deer-flow/backend/packages/harness/deerflow/client.py

"""DeerFlowClient — Embedded Python client for DeerFlow agent system.

Provides direct programmatic access to DeerFlow's agent capabilities
without requiring LangGraph Server or Gateway API processes.

Usage:
    from deerflow.client import DeerFlowClient

    client = DeerFlowClient()
    response = client.chat("Analyze this paper for me", thread_id="my-thread")
    print(response)

    # Streaming
    for event in client.stream("hello"):
        print(event)
"""

import asyncio
import concurrent.futures
import copy
import logging
import mimetypes
import os
import shutil
import uuid
from collections.abc import Generator, Mapping, Sequence
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Literal

from langchain.agents import create_agent
from langchain.agents.middleware import AgentMiddleware
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
from langchain_core.runnables import RunnableConfig

from deerflow.agents.lead_agent.agent import _authorize_model_name, build_middlewares
from deerflow.agents.lead_agent.prompt import apply_prompt_template, get_enabled_skills_for_config
from deerflow.agents.thread_state import get_thread_state_schema, normalize_middleware_state_schemas
from deerflow.authz.principal import build_principal_from_context
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,
    atomic_write_extensions_config,
    extensions_config_file_lock,
    extensions_config_write_lock,
    get_extensions_config,
    reload_extensions_config,
)
from deerflow.config.paths import get_paths
from deerflow.config.subagent_runtime_config import SubagentRuntimeConfig
from deerflow.models import create_chat_model
from deerflow.runtime import CheckpointStateAccessor
from deerflow.runtime.checkpoint_mode import (
    ensure_checkpoint_mode_compatible,
    freeze_checkpoint_channel_mode,
    freeze_checkpoint_snapshot_frequency,
    inject_checkpoint_mode,
)
from deerflow.runtime.goal import DEFAULT_MAX_GOAL_CONTINUATIONS, build_goal_state, goal_thread_lock, read_thread_goal, write_thread_goal
from deerflow.runtime.user_context import get_effective_user_id
from deerflow.skills.describe import build_skill_search_setup
from deerflow.skills.storage import get_or_new_user_skill_storage
from deerflow.subagents.capacity import configure_subagent_execution_capacity
from deerflow.tools.builtins.tool_search import assemble_deferred_tools, build_mcp_routing_middleware, get_mcp_routing_hints_prompt_section
from deerflow.trace_context import DEERFLOW_TRACE_METADATA_KEY, bind_trace_id, ensure_trace_id, generate_trace_id, get_current_trace_id, reset_trace_id
from deerflow.tracing import build_tracing_callbacks, inject_langfuse_metadata
from deerflow.uploads.manager import (
    claim_unique_filename,
    delete_file_safe,
    enrich_file_listing,
    ensure_uploads_dir,
    get_uploads_dir,
    list_files_in_dir,
    upload_artifact_url,
    upload_virtual_path,
)
from deerflow.utils.thread_id import resolve_thread_id, validate_thread_id

logger = logging.getLogger(__name__)

_EMBEDDED_AUTHORIZATION_CONTEXT_KEYS = frozenset(
    {
        "user_id",
        "user_role",
        "oauth_provider",
        "oauth_id",
        "channel_user_id",
        "is_internal",
        "authz_attributes",
    }
)


def _run_async_from_sync(coro):
    """Run an async helper from this synchronous client API."""
    try:
        loop = asyncio.get_running_loop()
    except RuntimeError:
        loop = None

    if loop is not None and loop.is_running():
        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
            return executor.submit(asyncio.run, coro).result()
    return asyncio.run(coro)


StreamEventType = Literal["values", "messages-tuple", "custom", "end"]


@dataclass
class StreamEvent:
    """A single event from the streaming agent response.

    Event types align with the LangGraph SSE protocol:
        - ``"values"``: Full state snapshot (title, messages, artifacts).
        - ``"messages-tuple"``: Per-message update (AI text, tool calls, tool results).
        - ``"end"``: Stream finished.

    Attributes:
        type: Event type.
        data: Event payload. Contents vary by type.
    """

    type: StreamEventType
    data: dict[str, Any] = field(default_factory=dict)


class DeerFlowClient:
    """Embedded Python client for DeerFlow agent system.

    Provides direct programmatic access to DeerFlow's agent capabilities
    without requiring LangGraph Server or Gateway API processes.

    Note:
        Multi-turn conversations require a ``checkpointer``. Without one,
        each ``stream()`` / ``chat()`` call is stateless — ``thread_id``
        is only used for file isolation (uploads / artifacts).

        The system prompt (including date, memory, and skills context) is
        generated when the internal agent is first created and cached until
        the configuration key changes. Call :meth:`reset_agent` to force
        a refresh in long-running processes.

    Example::

        from deerflow.client import DeerFlowClient

        client = DeerFlowClient()

        # Simple one-shot
        print(client.chat("hello"))

        # Streaming
        for event in client.stream("hello"):
            print(event.type, event.data)

        # Configuration queries
        print(client.list_models())
        print(client.list_skills())
    """

    def __init__(
        self,
        config_path: str | None = None,
        checkpointer=None,
        *,
        model_name: str | None = None,
        thinking_enabled: bool = True,
        subagent_enabled: bool = False,
        plan_mode: bool = False,
        agent_name: str | None = None,
        available_skills: set[str] | None = None,
        middlewares: Sequence[AgentMiddleware] | None = None,
        environment: str | None = None,
    ):
        """Initialize the client.

        Loads configuration but defers agent creation to first use.

        Args:
            config_path: Path to config.yaml. Uses default resolution if None.
            checkpointer: LangGraph checkpointer instance for state persistence.
                Required for multi-turn conversations on the same thread_id.
                Without a checkpointer, each call is stateless.
            model_name: Override the default model name from config.
            thinking_enabled: Enable model's extended thinking.
            subagent_enabled: Enable subagent delegation.
            plan_mode: Enable TodoList middleware for plan mode.
            agent_name: Name of the agent to use.
            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.
            environment: Deployment environment label that ends up in
                ``langfuse_tags`` (e.g. ``"production"`` / ``"staging"``).
                When ``None`` the worker/client falls back to the
                ``DEER_FLOW_ENV`` or ``ENVIRONMENT`` env vars. Pass an
                explicit value for programmatic callers that do not want
                env-var coupling.
        """
        if config_path is not None:
            reload_app_config(config_path)
        self._app_config = get_app_config()
        runtime_config = getattr(self._app_config, "subagent_runtime", None)
        if not isinstance(runtime_config, SubagentRuntimeConfig):
            # Preserve compatibility with lightweight embedded/test configs
            # created before the startup-only section existed.
            runtime_config = SubagentRuntimeConfig()
        configure_subagent_execution_capacity(runtime_config)
        self._subagent_execution_capacity = runtime_config.max_running
        self._checkpoint_channel_mode = freeze_checkpoint_channel_mode(self._app_config.database.checkpoint_channel_mode)
        self._checkpoint_snapshot_frequency = freeze_checkpoint_snapshot_frequency(self._app_config.database.checkpoint_delta.snapshot_frequency)

        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}")

        self._checkpointer = checkpointer
        self._model_name = model_name
        self._thinking_enabled = thinking_enabled
        self._subagent_enabled = subagent_enabled
        self._plan_mode = plan_mode
        self._agent_name = agent_name
        self._available_skills = set(available_skills) if available_skills is not None else None
        self._middlewares = list(middlewares) if middlewares else []
        self._environment = environment

        # Lazy agent — created on first call, recreated when config changes.
        self._agent = None
        self._agent_config_key: tuple | None = None

    def reset_agent(self) -> None:
        """Force the internal agent to be recreated on the next call.

        Use this after external changes (e.g. memory updates, skill
        installations) that should be reflected in the system prompt
        or tool set.
        """
        self._agent = None
        self._agent_config_key = None

    # ------------------------------------------------------------------
    # Internal helpers
    # ------------------------------------------------------------------

    @staticmethod
    def _atomic_write_json(path: Path, data: dict) -> None:
        """Write JSON to *path* atomically (temp file + replace)."""
        atomic_write_extensions_config(path, data)

    def _get_runnable_config(self, thread_id: str, **overrides) -> RunnableConfig:
        """Build a RunnableConfig for agent invocation."""
        configurable = {
            "thread_id": thread_id,
            "model_name": overrides.get("model_name", self._model_name),
            "thinking_enabled": overrides.get("thinking_enabled", self._thinking_enabled),
            "is_plan_mode": overrides.get("plan_mode", self._plan_mode),
            "subagent_enabled": overrides.get("subagent_enabled", self._subagent_enabled),
        }
        return RunnableConfig(
            configurable=configurable,
            recursion_limit=overrides.get("recursion_limit", 100),
        )

    def _ensure_agent(self, config: RunnableConfig, *, context: Mapping[str, Any] | None = None):
        """Create (or recreate) the agent when config-dependent params change."""
        cfg = dict(config.get("configurable", {}) or {})
        if context is not None:
            cfg.update(context)

        authorization_identity = None
        if self._app_config.authorization.enabled:
            principal = build_principal_from_context(
                cfg,
                default_role=self._app_config.authorization.default_role,
            )
            authorization_identity = (
                principal.user_id,
                principal.role,
                principal.oauth_provider,
                principal.oauth_id,
                principal.channel_user_id,
                principal.is_internal,
                copy.deepcopy(principal.attributes),
            )
        key = (
            cfg.get("model_name"),
            cfg.get("thinking_enabled"),
            cfg.get("is_plan_mode"),
            cfg.get("subagent_enabled"),
            cfg.get("max_concurrent_subagents"),
            cfg.get("max_total_subagents"),
            self._agent_name,
            frozenset(self._available_skills) if self._available_skills is not None else None,
            self._checkpoint_channel_mode,
            self._checkpoint_snapshot_frequency,
            authorization_identity,
        )

        if self._agent is not None and self._agent_config_key == key:
            return

        thinking_enabled = cfg.get("thinking_enabled", True)
        model_name = cfg.get("model_name")
        # Phase 3: enforce model:use authorization on the embedded/library path
        # too, mirroring the Gateway runtime path in ``_make_lead_agent`` so the
        # role-scoped model policy cannot be bypassed by constructing the agent
        # through ``DeerFlowClient``. Resolve the ``None`` default to a concrete
        # name first (what ``create_chat_model(name=None)`` would pick) so the
        # policy covers the implicit default model. ``cfg`` already carries the
        # identity that ``apply_tool_authorization`` reads below.
        if model_name is None and self._app_config.models:
            model_name = self._app_config.models[0].name
        model_name = _authorize_model_name(model_name, context=cfg, app_config=self._app_config)
        subagent_enabled = cfg.get("subagent_enabled", False)
        from deerflow.config.subagents_config import effective_subagent_concurrency

        # Lightweight integrations and older tests may construct a client via
        # ``__new__`` and inject only ``_app_config``. Production clients keep
        # the startup snapshot set by ``__init__``; the fallback preserves the
        # pre-snapshot construction contract without consulting global state.
        subagent_execution_capacity = getattr(
            self,
            "_subagent_execution_capacity",
            int(getattr(getattr(self._app_config, "subagent_runtime", None), "max_running", 3)),
        )
        max_concurrent_subagents = effective_subagent_concurrency(
            cfg.get("max_concurrent_subagents"),
            self._app_config,
            execution_capacity=subagent_execution_capacity,
        )
        max_total_subagents = cfg.get("max_total_subagents", self._app_config.subagents.max_total_per_run)

        tools = self._get_tools(model_name=model_name, subagent_enabled=subagent_enabled)

        # Add framework-provided tools before authorization so Layer 1 sees
        # every capability that can become model-visible.
        skills_list = get_enabled_skills_for_config(self._app_config)
        if self._available_skills is not None:
            skills_list = [s for s in skills_list if s.name in self._available_skills]
        skill_setup = build_skill_search_setup(
            skills_list,
            enabled=self._app_config.skills.deferred_discovery,
            container_base_path=self._app_config.skills.container_path,
        )
        late_tools = []
        if skill_setup.describe_skill_tool:
            late_tools.append(skill_setup.describe_skill_tool)

        # Apply authorization Layer 1 before deferred assembly.
        from deerflow.authz.tool_filter import apply_tool_authorization

        configured_tool_ids = {id(tool) for tool in tools}
        authorized_tools, _authz_provider = apply_tool_authorization(
            [*tools, *late_tools],
            context=cfg,
            app_config=self._app_config,
        )
        tools = [tool for tool in authorized_tools if id(tool) in configured_tool_ids]
        late_tools = [tool for tool in authorized_tools if id(tool) not in configured_tool_ids]
        final_tools, deferred_setup = assemble_deferred_tools(tools, enabled=self._app_config.tool_search.enabled)
        final_tools.extend(late_tools)
        mcp_routing_middleware = build_mcp_routing_middleware(
            final_tools,
            deferred_setup,
            top_k=self._app_config.tool_search.auto_promote_top_k,
        )
        mcp_routing_hints_section = get_mcp_routing_hints_prompt_section(authorized_tools, deferred_names=deferred_setup.deferred_names)

        effective_user_id = cfg.get("user_id") or get_effective_user_id()

        kwargs: dict[str, Any] = {
            # attach_tracing=False because ``stream()`` injects tracing
            # callbacks at the graph invocation root so a single embedded run
            # produces one trace with correct session_id / user_id propagation.
            # Attaching them again on the model would emit duplicate spans.
            "model": create_chat_model(name=model_name, thinking_enabled=thinking_enabled, attach_tracing=False),
            "tools": final_tools,
            "middleware": normalize_middleware_state_schemas(
                build_middlewares(
                    config,
                    model_name=model_name,
                    agent_name=self._agent_name,
                    available_skills=self._available_skills,
                    custom_middlewares=self._middlewares,
                    app_config=self._app_config,
                    deferred_setup=deferred_setup,
                    mcp_routing_middleware=mcp_routing_middleware,
                    user_id=effective_user_id,
                    authorization_provider=_authz_provider,
                    subagent_execution_capacity=subagent_execution_capacity,
                ),
                self._checkpoint_channel_mode,
                self._checkpoint_snapshot_frequency,
            ),
            "system_prompt": apply_prompt_template(
                subagent_enabled=subagent_enabled,
                max_concurrent_subagents=max_concurrent_subagents,
                max_total_subagents=max_total_subagents,
                agent_name=self._agent_name,
                available_skills=self._available_skills,
                app_config=self._app_config,
                deferred_names=deferred_setup.deferred_names,
                mcp_routing_hints_section=mcp_routing_hints_section,
                user_id=effective_user_id,
                skill_names=skill_setup.skill_names or None,
                subagent_execution_capacity=subagent_execution_capacity,
            ),
            "state_schema": get_thread_state_schema(self._checkpoint_channel_mode, self._checkpoint_snapshot_frequency),
        }
        checkpointer = self._checkpointer
        if checkpointer is None:
            from deerflow.runtime.checkpointer import get_checkpointer

            checkpointer = get_checkpointer()
        if checkpointer is not None:
            kwargs["checkpointer"] = checkpointer

        self._agent = create_agent(**kwargs)
        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):
        """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)

    @staticmethod
    def _serialize_tool_calls(tool_calls) -> list[dict]:
        """Reshape LangChain tool_calls into the wire format used in events."""
        return [{"name": tc["name"], "args": tc["args"], "id": tc.get("id")} for tc in tool_calls]

    @staticmethod
    def _serialize_additional_kwargs(msg) -> dict[str, Any] | None:
        """Copy message additional_kwargs when present."""
        additional_kwargs = getattr(msg, "additional_kwargs", None)
        if isinstance(additional_kwargs, dict) and additional_kwargs:
            return dict(additional_kwargs)
        return None

    @staticmethod
    def _ai_text_event(msg_id: str | None, text: str, usage: dict | None, additional_kwargs: dict[str, Any] | None = None) -> "StreamEvent":
        """Build a ``messages-tuple`` AI text event."""
        data: dict[str, Any] = {"type": "ai", "content": text, "id": msg_id}
        if usage:
            data["usage_metadata"] = usage
        if additional_kwargs:
            data["additional_kwargs"] = additional_kwargs
        return StreamEvent(type="messages-tuple", data=data)

    @staticmethod
    def _ai_tool_calls_event(msg_id: str | None, tool_calls, additional_kwargs: dict[str, Any] | None = None) -> "StreamEvent":
        """Build a ``messages-tuple`` AI tool-calls event."""
        data: dict[str, Any] = {
            "type": "ai",
            "content": "",
            "id": msg_id,
            "tool_calls": DeerFlowClient._serialize_tool_calls(tool_calls),
        }
        if additional_kwargs:
            data["additional_kwargs"] = additional_kwargs
        return StreamEvent(type="messages-tuple", data=data)

    @staticmethod
    def _tool_message_event(msg: ToolMessage) -> "StreamEvent":
        """Build a ``messages-tuple`` tool-result event from a ToolMessage."""
        data: dict[str, Any] = {
            "type": "tool",
            "content": DeerFlowClient._extract_text(msg.content),
            "name": msg.name,
            "tool_call_id": msg.tool_call_id,
            "id": msg.id,
        }
        if (artifact := getattr(msg, "artifact", None)) is not None:
            data["artifact"] = artifact
        return StreamEvent(type="messages-tuple", data=data)

    @staticmethod
    def _serialize_message(msg) -> dict:
        """Serialize a LangChain message to a plain dict for values events."""
        if isinstance(msg, AIMessage):
            d: dict[str, Any] = {"type": "ai", "content": msg.content, "id": getattr(msg, "id", None)}
            if msg.tool_calls:
                d["tool_calls"] = DeerFlowClient._serialize_tool_calls(msg.tool_calls)
            if getattr(msg, "usage_metadata", None):
                d["usage_metadata"] = msg.usage_metadata
            if additional_kwargs := DeerFlowClient._serialize_additional_kwargs(msg):
                d["additional_kwargs"] = additional_kwargs
            return d
        if isinstance(msg, ToolMessage):
            d = {
                "type": "tool",
                "content": DeerFlowClient._extract_text(msg.content),
                "name": getattr(msg, "name", None),
                "tool_call_id": getattr(msg, "tool_call_id", None),
                "id": getattr(msg, "id", None),
            }
            if additional_kwargs := DeerFlowClient._serialize_additional_kwargs(msg):
                d["additional_kwargs"] = additional_kwargs
            if (artifact := getattr(msg, "artifact", None)) is not None:
                d["artifact"] = artifact
            return d
        if isinstance(msg, HumanMessage):
            d = {"type": "human", "content": msg.content, "id": getattr(msg, "id", None)}
            if additional_kwargs := DeerFlowClient._serialize_additional_kwargs(msg):
                d["additional_kwargs"] = additional_kwargs
            return d
        if isinstance(msg, SystemMessage):
            d = {"type": "system", "content": msg.content, "id": getattr(msg, "id", None)}
            if additional_kwargs := DeerFlowClient._serialize_additional_kwargs(msg):
                d["additional_kwargs"] = additional_kwargs
            return d
        return {"type": "unknown", "content": str(msg), "id": getattr(msg, "id", None)}

    @staticmethod
    def _extract_text(content) -> str:
        """Extract plain text from AIMessage content (str or list of blocks).

        String chunks are concatenated without separators to avoid corrupting
        token/character deltas or chunked JSON payloads. Dict-based text blocks
        are treated as full text blocks and joined with newlines to preserve
        readability.
        """
        if isinstance(content, str):
            return content
        if isinstance(content, list):
            if content and all(isinstance(block, str) for block in content):
                chunk_like = len(content) > 1 and all(isinstance(block, str) and len(block) <= 20 and any(ch in block for ch in '{}[]":,') for block in content)
                return "".join(content) if chunk_like else "\n".join(content)

            pieces: list[str] = []
            pending_str_parts: list[str] = []

            def flush_pending_str_parts() -> None:
                if pending_str_parts:
                    pieces.append("".join(pending_str_parts))
                    pending_str_parts.clear()

            for block in content:
                if isinstance(block, str):
                    pending_str_parts.append(block)
                elif isinstance(block, dict):
                    flush_pending_str_parts()
                    text_val = block.get("text")
                    if isinstance(text_val, str):
                        pieces.append(text_val)

            flush_pending_str_parts()
            return "\n".join(pieces) if pieces else ""
        return str(content)

    # ------------------------------------------------------------------
    # Public API — threads
    # ------------------------------------------------------------------

    def _get_thread_checkpointer(self):
        checkpointer = self._checkpointer
        if checkpointer is None:
            from deerflow.runtime.checkpointer.provider import get_checkpointer

            checkpointer = get_checkpointer()
        return checkpointer

    def get_goal(self, thread_id: str) -> dict:
        """Return the active goal for a thread, if any."""
        validate_thread_id(thread_id)
        checkpointer = self._get_thread_checkpointer()
        goal = _run_async_from_sync(read_thread_goal(checkpointer, thread_id))
        return {"goal": goal}

    def set_goal(
        self,
        thread_id: str,
        objective: str,
        *,
        max_continuations: int = DEFAULT_MAX_GOAL_CONTINUATIONS,
    ) -> dict:
        """Set or replace a thread-scoped goal."""
        validate_thread_id(thread_id)
        checkpointer = self._get_thread_checkpointer()
        goal = build_goal_state(objective, max_continuations=max_continuations)

        async def _set_goal() -> None:
            async with goal_thread_lock(thread_id):
                await write_thread_goal(checkpointer, thread_id, goal, create_if_missing=True)

        _run_async_from_sync(_set_goal())
        return {"goal": goal}

    def clear_goal(self, thread_id: str) -> dict:
        """Clear the active goal for a thread."""
        validate_thread_id(thread_id)
        checkpointer = self._get_thread_checkpointer()

        async def _clear_goal() -> None:
            async with goal_thread_lock(thread_id):
                await write_thread_goal(checkpointer, thread_id, None)

        try:
            _run_async_from_sync(_clear_goal())
        except LookupError:
            pass
        return {"goal": None}

    def list_threads(self, limit: int = 10) -> dict:
        """List the recent N threads.

        Args:
            limit: Maximum number of threads to return. Default is 10.

        Returns:
            Dict with "thread_list" key containing list of thread info dicts,
            sorted by thread creation time descending.
        """
        checkpointer = self._get_thread_checkpointer()

        thread_info_map = {}

        for cp in checkpointer.list(config=None, limit=limit):
            cfg = cp.config.get("configurable", {})
            thread_id = cfg.get("thread_id")
            if not thread_id:
                continue

            ts = cp.checkpoint.get("ts")
            checkpoint_id = cfg.get("checkpoint_id")

            if thread_id not in thread_info_map:
                channel_values = cp.checkpoint.get("channel_values", {})
                thread_info_map[thread_id] = {
                    "thread_id": thread_id,
                    "created_at": ts,
                    "updated_at": ts,
                    "latest_checkpoint_id": checkpoint_id,
                    "title": channel_values.get("title"),
                }
            else:
                # Explicitly compare timestamps to ensure accuracy when iterating over unordered namespaces.
                # Treat None as "missing" and only compare when existing values are non-None.
                if ts is not None:
                    current_created = thread_info_map[thread_id]["created_at"]
                    if current_created is None or ts < current_created:
                        thread_info_map[thread_id]["created_at"] = ts

                    current_updated = thread_info_map[thread_id]["updated_at"]
                    if current_updated is None or ts > current_updated:
                        thread_info_map[thread_id]["updated_at"] = ts
                        thread_info_map[thread_id]["latest_checkpoint_id"] = checkpoint_id
                        channel_values = cp.checkpoint.get("channel_values", {})
                        thread_info_map[thread_id]["title"] = channel_values.get("title")

        threads = list(thread_info_map.values())
        threads.sort(key=lambda x: x.get("created_at") or "", reverse=True)

        return {"thread_list": threads[:limit]}

    def get_thread(self, thread_id: str) -> dict:
        """Get the complete materialized checkpoint history for a thread."""
        checkpointer = self._get_thread_checkpointer()
        config = self._get_runnable_config(thread_id)
        self._ensure_agent(config)
        if self._agent is None:
            raise RuntimeError("Agent was not initialized")

        accessor = CheckpointStateAccessor.bind(
            self._agent,
            checkpointer,
            mode=self._checkpoint_channel_mode,
        )
        # One streaming walk collects pending_writes per checkpoint id; a
        # per-snapshot get_tuple would cost one round-trip per checkpoint.
        pending_writes_by_checkpoint: dict[str, list] = {}
        for raw_tuple in checkpointer.list(config):
            raw_checkpoint_id = raw_tuple.config.get("configurable", {}).get("checkpoint_id")
            if raw_checkpoint_id:
                pending_writes_by_checkpoint[raw_checkpoint_id] = list(getattr(raw_tuple, "pending_writes", ()) or ())

        checkpoints = []
        for snapshot in accessor.history(config):
            values = dict(snapshot.values or {})
            if "messages" in values:
                values["messages"] = [self._serialize_message(message) if hasattr(message, "content") else message for message in values["messages"]]

            snapshot_config = snapshot.config or {}
            configurable = snapshot_config.get("configurable", {})
            parent_config = snapshot.parent_config or {}
            parent_configurable = parent_config.get("configurable", {})
            pending_writes = pending_writes_by_checkpoint.get(configurable.get("checkpoint_id"), [])

            checkpoints.append(
                {
                    "checkpoint_id": configurable.get("checkpoint_id"),
                    "parent_checkpoint_id": parent_configurable.get("checkpoint_id"),
                    "ts": snapshot.created_at,
                    "metadata": snapshot.metadata,
                    "values": values,
                    "pending_writes": [{"task_id": write[0], "channel": write[1], "value": write[2]} for write in pending_writes],
                }
            )

        checkpoints.sort(key=lambda checkpoint: checkpoint["ts"] or "")
        return {"thread_id": thread_id, "checkpoints": checkpoints}

    # ------------------------------------------------------------------
    # Public API — conversation
    # ------------------------------------------------------------------

    def stream(
        self,
        message: str,
        *,
        thread_id: str | None = None,
        **kwargs,
    ) -> Generator[StreamEvent, None, None]:
        """Stream a conversation turn with a DeerFlow request trace context.

        The embedded entry point, and like every other one it binds a trace id
        for the turn so logs, Langfuse metadata, and delegated work correlate.
        A caller that opened its own scope with ``request_trace_context`` keeps
        that id; otherwise the turn gets a fresh one.
        """
        # Resolve the id once, without mutating the caller's context.
        trace_id = get_current_trace_id() or generate_trace_id()

        # Bind the trace id only around each ``next()`` step, never across a
        # ``yield``. ``stream()`` is a sync generator, which shares the
        # caller's context — a ``with ensure_trace_context(): yield from ...``
        # would (1) leak the id into the caller's context between yields and
        # (2) risk ``ValueError: Token was created in a different Context``
        # when GC finalizes an abandoned generator in a different context.
        # Per-step set/reset keeps LangGraph node execution and its log
        # records inside the binding while returning control to the caller
        # with the ContextVar restored.
        inner = self._stream_turn(message, thread_id=thread_id, **kwargs)
        _EXHAUSTED = object()
        try:
            while True:
                token = bind_trace_id(trace_id)
                try:
                    try:
                        event = next(inner)
                    except StopIteration:
                        event = _EXHAUSTED
                finally:
                    reset_trace_id(token)
                if event is _EXHAUSTED:
                    break
                yield event
        finally:
            # close() drives the inner generator's finally path (GeneratorExit
            # on an abandoned stream), which still logs and fires callbacks --
            # bind the turn's id around it so that cleanup correlates with the
            # turn it belongs to. Set and reset in this same frame, never
            # across a yield, so the per-step cross-context safety holds even
            # when GC closes the generator from another Context.
            token = bind_trace_id(trace_id)
            try:
                inner.close()
            finally:
                reset_trace_id(token)

    def _stream_turn(
        self,
        message: str,
        *,
        thread_id: str | None = None,
        **kwargs,
    ) -> Generator[StreamEvent, None, None]:
        """Stream a conversation turn, yielding events incrementally.

        Each call sends one user message and yields events until the agent
        finishes its turn. A ``checkpointer`` must be provided at init time
        for multi-turn context to be preserved across calls.

        Event types align with the LangGraph SSE protocol so that
        consumers can switch between HTTP streaming and embedded mode
        without changing their event-handling logic.

        Token-level streaming
        ~~~~~~~~~~~~~~~~~~~~~
        This method subscribes to LangGraph's ``messages`` stream mode, so
        ``messages-tuple`` events for AI text are emitted as **deltas** as
        the model generates tokens, not as one cumulative dump at node
        completion.  Each delta carries a stable ``id`` — consumers that
        want the full text must accumulate ``content`` per ``id``.
        ``chat()`` already does this for you.

        Tool calls and tool results are still emitted once per logical
        message.  ``values`` events continue to carry full state snapshots
        after each graph node finishes; AI text already delivered via the
        ``messages`` stream is **not** re-synthesized from the snapshot to
        avoid duplicate deliveries.

        Why not reuse Gateway's ``run_agent``?
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        Gateway (``runtime/runs/worker.py``) has a complete streaming
        pipeline: ``run_agent`` → ``StreamBridge`` → ``sse_consumer``.  It
        looks like this client duplicates that work, but the two paths
        serve different audiences and **cannot** share execution:

        * ``run_agent`` is ``async def`` and uses ``agent.astream()``;
          this method is a sync generator using ``agent.stream()`` so
          callers can write ``for event in client.stream(...)`` without
          touching asyncio.  Bridging the two would require spinning up
          an event loop + thread per call.
        * Gateway events are JSON-serialized by ``serialize()`` for SSE
          wire transmission.  This client yields in-process stream event
          payloads directly as Python data structures (``StreamEvent``
          with ``data`` as a plain ``dict``), without the extra
          JSON/SSE serialization layer used for HTTP delivery.
        * ``StreamBridge`` is an asyncio-queue decoupling producers from
          consumers across an HTTP boundary (``Last-Event-ID`` replay,
          heartbeats, multi-subscriber fan-out).  A single in-process
          caller with a direct iterator needs none of that.

        So ``DeerFlowClient.stream()`` is a parallel, sync, in-process
        consumer of the same ``create_agent()`` factory — not a wrapper
        around Gateway.  The two paths **should** stay in sync on which
        LangGraph stream modes they subscribe to; that invariant is
        enforced by ``tests/test_client.py::test_messages_mode_emits_token_deltas``
        rather than by a shared constant, because the three layers
        (Graph, Platform SDK, HTTP) each use their own naming
        (``messages`` vs ``messages-tuple``) and cannot literally share
        a string.

        Args:
            message: User message text.
            thread_id: Thread ID for conversation context. Auto-generated if None.
            **kwargs: Override client defaults (model_name, thinking_enabled,
                plan_mode, subagent_enabled, recursion_limit). Trusted embedded
                callers may also provide user_id, user_role, oauth_provider,
                oauth_id, channel_user_id, is_internal, and authz_attributes.

        Yields:
            StreamEvent with one of:
            - type="values"          data={"title": str|None, "messages": [...], "artifacts": [...]}
            - type="custom"          data={...}
            - type="messages-tuple"  data={"type": "ai", "content": <delta>, "id": str}
            - type="messages-tuple"  data={"type": "ai", "content": <delta>, "id": str, "usage_metadata": {...}}
            - type="messages-tuple"  data={"type": "ai", "content": "", "id": str, "tool_calls": [...]}
            - type="messages-tuple"  data={"type": "ai", "content": "", "id": str, "additional_kwargs": {...}}
            - type="messages-tuple"  data={"type": "tool", "content": str, "name": str, "tool_call_id": str, "id": str}
              Tool results also include ``"artifact"`` when the source ToolMessage has a non-None artifact.
            - type="end"             data={"usage": {"input_tokens": int, "output_tokens": int, "total_tokens": int}}
        """
        thread_id = resolve_thread_id(thread_id)

        config = self._get_runnable_config(thread_id, **kwargs)
        inject_checkpoint_mode(config, self._checkpoint_channel_mode)
        checkpoint_config = {
            "configurable": {
                "thread_id": thread_id,
                "checkpoint_ns": "",
            }
        }
        checkpointer = self._checkpointer
        if checkpointer is None:
            from deerflow.runtime.checkpointer import get_checkpointer

            checkpointer = get_checkpointer()
        if checkpointer is not None:
            ensure_checkpoint_mode_compatible(
                checkpointer,
                checkpoint_config,
                self._checkpoint_channel_mode,
            )

        # Inject tracing callbacks and Langfuse trace metadata at the graph
        # invocation root so the embedded client matches the gateway worker's
        # behaviour: a single ``stream()`` produces one trace with all node /
        # LLM / tool calls nested under it, and the trace carries the reserved
        # ``langfuse_session_id`` / ``langfuse_user_id`` keys that the Langfuse
        # CallbackHandler lifts onto the root trace's ``sessionId`` / ``userId``.
        tracing_callbacks = build_tracing_callbacks()
        if tracing_callbacks:
            existing_callbacks = list(config.get("callbacks") or [])
            config["callbacks"] = [*existing_callbacks, *tracing_callbacks]

        run_id = str(uuid.uuid4())
        context: dict[str, Any] = {"thread_id": thread_id, "run_id": run_id}
        for key in _EMBEDDED_AUTHORIZATION_CONTEXT_KEYS:
            if key in kwargs:
                context[key] = kwargs[key]

        configurable = config.get("configurable") or {}
        deerflow_trace_id = ensure_trace_id()
        effective_user_id = context.get("user_id") or get_effective_user_id()
        if self._app_config.authorization.enabled:
            # Match the existing user-scoped storage/tracing identity when an
            # embedded caller relies on CurrentUser instead of an explicit
            # user_id override. Layer 1, Layer 2, and the agent cache must see
            # the same actor.
            context["user_id"] = effective_user_id
        inject_langfuse_metadata(
            config,
            thread_id=thread_id,
            user_id=effective_user_id,
            assistant_id=self._agent_name or "lead-agent",
            model_name=configurable.get("model_name") or self._model_name,
            environment=self._environment or os.environ.get("DEER_FLOW_ENV") or os.environ.get("ENVIRONMENT"),
            deerflow_trace_id=deerflow_trace_id,
        )

        self._ensure_agent(config, context=context)

        state: dict[str, Any] = {"messages": [HumanMessage(content=message, additional_kwargs={"run_id": run_id})]}
        context[DEERFLOW_TRACE_METADATA_KEY] = deerflow_trace_id
        if self._agent_name:
            context["agent_name"] = self._agent_name

        seen_ids: set[str] = set()
        # Cross-mode handoff: ids already streamed via LangGraph ``messages``
        # mode so the ``values`` path skips re-synthesis of the same message.
        streamed_ids: set[str] = set()
        # The same message id carries identical cumulative ``usage_metadata``
        # in both the final ``messages`` chunk and the values snapshot —
        # count it only on whichever arrives first.
        counted_usage_ids: set[str] = set()
        sent_additional_kwargs_by_id: dict[str, dict[str, Any]] = {}
        cumulative_usage: dict[str, int] = {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0}

        def _account_usage(msg_id: str | None, usage: Any) -> dict | None:
            """Add *usage* to cumulative totals if this id has not been counted.

            ``usage`` is a ``langchain_core.messages.UsageMetadata`` TypedDict
            or ``None``; typed as ``Any`` because TypedDicts are not
            structurally assignable to plain ``dict`` under strict type
            checking.  Returns the normalized usage dict (for attaching
            to an event) when we accepted it, otherwise ``None``.
            """
            if not usage:
                return None
            if msg_id and msg_id in counted_usage_ids:
                return None
            if msg_id:
                counted_usage_ids.add(msg_id)
            input_tokens = usage.get("input_tokens", 0) or 0
            output_tokens = usage.get("output_tokens", 0) or 0
            total_tokens = usage.get("total_tokens", 0) or 0
            cumulative_usage["input_tokens"] += input_tokens
            cumulative_usage["output_tokens"] += output_tokens
            cumulative_usage["total_tokens"] += total_tokens
            return {
                "input_tokens": input_tokens,
                "output_tokens": output_tokens,
                "total_tokens": total_tokens,
            }

        def _unsent_additional_kwargs(msg_id: str | None, additional_kwargs: dict[str, Any] | None) -> dict[str, Any] | None:
            if not additional_kwargs:
                return None
            if not msg_id:
                return additional_kwargs

            sent = sent_additional_kwargs_by_id.setdefault(msg_id, {})
            delta = {key: value for key, value in additional_kwargs.items() if sent.get(key) != value}
            if not delta:
                return None

            sent.update(delta)
            return delta

        for item in self._agent.stream(
            state,
            config=config,
            context=context,
            stream_mode=["values", "messages", "custom"],
        ):
            if isinstance(item, tuple) and len(item) == 2:
                mode, chunk = item
                mode = str(mode)
            else:
                mode, chunk = "values", item

            if mode == "custom":
                yield StreamEvent(type="custom", data=chunk)
                continue

            if mode == "messages":
                # LangGraph ``messages`` mode emits ``(message_chunk, metadata)``.
                if isinstance(chunk, tuple) and len(chunk) == 2:
                    msg_chunk, _metadata = chunk
                else:
                    msg_chunk = chunk

                msg_id = getattr(msg_chunk, "id", None)

                if isinstance(msg_chunk, AIMessage):
                    text = self._extract_text(msg_chunk.content)
                    additional_kwargs = self._serialize_additional_kwargs(msg_chunk)
                    counted_usage = _account_usage(msg_id, msg_chunk.usage_metadata)
                    sent_additional_kwargs = False

                    if text:
                        if msg_id:
                            streamed_ids.add(msg_id)
                        additional_kwargs_delta = _unsent_additional_kwargs(msg_id, additional_kwargs)
                        yield self._ai_text_event(
                            msg_id,
                            text,
                            counted_usage,
                            additional_kwargs_delta,
                        )
                        sent_additional_kwargs = bool(additional_kwargs_delta)

                    if msg_chunk.tool_calls:
                        if msg_id:
                            streamed_ids.add(msg_id)
                        additional_kwargs_delta = None if sent_additional_kwargs else _unsent_additional_kwargs(msg_id, additional_kwargs)
                        yield self._ai_tool_calls_event(
                            msg_id,
                            msg_chunk.tool_calls,
                            additional_kwargs_delta,
                        )

                elif isinstance(msg_chunk, ToolMessage):
                    if msg_id:
                        streamed_ids.add(msg_id)
                    yield self._tool_message_event(msg_chunk)
                continue

            # mode == "values"
            messages = chunk.get("messages", [])

            for msg in messages:
                msg_id = getattr(msg, "id", None)
                if msg_id and msg_id in seen_ids:
                    continue
                if msg_id:
                    seen_ids.add(msg_id)

                # Already streamed via ``messages`` mode; only (defensively)
                # capture usage here and skip re-synthesizing the event.
                if msg_id and msg_id in streamed_ids:
                    if isinstance(msg, AIMessage):
                        _account_usage(msg_id, getattr(msg, "usage_metadata", None))
                        additional_kwargs = self._serialize_additional_kwargs(msg)
                        additional_kwargs_delta = _unsent_additional_kwargs(msg_id, additional_kwargs)
                        if additional_kwargs_delta:
                            # Metadata-only follow-up: ``messages-tuple`` has no
                            # dedicated attribution event, so clients should
                            # merge this empty-content AI event by message id
                            # and ignore it for text rendering.
                            yield self._ai_text_event(msg_id, "", None, additional_kwargs_delta)
                    continue

                if isinstance(msg, AIMessage):
                    counted_usage = _account_usage(msg_id, msg.usage_metadata)
                    additional_kwargs = self._serialize_additional_kwargs(msg)
                    sent_additional_kwargs = False

                    if msg.tool_calls:
                        additional_kwargs_delta = _unsent_additional_kwargs(msg_id, additional_kwargs)
                        yield self._ai_tool_calls_event(
                            msg_id,
                            msg.tool_calls,
                            additional_kwargs_delta,
                        )
                        sent_additional_kwargs = bool(additional_kwargs_delta)

                    text = self._extract_text(msg.content)
                    if text:
                        additional_kwargs_delta = None if sent_additional_kwargs else _unsent_additional_kwargs(msg_id, additional_kwargs)
                        yield self._ai_text_event(
                            msg_id,
                            text,
                            counted_usage,
                            additional_kwargs_delta,
                        )
                    elif msg_id:
                        additional_kwargs_delta = None if sent_additional_kwargs else _unsent_additional_kwargs(msg_id, additional_kwargs)
                        if not additional_kwargs_delta:
                            continue
                        # See the metadata-only follow-up convention above.
                        yield self._ai_text_event(msg_id, "", None, additional_kwargs_delta)

                elif isinstance(msg, ToolMessage):
                    yield self._tool_message_event(msg)

            # Emit a values event for each state snapshot
            yield StreamEvent(
                type="values",
                data={
                    "title": chunk.get("title"),
                    "messages": [self._serialize_message(m) for m in messages],
                    "artifacts": chunk.get("artifacts", []),
                },
            )

        yield StreamEvent(type="end", data={"usage": cumulative_usage})

    def chat(self, message: str, *, thread_id: str | None = None, **kwargs) -> str:
        """Send a message and return the final text response.

        Convenience wrapper around :meth:`stream` that accumulates delta
        ``messages-tuple`` events per ``id`` and returns the text of the
        **last** AI message to complete.  Intermediate AI messages (e.g.
        planner drafts) are discarded — only the final id's accumulated
        text is returned.  Use :meth:`stream` directly if you need every
        delta as it arrives.

        Args:
            message: User message text.
            thread_id: Thread ID for conversation context. Auto-generated if None.
            **kwargs: Override client defaults (same as stream()).

        Returns:
            The accumulated text of the last AI message, or empty string
            if no AI text was produced.
        """
        # Per-id delta lists joined once at the end — avoids the O(n²) cost
        # of repeated ``str + str`` on a growing buffer for long responses.
        chunks: dict[str, list[str]] = {}
        last_id: str = ""
        for event in self.stream(message, thread_id=thread_id, **kwargs):
            if event.type == "messages-tuple" and event.data.get("type") == "ai":
                msg_id = event.data.get("id") or ""
                delta = event.data.get("content", "")
                if delta:
                    chunks.setdefault(msg_id, []).append(delta)
                    last_id = msg_id
        return "".join(chunks.get(last_id, ()))

    # ------------------------------------------------------------------
    # Public API — configuration queries
    # ------------------------------------------------------------------

    def list_models(self) -> dict:
        """List available models from configuration.

        Returns:
            Dict with "models" key containing list of model info dicts,
            matching the Gateway API ``ModelsListResponse`` schema.
        """
        token_usage_enabled = getattr(getattr(self._app_config, "token_usage", None), "enabled", False)
        if not isinstance(token_usage_enabled, bool):
            token_usage_enabled = False

        return {
            "models": [
                {
                    "name": model.name,
                    "model": getattr(model, "model", None),
                    "display_name": getattr(model, "display_name", None),
                    "description": getattr(model, "description", None),
                    "supports_thinking": getattr(model, "supports_thinking", False),
                    "supports_reasoning_effort": getattr(model, "supports_reasoning_effort", False),
                }
                for model in self._app_config.models
            ],
            "token_usage": {"enabled": token_usage_enabled},
        }

    def list_skills(self, enabled_only: bool = False) -> dict:
        """List available skills.

        Args:
            enabled_only: If True, only return enabled skills.

        Returns:
            Dict with "skills" key containing list of skill info dicts,
            matching the Gateway API ``SkillsListResponse`` schema.
        """
        storage = get_or_new_user_skill_storage(get_effective_user_id(), app_config=self._app_config)
        return {
            "skills": [
                {
                    "name": s.name,
                    "description": s.description,
                    "license": s.license,
                    "category": s.category,
                    "enabled": s.enabled,
                }
                for s in storage.load_skills(enabled_only=enabled_only)
            ]
        }

    def get_memory(self) -> dict:
        """Get current memory data.

        Returns:
            Memory data dict (see src/agents/memory/updater.py for structure).
        """
        from deerflow.agents.memory import get_memory_manager

        return get_memory_manager().get_memory(user_id=get_effective_user_id())

    def export_memory(self) -> dict:
        """Export current memory data for backup or transfer."""
        from deerflow.agents.memory import get_memory_manager

        return get_memory_manager().get_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 import get_memory_manager

        return get_memory_manager().import_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.

        Args:
            name: Model name.

        Returns:
            Model info dict matching the Gateway API ``ModelResponse``
            schema, or None if not found.
        """
        model = self._app_config.get_model_config(name)
        if model is None:
            return None
        return {
            "name": model.name,
            "model": getattr(model, "model", None),
            "display_name": getattr(model, "display_name", None),
            "description": getattr(model, "description", None),
            "supports_thinking": getattr(model, "supports_thinking", False),
            "supports_reasoning_effort": getattr(model, "supports_reasoning_effort", False),
        }

    # ------------------------------------------------------------------
    # Public API — MCP configuration
    # ------------------------------------------------------------------

    def get_mcp_config(self) -> dict:
        """Get MCP server configurations.

        Returns:
            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()}}

    def update_mcp_config(self, mcp_servers: dict[str, dict]) -> dict:
        """Update MCP server configurations.

        Writes to extensions_config.json and reloads the cache.

        Args:
            mcp_servers: Dict mapping server name to config dict.
                Each value should contain keys like enabled, type, command, args, env, url, etc.

        Returns:
            Dict with "mcp_servers" key, matching the Gateway API
            ``McpConfigResponse`` schema.

        Raises:
            OSError: If the config file cannot be written.
        """
        config_path = ExtensionsConfig.resolve_config_path()
        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.")

        with extensions_config_write_lock, extensions_config_file_lock(config_path):
            # The singleton is process-local, so re-read the shared file under
            # the cross-process lock before merging the replacement MCP map.
            current_config = ExtensionsConfig.from_file(config_path)
            config_data = current_config.to_file_dict()
            config_data["mcpServers"] = mcp_servers

            self._atomic_write_json(config_path, config_data)
            reloaded = reload_extensions_config()

        self._agent = None
        self._agent_config_key = None
        return {"mcp_servers": {name: server.model_dump() for name, server in reloaded.mcp_servers.items()}}

    # ------------------------------------------------------------------
    # Public API — skills management
    # ------------------------------------------------------------------

    def get_skill(self, name: str) -> dict | None:
        """Get a specific skill by name.

        Args:
            name: Skill name.

        Returns:
            Skill info dict, or None if not found.
        """
        storage = get_or_new_user_skill_storage(get_effective_user_id(), app_config=self._app_config)
        skill = next((s for s in storage.load_skills(enabled_only=False) if s.name == name), None)
        if skill is None:
            return None
        return {
            "name": skill.name,
            "description": skill.description,
            "license": skill.license,
            "category": skill.category,
            "enabled": skill.enabled,
        }

    def update_skill(self, name: str, *, enabled: bool) -> dict:
        """Update a skill's enabled status.

        Args:
            name: Skill name.
            enabled: New enabled status.

        Returns:
            Updated skill info dict.

        Raises:
            ValueError: If the skill is not found.
            OSError: If the config file cannot be written.
        """
        storage = get_or_new_user_skill_storage(get_effective_user_id(), app_config=self._app_config)
        skills = storage.load_skills(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")

        # PUBLIC skills → global extensions_config.json (shared state).
        # CUSTOM / LEGACY skills → per-user _skill_states.json (isolated state).
        from deerflow.skills.types import SkillCategory

        if skill.category == SkillCategory.PUBLIC:
            config_path = ExtensionsConfig.resolve_config_path()
            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.")

            from deerflow.skills.projection import skill_projection_mutation

            removal_names = (name,) if not enabled else ()
            with skill_projection_mutation(storage, "public", remove_names=removal_names):
                with extensions_config_write_lock, extensions_config_file_lock(config_path):
                    # The projection lock is cross-process, but the singleton
                    # cache is not. Reload from disk under the config lock.
                    extensions_config = ExtensionsConfig.from_file(config_path)
                    extensions_config.skills[name] = SkillStateConfig(enabled=enabled)

                    config_data = extensions_config.to_file_dict()

                    self._atomic_write_json(config_path, config_data)
                    reload_extensions_config()
        else:
            # CUSTOM / LEGACY: write per-user state
            from deerflow.skills.storage.user_scoped_skill_storage import UserScopedSkillStorage

            if isinstance(storage, UserScopedSkillStorage):
                storage.set_skill_enabled_state(name, enabled)
            else:
                # Fallback for non-user-scoped storage (unlikely in practice)
                config_path = ExtensionsConfig.resolve_config_path()
                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.")
                with extensions_config_write_lock, extensions_config_file_lock(config_path):
                    extensions_config = ExtensionsConfig.from_file(config_path)
                    extensions_config.skills[name] = SkillStateConfig(enabled=enabled)
                    config_data = extensions_config.to_file_dict()
                    self._atomic_write_json(config_path, config_data)
                    reload_extensions_config()

        # Invalidate the prompt cache for this caller (and for all users if
        # the changed skill is PUBLIC, since PUBLIC state is shared). Mirrors
        # what ``routers/skills.py::update_skill`` does — without this the
        # cached enabled-state would stay stale until process restart. See
        # review feedback on PR #3889.
        try:
            from deerflow.agents.lead_agent.prompt import clear_skills_system_prompt_cache, invalidate_user_skill_cache

            skill_category_value = skill.category.value if hasattr(skill.category, "value") else skill.category
            if skill_category_value == SkillCategory.PUBLIC.value:
                clear_skills_system_prompt_cache()
            else:
                invalidate_user_skill_cache(get_effective_user_id())
        except Exception as exc:
            # Don't let cache-invalidation failures mask the actual write
            # success — log and continue. The stale-cache window is bounded
            # by the next config reload.
            import logging

            logging.getLogger(__name__).warning("Failed to invalidate skills prompt cache after update_skill: %s", exc)

        self._agent = None
        self._agent_config_key = None

        updated = next((s for s in storage.load_skills(enabled_only=False) if s.name == name), None)
        if updated is None:
            raise RuntimeError(f"Skill '{name}' disappeared after update")
        return {
            "name": updated.name,
            "description": updated.description,
            "license": updated.license,
            "category": updated.category,
            "enabled": updated.enabled,
        }

    def install_skill(self, skill_path: str | Path) -> dict:
        """Install a skill from a .skill archive (ZIP).

        Args:
            skill_path: Path to the .skill file.

        Returns:
            Dict with success, skill_name, message.

        Raises:
            FileNotFoundError: If the file does not exist.
            ValueError: If the file is invalid.
        """
        return get_or_new_user_skill_storage(get_effective_user_id(), app_config=self._app_config).install_skill_from_archive(skill_path)

    # ------------------------------------------------------------------
    # Public API — memory management
    # ------------------------------------------------------------------

    def reload_memory(self) -> dict:
        """Reload memory data from file, forcing cache invalidation.

        Returns:
            The reloaded memory data dict.

        Backends without a reload concept (e.g. noop) fall back to
        ``get_memory``; a backend that exposes neither (a minimal ``add`` +
        ``get_context`` backend) raises ``NotImplementedError`` so the caller
        sees a clean unsupported-op error instead of an uncaught propagation.
        """
        from deerflow.agents.memory import get_memory_manager

        manager = get_memory_manager()
        user_id = get_effective_user_id()
        try:
            return manager.reload_memory(user_id=user_id)
        except NotImplementedError:
            pass  # no reload concept; fall back to current memory below
        try:
            return manager.get_memory(user_id=user_id)
        except NotImplementedError:
            raise NotImplementedError(f"reload_memory not supported by memory backend {type(manager).__name__}: implements neither reload_memory nor get_memory") from None

    def clear_memory(self) -> dict:
        """Clear all persisted memory data."""
        from deerflow.agents.memory import get_memory_manager

        return get_memory_manager().clear_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 import get_memory_manager

        manager = get_memory_manager()
        memory_data, fact_id = manager.create_fact(content=content, category=category, confidence=confidence, user_id=get_effective_user_id())
        if fact_id is None:
            raise ValueError("Fact was not stored because the configured memory.max_facts capacity policy evicted it")
        return memory_data

    def delete_memory_fact(self, fact_id: str) -> dict:
        """Delete a single fact from memory by fact id."""
        from deerflow.agents.memory import get_memory_manager

        manager = get_memory_manager()
        return manager.delete_fact(fact_id, user_id=get_effective_user_id())

    def update_memory_fact(
        self,
        fact_id: str,
        content: str | None = None,
        category: str | None = None,
        confidence: float | None = None,
    ) -> dict:
        """Update a single fact manually, preserving omitted fields."""
        from deerflow.agents.memory import get_memory_manager

        manager = get_memory_manager()
        return manager.update_fact(
            fact_id=fact_id,
            content=content,
            category=category,
            confidence=confidence,
            user_id=get_effective_user_id(),
        )

    def get_memory_config(self) -> dict:
        """Get memory system configuration.

        Returns:
            Memory config dict.
        """
        from deerflow.config.memory_config import get_memory_config

        config = get_memory_config()
        return {
            "enabled": config.enabled,
            "mode": config.mode,
            "injection_enabled": config.injection_enabled,
            "shutdown_flush_timeout_seconds": config.shutdown_flush_timeout_seconds,
            "manager_class": config.manager_class,
            "backend_config": config.backend_config,
        }

    def get_memory_status(self) -> dict:
        """Get memory status: config + current data.

        Returns:
            Dict with "config" and "data" keys.
        """
        return {
            "config": self.get_memory_config(),
            "data": self.get_memory(),
        }

    # ------------------------------------------------------------------
    # Public API — file uploads
    # ------------------------------------------------------------------

    def upload_files(self, thread_id: str, files: list[str | Path]) -> dict:
        """Upload local files into a thread's uploads directory.

        For PDF, PPT, Excel, and Word files, they are also converted to Markdown.

        Args:
            thread_id: Target thread ID.
            files: List of local file paths to upload.

        Returns:
            Dict with success, files, message — matching the Gateway API
            ``UploadResponse`` schema.

        Raises:
            FileNotFoundError: If any file does not exist.
            ValueError: If any supplied path exists but is not a regular file.
        """
        validate_thread_id(thread_id)
        from deerflow.utils.file_conversion import CONVERTIBLE_EXTENSIONS, convert_file_to_markdown

        # Validate all files upfront to avoid partial uploads.
        resolved_files = []
        seen_names: set[str] = set()
        has_convertible_file = False
        for f in files:
            p = Path(f)
            if not p.exists():
                raise FileNotFoundError(f"File not found: {f}")
            if not p.is_file():
                raise ValueError(f"Path is not a file: {f}")
            dest_name = claim_unique_filename(p.name, seen_names)
            resolved_files.append((p, dest_name))
            if not has_convertible_file and p.suffix.lower() in CONVERTIBLE_EXTENSIONS:
                has_convertible_file = True

        uploads_dir = ensure_uploads_dir(thread_id)
        uploaded_files: list[dict] = []

        conversion_pool = None
        if has_convertible_file:
            try:
                asyncio.get_running_loop()
            except RuntimeError:
                conversion_pool = None
            else:
                import concurrent.futures

                # Reuse one worker when already inside an event loop to avoid
                # creating a new ThreadPoolExecutor per converted file.
                conversion_pool = concurrent.futures.ThreadPoolExecutor(max_workers=1)

        def _convert_in_thread(path: Path, output_path: Path | None = None):
            return asyncio.run(convert_file_to_markdown(path, output_path=output_path))

        try:
            for src_path, dest_name in resolved_files:
                dest = uploads_dir / dest_name
                shutil.copy2(src_path, dest)

                info: dict[str, Any] = {
                    "filename": dest_name,
                    "size": dest.stat().st_size,
                    "path": str(dest),
                    "virtual_path": upload_virtual_path(dest_name),
                    "artifact_url": upload_artifact_url(thread_id, dest_name),
                }
                if dest_name != src_path.name:
                    info["original_filename"] = src_path.name

                if src_path.suffix.lower() in CONVERTIBLE_EXTENSIONS:
                    # Reserve companion .md name before convert so two stems
                    # that collapse to the same .md (or a prior .md upload)
                    # cannot silently overwrite each other.
                    provisional_md_name = Path(dest_name).with_suffix(".md").name
                    unique_md_name = claim_unique_filename(provisional_md_name, seen_names)
                    md_output = dest.with_name(unique_md_name)
                    try:
                        if conversion_pool is not None:
                            md_path = conversion_pool.submit(_convert_in_thread, dest, md_output).result()
                        else:
                            md_path = asyncio.run(convert_file_to_markdown(dest, output_path=md_output))
                    except Exception:
                        logger.warning(
                            "Failed to convert %s to markdown",
                            src_path.name,
                            exc_info=True,
                        )
                        md_path = None

                    if md_path is not None:
                        info["markdown_file"] = md_path.name
                        info["markdown_path"] = str(uploads_dir / md_path.name)
                        info["markdown_virtual_path"] = upload_virtual_path(md_path.name)
                        info["markdown_artifact_url"] = upload_artifact_url(thread_id, md_path.name)
                    else:
                        # Conversion failed and wrote nothing, so release the
                        # claim; holding it would rename a later same-stem
                        # upload against a name nothing occupies.
                        seen_names.discard(unique_md_name)

                uploaded_files.append(info)
        finally:
            if conversion_pool is not None:
                conversion_pool.shutdown(wait=True)

        return {
            "success": True,
            "files": uploaded_files,
            "message": f"Successfully uploaded {len(uploaded_files)} file(s)",
        }

    def list_uploads(self, thread_id: str) -> dict:
        """List files in a thread's uploads directory.

        Args:
            thread_id: Thread ID.

        Returns:
            Dict with "files" and "count" keys, matching the Gateway API
            ``list_uploaded_files`` response.
        """
        validate_thread_id(thread_id)
        uploads_dir = get_uploads_dir(thread_id)
        result = list_files_in_dir(uploads_dir)
        return enrich_file_listing(result, thread_id)

    def delete_upload(self, thread_id: str, filename: str) -> dict:
        """Delete a file from a thread's uploads directory.

        Args:
            thread_id: Thread ID.
            filename: Filename to delete.

        Returns:
            Dict with success and message, matching the Gateway API
            ``delete_uploaded_file`` response.

        Raises:
            FileNotFoundError: If the file does not exist.
            PermissionError: If path traversal is detected.
        """
        validate_thread_id(thread_id)
        from deerflow.utils.file_conversion import CONVERTIBLE_EXTENSIONS

        uploads_dir = get_uploads_dir(thread_id)
        return delete_file_safe(uploads_dir, filename, convertible_extensions=CONVERTIBLE_EXTENSIONS)

    # ------------------------------------------------------------------
    # Public API — artifacts
    # ------------------------------------------------------------------

    def get_artifact(self, thread_id: str, path: str) -> tuple[bytes, str]:
        """Read an artifact file produced by the agent.

        Args:
            thread_id: Thread ID.
            path: Virtual path (e.g. "mnt/user-data/outputs/file.txt").

        Returns:
            Tuple of (file_bytes, mime_type).

        Raises:
            FileNotFoundError: If the artifact does not exist.
            ValueError: If the path is invalid.
        """
        validate_thread_id(thread_id)
        try:
            actual = get_paths().resolve_virtual_path(thread_id, path, user_id=get_effective_user_id())
        except ValueError as exc:
            if "traversal" in str(exc):
                from deerflow.uploads.manager import PathTraversalError

                raise PathTraversalError("Path traversal detected") from exc
            raise
        if not actual.exists():
            raise FileNotFoundError(f"Artifact not found: {path}")
        if not actual.is_file():
            raise ValueError(f"Path is not a file: {path}")

        mime_type, _ = mimetypes.guess_type(actual)
        return actual.read_bytes(), mime_type or "application/octet-stream"