get_thread_id(runtime: ToolRuntime[ContextT, ThreadState] | None) -> str | None:
     if runtime is None:
         return None
-    if runtime.context and runtime.context.get("thread_id"):
-        return runtime.context.get("thread_id")
-    return runtime.config.get("configurable", {}).get("thread_id")
+    return runtime.context.thread_id or None
 
 
 def _history_record(*, action: str, file_path: str, prev_content: str | None, new_content: str | None, thread_id: str | None, scanner: dict[str, Any]) -> dict[str, Any]:
@@ -62,8 +63,8 @@ def _history_record(*, action: str, file_path: str, prev_content: str | None, ne
     }
 
 
-async def _scan_or_raise(content: str, *, executable: bool, location: str) -> dict[str, str]:
-    result = await scan_skill_content(content, executable=executable, location=location)
+async def _scan_or_raise(app_config: "AppConfig", content: str, *, executable: bool, location: str) -> dict[str, str]:
+    result = await scan_skill_content(app_config, content, executable=executable, location=location)
     if result.decision == "block":
         raise ValueError(f"Security scan blocked the write: {result.reason}")
     if executable and result.decision != "allow":
@@ -96,50 +97,55 @@ async def _skill_manage_impl(
         replace: Replacement text for patch.
         expected_count: Optional expected number of replacements for patch.
     """
+    from deerflow.config.deer_flow_context import resolve_context
+
     name = validate_skill_name(name)
     lock = _get_lock(name)
     thread_id = _get_thread_id(runtime)
+    app_config = resolve_context(runtime).app_config
 
     async with lock:
         if action == "create":
-            if await _to_thread(custom_skill_exists, name):
+            if await _to_thread(custom_skill_exists, name, app_config):
                 raise ValueError(f"Custom skill '{name}' already exists.")
             if content is None:
                 raise ValueError("content is required for create.")
             await _to_thread(validate_skill_markdown_content, name, content)
-            scan = await _scan_or_raise(content, executable=False, location=f"{name}/SKILL.md")
-            skill_file = await _to_thread(get_custom_skill_file, name)
+            scan = await _scan_or_raise(app_config, content, executable=False, location=f"{name}/SKILL.md")
+            skill_file = await _to_thread(get_custom_skill_file, name, app_config)
             await _to_thread(atomic_write, skill_file, content)
             await _to_thread(
                 append_history,
                 name,
                 _history_record(action="create", file_path="SKILL.md", prev_content=None, new_content=content, thread_id=thread_id, scanner=scan),
+                app_config,
             )
-            await refresh_skills_system_prompt_cache_async()
+            await refresh_skills_system_prompt_cache_async(app_config)
             return f"Created custom skill '{name}'."
 
         if action == "edit":
-            await _to_thread(ensure_custom_skill_is_editable, name)
+            await _to_thread(ensure_custom_skill_is_editable, name, app_config)
             if content is None:
                 raise ValueError("content is required for edit.")
             await _to_thread(validate_skill_markdown_content, name, content)
-            scan = await _scan_or_raise(content, executable=False, location=f"{name}/SKILL.md")
-            skill_file = await _to_thread(get_custom_skill_file, name)
+            scan = await _scan_or_raise(app_config, content, executable=False, location=f"{name}/SKILL.md")
+            skill_file = await _to_thread(get_custom_skill_file, name, app_config)
             prev_content = await _to_thread(skill_file.read_text, encoding="utf-8")
             await _to_thread(atomic_write, skill_file, content)
             await _to_thread(
                 append_history,
                 name,
                 _history_record(action="edit", file_path="SKILL.md", prev_content=prev_content, new_content=content, thread_id=thread_id, scanner=scan),
+                app_config,
             )
-            await refresh_skills_system_prompt_cache_async()
+            await refresh_skills_system_prompt_cache_async(app_config)
             return f"Updated custom skill '{name}'."
 
         if action == "patch":
-            await _to_thread(ensure_custom_skill_is_editable, name)
+            await _to_thread(ensure_custom_skill_is_editable, name, app_config)
             if find is None or replace is None:
                 raise ValueError("find and replace are required for patch.")
-            skill_file = await _to_thread(get_custom_skill_file, name)
+            skill_file = await _to_thread(get_custom_skill_file, name, app_config)
             prev_content = await _to_thread(skill_file.read_text, encoding="utf-8")
             occurrences = prev_content.count(find)
             if occurrences == 0:
@@ -149,51 +155,54 @@ async def _skill_manage_impl(
             replacement_count = expected_count if expected_count is not None else 1
             new_content = prev_content.replace(find, replace, replacement_count)
             await _to_thread(validate_skill_markdown_content, name, new_content)
-            scan = await _scan_or_raise(new_content, executable=False, location=f"{name}/SKILL.md")
+            scan = await _scan_or_raise(app_config, new_content, executable=False, location=f"{name}/SKILL.md")
             await _to_thread(atomic_write, skill_file, new_content)
             await _to_thread(
                 append_history,
                 name,
                 _history_record(action="patch", file_path="SKILL.md", prev_content=prev_content, new_content=new_content, thread_id=thread_id, scanner=scan),
+                app_config,
             )
-            await refresh_skills_system_prompt_cache_async()
+            await refresh_skills_system_prompt_cache_async(app_config)
             return f"Patched custom skill '{name}' ({replacement_count} replacement(s) applied, {occurrences} match(es) found)."
 
         if action == "delete":
-            await _to_thread(ensure_custom_skill_is_editable, name)
-            skill_dir = await _to_thread(get_custom_skill_dir, name)
-            prev_content = await _to_thread(read_custom_skill_content, name)
+            await _to_thread(ensure_custom_skill_is_editable, name, app_config)
+            skill_dir = await _to_thread(get_custom_skill_dir, name, app_config)
+            prev_content = await _to_thread(read_custom_skill_content, name, app_config)
             await _to_thread(
                 append_history,
                 name,
                 _history_record(action="delete", file_path="SKILL.md", prev_content=prev_content, new_content=None, thread_id=thread_id, scanner={"decision": "allow", "reason": "Deletion requested."}),
+                app_config,
             )
             await _to_thread(shutil.rmtree, skill_dir)
-            await refresh_skills_system_prompt_cache_async()
+            await refresh_skills_system_prompt_cache_async(app_config)
             return f"Deleted custom skill '{name}'."
 
         if action == "write_file":
-            await _to_thread(ensure_custom_skill_is_editable, name)
+            await _to_thread(ensure_custom_skill_is_editable, name, app_config)
             if path is None or content is None:
                 raise ValueError("path and content are required for write_file.")
-            target = await _to_thread(ensure_safe_support_path, name, path)
+            target = await _to_thread(ensure_safe_support_path, name, path, app_config)
             exists = await _to_thread(target.exists)
             prev_content = await _to_thread(target.read_text, encoding="utf-8") if exists else None
             executable = "scripts/" in path or path.startswith("scripts/")
-            scan = await _scan_or_raise(content, executable=executable, location=f"{name}/{path}")
+            scan = await _scan_or_raise(app_config, content, executable=executable, location=f"{name}/{path}")
             await _to_thread(atomic_write, target, content)
             await _to_thread(
                 append_history,
                 name,
                 _history_record(action="write_file", file_path=path, prev_content=prev_content, new_content=content, thread_id=thread_id, scanner=scan),
+                app_config,
             )
             return f"Wrote '{path}' for custom skill '{name}'."
 
         if action == "remove_file":
-            await _to_thread(ensure_custom_skill_is_editable, name)
+            await _to_thread(ensure_custom_skill_is_editable, name, app_config)
             if path is None:
                 raise ValueError("path is required for remove_file.")
-            target = await _to_thread(ensure_safe_support_path, name, path)
+            target = await _to_thread(ensure_safe_support_path, name, path, app_config)
             if not await _to_thread(target.exists):
                 raise FileNotFoundError(f"Supporting file '{path}' not found for skill '{name}'.")
             prev_content = await _to_thread(target.read_text, encoding="utf-8")
@@ -202,10 +211,11 @@ async def _skill_manage_impl(
                 append_history,
                 name,
                 _history_record(action="remove_file", file_path=path, prev_content=prev_content, new_content=None, thread_id=thread_id, scanner={"decision": "allow", "reason": "Deletion requested."}),
+                app_config,
             )
             return f"Removed '{path}' from custom skill '{name}'."
 
-        if await _to_thread(public_skill_exists, name):
+        if await _to_thread(public_skill_exists, name, app_config):
             raise ValueError(f"'{name}' is a built-in skill. To customise it, create a new skill with the same name under skills/custom/.")
         raise ValueError(f"Unsupported action '{action}'.")
 

backend/packages/harness/deerflow/tools/tools.py

@@ -2,7 +2,7 @@ import logging
 
 from langchain.tools import BaseTool
 
-from deerflow.config import get_app_config
+from deerflow.config.app_config import AppConfig
 from deerflow.reflection import resolve_variable
 from deerflow.sandbox.security import is_host_bash_allowed
 from deerflow.tools.builtins import ask_clarification_tool, present_file_tool, task_tool, view_image_tool
@@ -37,6 +37,8 @@ def get_available_tools(
     include_mcp: bool = True,
     model_name: str | None = None,
     subagent_enabled: bool = False,
+    *,
+    app_config: AppConfig,
 ) -> list[BaseTool]:
     """Get all available tools from config.
 
@@ -48,11 +50,12 @@ def get_available_tools(
         include_mcp: Whether to include tools from MCP servers (default: True).
         model_name: Optional model name to determine if vision tools should be included.
         subagent_enabled: Whether to include subagent tools (task, task_status).
+        app_config: Application config — required.
 
     Returns:
         List of available tools.
     """
-    config = get_app_config()
+    config = app_config
     tool_configs = [tool for tool in config.tools if groups is None or tool.group in groups]
 
     # Do not expose host bash by default when LocalSandboxProvider is active.
@@ -138,10 +141,9 @@ def get_available_tools(
     # Add invoke_acp_agent tool if any ACP agents are configured
     acp_tools: list[BaseTool] = []
     try:
-        from deerflow.config.acp_config import get_acp_agents
         from deerflow.tools.builtins.invoke_acp_agent_tool import build_invoke_acp_agent_tool
 
-        acp_agents = get_acp_agents()
+        acp_agents = config.acp_agents
         if acp_agents:
             acp_tools.append(build_invoke_acp_agent_tool(acp_agents))
             logger.info(f"Including invoke_acp_agent tool ({len(acp_agents)} agent(s): {list(acp_agents.keys())})")

backend/packages/harness/deerflow/utils/file_conversion.py

@@ -19,8 +19,6 @@ import logging
 import re
 from pathlib import Path
 
-from deerflow.config.app_config import get_app_config
-
 logger = logging.getLogger(__name__)
 
 # File extensions that should be converted to markdown
@@ -135,7 +133,7 @@ def _do_convert(file_path: Path, pdf_converter: str) -> str:
     return _convert_with_markitdown(file_path)
 
 
-async def convert_file_to_markdown(file_path: Path) -> Path | None:
+async def convert_file_to_markdown(file_path: Path, app_config: object | None = None) -> Path | None:
     """Convert a supported document file to Markdown.
 
     PDF files are handled with a two-converter strategy (see module docstring).
@@ -144,12 +142,14 @@ async def convert_file_to_markdown(file_path: Path) -> Path | None:
 
     Args:
         file_path: Path to the file to convert.
+        app_config: Optional AppConfig (for pdf_converter preference). When
+            omitted, defaults to ``auto``.
 
     Returns:
         Path to the generated .md file, or None if conversion failed.
     """
     try:
-        pdf_converter = _get_pdf_converter()
+        pdf_converter = _get_pdf_converter(app_config)
         file_size = file_path.stat().st_size
 
         if file_size > _ASYNC_THRESHOLD_BYTES:
@@ -288,28 +288,20 @@ def extract_outline(md_path: Path) -> list[dict]:
     return outline
 
 
-def _get_uploads_config_value(key: str, default: object) -> object:
-    """Read a value from the uploads config, supporting dict and attribute access."""
-    cfg = get_app_config()
-    uploads_cfg = getattr(cfg, "uploads", None)
-    if isinstance(uploads_cfg, dict):
-        return uploads_cfg.get(key, default)
-    return getattr(uploads_cfg, key, default)
-
-
-def _get_pdf_converter() -> str:
+def _get_pdf_converter(app_config: object | None) -> str:
     """Read pdf_converter setting from app config, defaulting to 'auto'.
 
     Normalizes the value to lowercase and validates it against the allowed set
     so that values like 'AUTO' or 'MarkItDown' from config.yaml don't silently
     fall through to unexpected behaviour.
     """
-    try:
-        raw = str(_get_uploads_config_value("pdf_converter", "auto")).strip().lower()
-        if raw not in _ALLOWED_PDF_CONVERTERS:
-            logger.warning("Invalid pdf_converter value %r; falling back to 'auto'", raw)
-            return "auto"
-        return raw
-    except Exception:
-        pass
-    return "auto"
+    if app_config is None:
+        return "auto"
+    uploads_cfg = getattr(app_config, "uploads", None)
+    if uploads_cfg is None:
+        return "auto"
+    raw = str(getattr(uploads_cfg, "pdf_converter", "auto")).strip().lower()
+    if raw not in _ALLOWED_PDF_CONVERTERS:
+        logger.warning("Invalid pdf_converter value %r; falling back to 'auto'", raw)
+        return "auto"
+    return raw

backend/pyproject.toml

@@ -38,9 +38,6 @@ markers = [
     "no_auto_user: disable the conftest autouse contextvar fixture for this test",
 ]
 
-[tool.uv]
-index-url = "https://pypi.org/simple"
-
 [tool.uv.workspace]
 members = ["packages/harness"]
 

backend/scripts/migrate_user_isolation.py

@@ -5,10 +5,11 @@ Usage:
 
 The script is idempotent — re-running it after a successful migration is a no-op.
 """
-
 import argparse
+import json
 import logging
 import shutil
+from pathlib import Path
 
 from deerflow.config.paths import Paths, get_paths
 

backend/tests/_router_auth_helpers.py

@@ -29,6 +29,7 @@ apps with the real middleware — those should not use this module.
 from __future__ import annotations
 
 from collections.abc import Callable
+from typing import ParamSpec, TypeVar
 from unittest.mock import AsyncMock, MagicMock
 from uuid import uuid4
 
@@ -112,7 +113,11 @@ def make_authed_test_app(
     return app
 
 
-def call_unwrapped[*P, R](decorated: Callable[P, R], /, *args: P.args, **kwargs: P.kwargs) -> R:
+_P = ParamSpec("_P")
+_R = TypeVar("_R")
+
+
+def call_unwrapped(decorated: Callable[_P, _R], /, *args: _P.args, **kwargs: _P.kwargs) -> _R:
     """Invoke the underlying function of a ``@require_permission``-decorated route.
 
     ``functools.wraps`` sets ``__wrapped__`` on each layer; we walk all