ruff3

Add a "Join our Discord" entry to the account dropdown's external-links
group, opening https://discord.gg/wdNEHETs87 in a new tab. lucide-react
has no Discord brand glyph, so include a small inline Discord SVG icon
(brand color). Add the joinDiscord label to all 8 locales.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Dockerfile

@@ -14,10 +14,22 @@ COPY . .
 
 COPY --from=node /app/web/dist ./web/dist
 
-RUN apt update \
+RUN apt-get update \
-    && apt install gcc -y \
+    && apt-get install -y --no-install-recommends gcc ca-certificates curl gnupg \
+    # Install the Docker CLI (client only) so the optional langbot_box
+    # service can drive the mounted host Docker socket and create sandbox
+    # containers. The same image powers langbot / plugin_runtime / box; only
+    # box uses the client. Arch-aware via dpkg so multi-arch builds work.
+    && install -m 0755 -d /etc/apt/keyrings \
+    && curl -fsSL https://download.docker.com/linux/debian/gpg -o /etc/apt/keyrings/docker.asc \
+    && chmod a+r /etc/apt/keyrings/docker.asc \
+    && echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/debian $(. /etc/os-release && echo \"$VERSION_CODENAME\") stable" > /etc/apt/sources.list.d/docker.list \
+    && apt-get update \
+    && apt-get install -y --no-install-recommends docker-ce-cli \
     && python -m pip install --no-cache-dir uv \
     && uv sync \
+    && apt-get purge -y --auto-remove curl gnupg \
+    && rm -rf /var/lib/apt/lists/* \
     && touch /.dockerenv
 
 CMD [ "uv", "run", "--no-sync", "main.py" ]

README_CN.md

@@ -38,7 +38,7 @@ LangBot 是一个**开源的生产级平台**，用于构建 AI 驱动的即时
 
 ### 核心能力
 
-- **AI 对话与 Agent** — 多轮对话、工具调用、多模态、流式输出。自带 RAG（知识库），深度集成 [Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org) 等 LLMOps 平台。
+- **AI 对话与 Agent** — 多轮对话、工具调用、多模态、流式输出。自带 RAG（知识库），深度集成 [Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org)、[Deerflow](https://deerflow.tech)、[Weknora](https://weknora.weixin.qq.com)等 LLMOps 平台。
 - **全平台支持** — 一套代码，覆盖 QQ、微信、企业微信、飞书、钉钉、Discord、Telegram、Slack、LINE、KOOK 等平台。
 - **生产就绪** — 访问控制、限速、敏感词过滤、全面监控与异常处理，已被多家企业采用。
 - **插件生态** — 数百个插件，跨进程的事件驱动架构，组件扩展，适配 [MCP 协议](https://modelcontextprotocol.io/)。

README_ES.md

@@ -37,7 +37,7 @@ LangBot es una **plataforma de código abierto y grado de producción** para con
 
 ### Capacidades Clave
 
-- **Conversaciones e Agentes IA** — Diálogos de múltiples turnos, llamadas a herramientas, soporte multimodal, salida en streaming. RAG (base de conocimientos) incorporado con integración profunda con [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **Conversaciones e Agentes IA** — Diálogos de múltiples turnos, llamadas a herramientas, soporte multimodal, salida en streaming. RAG (base de conocimientos) incorporado con integración profunda con [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org), [Deerflow](https://deerflow.tech)、[Weknora](https://weknora.weixin.qq.com).
 - **Soporte Universal de Plataformas de MI** — Un solo código base para Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
 - **Listo para Producción** — Control de acceso, limitación de velocidad, filtrado de palabras sensibles, monitoreo completo y manejo de excepciones. De confianza para empresas.
 - **Ecosistema de Plugins** — Cientos de plugins, arquitectura basada en eventos, extensiones de componentes y soporte del [protocolo MCP](https://modelcontextprotocol.io/).

README_FR.md

@@ -37,7 +37,7 @@ LangBot est une **plateforme open-source de niveau production** pour créer des
 
 ### Capacités Clés
 
-- **Conversations IA & Agents** — Dialogues multi-tours, appels d'outils, support multimodal, sortie en streaming. RAG (base de connaissances) intégré avec intégration profonde de [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **Conversations IA & Agents** — Dialogues multi-tours, appels d'outils, support multimodal, sortie en streaming. RAG (base de connaissances) intégré avec intégration profonde de [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org), [Deerflow](https://deerflow.tech), [Weknora](https://weknora.weixin.qq.com).
 - **Support Universel des Plateformes de MI** — Un seul code pour Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
 - **Prêt pour la Production** — Contrôle d'accès, limitation de débit, filtrage de mots sensibles, surveillance complète et gestion des exceptions. Approuvé par les entreprises.
 - **Écosystème de Plugins** — Des centaines de plugins, architecture événementielle, extensions de composants, et support du [protocole MCP](https://modelcontextprotocol.io/).

README_JP.md

@@ -37,7 +37,7 @@ LangBot は、AI搭載のインスタントメッセージングボットを構
 
 ### 主な機能
 
-- **AI対話とエージェント** — マルチターン対話、ツール呼び出し、マルチモーダル対応、ストリーミング出力。RAG（ナレッジベース）を内蔵し、[Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org) と深く統合。
+- **AI対話とエージェント** — マルチターン対話、ツール呼び出し、マルチモーダル対応、ストリーミング出力。RAG（ナレッジベース）を内蔵し、[Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org)、[Deerflow](https://deerflow.tech)、[Weknora](https://weknora.weixin.qq.com) と深く統合。
 - **ユニバーサルIMプラットフォーム対応** — 単一のコードベースで Discord、Telegram、Slack、LINE、QQ、WeChat、WeCom、Lark、DingTalk、KOOK に対応。
 - **本番環境対応** — アクセス制御、レート制限、センシティブワードフィルタリング、包括的な監視、例外処理を搭載。エンタープライズの信頼に応える品質。
 - **プラグインエコシステム** — 数百のプラグイン、イベント駆動アーキテクチャ、コンポーネント拡張、[MCPプロトコル](https://modelcontextprotocol.io/)対応。

README_KO.md

@@ -37,7 +37,7 @@ LangBot은 AI 기반 인스턴트 메시징 봇을 구축하기 위한 **오픈
 
 ### 핵심 기능
 
-- **AI 대화 및 에이전트** — 멀티턴 대화, 도구 호출, 멀티모달 지원, 스트리밍 출력. 내장 RAG(지식 베이스)와 [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org) 심층 통합.
+- **AI 대화 및 에이전트** — 멀티턴 대화, 도구 호출, 멀티모달 지원, 스트리밍 출력. 내장 RAG(지식 베이스)와 [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org), [Deerflow](https://deerflow.tech), [Weknora](https://weknora.weixin.qq.com) 심층 통합.
 - **유니버설 IM 플랫폼 지원** — 단일 코드베이스로 Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK 지원.
 - **프로덕션 레디** — 접근 제어, 속도 제한, 민감어 필터링, 종합 모니터링 및 예외 처리. 기업 환경에서 검증됨.
 - **플러그인 생태계** — 수백 개의 플러그인, 이벤트 기반 아키텍처, 컴포넌트 확장, [MCP 프로토콜](https://modelcontextprotocol.io/) 지원.

README_RU.md

@@ -37,7 +37,7 @@ LangBot — это **платформа с открытым исходным к
 
 ### Ключевые возможности
 
-- **ИИ-диалоги и агенты** — Многораундовые диалоги, вызов инструментов, мультимодальная поддержка, потоковый вывод. Встроенная реализация RAG (база знаний) с глубокой интеграцией в [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **ИИ-диалоги и агенты** — Многораундовые диалоги, вызов инструментов, мультимодальная поддержка, потоковый вывод. Встроенная реализация RAG (база знаний) с глубокой интеграцией в [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org), [Deerflow](https://deerflow.tech), [Weknora](https://weknora.weixin.qq.com).
 - **Универсальная поддержка IM-платформ** — Единая кодовая база для Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
 - **Готовность к продакшену** — Контроль доступа, ограничение скорости, фильтрация чувствительных слов, комплексный мониторинг и обработка исключений. Проверено в корпоративной среде.
 - **Экосистема плагинов** — Сотни плагинов, событийно-ориентированная архитектура, расширения компонентов и поддержка [протокола MCP](https://modelcontextprotocol.io/).

README_TW.md

@@ -39,7 +39,7 @@ LangBot 是一個**開源的生產級平台**，用於建構 AI 驅動的即時
 
 ### 核心能力
 
-- **AI 對話與 Agent** — 多輪對話、工具調用、多模態、流式輸出。自帶 RAG（知識庫），深度整合 [Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org) 等 LLMOps 平台。
+- **AI 對話與 Agent** — 多輪對話、工具調用、多模態、流式輸出。自帶 RAG（知識庫），深度整合 [Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org)、 [Deerflow](https://deerflow.tech)、[Weknora](https://weknora.weixin.qq.com)等 LLMOps 平台。
 - **全平台支援** — 一套程式碼，覆蓋 QQ、微信、企業微信、飛書、釘釘、Discord、Telegram、Slack、LINE、KOOK 等平台。
 - **生產就緒** — 存取控制、限速、敏感詞過濾、全面監控與異常處理，已被多家企業採用。
 - **外掛生態** — 數百個外掛，事件驅動架構，組件擴展，適配 [MCP 協議](https://modelcontextprotocol.io/)。

README_VI.md

@@ -37,7 +37,7 @@ LangBot là một **nền tảng mã nguồn mở, cấp sản xuất** để x
 
 ### Khả năng chính
 
-- **Hội thoại AI & Agent** — Đối thoại nhiều lượt, gọi công cụ, hỗ trợ đa phương thức, đầu ra streaming. RAG (cơ sở kiến thức) tích hợp sẵn với tích hợp sâu vào [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **Hội thoại AI & Agent** — Đối thoại nhiều lượt, gọi công cụ, hỗ trợ đa phương thức, đầu ra streaming. RAG (cơ sở kiến thức) tích hợp sẵn với tích hợp sâu vào [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org), [Deerflow](https://deerflow.tech), [Weknora](https://weknora.weixin.qq.com).
 - **Hỗ trợ đa nền tảng IM** — Một mã nguồn cho Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
 - **Sẵn sàng cho sản xuất** — Kiểm soát truy cập, giới hạn tốc độ, lọc từ nhạy cảm, giám sát toàn diện và xử lý ngoại lệ. Được doanh nghiệp tin dùng.
 - **Hệ sinh thái Plugin** — Hàng trăm plugin, kiến trúc hướng sự kiện, mở rộng thành phần, và hỗ trợ [giao thức MCP](https://modelcontextprotocol.io/).

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "langbot"
-version = "4.10.0-beta.2"
+version = "4.10.0"
 description = "Production-grade platform for building agentic IM bots"
 readme = "README.md"
 license-files = ["LICENSE"]
@@ -70,7 +70,7 @@ dependencies = [
     "chromadb>=1.0.0,<2.0.0",
     "qdrant-client (>=1.15.1,<2.0.0)",
     "pyseekdb==1.1.0.post3",
-    "langbot-plugin==0.4.0",
+    "langbot-plugin==0.4.1",
     "asyncpg>=0.30.0",
     "line-bot-sdk>=3.19.0",
     "matrix-nio>=0.25.2",

src/langbot/__init__.py

@@ -1,3 +1,3 @@
 """LangBot - Production-grade platform for building agentic IM bots"""
 
-__version__ = '4.10.0-beta.2'
+__version__ = '4.10.0'

src/langbot/libs/deerflow_api/__init__.py

@@ -0,0 +1,5 @@
+from .client import AsyncDeerFlowClient
+from .errors import DeerFlowAPIError
+from . import stream_utils
+
+__all__ = ['AsyncDeerFlowClient', 'DeerFlowAPIError', 'stream_utils']

src/langbot/libs/deerflow_api/client.py

@@ -0,0 +1,204 @@
+"""DeerFlow LangGraph HTTP API 客户端
+
+参考 astrbot 的 deerflow_api_client 实现，使用 httpx 适配 LangBot 风格。
+"""
+
+from __future__ import annotations
+
+import codecs
+import json
+import typing
+from collections.abc import AsyncGenerator
+
+import httpx
+
+from .errors import DeerFlowAPIError
+
+
+SSE_MAX_BUFFER_CHARS = 1_048_576
+
+
+def _normalize_sse_newlines(text: str) -> str:
+    """规范化 CRLF/CR 为 LF，确保 SSE 块分割稳定"""
+    return text.replace('\r\n', '\n').replace('\r', '\n')
+
+
+def _parse_sse_data_lines(data_lines: list[str]) -> typing.Any:
+    raw_data = '\n'.join(data_lines)
+    try:
+        return json.loads(raw_data)
+    except json.JSONDecodeError:
+        # 某些 LangGraph 兼容服务端会在单个 SSE 事件中用多个 data 行
+        # 发送多段 JSON 片段（例如 tuple payload）
+        parsed_lines: list[typing.Any] = []
+        can_parse_all = True
+        for line in data_lines:
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                parsed_lines.append(json.loads(line))
+            except json.JSONDecodeError:
+                can_parse_all = False
+                break
+        if can_parse_all and parsed_lines:
+            return parsed_lines[0] if len(parsed_lines) == 1 else parsed_lines
+        return raw_data
+
+
+def _parse_sse_block(block: str) -> dict[str, typing.Any] | None:
+    if not block.strip():
+        return None
+
+    event_name = 'message'
+    data_lines: list[str] = []
+    for line in block.splitlines():
+        if line.startswith('event:'):
+            event_name = line[6:].strip()
+        elif line.startswith('data:'):
+            data_lines.append(line[5:].lstrip())
+
+    if not data_lines:
+        return None
+    return {'event': event_name, 'data': _parse_sse_data_lines(data_lines)}
+
+
+class AsyncDeerFlowClient:
+    """DeerFlow LangGraph HTTP API 客户端"""
+
+    api_base: str
+    headers: dict[str, str]
+
+    def __init__(
+        self,
+        api_base: str = 'http://127.0.0.1:2026',
+        api_key: str = '',
+        auth_header: str = '',
+    ) -> None:
+        self.api_base = api_base.rstrip('/')
+        self.headers: dict[str, str] = {}
+        if auth_header:
+            self.headers['Authorization'] = auth_header
+        elif api_key:
+            self.headers['Authorization'] = f'Bearer {api_key}'
+
+    async def create_thread(self, timeout: float = 20) -> dict[str, typing.Any]:
+        """创建一个新的 LangGraph thread
+
+        Returns:
+            包含 thread_id 等信息的字典
+        """
+        url = f'{self.api_base}/api/langgraph/threads'
+        payload = {'metadata': {}}
+
+        async with httpx.AsyncClient(
+            trust_env=True,
+            timeout=timeout,
+        ) as client:
+            response = await client.post(
+                url,
+                headers=self.headers,
+                json=payload,
+            )
+            if response.status_code not in (200, 201):
+                raise DeerFlowAPIError(
+                    operation='create thread',
+                    status=response.status_code,
+                    body=response.text,
+                    url=url,
+                )
+            return response.json()
+
+    async def delete_thread(self, thread_id: str, timeout: float = 20) -> None:
+        """删除指定 thread"""
+        url = f'{self.api_base}/api/threads/{thread_id}'
+
+        async with httpx.AsyncClient(
+            trust_env=True,
+            timeout=timeout,
+        ) as client:
+            response = await client.delete(url, headers=self.headers)
+            if response.status_code not in (200, 202, 204, 404):
+                raise DeerFlowAPIError(
+                    operation='delete thread',
+                    status=response.status_code,
+                    body=response.text,
+                    url=url,
+                    thread_id=thread_id,
+                )
+
+    async def stream_run(
+        self,
+        thread_id: str,
+        payload: dict[str, typing.Any],
+        timeout: float = 120,
+    ) -> AsyncGenerator[dict[str, typing.Any], None]:
+        """运行一次 LangGraph stream 请求，逐事件 yield
+
+        Yields:
+            事件字典 {'event': event_name, 'data': parsed_data}
+        """
+        url = f'{self.api_base}/api/langgraph/threads/{thread_id}/runs/stream'
+
+        # 流式请求使用单独的 read timeout 控制
+        stream_timeout = httpx.Timeout(
+            connect=min(timeout, 30),
+            read=timeout,
+            write=timeout,
+            pool=timeout,
+        )
+
+        async with httpx.AsyncClient(
+            trust_env=True,
+            timeout=stream_timeout,
+        ) as client:
+            async with client.stream(
+                'POST',
+                url,
+                headers={
+                    **self.headers,
+                    'Accept': 'text/event-stream',
+                    'Content-Type': 'application/json',
+                },
+                json=payload,
+            ) as resp:
+                if resp.status_code != 200:
+                    body = await resp.aread()
+                    raise DeerFlowAPIError(
+                        operation='runs/stream request',
+                        status=resp.status_code,
+                        body=body.decode('utf-8', errors='replace'),
+                        url=url,
+                        thread_id=thread_id,
+                    )
+
+                decoder = codecs.getincrementaldecoder('utf-8')('replace')
+                buffer = ''
+
+                async for chunk in resp.aiter_bytes(8192):
+                    buffer += _normalize_sse_newlines(decoder.decode(chunk))
+
+                    while '\n\n' in buffer:
+                        block, buffer = buffer.split('\n\n', 1)
+                        parsed = _parse_sse_block(block)
+                        if parsed is not None:
+                            yield parsed
+
+                    if len(buffer) > SSE_MAX_BUFFER_CHARS:
+                        # 缓冲区过大，强制 flush
+                        parsed = _parse_sse_block(buffer)
+                        if parsed is not None:
+                            yield parsed
+                        buffer = ''
+
+                # flush 剩余内容
+                buffer += _normalize_sse_newlines(decoder.decode(b'', final=True))
+                while '\n\n' in buffer:
+                    block, buffer = buffer.split('\n\n', 1)
+                    parsed = _parse_sse_block(block)
+                    if parsed is not None:
+                        yield parsed
+                if buffer.strip():
+                    parsed = _parse_sse_block(buffer)
+                    if parsed is not None:
+                        yield parsed

src/langbot/libs/deerflow_api/errors.py

@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+
+class DeerFlowAPIError(Exception):
+    """DeerFlow API 请求失败"""
+
+    def __init__(
+        self,
+        *,
+        operation: str = '',
+        status: int = 0,
+        body: str = '',
+        url: str = '',
+        thread_id: str | None = None,
+        message: str = '',
+    ) -> None:
+        self.operation = operation
+        self.status = status
+        self.body = body
+        self.url = url
+        self.thread_id = thread_id
+
+        if message:
+            super().__init__(message)
+            return
+
+        msg = f'DeerFlow {operation} failed: status={status}, url={url}, body={body}'
+        if thread_id is not None:
+            msg = f'DeerFlow {operation} failed: thread_id={thread_id}, status={status}, url={url}, body={body}'
+        super().__init__(msg)

src/langbot/libs/deerflow_api/stream_utils.py

@@ -0,0 +1,212 @@
+"""DeerFlow LangGraph 流式响应解析工具
+
+参考 astrbot 实现的 deerflow_stream_utils。
+"""
+
+from __future__ import annotations
+
+import typing
+from collections.abc import Iterable
+
+
+def extract_text(content: typing.Any) -> str:
+    """从消息 content 中提取纯文本"""
+    if isinstance(content, str):
+        return content
+    if isinstance(content, dict):
+        if isinstance(content.get('text'), str):
+            return content['text']
+        if 'content' in content:
+            return extract_text(content.get('content'))
+        if 'kwargs' in content and isinstance(content['kwargs'], dict):
+            return extract_text(content['kwargs'].get('content'))
+    if isinstance(content, list):
+        parts: list[str] = []
+        for item in content:
+            if isinstance(item, str):
+                parts.append(item)
+            elif isinstance(item, dict):
+                item_type = item.get('type')
+                if item_type == 'text' and isinstance(item.get('text'), str):
+                    parts.append(item['text'])
+                elif 'content' in item:
+                    parts.append(extract_text(item['content']))
+        return '\n'.join([p for p in parts if p]).strip()
+    return str(content) if content is not None else ''
+
+
+def extract_messages_from_values_data(data: typing.Any) -> list[typing.Any]:
+    """从 values 事件中提取 messages 列表"""
+    candidates: list[typing.Any] = []
+    if isinstance(data, dict):
+        candidates.append(data)
+        if isinstance(data.get('values'), dict):
+            candidates.append(data['values'])
+    elif isinstance(data, list):
+        candidates.extend([x for x in data if isinstance(x, dict)])
+
+    for item in candidates:
+        messages = item.get('messages')
+        if isinstance(messages, list):
+            return messages
+    return []
+
+
+def is_ai_message(message: dict[str, typing.Any]) -> bool:
+    """判断是否为 AI/assistant 消息"""
+    role = str(message.get('role', '')).lower()
+    if role in {'assistant', 'ai'}:
+        return True
+
+    msg_type = str(message.get('type', '')).lower()
+    if msg_type in {'ai', 'assistant', 'aimessage', 'aimessagechunk'}:
+        return True
+    if 'ai' in msg_type and all(token not in msg_type for token in ('human', 'tool', 'system')):
+        return True
+    return False
+
+
+def extract_latest_ai_text(messages: Iterable[typing.Any]) -> str:
+    """获取最近一条 AI 消息的文本内容"""
+    if isinstance(messages, (list, tuple)):
+        iterable = reversed(messages)
+    else:
+        iterable = reversed(list(messages))
+
+    for msg in iterable:
+        if not isinstance(msg, dict):
+            continue
+        if is_ai_message(msg):
+            text = extract_text(msg.get('content'))
+            if text:
+                return text
+    return ''
+
+
+def extract_latest_ai_message(messages: Iterable[typing.Any]) -> dict[str, typing.Any] | None:
+    """获取最近一条 AI 消息对象"""
+    if isinstance(messages, (list, tuple)):
+        iterable = reversed(messages)
+    else:
+        iterable = reversed(list(messages))
+
+    for msg in iterable:
+        if not isinstance(msg, dict):
+            continue
+        if is_ai_message(msg):
+            return msg
+    return None
+
+
+def is_clarification_tool_message(message: dict[str, typing.Any]) -> bool:
+    """判断是否为澄清问题工具消息"""
+    msg_type = str(message.get('type', '')).lower()
+    tool_name = str(message.get('name', '')).lower()
+    return msg_type == 'tool' and tool_name == 'ask_clarification'
+
+
+def extract_latest_clarification_text(messages: Iterable[typing.Any]) -> str:
+    """提取最近的澄清问题文本"""
+    if isinstance(messages, (list, tuple)):
+        iterable = reversed(messages)
+    else:
+        iterable = reversed(list(messages))
+
+    for msg in iterable:
+        if not isinstance(msg, dict):
+            continue
+        if is_clarification_tool_message(msg):
+            text = extract_text(msg.get('content'))
+            if text:
+                return text
+    return ''
+
+
+def get_message_id(message: typing.Any) -> str:
+    """提取消息 ID"""
+    if not isinstance(message, dict):
+        return ''
+    msg_id = message.get('id')
+    return msg_id if isinstance(msg_id, str) else ''
+
+
+def extract_event_message_obj(data: typing.Any) -> dict[str, typing.Any] | None:
+    """从事件 data 中提取消息对象"""
+    msg_obj = data
+    if isinstance(data, (list, tuple)) and data:
+        msg_obj = data[0]
+    if isinstance(msg_obj, dict) and isinstance(msg_obj.get('data'), dict):
+        msg_obj = msg_obj['data']
+    return msg_obj if isinstance(msg_obj, dict) else None
+
+
+def extract_ai_delta_from_event_data(data: typing.Any) -> str:
+    """从 messages-tuple 事件中提取 AI delta 文本"""
+    msg_obj = extract_event_message_obj(data)
+    if not msg_obj:
+        return ''
+    if is_ai_message(msg_obj):
+        return extract_text(msg_obj.get('content'))
+    return ''
+
+
+def extract_clarification_from_event_data(data: typing.Any) -> str:
+    """从事件中提取澄清问题"""
+    msg_obj = extract_event_message_obj(data)
+    if not msg_obj:
+        return ''
+    if is_clarification_tool_message(msg_obj):
+        return extract_text(msg_obj.get('content'))
+    return ''
+
+
+def _iter_custom_event_items(data: typing.Any) -> list[dict[str, typing.Any]]:
+    items: list[dict[str, typing.Any]] = []
+    if isinstance(data, dict):
+        return [data]
+    if isinstance(data, list):
+        for item in data:
+            if isinstance(item, dict):
+                items.append(item)
+            elif isinstance(item, (list, tuple)):
+                for nested in item:
+                    if isinstance(nested, dict):
+                        items.append(nested)
+    return items
+
+
+def extract_task_failures_from_custom_event(data: typing.Any) -> list[str]:
+    """从 custom 事件中提取子任务失败信息"""
+    failures: list[str] = []
+    for item in _iter_custom_event_items(data):
+        event_type = str(item.get('type', '')).lower()
+        if event_type not in {'task_failed', 'task_timed_out'}:
+            continue
+
+        task_id = str(item.get('task_id', '')).strip()
+        error_text = extract_text(item.get('error')).strip()
+        if task_id and error_text:
+            failures.append(f'{task_id}: {error_text}')
+        elif error_text:
+            failures.append(error_text)
+        elif task_id:
+            failures.append(f'{task_id}: unknown error')
+        else:
+            failures.append('unknown task failure')
+    return failures
+
+
+def build_task_failure_summary(failures: list[str]) -> str:
+    """构建任务失败摘要"""
+    if not failures:
+        return ''
+    deduped: list[str] = []
+    seen: set[str] = set()
+    for failure in failures:
+        if failure not in seen:
+            seen.add(failure)
+            deduped.append(failure)
+    if len(deduped) == 1:
+        return f'DeerFlow subtask failed: {deduped[0]}'
+    joined = '\n'.join([f'- {item}' for item in deduped[:5]])
+    return f'DeerFlow subtasks failed:\n{joined}'

src/langbot/libs/weknora_api/__init__.py

@@ -0,0 +1,4 @@
+from .client import AsyncWeKnoraClient
+from .errors import WeKnoraAPIError
+
+__all__ = ['AsyncWeKnoraClient', 'WeKnoraAPIError']

src/langbot/libs/weknora_api/client.py

@@ -0,0 +1,180 @@
+from __future__ import annotations
+
+import httpx
+import typing
+import json
+
+from .errors import WeKnoraAPIError
+
+
+class AsyncWeKnoraClient:
+    """WeKnora API 客户端"""
+
+    api_key: str
+    base_url: str
+
+    def __init__(
+        self,
+        api_key: str,
+        base_url: str = 'http://localhost:80/api/v1',
+    ) -> None:
+        self.api_key = api_key
+        self.base_url = base_url
+
+    async def create_session(
+        self,
+        title: str = '',
+        description: str = '',
+        timeout: float = 30.0,
+    ) -> str:
+        """创建会话，返回 session_id"""
+        async with httpx.AsyncClient(
+            base_url=self.base_url,
+            trust_env=True,
+            timeout=timeout,
+        ) as client:
+            payload: dict[str, typing.Any] = {}
+            if title:
+                payload['title'] = title
+            if description:
+                payload['description'] = description
+
+            response = await client.post(
+                '/sessions',
+                headers={
+                    'X-API-Key': self.api_key,
+                    'Content-Type': 'application/json',
+                },
+                json=payload,
+            )
+
+            if response.status_code not in (200, 201):
+                raise WeKnoraAPIError(f'{response.status_code} {response.text}')
+
+            data = response.json()
+            return data['data']['id']
+
+    async def agent_chat(
+        self,
+        session_id: str,
+        query: str,
+        user: str,
+        agent_id: str = '',
+        knowledge_base_ids: list[str] | None = None,
+        web_search_enabled: bool = False,
+        timeout: float = 120.0,
+    ) -> typing.AsyncGenerator[dict[str, typing.Any], None]:
+        """
+        Agent 智能对话（SSE 流式）
+
+        响应事件类型:
+        - agent_query: Agent 开始处理
+        - thinking: 思考过程
+        - tool_call: 工具调用
+        - tool_result: 工具结果
+        - references: 知识库引用
+        - answer: 回答内容
+        - reflection: 反思
+        - session_title: 会话标题
+        - error: 错误
+        """
+        if knowledge_base_ids is None:
+            knowledge_base_ids = []
+
+        async with httpx.AsyncClient(
+            base_url=self.base_url,
+            trust_env=True,
+            timeout=timeout,
+        ) as client:
+            payload: dict[str, typing.Any] = {
+                'query': query,
+                'agent_enabled': True,
+                'channel': 'im',
+            }
+            if agent_id:
+                payload['agent_id'] = agent_id
+            if knowledge_base_ids:
+                payload['knowledge_base_ids'] = knowledge_base_ids
+            if web_search_enabled:
+                payload['web_search_enabled'] = True
+
+            async with client.stream(
+                'POST',
+                f'/agent-chat/{session_id}',
+                headers={
+                    'X-API-Key': self.api_key,
+                    'Content-Type': 'application/json',
+                },
+                json=payload,
+            ) as r:
+                async for chunk in r.aiter_lines():
+                    if r.status_code != 200:
+                        raise WeKnoraAPIError(f'{r.status_code} {chunk}')
+                    if chunk.strip() == '':
+                        continue
+                    if chunk.startswith('data:'):
+                        try:
+                            data = json.loads(chunk[5:].strip())
+                        except json.JSONDecodeError:
+                            continue
+                        yield data
+                        # 收到 error 事件后主动结束流，避免上层未 raise 时持续等待
+                        if data.get('response_type') == 'error':
+                            return
+
+    async def knowledge_chat(
+        self,
+        session_id: str,
+        query: str,
+        user: str,
+        agent_id: str = 'builtin-quick-answer',
+        knowledge_base_ids: list[str] | None = None,
+        timeout: float = 120.0,
+    ) -> typing.AsyncGenerator[dict[str, typing.Any], None]:
+        """
+        知识库 RAG 问答（SSE 流式）
+
+        响应事件类型:
+        - references: 知识库引用
+        - answer: 回答内容
+        """
+        if knowledge_base_ids is None:
+            knowledge_base_ids = []
+
+        async with httpx.AsyncClient(
+            base_url=self.base_url,
+            trust_env=True,
+            timeout=timeout,
+        ) as client:
+            payload: dict[str, typing.Any] = {
+                'query': query,
+                'channel': 'im',
+            }
+            if agent_id:
+                payload['agent_id'] = agent_id
+            if knowledge_base_ids:
+                payload['knowledge_base_ids'] = knowledge_base_ids
+
+            async with client.stream(
+                'POST',
+                f'/knowledge-chat/{session_id}',
+                headers={
+                    'X-API-Key': self.api_key,
+                    'Content-Type': 'application/json',
+                },
+                json=payload,
+            ) as r:
+                async for chunk in r.aiter_lines():
+                    if r.status_code != 200:
+                        raise WeKnoraAPIError(f'{r.status_code} {chunk}')
+                    if chunk.strip() == '':
+                        continue
+                    if chunk.startswith('data:'):
+                        try:
+                            data = json.loads(chunk[5:].strip())
+                        except json.JSONDecodeError:
+                            continue
+                        yield data
+                        # 收到 error 事件后主动结束流，避免上层未 raise 时持续等待
+                        if data.get('response_type') == 'error':
+                            return

src/langbot/libs/weknora_api/errors.py

@@ -0,0 +1,6 @@
+class WeKnoraAPIError(Exception):
+    """WeKnora API 请求失败"""
+
+    def __init__(self, message: str = ''):
+        self.message = message
+        super().__init__(self.message)

src/langbot/pkg/core/migrations/m042_weknora_api.py

@@ -0,0 +1,27 @@
+from __future__ import annotations
+
+from .. import migration
+
+
+@migration.migration_class('weknora-api-config', 42)
+class WeKnoraAPICfgMigration(migration.Migration):
+    """WeKnora API 配置迁移"""
+
+    async def need_migrate(self) -> bool:
+        """判断当前环境是否需要运行此迁移"""
+        return 'weknora-api' not in self.ap.provider_cfg.data
+
+    async def run(self):
+        """执行迁移"""
+        self.ap.provider_cfg.data['weknora-api'] = {
+            'base-url': 'http://localhost:8080/api/v1',
+            'app-type': 'agent',
+            'api-key': '',
+            'agent-id': 'builtin-smart-reasoning',
+            'knowledge-base-ids': [],
+            'web-search-enabled': False,
+            'timeout': 120,
+            'base-prompt': '请回答用户的问题。',
+        }
+
+        await self.ap.provider_cfg.dump_config()

src/langbot/pkg/core/migrations/m043_deerflow_api.py

@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from .. import migration
+
+
+@migration.migration_class('deerflow-api-config', 43)
+class DeerFlowAPICfgMigration(migration.Migration):
+    """DeerFlow API 配置迁移"""
+
+    async def need_migrate(self) -> bool:
+        """判断当前环境是否需要运行此迁移"""
+        return 'deerflow-api' not in self.ap.provider_cfg.data
+
+    async def run(self):
+        """执行迁移"""
+        self.ap.provider_cfg.data['deerflow-api'] = {
+            'api-base': 'http://127.0.0.1:2026',
+            'api-key': '',
+            'auth-header': '',
+            'assistant-id': 'lead_agent',
+            'model-name': '',
+            'thinking-enabled': False,
+            'plan-mode': False,
+            'subagent-enabled': False,
+            'max-concurrent-subagents': 3,
+            'timeout': 300,
+            'recursion-limit': 1000,
+        }
+
+        await self.ap.provider_cfg.dump_config()

src/langbot/pkg/plugin/connector.py

@@ -459,7 +459,7 @@ class PluginRuntimeConnector(ManagedRuntimeConnector):
                                         )
 
                                     file_bytes = download_resp.content
-                                    self._extract_deps_metadata(file_bytes, task_context)
+                                    self._inspect_plugin_package(file_bytes, task_context)
                                     file_key = await self.handler.send_file(file_bytes, 'lbpkg')
                                     install_info['plugin_file_key'] = file_key
                                     self.ap.logger.info(f'Transfered file {file_key} to plugin runtime')

src/langbot/pkg/provider/modelmgr/modelmgr.py

@@ -143,49 +143,83 @@ class ModelManager:
         # get the latest models from space
         space_models = await self.ap.space_service.get_models()
 
-        exists_llm_models_uuids = [m['uuid'] for m in await self.ap.llm_model_service.get_llm_models()]
+        # Index existing models by uuid. Space reuses a model's uuid across
-        exists_embedding_models_uuids = [
+        # renames / re-specs (e.g. the uuid that used to be ``claude-opus-4-6``
-            m['uuid'] for m in await self.ap.embedding_models_service.get_embedding_models()
+        # may later become ``claude-opus-4-7``). So for Space-managed models we
-        ]
+        # upsert: create when the uuid is new, otherwise update name/abilities/
+        # ranking to track Space. Models owned by other providers are never
+        # touched, even on an (unexpected) uuid collision.
+        existing_llm_models = {m['uuid']: m for m in await self.ap.llm_model_service.get_llm_models()}
+        existing_embedding_models = {
+            m['uuid']: m for m in await self.ap.embedding_models_service.get_embedding_models()
+        }
+
+        created = 0
+        updated = 0
 
         for space_model in space_models:
             if space_model.category == 'chat':
-                uuid = space_model.uuid
+                existing = existing_llm_models.get(space_model.uuid)
-
+                if existing is None:
-                if uuid in exists_llm_models_uuids:
+                    # model will be automatically loaded
-                    continue
+                    await self.ap.llm_model_service.create_llm_model(
-
+                        {
-                # model will be automatically loaded
+                            'uuid': space_model.uuid,
-                await self.ap.llm_model_service.create_llm_model(
+                            'name': space_model.model_id,
-                    {
+                            'provider_uuid': space_model_provider.uuid,
-                        'uuid': space_model.uuid,
+                            'abilities': space_model.llm_abilities or [],
+                            'extra_args': {},
+                            'prefered_ranking': space_model.featured_order,
+                        },
+                        preserve_uuid=True,
+                        auto_set_to_default_pipeline=False,
+                    )
+                    created += 1
+                elif existing.get('provider_uuid') == space_model_provider.uuid:
+                    desired = {
                         'name': space_model.model_id,
                         'provider_uuid': space_model_provider.uuid,
                         'abilities': space_model.llm_abilities or [],
-                        'extra_args': {},
                         'prefered_ranking': space_model.featured_order,
-                    },
+                    }
-                    preserve_uuid=True,
+                    if (
-                    auto_set_to_default_pipeline=False,
+                        existing.get('name') != desired['name']
-                )
+                        or list(existing.get('abilities') or []) != list(desired['abilities'])
+                        or existing.get('prefered_ranking') != desired['prefered_ranking']
+                    ):
+                        await self.ap.llm_model_service.update_llm_model(space_model.uuid, dict(desired))
+                        updated += 1
 
             elif space_model.category == 'embedding':
-                uuid = space_model.uuid
+                existing = existing_embedding_models.get(space_model.uuid)
-
+                if existing is None:
-                if uuid in exists_embedding_models_uuids:
+                    # model will be automatically loaded
-                    continue
+                    await self.ap.embedding_models_service.create_embedding_model(
-
+                        {
-                # model will be automatically loaded
+                            'uuid': space_model.uuid,
-                await self.ap.embedding_models_service.create_embedding_model(
+                            'name': space_model.model_id,
-                    {
+                            'provider_uuid': space_model_provider.uuid,
-                        'uuid': space_model.uuid,
+                            'extra_args': {},
+                            'prefered_ranking': space_model.featured_order,
+                        },
+                        preserve_uuid=True,
+                    )
+                    created += 1
+                elif existing.get('provider_uuid') == space_model_provider.uuid:
+                    desired = {
                         'name': space_model.model_id,
                         'provider_uuid': space_model_provider.uuid,
-                        'extra_args': {},
                         'prefered_ranking': space_model.featured_order,
-                    },
+                    }
-                    preserve_uuid=True,
+                    if (
-                )
+                        existing.get('name') != desired['name']
+                        or existing.get('prefered_ranking') != desired['prefered_ranking']
+                    ):
+                        await self.ap.embedding_models_service.update_embedding_model(space_model.uuid, dict(desired))
+                        updated += 1
+
+        if created or updated:
+            self.ap.logger.info(f'Synced models from LangBot Space: {created} added, {updated} updated.')
 
     async def init_temporary_runtime_llm_model(
         self,

src/langbot/pkg/provider/runners/deerflowapi.py

@@ -0,0 +1,511 @@
+"""DeerFlow LangGraph API Runner
+
+参考 astrbot 的 deerflow_agent_runner 实现，适配 LangBot 的 Runner 接口。
+
+特点：
+- 使用 LangGraph HTTP API 接入 deer-flow 后端
+- 自动管理 thread_id（按 session 隔离）
+- 支持 SSE 流式响应解析
+- 支持 streaming/非流式两种输出
+- 处理 values / messages-tuple / custom 三种事件
+"""
+
+from __future__ import annotations
+
+import asyncio
+import hashlib
+import json
+import typing
+from collections import deque
+from dataclasses import dataclass, field
+
+
+from langbot.pkg.provider import runner
+from langbot.pkg.core import app
+import langbot_plugin.api.entities.builtin.provider.message as provider_message
+import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+from langbot.libs.deerflow_api import client, errors, stream_utils
+
+
+_MAX_VALUES_HISTORY = 200
+
+
+@dataclass
+class _StreamState:
+    """流式状态跟踪"""
+
+    latest_text: str = ''
+    prev_text_for_streaming: str = ''
+    clarification_text: str = ''
+    task_failures: list[str] = field(default_factory=list)
+    seen_message_ids: set[str] = field(default_factory=set)
+    seen_message_order: deque[str] = field(default_factory=deque)
+    no_id_message_fingerprints: dict[int, str] = field(default_factory=dict)
+    baseline_initialized: bool = False
+    has_values_text: bool = False
+    run_values_messages: list[dict[str, typing.Any]] = field(default_factory=list)
+    timed_out: bool = False
+
+
+@runner.runner_class('deerflow-api')
+class DeerFlowAPIRunner(runner.RequestRunner):
+    """DeerFlow LangGraph API 对话请求器"""
+
+    deerflow_client: client.AsyncDeerFlowClient
+
+    def __init__(self, ap: app.Application, pipeline_config: dict):
+        super().__init__(ap, pipeline_config)
+
+        cfg = self.pipeline_config['ai']['deerflow-api']
+
+        api_base = cfg.get('api-base', '').strip()
+        if not api_base or not api_base.startswith(('http://', 'https://')):
+            raise errors.DeerFlowAPIError(
+                message='DeerFlow API Base URL 格式错误，必须以 http:// 或 https:// 开头',
+            )
+
+        self.api_base = api_base
+        self.api_key = cfg.get('api-key', '')
+        self.auth_header = cfg.get('auth-header', '')
+        self.assistant_id = cfg.get('assistant-id', 'lead_agent')
+        self.model_name = cfg.get('model-name', '')
+        self.thinking_enabled = bool(cfg.get('thinking-enabled', False))
+        self.plan_mode = bool(cfg.get('plan-mode', False))
+        self.subagent_enabled = bool(cfg.get('subagent-enabled', False))
+        self.max_concurrent_subagents = int(cfg.get('max-concurrent-subagents', 3))
+        self.timeout = int(cfg.get('timeout', 300))
+        self.recursion_limit = int(cfg.get('recursion-limit', 1000))
+
+        self.deerflow_client = client.AsyncDeerFlowClient(
+            api_base=self.api_base,
+            api_key=self.api_key,
+            auth_header=self.auth_header,
+        )
+
+    # ------------------------------------------------------------------
+    # 辅助方法
+    # ------------------------------------------------------------------
+
+    def _fingerprint_message(self, message: dict[str, typing.Any]) -> str:
+        try:
+            raw = json.dumps(message, sort_keys=True, ensure_ascii=False, default=str)
+        except (TypeError, ValueError):
+            raw = repr(message)
+        return hashlib.sha1(raw.encode('utf-8', errors='ignore')).hexdigest()
+
+    def _remember_seen_message_id(self, state: _StreamState, msg_id: str) -> None:
+        if not msg_id or msg_id in state.seen_message_ids:
+            return
+        state.seen_message_ids.add(msg_id)
+        state.seen_message_order.append(msg_id)
+        while len(state.seen_message_order) > _MAX_VALUES_HISTORY:
+            dropped = state.seen_message_order.popleft()
+            state.seen_message_ids.discard(dropped)
+
+    def _extract_new_messages_from_values(
+        self,
+        values_messages: list[typing.Any],
+        state: _StreamState,
+    ) -> list[dict[str, typing.Any]]:
+        new_messages: list[dict[str, typing.Any]] = []
+        no_id_indexes_seen: set[int] = set()
+        for idx, msg in enumerate(values_messages):
+            if not isinstance(msg, dict):
+                continue
+            msg_id = stream_utils.get_message_id(msg)
+            if msg_id:
+                if msg_id in state.seen_message_ids:
+                    continue
+                self._remember_seen_message_id(state, msg_id)
+                new_messages.append(msg)
+                continue
+
+            no_id_indexes_seen.add(idx)
+            fp = self._fingerprint_message(msg)
+            if state.no_id_message_fingerprints.get(idx) == fp:
+                continue
+            state.no_id_message_fingerprints[idx] = fp
+            new_messages.append(msg)
+
+        for idx in list(state.no_id_message_fingerprints.keys()):
+            if idx not in no_id_indexes_seen:
+                state.no_id_message_fingerprints.pop(idx, None)
+        return new_messages
+
+    # ------------------------------------------------------------------
+    # 用户输入处理
+    # ------------------------------------------------------------------
+
+    def _build_user_content(
+        self,
+        prompt: str,
+        image_urls: list[str],
+    ) -> typing.Any:
+        """构建 LangGraph 兼容的 user content（支持多模态）"""
+        if not image_urls:
+            return prompt
+
+        content: list[dict[str, typing.Any]] = []
+        if prompt:
+            content.append({'type': 'text', 'text': prompt})
+        for url in image_urls:
+            if not isinstance(url, str):
+                continue
+            url = url.strip()
+            if not url:
+                continue
+            if url.startswith(('http://', 'https://', 'data:')):
+                content.append({'type': 'image_url', 'image_url': {'url': url}})
+        return content if content else prompt
+
+    def _preprocess_user_message(
+        self,
+        query: pipeline_query.Query,
+    ) -> tuple[str, list[str]]:
+        """提取用户消息的纯文本与图片 URL 列表"""
+        plain_text = ''
+        image_urls: list[str] = []
+
+        if isinstance(query.user_message.content, str):
+            plain_text = query.user_message.content
+        elif isinstance(query.user_message.content, list):
+            for ce in query.user_message.content:
+                if ce.type == 'text':
+                    plain_text += ce.text
+                elif ce.type == 'image_base64':
+                    # 转换为 data URI 形式
+                    b64 = getattr(ce, 'image_base64', '')
+                    if b64:
+                        if not b64.startswith('data:'):
+                            b64 = f'data:image/png;base64,{b64}'
+                        image_urls.append(b64)
+                elif ce.type == 'image_url':
+                    url = getattr(ce, 'image_url', '')
+                    if url:
+                        image_urls.append(url)
+
+        return plain_text, image_urls
+
+    # ------------------------------------------------------------------
+    # 请求构造
+    # ------------------------------------------------------------------
+
+    def _build_messages(
+        self,
+        prompt: str,
+        image_urls: list[str],
+        system_prompt: str = '',
+    ) -> list[dict[str, typing.Any]]:
+        messages: list[dict[str, typing.Any]] = []
+        if system_prompt:
+            messages.append({'role': 'system', 'content': system_prompt})
+        messages.append(
+            {
+                'role': 'user',
+                'content': self._build_user_content(prompt, image_urls),
+            }
+        )
+        return messages
+
+    def _build_runtime_configurable(self, thread_id: str) -> dict[str, typing.Any]:
+        cfg: dict[str, typing.Any] = {
+            'thread_id': thread_id,
+            'thinking_enabled': self.thinking_enabled,
+            'is_plan_mode': self.plan_mode,
+            'subagent_enabled': self.subagent_enabled,
+        }
+        if self.subagent_enabled:
+            cfg['max_concurrent_subagents'] = self.max_concurrent_subagents
+        if self.model_name:
+            cfg['model_name'] = self.model_name
+        return cfg
+
+    def _build_payload(
+        self,
+        thread_id: str,
+        prompt: str,
+        image_urls: list[str],
+        system_prompt: str = '',
+    ) -> dict[str, typing.Any]:
+        runtime_configurable = self._build_runtime_configurable(thread_id)
+        return {
+            'assistant_id': self.assistant_id,
+            'input': {
+                'messages': self._build_messages(prompt, image_urls, system_prompt),
+            },
+            'stream_mode': ['values', 'messages-tuple', 'custom'],
+            # DeerFlow 2.0 从 config.configurable 读取运行时覆盖
+            # 同时保留 context 字段做向后兼容
+            'context': dict(runtime_configurable),
+            'config': {
+                'recursion_limit': self.recursion_limit,
+                'configurable': runtime_configurable,
+            },
+        }
+
+    # ------------------------------------------------------------------
+    # Session/Thread 管理
+    # ------------------------------------------------------------------
+
+    async def _ensure_thread_id(self, query: pipeline_query.Query) -> str:
+        """从 query.session 取/创建 deerflow thread_id
+
+        LangBot 使用 `query.session.using_conversation.uuid` 持久化 conversation id，
+        我们复用这个字段存储 deerflow thread_id（与 Dify Runner 同样做法）。
+        """
+        thread_id = query.session.using_conversation.uuid or ''
+        if thread_id:
+            return thread_id
+
+        thread = await self.deerflow_client.create_thread(timeout=min(30, self.timeout))
+        thread_id = thread.get('thread_id', '')
+        if not thread_id:
+            raise errors.DeerFlowAPIError(message=f'DeerFlow create thread 返回数据缺少 thread_id: {thread}')
+
+        query.session.using_conversation.uuid = thread_id
+        return thread_id
+
+    # ------------------------------------------------------------------
+    # 流式事件处理
+    # ------------------------------------------------------------------
+
+    def _handle_values_event(
+        self,
+        data: typing.Any,
+        state: _StreamState,
+    ) -> str | None:
+        """处理 values 事件，返回新的完整文本（增量基础上的全量）"""
+        values_messages = stream_utils.extract_messages_from_values_data(data)
+        if not values_messages:
+            return None
+
+        new_messages: list[dict[str, typing.Any]] = []
+        if not state.baseline_initialized:
+            state.baseline_initialized = True
+            for idx, msg in enumerate(values_messages):
+                if not isinstance(msg, dict):
+                    continue
+                new_messages.append(msg)
+                msg_id = stream_utils.get_message_id(msg)
+                if msg_id:
+                    self._remember_seen_message_id(state, msg_id)
+                    continue
+                state.no_id_message_fingerprints[idx] = self._fingerprint_message(msg)
+        else:
+            new_messages = self._extract_new_messages_from_values(values_messages, state)
+
+        latest_text = ''
+        if new_messages:
+            state.run_values_messages.extend(new_messages)
+            if len(state.run_values_messages) > _MAX_VALUES_HISTORY:
+                state.run_values_messages = state.run_values_messages[-_MAX_VALUES_HISTORY:]
+            latest_text = stream_utils.extract_latest_ai_text(state.run_values_messages)
+            if latest_text:
+                state.has_values_text = True
+            latest_clarification = stream_utils.extract_latest_clarification_text(
+                state.run_values_messages,
+            )
+            if latest_clarification:
+                state.clarification_text = latest_clarification
+
+        return latest_text or None
+
+    def _handle_message_event(
+        self,
+        data: typing.Any,
+        state: _StreamState,
+    ) -> str | None:
+        """处理 messages-tuple 事件，返回增量文本
+
+        当 values 事件已经提供完整文本时，跳过 messages-tuple 的增量
+        """
+        delta = stream_utils.extract_ai_delta_from_event_data(data)
+        if delta and not state.has_values_text:
+            state.latest_text += delta
+            return delta
+
+        maybe_clar = stream_utils.extract_clarification_from_event_data(data)
+        if maybe_clar:
+            state.clarification_text = maybe_clar
+        return None
+
+    def _build_final_text(self, state: _StreamState) -> str:
+        """构建最终输出文本"""
+        if state.clarification_text:
+            return state.clarification_text
+
+        # 优先使用最后一条 AI message 的文本
+        latest_ai = stream_utils.extract_latest_ai_message(state.run_values_messages)
+        if latest_ai:
+            text = stream_utils.extract_text(latest_ai.get('content'))
+            if text:
+                if state.timed_out:
+                    text += f'\n\nDeerFlow stream 在 {self.timeout}s 后超时，返回部分结果。'
+                return text
+
+        if state.latest_text:
+            text = state.latest_text
+            if state.timed_out:
+                text += f'\n\nDeerFlow stream 在 {self.timeout}s 后超时，返回部分结果。'
+            return text
+
+        # 提取任务失败信息作兜底
+        failure_text = stream_utils.build_task_failure_summary(state.task_failures)
+        if failure_text:
+            return failure_text
+
+        return 'DeerFlow 返回空响应'
+
+    # ------------------------------------------------------------------
+    # 主流程
+    # ------------------------------------------------------------------
+
+    async def _stream_messages_chunk(
+        self,
+        query: pipeline_query.Query,
+    ) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
+        """流式输出生成器"""
+        plain_text, image_urls = self._preprocess_user_message(query)
+
+        system_prompt = ''
+        # LangBot 的 pipeline 通常通过 prompt-preprocess 已注入 system prompt
+        # 这里保持空，让 prompt-preprocess 的内容作为 user message 一并送给 deerflow
+
+        thread_id = await self._ensure_thread_id(query)
+        payload = self._build_payload(
+            thread_id=thread_id,
+            prompt=plain_text or 'continue',
+            image_urls=image_urls,
+            system_prompt=system_prompt,
+        )
+
+        state = _StreamState()
+        prev_text = ''
+        message_idx = 0
+
+        try:
+            async for event in self.deerflow_client.stream_run(
+                thread_id=thread_id,
+                payload=payload,
+                timeout=self.timeout,
+            ):
+                event_type = event.get('event')
+                data = event.get('data')
+
+                if event_type == 'values':
+                    new_full = self._handle_values_event(data, state)
+                    if new_full and new_full != prev_text:
+                        delta = new_full[len(prev_text) :] if new_full.startswith(prev_text) else new_full
+                        prev_text = new_full
+                        if delta:
+                            message_idx += 1
+                            yield provider_message.MessageChunk(
+                                role='assistant',
+                                content=new_full,
+                                is_final=False,
+                            )
+                    continue
+
+                if event_type in {'messages-tuple', 'messages', 'message'}:
+                    delta = self._handle_message_event(data, state)
+                    if delta:
+                        prev_text = state.latest_text
+                        message_idx += 1
+                        yield provider_message.MessageChunk(
+                            role='assistant',
+                            content=prev_text,
+                            is_final=False,
+                        )
+                    continue
+
+                if event_type == 'custom':
+                    state.task_failures.extend(
+                        stream_utils.extract_task_failures_from_custom_event(data),
+                    )
+                    continue
+
+                if event_type == 'error':
+                    raise errors.DeerFlowAPIError(message=f'DeerFlow stream error event: {data}')
+
+                if event_type == 'end':
+                    break
+        except (asyncio.TimeoutError, TimeoutError):
+            self.ap.logger.warning(f'DeerFlow stream timed out after {self.timeout}s for thread_id={thread_id}')
+            state.timed_out = True
+
+        # 最终消息
+        final_text = self._build_final_text(state)
+        yield provider_message.MessageChunk(
+            role='assistant',
+            content=final_text,
+            is_final=True,
+        )
+
+    async def _messages(
+        self,
+        query: pipeline_query.Query,
+    ) -> typing.AsyncGenerator[provider_message.Message, None]:
+        """非流式聚合输出"""
+        plain_text, image_urls = self._preprocess_user_message(query)
+
+        thread_id = await self._ensure_thread_id(query)
+        payload = self._build_payload(
+            thread_id=thread_id,
+            prompt=plain_text or 'continue',
+            image_urls=image_urls,
+        )
+
+        state = _StreamState()
+
+        try:
+            async for event in self.deerflow_client.stream_run(
+                thread_id=thread_id,
+                payload=payload,
+                timeout=self.timeout,
+            ):
+                event_type = event.get('event')
+                data = event.get('data')
+
+                if event_type == 'values':
+                    self._handle_values_event(data, state)
+                    continue
+
+                if event_type in {'messages-tuple', 'messages', 'message'}:
+                    self._handle_message_event(data, state)
+                    continue
+
+                if event_type == 'custom':
+                    state.task_failures.extend(
+                        stream_utils.extract_task_failures_from_custom_event(data),
+                    )
+                    continue
+
+                if event_type == 'error':
+                    raise errors.DeerFlowAPIError(message=f'DeerFlow stream error event: {data}')
+
+                if event_type == 'end':
+                    break
+        except (asyncio.TimeoutError, TimeoutError):
+            self.ap.logger.warning(f'DeerFlow stream timed out after {self.timeout}s for thread_id={thread_id}')
+            state.timed_out = True
+
+        final_text = self._build_final_text(state)
+        yield provider_message.Message(
+            role='assistant',
+            content=final_text,
+        )
+
+    async def run(
+        self,
+        query: pipeline_query.Query,
+    ) -> typing.AsyncGenerator[provider_message.Message, None]:
+        """主入口：根据 adapter 是否支持流式输出，选择流式或非流式"""
+        if await query.adapter.is_stream_output_supported():
+            msg_idx = 0
+            async for msg in self._stream_messages_chunk(query):
+                msg_idx += 1
+                msg.msg_sequence = msg_idx
+                yield msg
+        else:
+            async for msg in self._messages(query):
+                yield msg

src/langbot/pkg/provider/runners/weknoraapi.py

@@ -0,0 +1,351 @@
+from __future__ import annotations
+
+import typing
+import json
+
+
+from langbot.pkg.provider import runner
+from langbot.pkg.core import app
+import langbot_plugin.api.entities.builtin.provider.message as provider_message
+import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+from langbot.libs.weknora_api import client, errors
+
+
+@runner.runner_class('weknora-api')
+class WeKnoraAPIRunner(runner.RequestRunner):
+    """WeKnora API 对话请求器"""
+
+    weknora_client: client.AsyncWeKnoraClient
+
+    def __init__(self, ap: app.Application, pipeline_config: dict):
+        super().__init__(ap, pipeline_config)
+
+        valid_app_types = ['chat', 'agent']
+        if self.pipeline_config['ai']['weknora-api']['app-type'] not in valid_app_types:
+            raise errors.WeKnoraAPIError(
+                f'不支持的 WeKnora 应用类型: {self.pipeline_config["ai"]["weknora-api"]["app-type"]}'
+            )
+
+        api_key = self.pipeline_config['ai']['weknora-api'].get('api-key', '').strip()
+        if not api_key:
+            raise errors.WeKnoraAPIError(
+                'WeKnora API Key 未配置，请在流水线的 WeKnora API 配置中填入 API Key '
+                '(从 WeKnora 前端 设置 → API Keys 生成)'
+            )
+
+        base_url = self.pipeline_config['ai']['weknora-api'].get('base-url', '').strip()
+        if not base_url:
+            raise errors.WeKnoraAPIError('WeKnora Base URL 未配置，请填入服务器地址，例如 http://localhost:8080/api/v1')
+
+        self.weknora_client = client.AsyncWeKnoraClient(
+            api_key=api_key,
+            base_url=base_url,
+        )
+
+    async def _extract_plain_text(self, query: pipeline_query.Query) -> str:
+        """从用户消息中提取纯文本内容"""
+        plain_text = ''
+        if isinstance(query.user_message.content, str):
+            plain_text = query.user_message.content
+        elif isinstance(query.user_message.content, list):
+            for ce in query.user_message.content:
+                if ce.type == 'text':
+                    plain_text += ce.text
+
+        if not plain_text:
+            plain_text = self.pipeline_config['ai']['weknora-api'].get('base-prompt', '')
+
+        return plain_text
+
+    async def _ensure_session(self, query: pipeline_query.Query) -> str:
+        """确保会话存在，如果不存在则创建"""
+        session_id = query.session.using_conversation.uuid or ''
+
+        if not session_id:
+            user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
+            session_id = await self.weknora_client.create_session(title=f'IM Chat - {user_tag}')
+            query.session.using_conversation.uuid = session_id
+
+        return session_id
+
+    async def _agent_chat_messages(
+        self, query: pipeline_query.Query
+    ) -> typing.AsyncGenerator[provider_message.Message, None]:
+        """调用 Agent 智能对话（非流式聚合输出）"""
+        session_id = await self._ensure_session(query)
+        plain_text = await self._extract_plain_text(query)
+        user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
+
+        config = self.pipeline_config['ai']['weknora-api']
+        agent_id = config.get('agent-id', 'builtin-smart-reasoning')
+        knowledge_base_ids = config.get('knowledge-base-ids', [])
+        web_search_enabled = config.get('web-search-enabled', False)
+        timeout = config.get('timeout', 120)
+
+        full_answer = ''
+        chunk = None
+
+        async for chunk in self.weknora_client.agent_chat(
+            session_id=session_id,
+            query=plain_text,
+            user=user_tag,
+            agent_id=agent_id,
+            knowledge_base_ids=knowledge_base_ids,
+            web_search_enabled=web_search_enabled,
+            timeout=timeout,
+        ):
+            self.ap.logger.debug('weknora-agent-chunk: ' + str(chunk))
+
+            response_type = chunk.get('response_type', '')
+            content = chunk.get('content', '')
+
+            if response_type == 'tool_call':
+                # 工具调用
+                tool_data = chunk.get('data', {})
+                tool_name = tool_data.get('tool_name', '')
+                if tool_name:
+                    yield provider_message.Message(
+                        role='assistant',
+                        tool_calls=[
+                            provider_message.ToolCall(
+                                id=chunk.get('id', ''),
+                                type='function',
+                                function=provider_message.FunctionCall(
+                                    name=tool_name,
+                                    arguments=json.dumps(tool_data.get('arguments', {})),
+                                ),
+                            )
+                        ],
+                    )
+
+            elif response_type == 'answer':
+                if content:
+                    full_answer += content
+
+            elif response_type == 'error':
+                raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
+
+        if chunk is None:
+            raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
+
+        if full_answer:
+            yield provider_message.Message(
+                role='assistant',
+                content=full_answer,
+            )
+
+    async def _chat_messages(
+        self, query: pipeline_query.Query
+    ) -> typing.AsyncGenerator[provider_message.Message, None]:
+        """调用知识库 RAG 问答（非流式聚合输出）"""
+        session_id = await self._ensure_session(query)
+        plain_text = await self._extract_plain_text(query)
+        user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
+
+        config = self.pipeline_config['ai']['weknora-api']
+        agent_id = config.get('agent-id', 'builtin-quick-answer')
+        knowledge_base_ids = config.get('knowledge-base-ids', [])
+        timeout = config.get('timeout', 120)
+
+        full_answer = ''
+        chunk = None
+
+        async for chunk in self.weknora_client.knowledge_chat(
+            session_id=session_id,
+            query=plain_text,
+            user=user_tag,
+            agent_id=agent_id,
+            knowledge_base_ids=knowledge_base_ids,
+            timeout=timeout,
+        ):
+            self.ap.logger.debug('weknora-chat-chunk: ' + str(chunk))
+
+            response_type = chunk.get('response_type', '')
+            content = chunk.get('content', '')
+
+            if response_type == 'answer':
+                if content:
+                    full_answer += content
+
+            elif response_type == 'error':
+                raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
+
+        if chunk is None:
+            raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
+
+        if full_answer:
+            yield provider_message.Message(
+                role='assistant',
+                content=full_answer,
+            )
+
+    async def _agent_chat_messages_chunk(
+        self, query: pipeline_query.Query
+    ) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
+        """调用 Agent 智能对话（流式输出）"""
+        session_id = await self._ensure_session(query)
+        plain_text = await self._extract_plain_text(query)
+        user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
+
+        config = self.pipeline_config['ai']['weknora-api']
+        agent_id = config.get('agent-id', 'builtin-smart-reasoning')
+        knowledge_base_ids = config.get('knowledge-base-ids', [])
+        web_search_enabled = config.get('web-search-enabled', False)
+        timeout = config.get('timeout', 120)
+
+        pending_answer = ''
+        message_idx = 0
+        is_final = False
+        chunk = None
+
+        async for chunk in self.weknora_client.agent_chat(
+            session_id=session_id,
+            query=plain_text,
+            user=user_tag,
+            agent_id=agent_id,
+            knowledge_base_ids=knowledge_base_ids,
+            web_search_enabled=web_search_enabled,
+            timeout=timeout,
+        ):
+            self.ap.logger.debug('weknora-agent-chunk: ' + str(chunk))
+
+            response_type = chunk.get('response_type', '')
+            content = chunk.get('content', '')
+            done = chunk.get('done', False)
+
+            if response_type == 'tool_call':
+                tool_data = chunk.get('data', {})
+                tool_name = tool_data.get('tool_name', '')
+                if tool_name:
+                    message_idx += 1
+                    yield provider_message.MessageChunk(
+                        role='assistant',
+                        tool_calls=[
+                            provider_message.ToolCall(
+                                id=chunk.get('id', ''),
+                                type='function',
+                                function=provider_message.FunctionCall(
+                                    name=tool_name,
+                                    arguments=json.dumps(tool_data.get('arguments', {})),
+                                ),
+                            )
+                        ],
+                    )
+
+            elif response_type == 'answer':
+                message_idx += 1
+                if content:
+                    pending_answer += content
+
+                if done:
+                    is_final = True
+
+                # 每 8 个 chunk 输出一次，或最终输出
+                if message_idx % 8 == 0 or is_final:
+                    yield provider_message.MessageChunk(
+                        role='assistant',
+                        content=pending_answer,
+                        is_final=is_final,
+                    )
+
+            elif response_type == 'error':
+                raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
+
+        if chunk is None:
+            raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
+
+        # 确保最终消息已发出
+        if not is_final and pending_answer:
+            yield provider_message.MessageChunk(
+                role='assistant',
+                content=pending_answer,
+                is_final=True,
+            )
+
+    async def _chat_messages_chunk(
+        self, query: pipeline_query.Query
+    ) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
+        """调用知识库 RAG 问答（流式输出）"""
+        session_id = await self._ensure_session(query)
+        plain_text = await self._extract_plain_text(query)
+        user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
+
+        config = self.pipeline_config['ai']['weknora-api']
+        agent_id = config.get('agent-id', 'builtin-quick-answer')
+        knowledge_base_ids = config.get('knowledge-base-ids', [])
+        timeout = config.get('timeout', 120)
+
+        pending_answer = ''
+        message_idx = 0
+        is_final = False
+        chunk = None
+
+        async for chunk in self.weknora_client.knowledge_chat(
+            session_id=session_id,
+            query=plain_text,
+            user=user_tag,
+            agent_id=agent_id,
+            knowledge_base_ids=knowledge_base_ids,
+            timeout=timeout,
+        ):
+            self.ap.logger.debug('weknora-chat-chunk: ' + str(chunk))
+
+            response_type = chunk.get('response_type', '')
+            content = chunk.get('content', '')
+            done = chunk.get('done', False)
+
+            if response_type == 'answer':
+                message_idx += 1
+                if content:
+                    pending_answer += content
+
+                if done:
+                    is_final = True
+
+                if message_idx % 8 == 0 or is_final:
+                    yield provider_message.MessageChunk(
+                        role='assistant',
+                        content=pending_answer,
+                        is_final=is_final,
+                    )
+
+            elif response_type == 'error':
+                raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
+
+        if chunk is None:
+            raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
+
+        if not is_final and pending_answer:
+            yield provider_message.MessageChunk(
+                role='assistant',
+                content=pending_answer,
+                is_final=True,
+            )
+
+    async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
+        """运行请求"""
+        app_type = self.pipeline_config['ai']['weknora-api']['app-type']
+
+        if await query.adapter.is_stream_output_supported():
+            msg_idx = 0
+            if app_type == 'agent':
+                async for msg in self._agent_chat_messages_chunk(query):
+                    msg_idx += 1
+                    msg.msg_sequence = msg_idx
+                    yield msg
+            elif app_type == 'chat':
+                async for msg in self._chat_messages_chunk(query):
+                    msg_idx += 1
+                    msg.msg_sequence = msg_idx
+                    yield msg
+            else:
+                raise errors.WeKnoraAPIError(f'不支持的 WeKnora 应用类型: {app_type}')
+        else:
+            if app_type == 'agent':
+                async for msg in self._agent_chat_messages(query):
+                    yield msg
+            elif app_type == 'chat':
+                async for msg in self._chat_messages(query):
+                    yield msg
+            else:
+                raise errors.WeKnoraAPIError(f'不支持的 WeKnora 应用类型: {app_type}')

src/langbot/pkg/provider/tools/loaders/mcp.py

@@ -240,12 +240,13 @@ class RuntimeMCPSession:
                     return
                 if attempt >= self._MAX_RETRIES:
                     self.status = MCPSessionStatus.ERROR
-                    self.error_message = f'Failed after {self._MAX_RETRIES + 1} attempts: {e}'
+                    self.error_message = f'Failed after {self._MAX_RETRIES + 1} attempts: {self._describe_exception(e)}'
                     self._ready_event.set()
                     return
                 delay = self._RETRY_DELAYS[attempt]
                 self.ap.logger.warning(
-                    f'MCP session {self.server_name} failed (attempt {attempt + 1}), retrying in {delay}s: {e}'
+                    f'MCP session {self.server_name} failed (attempt {attempt + 1}), '
+                    f'retrying in {delay}s: {self._describe_exception(e)}'
                 )
                 await self._cleanup_box_stdio_session()
                 # Reset status for retry
@@ -254,6 +255,30 @@ class RuntimeMCPSession:
                 self.error_phase = None
                 await asyncio.sleep(delay)
 
+    @staticmethod
+    def _describe_exception(exc: BaseException) -> str:
+        """Flatten an exception into its underlying leaf messages.
+
+        anyio / the MCP client wrap real failures in a TaskGroup, whose own
+        message is the unhelpful "unhandled errors in a TaskGroup (N
+        sub-exception)". Recurse into ExceptionGroups so the actual cause
+        (e.g. ``httpx.HTTPStatusError: Client error '410 Gone'``) is surfaced.
+        """
+        leaves: list[str] = []
+
+        def visit(e: BaseException) -> None:
+            sub = getattr(e, 'exceptions', None)
+            if sub:  # ExceptionGroup / BaseExceptionGroup
+                for child in sub:
+                    visit(child)
+            else:
+                leaves.append(f'{type(e).__name__}: {e}')
+
+        visit(exc)
+        seen: set[str] = set()
+        unique = [m for m in leaves if not (m in seen or seen.add(m))]
+        return '; '.join(unique) if unique else f'{type(exc).__name__}: {exc}'
+
     _MONITOR_POLL_INTERVAL = 5
     _MONITOR_MAX_CONSECUTIVE_ERRORS = 3
 

src/langbot/templates/metadata/pipeline/ai.yaml

@@ -47,6 +47,14 @@ stages:
             label:
               en_US: Langflow API
               zh_Hans: Langflow API
+          - name: weknora-api
+            label:
+              en_US: WeKnora API
+              zh_Hans: WeKnora API
+          - name: deerflow-api
+            label:
+              en_US: DeerFlow API
+              zh_Hans: DeerFlow API
       - name: expire-time
         label:
           en_US: Conversation expire time (seconds)
@@ -653,3 +661,215 @@ stages:
         type: json
         required: false
         default: '{}'
+  - name: weknora-api
+    label:
+      en_US: WeKnora API
+      zh_Hans: WeKnora API
+    description:
+      en_US: Configure the WeKnora API of the pipeline
+      zh_Hans: 配置 WeKnora API
+    config:
+      - name: base-url
+        label:
+          en_US: Base URL
+          zh_Hans: 基础 URL
+        description:
+          en_US: The base URL of the WeKnora server (with /api/v1)
+          zh_Hans: WeKnora 服务器的基础 URL（包含 /api/v1）
+        type: string
+        required: true
+        default: 'http://localhost:8080/api/v1'
+      - name: api-key
+        label:
+          en_US: API Key
+          zh_Hans: API 密钥
+        description:
+          en_US: The API key for WeKnora, generated from WeKnora frontend Settings → API Keys
+          zh_Hans: WeKnora 的 API 密钥，从 WeKnora 前端 设置 → API Keys 生成
+        type: string
+        required: true
+        default: ''
+      - name: app-type
+        label:
+          en_US: App Type
+          zh_Hans: 应用类型
+        type: select
+        required: true
+        default: agent
+        options:
+          - name: agent
+            label:
+              en_US: Agent (Smart Reasoning)
+              zh_Hans: Agent（智能推理）
+          - name: chat
+            label:
+              en_US: Chat (Knowledge Base RAG)
+              zh_Hans: 聊天（知识库 RAG）
+      - name: agent-id
+        label:
+          en_US: Agent ID
+          zh_Hans: 智能体 ID
+        description:
+          en_US: The Agent ID to use. Built-in agents include builtin-quick-answer, builtin-smart-reasoning, builtin-data-analyst
+          zh_Hans: 要使用的智能体 ID。内置智能体：builtin-quick-answer、builtin-smart-reasoning、builtin-data-analyst
+        type: string
+        required: true
+        default: 'builtin-smart-reasoning'
+      - name: knowledge-base-ids
+        label:
+          en_US: Knowledge Base IDs
+          zh_Hans: 知识库 ID 列表
+        description:
+          en_US: List of WeKnora knowledge base IDs to use (one per line)
+          zh_Hans: 要使用的 WeKnora 知识库 ID 列表（每行一个）
+        type: array
+        required: false
+        default: []
+      - name: web-search-enabled
+        label:
+          en_US: Enable Web Search
+          zh_Hans: 启用网络搜索
+        description:
+          en_US: Whether to enable web search in agent mode
+          zh_Hans: 在 Agent 模式下是否启用网络搜索
+        type: boolean
+        required: false
+        default: false
+      - name: timeout
+        label:
+          en_US: Timeout
+          zh_Hans: 超时时间
+        description:
+          en_US: Request timeout in seconds
+          zh_Hans: 请求超时时间（秒）
+        type: integer
+        required: false
+        default: 120
+      - name: base-prompt
+        label:
+          en_US: Base Prompt
+          zh_Hans: 基础提示词
+        description:
+          en_US: Default prompt when user message is empty (e.g. only images)
+          zh_Hans: 当用户消息为空（例如仅图片）时使用的默认提示词
+        type: string
+        required: false
+        default: '请回答用户的问题。'
+  - name: deerflow-api
+    label:
+      en_US: DeerFlow API
+      zh_Hans: DeerFlow API
+    description:
+      en_US: Configure the DeerFlow LangGraph API of the pipeline
+      zh_Hans: 配置 DeerFlow LangGraph API
+    config:
+      - name: api-base
+        label:
+          en_US: API Base URL
+          zh_Hans: API 基础 URL
+        description:
+          en_US: The base URL of the DeerFlow server (e.g. http://127.0.0.1:2026)
+          zh_Hans: DeerFlow 服务器的基础 URL（例如 http://127.0.0.1:2026）
+        type: string
+        required: true
+        default: 'http://127.0.0.1:2026'
+      - name: api-key
+        label:
+          en_US: API Key
+          zh_Hans: API 密钥
+        description:
+          en_US: Optional API key for DeerFlow (leave empty if not required)
+          zh_Hans: DeerFlow 的 API 密钥（如果不需要可留空）
+        type: string
+        required: false
+        default: ''
+      - name: auth-header
+        label:
+          en_US: Auth Header Name
+          zh_Hans: 鉴权请求头名称
+        description:
+          en_US: Custom auth header name. Leave empty to use "x-api-key"
+          zh_Hans: 自定义鉴权请求头名称，留空则使用 "x-api-key"
+        type: string
+        required: false
+        default: ''
+      - name: assistant-id
+        label:
+          en_US: Assistant ID
+          zh_Hans: 助手 ID
+        description:
+          en_US: The DeerFlow assistant/graph id (default lead_agent)
+          zh_Hans: DeerFlow 助手/图 ID（默认 lead_agent）
+        type: string
+        required: true
+        default: 'lead_agent'
+      - name: model-name
+        label:
+          en_US: Model Name
+          zh_Hans: 模型名称
+        description:
+          en_US: Optional model override forwarded to DeerFlow configurable
+          zh_Hans: 可选的模型名称覆盖，会作为 configurable 转发给 DeerFlow
+        type: string
+        required: false
+        default: ''
+      - name: thinking-enabled
+        label:
+          en_US: Enable Thinking
+          zh_Hans: 启用思考
+        description:
+          en_US: Whether to enable DeerFlow thinking mode
+          zh_Hans: 是否启用 DeerFlow 思考模式
+        type: boolean
+        required: false
+        default: false
+      - name: plan-mode
+        label:
+          en_US: Plan Mode
+          zh_Hans: 规划模式
+        description:
+          en_US: Whether to enable DeerFlow plan mode
+          zh_Hans: 是否启用 DeerFlow 规划模式
+        type: boolean
+        required: false
+        default: false
+      - name: subagent-enabled
+        label:
+          en_US: Enable Subagents
+          zh_Hans: 启用子代理
+        description:
+          en_US: Whether to enable parallel subagent execution
+          zh_Hans: 是否启用并行子代理执行
+        type: boolean
+        required: false
+        default: false
+      - name: max-concurrent-subagents
+        label:
+          en_US: Max Concurrent Subagents
+          zh_Hans: 最大并发子代理数
+        description:
+          en_US: Maximum number of concurrent subagents (only effective when subagents are enabled)
+          zh_Hans: 最大并发子代理数（仅在启用子代理时生效）
+        type: integer
+        required: false
+        default: 3
+      - name: timeout
+        label:
+          en_US: Timeout
+          zh_Hans: 超时时间
+        description:
+          en_US: Request timeout in seconds (DeerFlow runs may take a long time)
+          zh_Hans: 请求超时时间（秒），DeerFlow 运行可能耗时较长
+        type: integer
+        required: false
+        default: 300
+      - name: recursion-limit
+        label:
+          en_US: Recursion Limit
+          zh_Hans: 递归上限
+        description:
+          en_US: LangGraph recursion limit for a single run
+          zh_Hans: 单次运行的 LangGraph 递归上限
+        type: integer
+        required: false
+        default: 1000

uv.lock

@@ -1899,7 +1899,7 @@ wheels = [
 
 [[package]]
 name = "langbot"
-version = "4.10.0b1"
+version = "4.10.0"
 source = { editable = "." }
 dependencies = [
     { name = "aiocqhttp" },
@@ -2013,7 +2013,7 @@ requires-dist = [
     { name = "ebooklib", specifier = ">=0.18" },
     { name = "gewechat-client", specifier = ">=0.1.5" },
     { name = "html2text", specifier = ">=2024.2.26" },
-    { name = "langbot-plugin", specifier = "==0.4.0" },
+    { name = "langbot-plugin", specifier = "==0.4.1" },
     { name = "langchain", specifier = ">=0.2.0" },
     { name = "langchain-core", specifier = ">=1.2.28" },
     { name = "langchain-text-splitters", specifier = ">=1.1.2" },
@@ -2076,7 +2076,7 @@ dev = [
 
 [[package]]
 name = "langbot-plugin"
-version = "0.4.0"
+version = "0.4.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiofiles" },
@@ -2096,9 +2096,9 @@ dependencies = [
     { name = "watchdog" },
     { name = "websockets" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9b/0d/709cb5641b802c4a92a177363675b2bd7d38f6921bd873001b759a98cf05/langbot_plugin-0.4.0.tar.gz", hash = "sha256:e8669676283f3fae434b65a53ffb6ea26ea5665922c88fa72d348bca7a5b2650", size = 288861, upload-time = "2026-06-03T02:53:17.046Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/b2/c1/b11ce66fb2537b257ff387b8b5b708e616e5a072ae04440e24807eb3b1cf/langbot_plugin-0.4.1.tar.gz", hash = "sha256:57d3f8cd6b6c33316792ebfa0c907b2240834a84f2b8c8034c6be7721b425059", size = 289249, upload-time = "2026-06-04T05:19:08.747Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/bb/14/17d2e42c54539fdc4a0287bc1975170d597d5f58604e011cfc352b53bd35/langbot_plugin-0.4.0-py3-none-any.whl", hash = "sha256:144cf5c7a4849c3db1485a13a698568fab0211a9ff7de6fc9965d72850aa9ef0", size = 203081, upload-time = "2026-06-03T02:53:15.762Z" },
+    { url = "https://files.pythonhosted.org/packages/72/e8/335023bb5e1310621c7b7d8ae4fcac179f119709eee9a8ba65b681f66a8e/langbot_plugin-0.4.1-py3-none-any.whl", hash = "sha256:a9c319a4abb6944ae3d9a491edbeb703842a87b42b4e3b1eafba666ec2beeee7", size = 203412, upload-time = "2026-06-04T05:19:09.936Z" },
 ]
 
 [[package]]

web/src/app/home/components/home-sidebar/HomeSidebar.tsx

@@ -119,6 +119,22 @@ function compareVersions(v1: string, v2: string): boolean {
   return false;
 }
 
+// Discord brand glyph (lucide-react has no Discord icon).
+function DiscordIcon({ className }: { className?: string }) {
+  return (
+    <svg
+      role="img"
+      viewBox="0 0 24 24"
+      fill="currentColor"
+      xmlns="http://www.w3.org/2000/svg"
+      className={className}
+      aria-hidden="true"
+    >
+      <path d="M20.317 4.3698a19.7913 19.7913 0 00-4.8851-1.5152.0741.0741 0 00-.0785.0371c-.211.3753-.4447.8648-.6083 1.2495-1.8447-.2762-3.68-.2762-5.4868 0-.1636-.3933-.4058-.8742-.6177-1.2495a.077.077 0 00-.0785-.037 19.7363 19.7363 0 00-4.8852 1.515.0699.0699 0 00-.0321.0277C.5334 9.0458-.319 13.5799.0992 18.0578a.0824.0824 0 00.0312.0561c2.0528 1.5076 4.0413 2.4228 5.9929 3.0294a.0777.0777 0 00.0842-.0276c.4616-.6304.8731-1.2952 1.226-1.9942a.076.076 0 00-.0416-.1057c-.6528-.2476-1.2743-.5495-1.8722-.8923a.077.077 0 01-.0076-.1277c.1258-.0943.2517-.1923.3718-.2914a.0743.0743 0 01.0776-.0105c3.9278 1.7933 8.18 1.7933 12.0614 0a.0739.0739 0 01.0785.0095c.1202.099.246.1981.3728.2924a.077.077 0 01-.0066.1276 12.2986 12.2986 0 01-1.873.8914.0766.0766 0 00-.0407.1067c.3604.698.7719 1.3628 1.225 1.9932a.076.076 0 00.0842.0286c1.961-.6067 3.9495-1.5219 6.0023-3.0294a.077.077 0 00.0313-.0552c.5004-5.177-.8382-9.6739-3.5485-13.6604a.061.061 0 00-.0312-.0286zM8.02 15.3312c-1.1825 0-2.1569-1.0857-2.1569-2.419 0-1.3332.9555-2.4189 2.157-2.4189 1.2108 0 2.1757 1.0952 2.1568 2.419 0 1.3332-.9555 2.4189-2.1569 2.4189zm7.9748 0c-1.1825 0-2.1569-1.0857-2.1569-2.419 0-1.3332.9554-2.4189 2.1569-2.4189 1.2108 0 2.1757 1.0952 2.1568 2.419 0 1.3332-.946 2.4189-2.1568 2.4189Z" />
+    </svg>
+  );
+}
+
 // IDs of sidebar entries that have collapsible entity sub-items
 const ENTITY_CATEGORY_IDS = [
   'bots',
@@ -2077,6 +2093,14 @@ export default function HomeSidebar({
                         </Badge>
                       )}
                     </DropdownMenuItem>
+                    <DropdownMenuItem
+                      onClick={() => {
+                        window.open('https://discord.gg/wdNEHETs87', '_blank');
+                      }}
+                    >
+                      <DiscordIcon className="text-[#5865F2]" />
+                      {t('common.joinDiscord')}
+                    </DropdownMenuItem>
                   </DropdownMenuGroup>
                   <DropdownMenuSeparator />
 

web/src/app/home/components/models-dialog/components/AddModelPopover.tsx

@@ -18,7 +18,7 @@ import {
   PopoverContent,
   PopoverTrigger,
 } from '@/components/ui/popover';
-import { Tabs, TabsContent, TabsList, TabsTrigger } from '@/components/ui/tabs';
+import { Tabs, TabsList, TabsTrigger } from '@/components/ui/tabs';
 import { useTranslation } from 'react-i18next';
 import { ScannedProviderModel } from '@/app/infra/entities/api';
 import {
@@ -298,20 +298,8 @@ export default function AddModelPopover({
           </div>
 
           <div className="overflow-y-auto flex-1 min-h-0">
-            <Tabs
+            {mode === 'manual' ? (
-              value={mode}
+              <div className="mt-3">
-              onValueChange={(v) => setMode(v as 'manual' | 'scan')}
-            >
-              {!trigger && (
-                <TabsList className="grid w-full grid-cols-2 mt-3">
-                  <TabsTrigger value="manual">
-                    {t('models.manualAdd')}
-                  </TabsTrigger>
-                  <TabsTrigger value="scan">{t('models.scanAdd')}</TabsTrigger>
-                </TabsList>
-              )}
-
-              <TabsContent value="manual" className="mt-3">
                 <div className="space-y-3">
                   <div className="space-y-2">
                     <Label>{t('models.modelName')}</Label>
@@ -390,9 +378,9 @@ export default function AddModelPopover({
                     </Button>
                   </div>
                 </div>
-              </TabsContent>
+              </div>
-
+            ) : (
-              <TabsContent value="scan" className="space-y-2 mt-0 pt-0">
+              <div className="space-y-2 mt-3">
                 {scanLoading ? (
                   <div className="flex items-center justify-center py-4">
                     <RefreshCw className="h-4 w-4 mr-2 animate-spin text-muted-foreground" />
@@ -565,8 +553,8 @@ export default function AddModelPopover({
                     />
                   </Button>
                 </div>
-              </TabsContent>
+              </div>
-            </Tabs>
+            )}
           </div>
         </Tabs>
       </PopoverContent>

web/src/app/home/plugins/components/plugin-install-task/PluginInstallProgressDialog.tsx

@@ -11,7 +11,7 @@ import {
   Download,
   Package,
   Server,
-  BookOpen,
+  Sparkles,
   CheckCircle2,
   XCircle,
   Loader2,
@@ -176,7 +176,7 @@ function TaskProgressContent({ task }: { task: PluginInstallTask }) {
   // MCP / Skill don't have the plugin's download + dependency-install stages;
   // show a single "installing → done/failed" row instead of plugin steps.
   const isPlugin = task.extensionType === 'plugin';
-  const simpleIcon = task.extensionType === 'mcp' ? Server : BookOpen;
+  const simpleIcon = task.extensionType === 'mcp' ? Server : Sparkles;
   const simpleInstallingLabel =
     task.extensionType === 'mcp'
       ? t('addExtension.installStage.mcpInstalling')

web/src/app/home/plugins/components/plugin-install-task/PluginInstallTaskQueue.tsx

@@ -9,9 +9,9 @@ import {
   Loader2,
   X,
   ListTodo,
-  Wrench,
+  Puzzle,
-  AudioWaveform,
+  Server,
-  Book,
+  Sparkles,
 } from 'lucide-react';
 import { Button } from '@/components/ui/button';
 import {
@@ -35,9 +35,9 @@ const STAGE_ICONS: Record<string, React.ElementType> = {
 };
 
 const EXTENSION_TYPE_ICONS: Record<string, React.ElementType> = {
-  plugin: Wrench,
+  plugin: Puzzle,
-  mcp: AudioWaveform,
+  mcp: Server,
-  skill: Book,
+  skill: Sparkles,
 };
 
 function TaskQueueItem({
@@ -54,7 +54,7 @@ function TaskQueueItem({
   const isError = task.stage === InstallStage.ERROR;
   const isRunning = !isDone && !isError;
   const StageIcon = STAGE_ICONS[task.stage] || Download;
-  const TypeIcon = EXTENSION_TYPE_ICONS[task.extensionType] || Wrench;
+  const TypeIcon = EXTENSION_TYPE_ICONS[task.extensionType] || Puzzle;
 
   const getTypeBadgeClass = () => {
     switch (task.extensionType) {

web/src/app/home/plugins/components/plugin-installed/PluginInstalledComponent.tsx

@@ -21,8 +21,7 @@ import { extractI18nObject } from '@/i18n/I18nProvider';
 import { toast } from 'sonner';
 import { useAsyncTask, AsyncTaskStatus } from '@/hooks/useAsyncTask';
 import { useSidebarData } from '@/app/home/components/home-sidebar/SidebarDataContext';
-import { Loader2, Puzzle } from 'lucide-react';
+import { Loader2, Puzzle, Server, Sparkles } from 'lucide-react';
-import { Wrench, AudioWaveform, Book } from 'lucide-react';
 
 export interface PluginInstalledComponentRef {
   refreshPluginList: () => void;
@@ -44,14 +43,18 @@ export const FilterOptions = [
   {
     value: 'plugin' as FilterType,
     labelKey: 'market.typePlugin',
-    icon: Wrench,
+    icon: Puzzle,
   },
   {
     value: 'mcp' as FilterType,
     labelKey: 'market.typeMCP',
-    icon: AudioWaveform,
+    icon: Server,
+  },
+  {
+    value: 'skill' as FilterType,
+    labelKey: 'market.typeSkill',
+    icon: Sparkles,
   },
-  { value: 'skill' as FilterType, labelKey: 'market.typeSkill', icon: Book },
 ];
 
 interface PluginInstalledComponentProps {

web/src/app/home/plugins/components/plugin-market/PluginMarketComponent.tsx

@@ -17,6 +17,9 @@ import { Separator } from '@/components/ui/separator';
 import { ToggleGroup, ToggleGroupItem } from '@/components/ui/toggle-group';
 import {
   Search,
+  Puzzle,
+  Server,
+  Sparkles,
   Wrench,
   AudioWaveform,
   Hash,
@@ -88,9 +91,9 @@ function MarketPageContent({
 
   const extensionTypeOptions = [
     { value: 'all', label: t('market.filters.allFormats'), icon: null },
-    { value: 'plugin', label: t('market.typePlugin'), icon: Wrench },
+    { value: 'plugin', label: t('market.typePlugin'), icon: Puzzle },
-    { value: 'mcp', label: t('market.typeMCP'), icon: AudioWaveform },
+    { value: 'mcp', label: t('market.typeMCP'), icon: Server },
-    { value: 'skill', label: t('market.typeSkill'), icon: Book },
+    { value: 'skill', label: t('market.typeSkill'), icon: Sparkles },
   ];
 
   const [searchQuery, setSearchQuery] = useState('');
@@ -142,7 +145,7 @@ function MarketPageContent({
     }
   }, [typeFilter, componentFilter, selectedTags, sortOption]);
 
-  const pageSize = 12; // 每页12个
+  const pageSize = 24; // 每页24个
   const searchTimeoutRef = useRef<NodeJS.Timeout | null>(null);
   const scrollContainerRef = useRef<HTMLDivElement | null>(null);
   const isComposingRef = useRef(false);

web/src/app/home/plugins/components/plugin-market/plugin-market-card/PluginMarketCardComponent.tsx

@@ -94,7 +94,7 @@ export default function PluginMarketCardComponent({
       role="button"
       tabIndex={0}
       aria-label={t('market.installCard', { name: cardVO.label })}
-      className="w-[100%] h-[10rem] cursor-pointer bg-white rounded-[10px] shadow-[0px_0px_4px_0_rgba(0,0,0,0.2)] p-3 sm:p-[1rem] hover:shadow-[0px_2px_8px_0_rgba(0,0,0,0.15)] transition-shadow duration-200 outline-none focus-visible:ring-[3px] focus-visible:ring-ring/50 dark:bg-[#1f1f22] dark:shadow-[0px_0px_4px_0_rgba(255,255,255,0.1)] dark:hover:shadow-[0px_2px_8px_0_rgba(255,255,255,0.15)] relative"
+      className="w-[100%] h-[10rem] cursor-pointer bg-white rounded-[10px] border border-border shadow-[0px_1px_2px_0_rgba(0,0,0,0.06)] p-3 sm:p-[1rem] hover:shadow-[0px_2px_5px_0_rgba(0,0,0,0.08)] transition-shadow duration-200 outline-none focus-visible:ring-[3px] focus-visible:ring-ring/50 dark:bg-[#1f1f22] dark:shadow-[0px_1px_2px_0_rgba(255,255,255,0.04)] dark:hover:shadow-[0px_2px_5px_0_rgba(255,255,255,0.07)] relative"
       onClick={handleInstallClick}
       onKeyDown={(event) => {
         if (event.key === 'Enter' || event.key === ' ') {

web/src/i18n/locales/en-US.ts

@@ -37,6 +37,7 @@ const enUS = {
     helpDocs: 'Get Help',
     featureRequest: 'Feature Request',
     starOnGitHub: 'Star on GitHub',
+    joinDiscord: 'Join our Discord',
     create: 'Create',
     edit: 'Edit',
     delete: 'Delete',
@@ -631,8 +632,8 @@ const enUS = {
   },
   market: {
     searchPlaceholder: 'Search plugins...',
-    searchResults: 'Found {{count}} plugins',
+    searchResults: 'Found {{count}} extensions',
-    totalPlugins: 'Total {{count}} plugins',
+    totalPlugins: 'Total {{count}} extensions',
     noPlugins: 'No plugins available',
     noResults: 'No relevant plugins found',
     loadingMore: 'Loading more...',

web/src/i18n/locales/es-ES.ts

@@ -40,6 +40,7 @@ const esES = {
     helpDocs: 'Obtener ayuda',
     featureRequest: 'Solicitar función',
     starOnGitHub: 'Dar estrella en GitHub',
+    joinDiscord: 'Únete a Discord',
     create: 'Crear',
     edit: 'Editar',
     delete: 'Eliminar',
@@ -644,8 +645,8 @@ const esES = {
   },
   market: {
     searchPlaceholder: 'Buscar plugins...',
-    searchResults: 'Se encontraron {{count}} plugins',
+    searchResults: 'Se encontraron {{count}} extensiones',
-    totalPlugins: 'Total {{count}} plugins',
+    totalPlugins: 'Total {{count}} extensiones',
     noPlugins: 'No hay plugins disponibles',
     noResults: 'No se encontraron plugins relevantes',
     loadingMore: 'Cargando más...',

web/src/i18n/locales/ja-JP.ts

@@ -38,6 +38,7 @@ const jaJP = {
     helpDocs: 'ヘルプドキュメント',
     featureRequest: '機能リクエスト',
     starOnGitHub: 'GitHubでStarする',
+    joinDiscord: 'Discord に参加',
     create: '作成',
     edit: '編集',
     delete: '削除',
@@ -636,8 +637,8 @@ const jaJP = {
   },
   market: {
     searchPlaceholder: 'プラグインを検索...',
-    searchResults: '{{count}} 個のプラグインが見つかりました',
+    searchResults: '{{count}} 個の拡張機能が見つかりました',
-    totalPlugins: '合計 {{count}} 個のプラグイン',
+    totalPlugins: '合計 {{count}} 個の拡張機能',
     noPlugins: '利用可能なプラグインがありません',
     noResults: '関連するプラグインが見つかりません',
     loadingMore: 'さらに読み込み中...',

web/src/i18n/locales/ru-RU.ts

@@ -38,6 +38,7 @@ const ruRU = {
     helpDocs: 'Помощь',
     featureRequest: 'Запрос функции',
     starOnGitHub: 'Поставить звезду на GitHub',
+    joinDiscord: 'Присоединиться к Discord',
     create: 'Создать',
     edit: 'Редактировать',
     delete: 'Удалить',
@@ -642,8 +643,8 @@ const ruRU = {
   },
   market: {
     searchPlaceholder: 'Поиск плагинов...',
-    searchResults: 'Найдено {{count}} плагинов',
+    searchResults: 'Найдено {{count}} расширений',
-    totalPlugins: 'Всего {{count}} плагинов',
+    totalPlugins: 'Всего {{count}} расширений',
     noPlugins: 'Нет доступных плагинов',
     noResults: 'Подходящие плагины не найдены',
     loadingMore: 'Загрузка ещё...',

web/src/i18n/locales/th-TH.ts

@@ -37,6 +37,7 @@ const thTH = {
     helpDocs: 'ขอความช่วยเหลือ',
     featureRequest: 'ขอฟีเจอร์ใหม่',
     starOnGitHub: 'ให้ดาวบน GitHub',
+    joinDiscord: 'เข้าร่วม Discord',
     create: 'สร้าง',
     edit: 'แก้ไข',
     delete: 'ลบ',
@@ -623,8 +624,8 @@ const thTH = {
   },
   market: {
     searchPlaceholder: 'ค้นหาปลั๊กอิน...',
-    searchResults: 'พบ {{count}} ปลั๊กอิน',
+    searchResults: 'พบ {{count}} ส่วนขยาย',
-    totalPlugins: 'ทั้งหมด {{count}} ปลั๊กอิน',
+    totalPlugins: 'ทั้งหมด {{count}} ส่วนขยาย',
     noPlugins: 'ไม่มีปลั๊กอินที่พร้อมใช้งาน',
     noResults: 'ไม่พบปลั๊กอินที่เกี่ยวข้อง',
     loadingMore: 'กำลังโหลดเพิ่มเติม...',

web/src/i18n/locales/vi-VN.ts

@@ -38,6 +38,7 @@ const viVN = {
     helpDocs: 'Trợ giúp',
     featureRequest: 'Yêu cầu tính năng',
     starOnGitHub: 'Star trên GitHub',
+    joinDiscord: 'Tham gia Discord',
     create: 'Tạo',
     edit: 'Chỉnh sửa',
     delete: 'Xóa',
@@ -637,8 +638,8 @@ const viVN = {
   },
   market: {
     searchPlaceholder: 'Tìm kiếm plugin...',
-    searchResults: 'Tìm thấy {{count}} plugin',
+    searchResults: 'Tìm thấy {{count}} tiện ích mở rộng',
-    totalPlugins: 'Tổng cộng {{count}} plugin',
+    totalPlugins: 'Tổng cộng {{count}} tiện ích mở rộng',
     noPlugins: 'Không có plugin nào',
     noResults: 'Không tìm thấy plugin liên quan',
     loadingMore: 'Đang tải thêm...',

web/src/i18n/locales/zh-Hans.ts

@@ -36,6 +36,7 @@ const zhHans = {
     helpDocs: '帮助文档',
     featureRequest: '需求建议',
     starOnGitHub: '在 GitHub 上 Star',
+    joinDiscord: '加入 Discord 社区',
     create: '创建',
     edit: '编辑',
     delete: '删除',
@@ -605,8 +606,8 @@ const zhHans = {
   },
   market: {
     searchPlaceholder: '搜索插件...',
-    searchResults: '搜索到 {{count}} 个插件',
+    searchResults: '搜索到 {{count}} 个扩展',
-    totalPlugins: '共 {{count}} 个插件',
+    totalPlugins: '共 {{count}} 个扩展',
     noPlugins: '暂无插件',
     noResults: '未找到相关插件',
     loadingMore: '加载更多...',

web/src/i18n/locales/zh-Hant.ts

@@ -36,6 +36,7 @@ const zhHant = {
     helpDocs: '輔助說明',
     featureRequest: '需求建議',
     starOnGitHub: '在 GitHub 上 Star',
+    joinDiscord: '加入 Discord 社群',
     create: '建立',
     edit: '編輯',
     delete: '刪除',
@@ -605,8 +606,8 @@ const zhHant = {
   },
   market: {
     searchPlaceholder: '搜尋插件...',
-    searchResults: '搜尋到 {{count}} 個插件',
+    searchResults: '搜尋到 {{count}} 個擴展',
-    totalPlugins: '共 {{count}} 個插件',
+    totalPlugins: '共 {{count}} 個擴展',
     noPlugins: '暫無插件',
     noResults: '未找到相關插件',
     loadingMore: '載入更多...',