feat: support multi-channel

fix: replace upsert syntax to support SQLite lower version
fix: improve web UI stability and conversation history restore
2026-06-02 18:17:11 +08:00 · 2026-02-26 18:34:08 +08:00 · 2026-02-26 10:44:04 +08:00 · 2026-02-26 10:35:20 +08:00 · 2026-02-25 18:01:39 +08:00 · 2026-02-25 01:34:16 +08:00
47 changed files with 5851 additions and 2139 deletions
--- a/README.md
+++ b/README.md
@@ -18,14 +18,15 @@
 -  ✅  **长期记忆：** 自动将对话记忆持久化至本地文件和数据库中，包括全局记忆和天级记忆，支持关键词及向量检索
 -  ✅  **技能系统：** 实现了Skills创建和运行的引擎，内置多种技能，并支持通过自然语言对话完成自定义Skills开发
 -  ✅  **多模态消息：** 支持对文本、图片、语音、文件等多类型消息进行解析、处理、生成、发送等操作
-  ✅  **多模型接入：** 支持OpenAI, Claude, Gemini, DeepSeek, MiniMax、GLM、Qwen、Kimi等国内外主流模型厂商
+-  ✅  **多模型接入：** 支持OpenAI, Claude, Gemini, DeepSeek, MiniMax、GLM、Qwen、Kimi、Doubao等国内外主流模型厂商
 -  ✅  **多端部署：** 支持运行在本地计算机或服务器，可集成到网页、飞书、钉钉、微信公众号、企业微信应用中使用
 -  ✅  **知识库：** 集成企业知识库能力，让Agent成为专属数字员工，基于[LinkAI](https://link-ai.tech)平台实现

 ## 声明

-1. 本项目遵循 [MIT开源协议](/LICENSE)，主要用于技术研究和学习，使用本项目时需遵守所在地法律法规、相关政策以及企业章程，禁止用于任何违法或侵犯他人权益的行为。任何个人、团队和企业，无论以何种方式使用该项目、对何对象提供服务，所产生的一切后果，本项目均不承担任何责任
-2. 成本与安全：Agent模式下Token使用量高于普通对话模式，请根据效果及成本综合选择模型。Agent具有访问所在操作系统的能力，请谨慎选择项目部署环境。同时项目也会持续升级安全机制、并降低模型消耗成本
+1. 本项目遵循 [MIT开源协议](/LICENSE)，主要用于技术研究和学习，使用本项目时需遵守所在地法律法规、相关政策以及企业章程，禁止用于任何违法或侵犯他人权益的行为。任何个人、团队和企业，无论以何种方式使用该项目、对何对象提供服务，所产生的一切后果，本项目均不承担任何责任。
+2. 成本与安全：Agent模式下Token使用量高于普通对话模式，请根据效果及成本综合选择模型。Agent具有访问所在操作系统的能力，请谨慎选择项目部署环境。同时项目也会持续升级安全机制、并降低模型消耗成本。
+3. CowAgent项目专注于开源技术开发，不会参与、授权或发行任何加密货币。

 ## 演示

@@ -90,7 +91,7 @@ bash <(curl -sS https://cdn.link-ai.tech/code/cow/run.sh)

 项目支持国内外主流厂商的模型接口，可选模型及配置说明参考：[模型说明](#模型说明)。

-> 注：Agent模式下推荐使用以下模型，可根据效果及成本综合选择：GLM(glm-4.7)、MiniMAx(MiniMax-M2.1)、Qwen(qwen3-max)、Claude(claude-opus-4-6、claude-sonnet-4-5、claude-sonnet-4-0)、Gemini(gemini-3-flash-preview、gemini-3-pro-preview)
+> 注：Agent模式下推荐使用以下模型，可根据效果及成本综合选择：MiniMax-M2.5、glm-5、kimi-k2.5、qwen3.5-plus、claude-sonnet-4-6、gemini-3.1-pro-preview

 同时支持使用 **LinkAI平台** 接口，可灵活切换 OpenAI、Claude、Gemini、DeepSeek、Qwen、Kimi 等多种常用模型，并支持知识库、工作流、插件等Agent能力，参考 [接口文档](https://docs.link-ai.tech/platform/api)。

@@ -136,9 +137,11 @@ pip3 install -r requirements-optional.txt
 # config.json 文件内容示例
 {
  "channel_type": "web",                                      # 接入渠道类型，默认为web，支持修改为:feishu,dingtalk,wechatcom_app,terminal,wechatmp,wechatmp_service
-  "model": "MiniMax-M2.1",                                    # 模型名称
+  "model": "MiniMax-M2.5",                                    # 模型名称
  "minimax_api_key": "",                                      # MiniMax API Key
  "zhipu_ai_api_key": "",                                     # 智谱GLM API Key
+  "moonshot_api_key": "",                                     # Kimi/Moonshot API Key
+  "ark_api_key": "",                                          # 豆包(火山方舟) API Key
  "dashscope_api_key": "",                                    # 百炼(通义千问)API Key
  "claude_api_key": "",                                       # Claude API Key
  "claude_api_base": "https://api.anthropic.com/v1",          # Claude API 地址，修改可接入三方代理平台
@@ -173,13 +176,13 @@ pip3 install -r requirements-optional.txt
 <details>
 <summary>2. 其他配置</summary>

-+ `model`: 模型名称，Agent模式下推荐使用 `glm-4.7`、`MiniMax-M2.1`、`qwen3-max`、`claude-opus-4-6`、`claude-sonnet-4-5`、`claude-sonnet-4-0`、`gemini-3-flash-preview`、`gemini-3-pro-preview`，全部模型名称参考[common/const.py](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/common/const.py)文件
+ `model`: 模型名称，Agent模式下推荐使用 `MiniMax-M2.5`、`glm-5`、`kimi-k2.5`、`qwen3.5-plus`、`claude-sonnet-4-6`、`gemini-3.1-pro-preview`，全部模型名称参考[common/const.py](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/common/const.py)文件
 + `character_desc`：普通对话模式下的机器人系统提示词。在Agent模式下该配置不生效，由工作空间中的文件内容构成。
 + `subscribe_msg`：订阅消息，公众号和企业微信channel中请填写，当被订阅时会自动回复， 可使用特殊占位符。目前支持的占位符有{trigger_prefix}，在程序中它会自动替换成bot的触发词。
 </details>

 <details>
-<summary>5. LinkAI配置</summary>
+<summary>3. LinkAI配置</summary>

 + `use_linkai`: 是否使用LinkAI接口，默认关闭，设置为true后可对接LinkAI平台，使用知识库、工作流、插件等能力, 参考[接口文档](https://docs.link-ai.tech/platform/api/chat)
 + `linkai_api_key`: LinkAI Api Key，可在 [控制台](https://link-ai.tech/console/interface) 创建
@@ -309,24 +312,24 @@ volumes:

 ```json
 {
-    "model": "MiniMax-M2.1",
+    "model": "MiniMax-M2.5",
    "minimax_api_key": ""
 }
 ```
- - `model`: 可填写 `MiniMax-M2.1、MiniMax-M2.1-lightning、MiniMax-M2、abab6.5-chat` 等
+ - `model`: 可填写 `MiniMax-M2.5、MiniMax-M2.1、MiniMax-M2.1-lightning、MiniMax-M2、abab6.5-chat` 等
 - `minimax_api_key`：MiniMax平台的API-KEY，在 [控制台](https://platform.minimaxi.com/user-center/basic-information/interface-key) 创建

 方式二：OpenAI兼容方式接入，配置如下：
 ```json
 {
  "bot_type": "chatGPT",
-  "model": "MiniMax-M2.1",
+  "model": "MiniMax-M2.5",
  "open_ai_api_base": "https://api.minimaxi.com/v1",
  "open_ai_api_key": ""
 }
 ```
 - `bot_type`: OpenAI兼容方式
- `model`: 可填 `MiniMax-M2.1、MiniMax-M2.1-lightning、MiniMax-M2`，参考[API文档](https://platform.minimaxi.com/document/%E5%AF%B9%E8%AF%9D?key=66701d281d57f38758d581d0#QklxsNSbaf6kM4j6wjO5eEek)
+- `model`: 可填 `MiniMax-M2.5、MiniMax-M2.1、MiniMax-M2.1-lightning、MiniMax-M2`，参考[API文档](https://platform.minimaxi.com/document/%E5%AF%B9%E8%AF%9D?key=66701d281d57f38758d581d0#QklxsNSbaf6kM4j6wjO5eEek)
 - `open_ai_api_base`: MiniMax平台API的 BASE URL
 - `open_ai_api_key`: MiniMax平台的API-KEY
 </details>
@@ -338,24 +341,24 @@ volumes:

 ```json
 {
-  "model": "glm-4.7",
+  "model": "glm-5",
  "zhipu_ai_api_key": ""
 }
 ```
- - `model`: 可填 `glm-4.7、glm-4-plus、glm-4-flash、glm-4-air、glm-4-airx、glm-4-long` 等, 参考 [glm-4系列模型编码](https://bigmodel.cn/dev/api/normal-model/glm-4)
+ - `model`: 可填 `glm-5、glm-4.7、glm-4-plus、glm-4-flash、glm-4-air、glm-4-airx、glm-4-long` 等, 参考 [glm系列模型编码](https://bigmodel.cn/dev/api/normal-model/glm-4)
 - `zhipu_ai_api_key`: 智谱AI平台的 API KEY，在 [控制台](https://www.bigmodel.cn/usercenter/proj-mgmt/apikeys) 创建

 方式二：OpenAI兼容方式接入，配置如下：
 ```json
 {
  "bot_type": "chatGPT",
-  "model": "glm-4.7",
+  "model": "glm-5",
  "open_ai_api_base": "https://open.bigmodel.cn/api/paas/v4",
  "open_ai_api_key": ""
 }
 ```
 - `bot_type`: OpenAI兼容方式
- `model`: 可填 `glm-4.7、glm-4.6、glm-4-plus、glm-4-flash、glm-4-air、glm-4-airx、glm-4-long` 等
+- `model`: 可填 `glm-5、glm-4.7、glm-4-plus、glm-4-flash、glm-4-air、glm-4-airx、glm-4-long` 等
 - `open_ai_api_base`: 智谱AI平台的 BASE URL
 - `open_ai_api_key`: 智谱AI平台的 API KEY
 </details>
@@ -367,18 +370,18 @@ volumes:

 ```json
 {
-    "model": "qwen3-max",
+    "model": "qwen3.5-plus",
    "dashscope_api_key": "sk-qVxxxxG"
 }
 ```
- - `model`: 可填写 `qwen3-max、qwen-max、qwen-plus、qwen-turbo、qwen-long、qwq-plus` 等
+ - `model`: 可填写 `qwen3.5-plus、qwen3-max、qwen-max、qwen-plus、qwen-turbo、qwen-long、qwq-plus` 等
 - `dashscope_api_key`: 通义千问的 API-KEY，参考 [官方文档](https://bailian.console.aliyun.com/?tab=api#/api) ，在 [控制台](https://bailian.console.aliyun.com/?tab=model#/api-key) 创建

 方式二：OpenAI兼容方式接入，配置如下：
 ```json
 {
  "bot_type": "chatGPT",
-  "model": "qwen3-max",
+  "model": "qwen3.5-plus",
  "open_ai_api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
  "open_ai_api_key": "sk-qVxxxxG"
 }
@@ -389,6 +392,53 @@ volumes:
 - `open_ai_api_key`: 通义千问的 API-KEY
 </details>

+<details>
+<summary>Kimi (Moonshot)</summary>
+
+方式一：官方接入，配置如下：
+
+```json
+{
+    "model": "kimi-k2.5",
+    "moonshot_api_key": ""
+}
+```
+ - `model`: 可填写 `kimi-k2.5、kimi-k2、moonshot-v1-8k、moonshot-v1-32k、moonshot-v1-128k`
+ - `moonshot_api_key`: Moonshot的API-KEY，在 [控制台](https://platform.moonshot.cn/console/api-keys) 创建
+ 
+方式二：OpenAI兼容方式接入，配置如下：
+```json
+{
+  "bot_type": "chatGPT",
+  "model": "kimi-k2.5",
+  "open_ai_api_base": "https://api.moonshot.cn/v1",
+  "open_ai_api_key": ""
+}
+```
+- `bot_type`: OpenAI兼容方式
+- `model`: 可填写 `kimi-k2.5、kimi-k2、moonshot-v1-8k、moonshot-v1-32k、moonshot-v1-128k`
+- `open_ai_api_base`: Moonshot的 BASE URL
+- `open_ai_api_key`: Moonshot的 API-KEY
+</details>
+
+<details>
+<summary>豆包 (Doubao)</summary>
+
+1. API Key创建：在 [火山方舟控制台](https://console.volcengine.com/ark/region:ark+cn-beijing/apikey) 创建API Key
+
+2. 填写配置
+
+```json
+{
+    "model": "doubao-seed-2-0-code-preview-260215",
+    "ark_api_key": "YOUR_API_KEY"
+}
+```
+ - `model`: 可填写 `doubao-seed-2-0-code-preview-260215、doubao-seed-2-0-pro-260215、doubao-seed-2-0-lite-260215、doubao-seed-2-0-mini-260215` 等
+ - `ark_api_key`: 火山方舟平台的 API Key，在 [控制台](https://console.volcengine.com/ark/region:ark+cn-beijing/apikey) 创建
+ - `ark_base_url`: 可选，默认为 `https://ark.cn-beijing.volces.com/api/v3`
+</details>
+
 <details>
 <summary>Claude</summary>

@@ -398,11 +448,11 @@ volumes:

 ```json
 {
-    "model": "claude-sonnet-4-5",
+    "model": "claude-sonnet-4-6",
    "claude_api_key": "YOUR_API_KEY"
 }
 ```
- - `model`: 参考 [官方模型ID](https://docs.anthropic.com/en/docs/about-claude/models/overview#model-aliases) ，支持 `claude-opus-4-6、claude-sonnet-4-5、claude-sonnet-4-0、claude-opus-4-0、claude-3-5-sonnet-latest` 等
+ - `model`: 参考 [官方模型ID](https://docs.anthropic.com/en/docs/about-claude/models/overview#model-aliases) ，支持 `claude-sonnet-4-6、claude-opus-4-6、claude-sonnet-4-5、claude-sonnet-4-0、claude-opus-4-0、claude-3-5-sonnet-latest` 等
 </details>

 <details>
@@ -411,11 +461,11 @@ volumes:
 API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn) 创建API Key ，配置如下
 ```json
 {
-    "model": "gemini-3-flash-preview",
+    "model": "gemini-3.1-pro-preview",
    "gemini_api_key": ""
 }
 ```
- - `model`: 参考[官方文档-模型列表](https://ai.google.dev/gemini-api/docs/models?hl=zh-cn)，支持 `gemini-3-flash-preview、gemini-3-pro-preview、gemini-2.5-pro、gemini-2.0-flash` 等
+ - `model`: 参考[官方文档-模型列表](https://ai.google.dev/gemini-api/docs/models?hl=zh-cn)，支持 `gemini-3.1-pro-preview、gemini-3-flash-preview、gemini-3-pro-preview、gemini-2.5-pro、gemini-2.0-flash` 等
 </details>

 <details>
@@ -441,35 +491,6 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)
 - `open_ai_api_base`: DeepSeek平台 BASE URL
 </details>

-<details>
-<summary>Kimi (Moonshot)</summary>
-
-方式一：官方接入，配置如下：
-
-```json
-{
-    "model": "moonshot-v1-128k",
-    "moonshot_api_key": ""
-}
-```
- - `model`: 可填写 `moonshot-v1-8k、moonshot-v1-32k、moonshot-v1-128k`
- - `moonshot_api_key`: Moonshot的API-KEY，在 [控制台](https://platform.moonshot.cn/console/api-keys) 创建
- 
-方式二：OpenAI兼容方式接入，配置如下：
-```json
-{
-  "bot_type": "chatGPT",
-  "model": "moonshot-v1-128k",
-  "open_ai_api_base": "https://api.moonshot.cn/v1",
-  "open_ai_api_key": ""
-}
-```
- `bot_type`: OpenAI兼容方式
- `model`: 可填写 `moonshot-v1-8k、moonshot-v1-32k、moonshot-v1-128k`
- `open_ai_api_base`: Moonshot的 BASE URL
- `open_ai_api_key`: Moonshot的 API-KEY
-</details>
-
 <details>
 <summary>Azure</summary>

@@ -587,10 +608,12 @@ API Key创建：在 [控制台](https://aistudio.google.com/app/apikey?hl=zh-cn)

 以下对可接入通道的配置方式进行说明，应用通道代码在项目的 `channel/` 目录下。

+支持同时可接入多个通道，配置时可通过逗号进行分割，例如 `"channel_type": "feishu,dingtalk"`。
+
 <details>
 <summary>1. Web</summary>

-项目启动后默认运行Web通道，配置如下：
+项目启动后会默认运行Web控制台，配置如下：

 ```json
 {
--- a/agent/chat/init.py
+++ b/agent/chat/init.py
@@ -0,0 +1,3 @@
+from agent.chat.service import ChatService
+
+__all__ = ["ChatService"]
--- a/agent/chat/service.py
+++ b/agent/chat/service.py
@@ -0,0 +1,169 @@
+"""
+ChatService - Wraps the Agent stream execution to produce CHAT protocol chunks.
+
+Translates agent events (message_update, message_end, tool_execution_end, etc.)
+into the CHAT socket protocol format (content chunks with segment_id, tool_calls chunks).
+"""
+
+import time
+from typing import Callable, Optional
+
+from common.log import logger
+
+
+class ChatService:
+    """
+    High-level service that runs an Agent for a given query and streams
+    the results as CHAT protocol chunks via a callback.
+
+    Usage:
+        svc = ChatService(agent_bridge)
+        svc.run(query, session_id, send_chunk_fn)
+    """
+
+    def __init__(self, agent_bridge):
+        """
+        :param agent_bridge: AgentBridge instance (manages agent lifecycle)
+        """
+        self.agent_bridge = agent_bridge
+
+    def run(self, query: str, session_id: str, send_chunk_fn: Callable[[dict], None]):
+        """
+        Run the agent for *query* and stream results back via *send_chunk_fn*.
+
+        The method blocks until the agent finishes. After it returns the SDK
+        will automatically send the final (streaming=false) message.
+
+        :param query: user query text
+        :param session_id: session identifier for agent isolation
+        :param send_chunk_fn: callable(chunk_data: dict) to send a streaming chunk
+        """
+        agent = self.agent_bridge.get_agent(session_id=session_id)
+        if agent is None:
+            raise RuntimeError("Failed to initialise agent for the session")
+
+        # State shared between the event callback and this method
+        state = _StreamState()
+
+        def on_event(event: dict):
+            """Translate agent events into CHAT protocol chunks."""
+            event_type = event.get("type")
+            data = event.get("data", {})
+
+            if event_type == "message_update":
+                # Incremental text delta
+                delta = data.get("delta", "")
+                if delta:
+                    send_chunk_fn({
+                        "chunk_type": "content",
+                        "delta": delta,
+                        "segment_id": state.segment_id,
+                    })
+
+            elif event_type == "message_end":
+                # A content segment finished.
+                tool_calls = data.get("tool_calls", [])
+                if tool_calls:
+                    # After tool_calls are executed the next content will be
+                    # a new segment; collect tool results until turn_end.
+                    state.pending_tool_results = []
+
+            elif event_type == "tool_execution_end":
+                tool_name = data.get("tool_name", "")
+                arguments = data.get("arguments", {})
+                result = data.get("result", "")
+                status = data.get("status", "unknown")
+                execution_time = data.get("execution_time", 0)
+                elapsed_str = f"{execution_time:.2f}s"
+
+                # Serialise result to string if needed
+                if not isinstance(result, str):
+                    import json
+                    try:
+                        result = json.dumps(result, ensure_ascii=False)
+                    except Exception:
+                        result = str(result)
+
+                tool_info = {
+                    "name": tool_name,
+                    "arguments": arguments,
+                    "result": result,
+                    "status": status,
+                    "elapsed": elapsed_str,
+                }
+
+                if state.pending_tool_results is not None:
+                    state.pending_tool_results.append(tool_info)
+
+            elif event_type == "turn_end":
+                has_tool_calls = data.get("has_tool_calls", False)
+                if has_tool_calls and state.pending_tool_results:
+                    # Flush collected tool results as a single tool_calls chunk
+                    send_chunk_fn({
+                        "chunk_type": "tool_calls",
+                        "tool_calls": state.pending_tool_results,
+                    })
+                    state.pending_tool_results = None
+                    # Next content belongs to a new segment
+                    state.segment_id += 1
+
+        # Run the agent with our event callback ---------------------------
+        logger.info(f"[ChatService] Starting agent run: session={session_id}, query={query[:80]}")
+
+        from config import conf
+        max_context_turns = conf().get("agent_max_context_turns", 30)
+
+        # Get full system prompt with skills
+        full_system_prompt = agent.get_full_system_prompt()
+
+        # Create a copy of messages for this execution
+        with agent.messages_lock:
+            messages_copy = agent.messages.copy()
+            original_length = len(agent.messages)
+
+        from agent.protocol.agent_stream import AgentStreamExecutor
+
+        executor = AgentStreamExecutor(
+            agent=agent,
+            model=agent.model,
+            system_prompt=full_system_prompt,
+            tools=agent.tools,
+            max_turns=agent.max_steps,
+            on_event=on_event,
+            messages=messages_copy,
+            max_context_turns=max_context_turns,
+        )
+
+        try:
+            response = executor.run_stream(query)
+        except Exception:
+            # If executor cleared messages (context overflow), sync back
+            if len(executor.messages) == 0:
+                with agent.messages_lock:
+                    agent.messages.clear()
+                    logger.info("[ChatService] Cleared agent message history after executor recovery")
+            raise
+
+        # Append only the NEW messages from this execution (thread-safe)
+        with agent.messages_lock:
+            new_messages = executor.messages[original_length:]
+            agent.messages.extend(new_messages)
+
+        # Store executor reference for files_to_send access
+        agent.stream_executor = executor
+
+        # Execute post-process tools
+        agent._execute_post_process_tools()
+
+        logger.info(f"[ChatService] Agent run completed: session={session_id}")
+
+
+
+class _StreamState:
+    """Mutable state shared between the event callback and the run method."""
+
+    def __init__(self):
+        self.segment_id: int = 0
+        # None means we are not accumulating tool results right now.
+        # A list means we are in the middle of a tool-execution phase.
+        self.pending_tool_results: Optional[list] = None
--- a/agent/memory/init.py
+++ b/agent/memory/init.py
@@ -1,11 +1,21 @@
 """
 Memory module for AgentMesh

-Provides long-term memory capabilities with hybrid search (vector + keyword)
+Provides both long-term memory (vector/keyword search) and short-term
+conversation history persistence (SQLite).
 """

 from agent.memory.manager import MemoryManager
 from agent.memory.config import MemoryConfig, get_default_memory_config, set_global_memory_config
 from agent.memory.embedding import create_embedding_provider
+from agent.memory.conversation_store import ConversationStore, get_conversation_store

-__all__ = ['MemoryManager', 'MemoryConfig', 'get_default_memory_config', 'set_global_memory_config', 'create_embedding_provider']
+__all__ = [
+    'MemoryManager',
+    'MemoryConfig',
+    'get_default_memory_config',
+    'set_global_memory_config',
+    'create_embedding_provider',
+    'ConversationStore',
+    'get_conversation_store',
+]
--- a/agent/memory/conversation_store.py
+++ b/agent/memory/conversation_store.py
@@ -0,0 +1,618 @@
+"""
+Conversation history persistence using SQLite.
+
+Design:
+- sessions table: per-session metadata (channel_type, last_active, msg_count)
+- messages table: individual messages stored as JSON, append-only
+- Pruning: age-based only (sessions not updated within N days are deleted)
+- Thread-safe via a single in-process lock
+
+Storage path: ~/cow/sessions/conversations.db
+"""
+
+from __future__ import annotations
+
+import json
+import sqlite3
+import threading
+import time
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+from common.log import logger
+
+
+# ---------------------------------------------------------------------------
+# Schema
+# ---------------------------------------------------------------------------
+
+_DDL = """
+CREATE TABLE IF NOT EXISTS sessions (
+    session_id   TEXT    PRIMARY KEY,
+    channel_type TEXT    NOT NULL DEFAULT '',
+    created_at   INTEGER NOT NULL,
+    last_active  INTEGER NOT NULL,
+    msg_count    INTEGER NOT NULL DEFAULT 0
+);
+
+CREATE TABLE IF NOT EXISTS messages (
+    id           INTEGER PRIMARY KEY AUTOINCREMENT,
+    session_id   TEXT    NOT NULL,
+    seq          INTEGER NOT NULL,
+    role         TEXT    NOT NULL,
+    content      TEXT    NOT NULL,
+    created_at   INTEGER NOT NULL,
+    UNIQUE (session_id, seq)
+);
+
+CREATE INDEX IF NOT EXISTS idx_messages_session
+    ON messages (session_id, seq);
+
+CREATE INDEX IF NOT EXISTS idx_sessions_last_active
+    ON sessions (last_active);
+"""
+
+# Migration: add channel_type column to existing databases that predate it.
+_MIGRATION_ADD_CHANNEL_TYPE = """
+ALTER TABLE sessions ADD COLUMN channel_type TEXT NOT NULL DEFAULT '';
+"""
+
+DEFAULT_MAX_AGE_DAYS: int = 30
+
+
+def _is_visible_user_message(content: Any) -> bool:
+    """
+    Return True when a user-role message represents actual user input
+    (not an internal tool_result injected by the agent loop).
+    """
+    if isinstance(content, str):
+        return bool(content.strip())
+    if isinstance(content, list):
+        return any(
+            isinstance(b, dict) and b.get("type") == "text"
+            for b in content
+        )
+    return False
+
+
+def _extract_display_text(content: Any) -> str:
+    """
+    Extract the human-readable text portion from a message content value.
+    Returns an empty string for tool_use / tool_result blocks.
+    """
+    if isinstance(content, str):
+        return content.strip()
+    if isinstance(content, list):
+        parts = [
+            b.get("text", "")
+            for b in content
+            if isinstance(b, dict) and b.get("type") == "text"
+        ]
+        return "\n".join(p for p in parts if p).strip()
+    return ""
+
+
+def _extract_tool_calls(content: Any) -> List[Dict[str, Any]]:
+    """
+    Extract tool_use blocks from an assistant message content.
+    Returns a list of {name, arguments} dicts (result filled in later).
+    """
+    if not isinstance(content, list):
+        return []
+    return [
+        {"id": b.get("id", ""), "name": b.get("name", ""), "arguments": b.get("input", {})}
+        for b in content
+        if isinstance(b, dict) and b.get("type") == "tool_use"
+    ]
+
+
+def _extract_tool_results(content: Any) -> Dict[str, str]:
+    """
+    Extract tool_result blocks from a user message, keyed by tool_use_id.
+    """
+    if not isinstance(content, list):
+        return {}
+    results = {}
+    for b in content:
+        if not isinstance(b, dict) or b.get("type") != "tool_result":
+            continue
+        tool_id = b.get("tool_use_id", "")
+        result_content = b.get("content", "")
+        if isinstance(result_content, list):
+            result_content = "\n".join(
+                rb.get("text", "") for rb in result_content
+                if isinstance(rb, dict) and rb.get("type") == "text"
+            )
+        results[tool_id] = str(result_content)
+    return results
+
+
+def _group_into_display_turns(
+    rows: List[tuple],
+) -> List[Dict[str, Any]]:
+    """
+    Convert raw (role, content_json, created_at) DB rows into display turns.
+
+    One display turn = one visible user message  +  one merged assistant reply.
+    All intermediate assistant messages (those carrying tool_use) and the final
+    assistant text reply produced for the same user query are collapsed into a
+    single assistant turn, exactly matching the live SSE rendering where tools
+    and the final answer appear inside the same bubble.
+
+    Grouping rules:
+    - A visible user message starts a new group.
+    - tool_result user messages are internal; their content is attached to the
+      matching tool_use entry via tool_use_id and they never become own turns.
+    - All assistant messages within a group are merged:
+        * tool_use blocks → tool_calls list (result filled from tool_results)
+        * text blocks → last non-empty text becomes the display content
+    """
+    # ------------------------------------------------------------------ #
+    # Pass 1: split rows into groups, each starting with a visible user msg
+    # ------------------------------------------------------------------ #
+    # group = (user_row | None, [subsequent_rows])
+    # user_row: (content, created_at)
+    groups: List[tuple] = []
+    cur_user: Optional[tuple] = None
+    cur_rest: List[tuple] = []
+    started = False
+
+    for role, raw_content, created_at in rows:
+        try:
+            content = json.loads(raw_content)
+        except Exception:
+            content = raw_content
+
+        if role == "user" and _is_visible_user_message(content):
+            if started:
+                groups.append((cur_user, cur_rest))
+            cur_user = (content, created_at)
+            cur_rest = []
+            started = True
+        else:
+            cur_rest.append((role, content, created_at))
+
+    if started:
+        groups.append((cur_user, cur_rest))
+
+    # ------------------------------------------------------------------ #
+    # Pass 2: build display turns from each group
+    # ------------------------------------------------------------------ #
+    turns: List[Dict[str, Any]] = []
+
+    for user_row, rest in groups:
+        # User turn
+        if user_row:
+            content, created_at = user_row
+            text = _extract_display_text(content)
+            if text:
+                turns.append({"role": "user", "content": text, "created_at": created_at})
+
+        # Collect all tool_calls and tool_results from the rest of the group
+        all_tool_calls: List[Dict[str, Any]] = []
+        tool_results: Dict[str, str] = {}
+        final_text = ""
+        final_ts: Optional[int] = None
+
+        for role, content, created_at in rest:
+            if role == "user":
+                tool_results.update(_extract_tool_results(content))
+            elif role == "assistant":
+                tcs = _extract_tool_calls(content)
+                all_tool_calls.extend(tcs)
+                t = _extract_display_text(content)
+                if t:
+                    final_text = t
+                final_ts = created_at
+
+        # Attach tool results to their matching tool_call entries
+        for tc in all_tool_calls:
+            tc["result"] = tool_results.get(tc.get("id", ""), "")
+
+        if final_text or all_tool_calls:
+            turns.append({
+                "role": "assistant",
+                "content": final_text,
+                "tool_calls": all_tool_calls,
+                "created_at": final_ts or (user_row[1] if user_row else 0),
+            })
+
+    return turns
+
+
+class ConversationStore:
+    """
+    SQLite-backed store for per-session conversation history.
+
+    Usage:
+        store = ConversationStore(db_path)
+        store.append_messages("user_123", new_messages, channel_type="feishu")
+        msgs = store.load_messages("user_123", max_turns=30)
+    """
+
+    def __init__(self, db_path: Path):
+        self._db_path = db_path
+        self._lock = threading.Lock()
+        self._init_db()
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+
+    def load_messages(
+        self,
+        session_id: str,
+        max_turns: int = 30,
+    ) -> List[Dict[str, Any]]:
+        """
+        Load the most recent messages for a session, for injection into the LLM.
+
+        ALL message types (user text, assistant tool_use, tool_result) are returned
+        in their original JSON form so the LLM can reconstruct the full context.
+
+        max_turns is a *visible-turn* count: we count only user messages whose
+        content is actual user text (not tool_result blocks).  This prevents
+        tool-heavy sessions from exhausting the turn budget prematurely.
+
+        Args:
+            session_id: Unique session identifier.
+            max_turns: Maximum number of visible user-assistant turns to keep.
+
+        Returns:
+            Chronologically ordered list of message dicts (role, content).
+        """
+        with self._lock:
+            conn = self._connect()
+            try:
+                rows = conn.execute(
+                    """
+                    SELECT seq, role, content
+                    FROM messages
+                    WHERE session_id = ?
+                    ORDER BY seq DESC
+                    """,
+                    (session_id,),
+                ).fetchall()
+            finally:
+                conn.close()
+
+        if not rows:
+            return []
+
+        # Walk newest-to-oldest counting *visible* user turns (actual user text,
+        # not tool_result injections).  Record the seq of every visible user
+        # message so we can find a clean cut point later.
+        visible_turn_seqs: List[int] = []  # newest first
+        for seq, role, raw_content in rows:
+            if role != "user":
+                continue
+            try:
+                content = json.loads(raw_content)
+            except Exception:
+                content = raw_content
+            if _is_visible_user_message(content):
+                visible_turn_seqs.append(seq)
+
+        # Determine the seq of the oldest visible user message we want to keep.
+        # If the total turns fit within max_turns, keep everything.
+        if len(visible_turn_seqs) <= max_turns:
+            cutoff_seq = None  # keep all
+        else:
+            # The Nth visible user message (0-indexed) is the oldest we keep.
+            cutoff_seq = visible_turn_seqs[max_turns - 1]
+
+        # Build result in chronological order, starting from cutoff.
+        # IMPORTANT: we start exactly at cutoff_seq (the visible user message),
+        # never mid-group, so tool_use / tool_result pairs are always complete.
+        result = []
+        for seq, role, raw_content in reversed(rows):
+            if cutoff_seq is not None and seq < cutoff_seq:
+                continue
+            try:
+                content = json.loads(raw_content)
+            except Exception:
+                content = raw_content
+            result.append({"role": role, "content": content})
+        return result
+
+    def append_messages(
+        self,
+        session_id: str,
+        messages: List[Dict[str, Any]],
+        channel_type: str = "",
+    ) -> None:
+        """
+        Append new messages to a session's history.
+
+        Seq numbers continue from the session's current maximum, so
+        concurrent callers on distinct sessions never collide.
+
+        Args:
+            session_id: Unique session identifier.
+            messages: List of message dicts to append.
+            channel_type: Source channel (e.g. "feishu", "web", "wechat").
+                          Only written on session creation; ignored on update.
+        """
+        if not messages:
+            return
+
+        now = int(time.time())
+        with self._lock:
+            conn = self._connect()
+            try:
+                with conn:
+                    # INSERT OR IGNORE creates the row on first visit;
+                    # the UPDATE always refreshes last_active.
+                    # Avoids ON CONFLICT...DO UPDATE (requires SQLite >= 3.24).
+                    conn.execute(
+                        """
+                        INSERT OR IGNORE INTO sessions
+                            (session_id, channel_type, created_at, last_active, msg_count)
+                        VALUES (?, ?, ?, ?, 0)
+                        """,
+                        (session_id, channel_type, now, now),
+                    )
+                    conn.execute(
+                        "UPDATE sessions SET last_active = ? WHERE session_id = ?",
+                        (now, session_id),
+                    )
+
+                    # Determine starting seq for the new batch.
+                    row = conn.execute(
+                        "SELECT COALESCE(MAX(seq), -1) FROM messages WHERE session_id = ?",
+                        (session_id,),
+                    ).fetchone()
+                    next_seq = row[0] + 1
+
+                    for msg in messages:
+                        role = msg.get("role", "")
+                        content = json.dumps(
+                            msg.get("content", ""), ensure_ascii=False
+                        )
+                        conn.execute(
+                            """
+                            INSERT OR IGNORE INTO messages
+                                (session_id, seq, role, content, created_at)
+                            VALUES (?, ?, ?, ?, ?)
+                            """,
+                            (session_id, next_seq, role, content, now),
+                        )
+                        next_seq += 1
+
+                    conn.execute(
+                        """
+                        UPDATE sessions
+                        SET msg_count = (
+                            SELECT COUNT(*) FROM messages WHERE session_id = ?
+                        )
+                        WHERE session_id = ?
+                        """,
+                        (session_id, session_id),
+                    )
+            finally:
+                conn.close()
+
+    def clear_session(self, session_id: str) -> None:
+        """Delete all messages and the session record for a given session_id."""
+        with self._lock:
+            conn = self._connect()
+            try:
+                with conn:
+                    conn.execute(
+                        "DELETE FROM messages WHERE session_id = ?", (session_id,)
+                    )
+                    conn.execute(
+                        "DELETE FROM sessions WHERE session_id = ?", (session_id,)
+                    )
+            finally:
+                conn.close()
+
+    def cleanup_old_sessions(self, max_age_days: Optional[int] = None) -> int:
+        """
+        Delete sessions that have not been active within max_age_days.
+
+        Args:
+            max_age_days: Override the default retention period.
+
+        Returns:
+            Number of sessions deleted.
+        """
+        try:
+            from config import conf
+            max_age = max_age_days or conf().get(
+                "conversation_max_age_days", DEFAULT_MAX_AGE_DAYS
+            )
+        except Exception:
+            max_age = max_age_days or DEFAULT_MAX_AGE_DAYS
+
+        cutoff = int(time.time()) - max_age * 86400
+        deleted = 0
+
+        with self._lock:
+            conn = self._connect()
+            try:
+                with conn:
+                    stale = conn.execute(
+                        "SELECT session_id FROM sessions WHERE last_active < ?",
+                        (cutoff,),
+                    ).fetchall()
+                    for (sid,) in stale:
+                        conn.execute(
+                            "DELETE FROM messages WHERE session_id = ?", (sid,)
+                        )
+                        conn.execute(
+                            "DELETE FROM sessions WHERE session_id = ?", (sid,)
+                        )
+                        deleted += 1
+            finally:
+                conn.close()
+
+        if deleted:
+            logger.info(f"[ConversationStore] Pruned {deleted} expired sessions")
+        return deleted
+
+    def load_history_page(
+        self,
+        session_id: str,
+        page: int = 1,
+        page_size: int = 20,
+    ) -> Dict[str, Any]:
+        """
+        Load a page of conversation history for UI display, grouped into turns.
+
+        Each "turn" maps to one of:
+          - A user message (role="user", content=str)
+          - An assistant message (role="assistant", content=str,
+            tool_calls=[{name, arguments, result}] when tools were used)
+
+        Internal tool_result user messages are merged into the preceding
+        assistant entry's tool_calls list and never appear as standalone items.
+
+        Pages are numbered from 1 (most recent).  Messages within a page are
+        returned in chronological order.
+
+        Returns:
+            {
+                "messages": [
+                    {
+                        "role": "user" | "assistant",
+                        "content": str,
+                        "tool_calls": [...],   # assistant only, may be []
+                        "created_at": int,
+                    },
+                    ...
+                ],
+                "total": <visible turn count>,
+                "page": <current page>,
+                "page_size": <page_size>,
+                "has_more": bool,
+            }
+        """
+        page = max(1, page)
+        with self._lock:
+            conn = self._connect()
+            try:
+                rows = conn.execute(
+                    """
+                    SELECT role, content, created_at
+                    FROM messages
+                    WHERE session_id = ?
+                    ORDER BY seq ASC
+                    """,
+                    (session_id,),
+                ).fetchall()
+            finally:
+                conn.close()
+
+        visible = _group_into_display_turns(rows)
+
+        total = len(visible)
+        offset = (page - 1) * page_size
+        page_items = list(reversed(visible))[offset: offset + page_size]
+        page_items = list(reversed(page_items))
+
+        return {
+            "messages": page_items,
+            "total": total,
+            "page": page,
+            "page_size": page_size,
+            "has_more": offset + page_size < total,
+        }
+
+    def get_stats(self) -> Dict[str, Any]:
+        """Return basic stats keyed by channel_type, for monitoring."""
+        with self._lock:
+            conn = self._connect()
+            try:
+                total_sessions = conn.execute(
+                    "SELECT COUNT(*) FROM sessions"
+                ).fetchone()[0]
+                total_messages = conn.execute(
+                    "SELECT COUNT(*) FROM messages"
+                ).fetchone()[0]
+                by_channel = conn.execute(
+                    """
+                    SELECT channel_type, COUNT(*) as cnt
+                    FROM sessions
+                    GROUP BY channel_type
+                    ORDER BY cnt DESC
+                    """
+                ).fetchall()
+                return {
+                    "total_sessions": total_sessions,
+                    "total_messages": total_messages,
+                    "by_channel": {row[0] or "unknown": row[1] for row in by_channel},
+                }
+            finally:
+                conn.close()
+
+    # ------------------------------------------------------------------
+    # Internal helpers
+    # ------------------------------------------------------------------
+
+    def _init_db(self) -> None:
+        self._db_path.parent.mkdir(parents=True, exist_ok=True)
+        conn = self._connect()
+        try:
+            conn.executescript(_DDL)
+            conn.commit()
+            self._migrate(conn)
+        finally:
+            conn.close()
+
+    def _migrate(self, conn: sqlite3.Connection) -> None:
+        """Apply incremental schema migrations on existing databases."""
+        cols = {
+            row[1]
+            for row in conn.execute("PRAGMA table_info(sessions)").fetchall()
+        }
+        if "channel_type" not in cols:
+            try:
+                conn.execute(_MIGRATION_ADD_CHANNEL_TYPE)
+                conn.commit()
+                logger.info("[ConversationStore] Migrated: added channel_type column")
+            except Exception as e:
+                logger.warning(f"[ConversationStore] Migration failed: {e}")
+
+    def _connect(self) -> sqlite3.Connection:
+        conn = sqlite3.connect(str(self._db_path), timeout=10)
+        conn.execute("PRAGMA journal_mode=WAL")
+        conn.execute("PRAGMA synchronous=NORMAL")
+        return conn
+
+
+# ---------------------------------------------------------------------------
+# Singleton
+# ---------------------------------------------------------------------------
+
+_store_instance: Optional[ConversationStore] = None
+_store_lock = threading.Lock()
+
+
+def get_conversation_store() -> ConversationStore:
+    """
+    Return the process-wide ConversationStore singleton.
+
+    Reuses the long-term memory database so the project stays with a single
+    SQLite file: ~/cow/memory/long-term/index.db
+    The conversation tables (sessions / messages) are separate from the
+    memory tables (memory_chunks / file_metadata) — no conflicts.
+    """
+    global _store_instance
+    if _store_instance is not None:
+        return _store_instance
+
+    with _store_lock:
+        if _store_instance is not None:
+            return _store_instance
+
+        try:
+            from agent.memory.config import get_default_memory_config
+            db_path = get_default_memory_config().get_db_path()
+        except Exception:
+            from common.utils import expand_path
+            db_path = Path(expand_path("~/cow")) / "memory" / "long-term" / "index.db"
+
+        _store_instance = ConversationStore(db_path)
+        logger.debug(f"[ConversationStore] Using shared DB at: {db_path}")
+        return _store_instance
--- a/agent/memory/service.py
+++ b/agent/memory/service.py
@@ -0,0 +1,167 @@
+"""
+Memory service for handling memory query operations via cloud protocol.
+
+Provides a unified interface for listing and reading memory files,
+callable from the cloud client (LinkAI) or a future web console.
+
+Memory file layout (under workspace_root):
+    MEMORY.md               -> type: global
+    memory/2026-02-20.md    -> type: daily
+"""
+
+import os
+from datetime import datetime
+from typing import Dict, List, Optional
+from pathlib import Path
+from common.log import logger
+
+
+class MemoryService:
+    """
+    High-level service for memory file queries.
+    Operates directly on the filesystem — no MemoryManager dependency.
+    """
+
+    def __init__(self, workspace_root: str):
+        """
+        :param workspace_root: Workspace root directory (e.g. ~/cow)
+        """
+        self.workspace_root = workspace_root
+        self.memory_dir = os.path.join(workspace_root, "memory")
+
+    # ------------------------------------------------------------------
+    # list — paginated file metadata
+    # ------------------------------------------------------------------
+    def list_files(self, page: int = 1, page_size: int = 20) -> dict:
+        """
+        List all memory files with metadata (without content).
+
+        Returns::
+
+            {
+                "page": 1,
+                "page_size": 20,
+                "total": 15,
+                "list": [
+                    {"filename": "MEMORY.md", "type": "global", "size": 2048, "updated_at": "2026-02-20 10:00:00"},
+                    {"filename": "2026-02-20.md", "type": "daily", "size": 512, "updated_at": "2026-02-20 09:30:00"},
+                    ...
+                ]
+            }
+        """
+        files: List[dict] = []
+
+        # 1. Global memory — MEMORY.md in workspace root
+        global_path = os.path.join(self.workspace_root, "MEMORY.md")
+        if os.path.isfile(global_path):
+            files.append(self._file_info(global_path, "MEMORY.md", "global"))
+
+        # 2. Daily memory files — memory/*.md (sorted newest first)
+        if os.path.isdir(self.memory_dir):
+            daily_files = []
+            for name in os.listdir(self.memory_dir):
+                full = os.path.join(self.memory_dir, name)
+                if os.path.isfile(full) and name.endswith(".md"):
+                    daily_files.append((name, full))
+            # Sort by filename descending (newest date first)
+            daily_files.sort(key=lambda x: x[0], reverse=True)
+            for name, full in daily_files:
+                files.append(self._file_info(full, name, "daily"))
+
+        total = len(files)
+
+        # Paginate
+        start = (page - 1) * page_size
+        end = start + page_size
+        page_items = files[start:end]
+
+        return {
+            "page": page,
+            "page_size": page_size,
+            "total": total,
+            "list": page_items,
+        }
+
+    # ------------------------------------------------------------------
+    # content — read a single file
+    # ------------------------------------------------------------------
+    def get_content(self, filename: str) -> dict:
+        """
+        Read the full content of a memory file.
+
+        :param filename: File name, e.g. ``MEMORY.md`` or ``2026-02-20.md``
+        :return: dict with ``filename`` and ``content``
+        :raises FileNotFoundError: if the file does not exist
+        """
+        path = self._resolve_path(filename)
+        if not os.path.isfile(path):
+            raise FileNotFoundError(f"Memory file not found: {filename}")
+
+        with open(path, "r", encoding="utf-8") as f:
+            content = f.read()
+
+        return {
+            "filename": filename,
+            "content": content,
+        }
+
+    # ------------------------------------------------------------------
+    # dispatch — single entry point for protocol messages
+    # ------------------------------------------------------------------
+    def dispatch(self, action: str, payload: Optional[dict] = None) -> dict:
+        """
+        Dispatch a memory management action.
+
+        :param action: ``list`` or ``content``
+        :param payload: action-specific payload
+        :return: protocol-compatible response dict
+        """
+        payload = payload or {}
+        try:
+            if action == "list":
+                page = payload.get("page", 1)
+                page_size = payload.get("page_size", 20)
+                result_payload = self.list_files(page=page, page_size=page_size)
+                return {"action": action, "code": 200, "message": "success", "payload": result_payload}
+
+            elif action == "content":
+                filename = payload.get("filename")
+                if not filename:
+                    return {"action": action, "code": 400, "message": "filename is required", "payload": None}
+                result_payload = self.get_content(filename)
+                return {"action": action, "code": 200, "message": "success", "payload": result_payload}
+
+            else:
+                return {"action": action, "code": 400, "message": f"unknown action: {action}", "payload": None}
+
+        except FileNotFoundError as e:
+            return {"action": action, "code": 404, "message": str(e), "payload": None}
+        except Exception as e:
+            logger.error(f"[MemoryService] dispatch error: action={action}, error={e}")
+            return {"action": action, "code": 500, "message": str(e), "payload": None}
+
+    # ------------------------------------------------------------------
+    # internal helpers
+    # ------------------------------------------------------------------
+    def _resolve_path(self, filename: str) -> str:
+        """
+        Resolve a filename to its absolute path.
+
+        - ``MEMORY.md`` → ``{workspace_root}/MEMORY.md``
+        - ``2026-02-20.md`` → ``{workspace_root}/memory/2026-02-20.md``
+        """
+        if filename == "MEMORY.md":
+            return os.path.join(self.workspace_root, filename)
+        return os.path.join(self.memory_dir, filename)
+
+    @staticmethod
+    def _file_info(path: str, filename: str, file_type: str) -> dict:
+        """Build a file metadata dict."""
+        stat = os.stat(path)
+        updated_at = datetime.fromtimestamp(stat.st_mtime).strftime("%Y-%m-%d %H:%M:%S")
+        return {
+            "filename": filename,
+            "type": file_type,
+            "size": stat.st_size,
+            "updated_at": updated_at,
+        }
--- a/agent/protocol/agent.py
+++ b/agent/protocol/agent.py
@@ -1,4 +1,5 @@
 import json
+import os
 import time
 import threading

@@ -61,7 +62,8 @@ class Agent:
                # Auto-create skill manager
                try:
                    from agent.skills import SkillManager
-                    self.skill_manager = SkillManager(workspace_dir=workspace_dir)
+                    custom_dir = os.path.join(workspace_dir, "skills") if workspace_dir else None
+                    self.skill_manager = SkillManager(custom_dir=custom_dir)
                    logger.debug(f"Initialized SkillManager with {len(self.skill_manager.skills)} skills")
                except Exception as e:
                    logger.warning(f"Failed to initialize SkillManager: {e}")
--- a/agent/protocol/agent_stream.py
+++ b/agent/protocol/agent_stream.py
@@ -501,7 +501,7 @@ class AgentStreamExecutor:

        # Prepare messages
        messages = self._prepare_messages()
-        logger.debug(f"Sending {len(messages)} messages to LLM")
+        logger.info(f"Sending {len(messages)} messages to LLM")

        # Prepare tool definitions (OpenAI/Claude format)
        tools_schema = None
@@ -574,7 +574,7 @@ class AgentStreamExecutor:
                        raise Exception(f"{error_msg} (Status: {status_code}, Code: {error_code}, Type: {error_type})")

                # Parse chunk
-                if isinstance(chunk, dict) and "choices" in chunk:
+                if isinstance(chunk, dict) and chunk.get("choices"):
                    choice = chunk["choices"][0]
                    delta = choice.get("delta", {})
                    
@@ -583,6 +583,11 @@ class AgentStreamExecutor:
                    if finish_reason:
                        stop_reason = finish_reason

+                    # Skip reasoning_content (internal thinking from models like GLM-5)
+                    reasoning_delta = delta.get("reasoning_content") or ""
+                    # if reasoning_delta:
+                    #     logger.debug(f"🧠 [thinking] {reasoning_delta[:100]}...")
+
                    # Handle text content
                    content_delta = delta.get("content") or ""
                    if content_delta:
--- a/agent/skills/init.py
+++ b/agent/skills/init.py
@@ -15,6 +15,7 @@ from agent.skills.types import (
 )
 from agent.skills.loader import SkillLoader
 from agent.skills.manager import SkillManager
+from agent.skills.service import SkillService
 from agent.skills.formatter import format_skills_for_prompt

 __all__ = [
@@ -25,5 +26,6 @@ __all__ = [
    "LoadSkillsResult",
    "SkillLoader",
    "SkillManager",
+    "SkillService",
    "format_skills_for_prompt",
 ]
--- a/agent/skills/loader.py
+++ b/agent/skills/loader.py
@@ -12,25 +12,20 @@ from agent.skills.frontmatter import parse_frontmatter, parse_metadata, parse_bo

 class SkillLoader:
    """Loads skills from various directories."""
-    
-    def __init__(self, workspace_dir: Optional[str] = None):
-        """
-        Initialize the skill loader.
-        
-        :param workspace_dir: Agent workspace directory (for workspace-specific skills)
-        """
-        self.workspace_dir = workspace_dir
+
+    def __init__(self):
+        pass
    
    def load_skills_from_dir(self, dir_path: str, source: str) -> LoadSkillsResult:
        """
        Load skills from a directory.
-        
+
        Discovery rules:
        - Direct .md files in the root directory
        - Recursive SKILL.md files under subdirectories
-        
+
        :param dir_path: Directory path to scan
-        :param source: Source identifier (e.g., 'managed', 'workspace', 'bundled')
+        :param source: Source identifier ('builtin' or 'custom')
        :return: LoadSkillsResult with skills and diagnostics
        """
        skills = []
@@ -216,61 +211,49 @@ class SkillLoader:
    
    def load_all_skills(
        self,
-        managed_dir: Optional[str] = None,
-        workspace_skills_dir: Optional[str] = None,
-        extra_dirs: Optional[List[str]] = None,
+        builtin_dir: Optional[str] = None,
+        custom_dir: Optional[str] = None,
    ) -> Dict[str, SkillEntry]:
        """
-        Load skills from all configured locations with precedence.
-        
+        Load skills from builtin and custom directories.
+
        Precedence (lowest to highest):
-        1. Extra directories
-        2. Managed skills directory
-        3. Workspace skills directory
-        
-        :param managed_dir: Managed skills directory (e.g., ~/.cow/skills)
-        :param workspace_skills_dir: Workspace skills directory (e.g., workspace/skills)
-        :param extra_dirs: Additional directories to load skills from
+        1. builtin  — project root ``skills/``, shipped with the codebase
+        2. custom   — workspace ``skills/``, installed via cloud console or skill creator
+
+        Same-name custom skills override builtin ones.
+
+        :param builtin_dir: Built-in skills directory
+        :param custom_dir: Custom skills directory
        :return: Dictionary mapping skill name to SkillEntry
        """
        skill_map: Dict[str, SkillEntry] = {}
        all_diagnostics = []
-        
-        # Load from extra directories (lowest precedence)
-        if extra_dirs:
-            for extra_dir in extra_dirs:
-                if not os.path.exists(extra_dir):
-                    continue
-                result = self.load_skills_from_dir(extra_dir, source='extra')
-                all_diagnostics.extend(result.diagnostics)
-                for skill in result.skills:
-                    entry = self._create_skill_entry(skill)
-                    skill_map[skill.name] = entry
-        
-        # Load from managed directory
-        if managed_dir and os.path.exists(managed_dir):
-            result = self.load_skills_from_dir(managed_dir, source='managed')
+
+        # Load builtin skills (lower precedence)
+        if builtin_dir and os.path.exists(builtin_dir):
+            result = self.load_skills_from_dir(builtin_dir, source='builtin')
            all_diagnostics.extend(result.diagnostics)
            for skill in result.skills:
                entry = self._create_skill_entry(skill)
                skill_map[skill.name] = entry
-        
-        # Load from workspace directory (highest precedence)
-        if workspace_skills_dir and os.path.exists(workspace_skills_dir):
-            result = self.load_skills_from_dir(workspace_skills_dir, source='workspace')
+
+        # Load custom skills (higher precedence, overrides builtin)
+        if custom_dir and os.path.exists(custom_dir):
+            result = self.load_skills_from_dir(custom_dir, source='custom')
            all_diagnostics.extend(result.diagnostics)
            for skill in result.skills:
                entry = self._create_skill_entry(skill)
                skill_map[skill.name] = entry
-        
+
        # Log diagnostics
        if all_diagnostics:
            logger.debug(f"Skill loading diagnostics: {len(all_diagnostics)} issues")
-            for diag in all_diagnostics[:5]:  # Log first 5
+            for diag in all_diagnostics[:5]:
                logger.debug(f"  - {diag}")
-        
-        logger.debug(f"Loaded {len(skill_map)} skills from all sources")
-        
+
+        logger.debug(f"Loaded {len(skill_map)} skills total")
+
        return skill_map
    
    def _create_skill_entry(self, skill: Skill) -> SkillEntry:
--- a/agent/skills/manager.py
+++ b/agent/skills/manager.py
@@ -3,6 +3,7 @@ Skill manager for managing skill lifecycle and operations.
 """

 import os
+import json
 from typing import Dict, List, Optional
 from pathlib import Path
 from common.log import logger
@@ -10,56 +11,131 @@ from agent.skills.types import Skill, SkillEntry, SkillSnapshot
 from agent.skills.loader import SkillLoader
 from agent.skills.formatter import format_skill_entries_for_prompt

+SKILLS_CONFIG_FILE = "skills_config.json"
+

 class SkillManager:
    """Manages skills for an agent."""
-    
+
    def __init__(
        self,
-        workspace_dir: Optional[str] = None,
-        managed_skills_dir: Optional[str] = None,
-        extra_dirs: Optional[List[str]] = None,
+        builtin_dir: Optional[str] = None,
+        custom_dir: Optional[str] = None,
        config: Optional[Dict] = None,
    ):
        """
        Initialize the skill manager.
-        
-        :param workspace_dir: Agent workspace directory
-        :param managed_skills_dir: Managed skills directory (e.g., ~/.cow/skills)
-        :param extra_dirs: Additional skill directories
+
+        :param builtin_dir: Built-in skills directory (project root ``skills/``)
+        :param custom_dir: Custom skills directory (workspace ``skills/``)
        :param config: Configuration dictionary
        """
-        self.workspace_dir = workspace_dir
-        self.managed_skills_dir = managed_skills_dir or self._get_default_managed_dir()
-        self.extra_dirs = extra_dirs or []
+        project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+        self.builtin_dir = builtin_dir or os.path.join(project_root, 'skills')
+        self.custom_dir = custom_dir or os.path.join(project_root, 'workspace', 'skills')
        self.config = config or {}
-        
-        self.loader = SkillLoader(workspace_dir=workspace_dir)
+        self._skills_config_path = os.path.join(self.custom_dir, SKILLS_CONFIG_FILE)
+
+        # skills_config: full skill metadata keyed by name
+        # { "web-fetch": {"name": ..., "description": ..., "source": ..., "enabled": true}, ... }
+        self.skills_config: Dict[str, dict] = {}
+
+        self.loader = SkillLoader()
        self.skills: Dict[str, SkillEntry] = {}
-        
+
        # Load skills on initialization
        self.refresh_skills()
-    
-    def _get_default_managed_dir(self) -> str:
-        """Get the default managed skills directory."""
-        # Use project root skills directory as default
-        import os
-        project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-        return os.path.join(project_root, 'skills')
-    
+
    def refresh_skills(self):
-        """Reload all skills from configured directories."""
-        workspace_skills_dir = None
-        if self.workspace_dir:
-            workspace_skills_dir = os.path.join(self.workspace_dir, 'skills')
-        
+        """Reload all skills from builtin and custom directories, then sync config."""
        self.skills = self.loader.load_all_skills(
-            managed_dir=self.managed_skills_dir,
-            workspace_skills_dir=workspace_skills_dir,
-            extra_dirs=self.extra_dirs,
+            builtin_dir=self.builtin_dir,
+            custom_dir=self.custom_dir,
        )
-        
+        self._sync_skills_config()
        logger.debug(f"SkillManager: Loaded {len(self.skills)} skills")
+
+    # ------------------------------------------------------------------
+    # skills_config.json management
+    # ------------------------------------------------------------------
+    def _load_skills_config(self) -> Dict[str, dict]:
+        """Load skills_config.json from custom_dir. Returns empty dict if not found."""
+        if not os.path.exists(self._skills_config_path):
+            return {}
+        try:
+            with open(self._skills_config_path, "r", encoding="utf-8") as f:
+                data = json.load(f)
+            if isinstance(data, dict):
+                return data
+        except Exception as e:
+            logger.warning(f"[SkillManager] Failed to load {SKILLS_CONFIG_FILE}: {e}")
+        return {}
+
+    def _save_skills_config(self):
+        """Persist skills_config to custom_dir/skills_config.json."""
+        os.makedirs(self.custom_dir, exist_ok=True)
+        try:
+            with open(self._skills_config_path, "w", encoding="utf-8") as f:
+                json.dump(self.skills_config, f, indent=4, ensure_ascii=False)
+        except Exception as e:
+            logger.error(f"[SkillManager] Failed to save {SKILLS_CONFIG_FILE}: {e}")
+
+    def _sync_skills_config(self):
+        """
+        Merge directory-scanned skills with the persisted config file.
+
+        - New skills discovered on disk are added with enabled=True.
+        - Skills that no longer exist on disk are removed.
+        - Existing entries preserve their enabled state; name/description/source
+          are refreshed from the latest scan.
+        """
+        saved = self._load_skills_config()
+        merged: Dict[str, dict] = {}
+
+        for name, entry in self.skills.items():
+            skill = entry.skill
+            prev = saved.get(name, {})
+            merged[name] = {
+                "name": name,
+                "description": skill.description,
+                "source": skill.source,
+                "enabled": prev.get("enabled", True),
+            }
+
+        self.skills_config = merged
+        self._save_skills_config()
+
+    def is_skill_enabled(self, name: str) -> bool:
+        """
+        Check if a skill is enabled according to skills_config.
+
+        :param name: skill name
+        :return: True if enabled (default True if not in config)
+        """
+        entry = self.skills_config.get(name)
+        if entry is None:
+            return True
+        return entry.get("enabled", True)
+
+    def set_skill_enabled(self, name: str, enabled: bool):
+        """
+        Set a skill's enabled state and persist.
+
+        :param name: skill name
+        :param enabled: True to enable, False to disable
+        """
+        if name not in self.skills_config:
+            raise ValueError(f"skill '{name}' not found in config")
+        self.skills_config[name]["enabled"] = enabled
+        self._save_skills_config()
+
+    def get_skills_config(self) -> Dict[str, dict]:
+        """
+        Return the full skills_config dict (for query API).
+
+        :return: copy of skills_config
+        """
+        return dict(self.skills_config)
    
    def get_skill(self, name: str) -> Optional[SkillEntry]:
        """
@@ -85,25 +161,24 @@ class SkillManager:
    ) -> List[SkillEntry]:
        """
        Filter skills based on criteria.
-        
+
        Simple rule: Skills are auto-enabled if requirements are met.
-        - Has required API keys → included
-        - Missing API keys → excluded
-        
+        - Has required API keys -> included
+        - Missing API keys -> excluded
+
        :param skill_filter: List of skill names to include (None = all)
-        :param include_disabled: Whether to include skills with disable_model_invocation=True
+        :param include_disabled: Whether to include disabled skills
        :return: Filtered list of skill entries
        """
        from agent.skills.config import should_include_skill
-        
+
        entries = list(self.skills.values())
-        
+
        # Check requirements (platform, binaries, env vars)
        entries = [e for e in entries if should_include_skill(e, self.config)]
-        
+
        # Apply skill filter
        if skill_filter is not None:
-            # Flatten and normalize skill names (handle both strings and nested lists)
            normalized = []
            for item in skill_filter:
                if isinstance(item, str):
@@ -111,20 +186,18 @@ class SkillManager:
                    if name:
                        normalized.append(name)
                elif isinstance(item, list):
-                    # Handle nested lists
                    for subitem in item:
                        if isinstance(subitem, str):
                            name = subitem.strip()
                            if name:
                                normalized.append(name)
-            
            if normalized:
                entries = [e for e in entries if e.skill.name in normalized]
-        
-        # Filter out disabled skills unless explicitly requested
+
+        # Filter out disabled skills based on skills_config.json
        if not include_disabled:
-            entries = [e for e in entries if not e.skill.disable_model_invocation]
-        
+            entries = [e for e in entries if self.is_skill_enabled(e.skill.name)]
+
        return entries
    
    def build_skills_prompt(
--- a/agent/skills/service.py
+++ b/agent/skills/service.py
@@ -0,0 +1,204 @@
+"""
+Skill service for handling skill CRUD operations.
+
+This service provides a unified interface for managing skills, which can be
+called from the cloud control client (LinkAI), the local web console, or any
+other management entry point.
+"""
+
+import os
+import shutil
+from typing import Dict, List, Optional
+from common.log import logger
+from agent.skills.types import Skill, SkillEntry
+from agent.skills.manager import SkillManager
+
+try:
+    import requests
+except ImportError:
+    requests = None
+
+
+class SkillService:
+    """
+    High-level service for skill lifecycle management.
+    Wraps SkillManager and provides network-aware operations such as
+    downloading skill files from remote URLs.
+    """
+
+    def __init__(self, skill_manager: SkillManager):
+        """
+        :param skill_manager: The SkillManager instance to operate on
+        """
+        self.manager = skill_manager
+
+    # ------------------------------------------------------------------
+    # query
+    # ------------------------------------------------------------------
+    def query(self) -> List[dict]:
+        """
+        Query all skills and return a serialisable list.
+        Reads from skills_config.json (refreshes from disk if needed).
+
+        :return: list of skill info dicts
+        """
+        self.manager.refresh_skills()
+        config = self.manager.get_skills_config()
+        result = list(config.values())
+        logger.info(f"[SkillService] query: {len(result)} skills found")
+        return result
+
+    # ------------------------------------------------------------------
+    # add / install
+    # ------------------------------------------------------------------
+    def add(self, payload: dict) -> None:
+        """
+        Add (install) a skill from a remote payload.
+
+        The payload follows the socket protocol::
+
+            {
+                "name": "web_search",
+                "type": "url",
+                "enabled": true,
+                "files": [
+                    {"url": "https://...", "path": "README.md"},
+                    {"url": "https://...", "path": "scripts/main.py"}
+                ]
+            }
+
+        Files are downloaded and saved under the custom skills directory
+        using *name* as the sub-directory.
+
+        :param payload: skill add payload from server
+        """
+        name = payload.get("name")
+        if not name:
+            raise ValueError("skill name is required")
+
+        files = payload.get("files", [])
+        if not files:
+            raise ValueError("skill files list is empty")
+
+        skill_dir = os.path.join(self.manager.custom_dir, name)
+        os.makedirs(skill_dir, exist_ok=True)
+
+        for file_info in files:
+            url = file_info.get("url")
+            rel_path = file_info.get("path")
+            if not url or not rel_path:
+                logger.warning(f"[SkillService] add: skip invalid file entry {file_info}")
+                continue
+            dest = os.path.join(skill_dir, rel_path)
+            self._download_file(url, dest)
+
+        # Reload to pick up the new skill and sync config
+        self.manager.refresh_skills()
+        logger.info(f"[SkillService] add: skill '{name}' installed ({len(files)} files)")
+
+    # ------------------------------------------------------------------
+    # open / close (enable / disable)
+    # ------------------------------------------------------------------
+    def open(self, payload: dict) -> None:
+        """
+        Enable a skill by name.
+
+        :param payload: {"name": "skill_name"}
+        """
+        name = payload.get("name")
+        if not name:
+            raise ValueError("skill name is required")
+        self.manager.set_skill_enabled(name, enabled=True)
+        logger.info(f"[SkillService] open: skill '{name}' enabled")
+
+    def close(self, payload: dict) -> None:
+        """
+        Disable a skill by name.
+
+        :param payload: {"name": "skill_name"}
+        """
+        name = payload.get("name")
+        if not name:
+            raise ValueError("skill name is required")
+        self.manager.set_skill_enabled(name, enabled=False)
+        logger.info(f"[SkillService] close: skill '{name}' disabled")
+
+    # ------------------------------------------------------------------
+    # delete
+    # ------------------------------------------------------------------
+    def delete(self, payload: dict) -> None:
+        """
+        Delete a skill by removing its directory entirely.
+
+        :param payload: {"name": "skill_name"}
+        """
+        name = payload.get("name")
+        if not name:
+            raise ValueError("skill name is required")
+
+        skill_dir = os.path.join(self.manager.custom_dir, name)
+        if os.path.exists(skill_dir):
+            shutil.rmtree(skill_dir)
+            logger.info(f"[SkillService] delete: removed directory {skill_dir}")
+        else:
+            logger.warning(f"[SkillService] delete: skill directory not found: {skill_dir}")
+
+        # Refresh will remove the deleted skill from config automatically
+        self.manager.refresh_skills()
+        logger.info(f"[SkillService] delete: skill '{name}' deleted")
+
+    # ------------------------------------------------------------------
+    # dispatch - single entry point for protocol messages
+    # ------------------------------------------------------------------
+    def dispatch(self, action: str, payload: Optional[dict] = None) -> dict:
+        """
+        Dispatch a skill management action and return a protocol-compatible
+        response dict.
+
+        :param action: one of query / add / open / close / delete
+        :param payload: action-specific payload (may be None for query)
+        :return: dict with action, code, message, payload
+        """
+        payload = payload or {}
+        try:
+            if action == "query":
+                result_payload = self.query()
+                return {"action": action, "code": 200, "message": "success", "payload": result_payload}
+            elif action == "add":
+                self.add(payload)
+            elif action == "open":
+                self.open(payload)
+            elif action == "close":
+                self.close(payload)
+            elif action == "delete":
+                self.delete(payload)
+            else:
+                return {"action": action, "code": 400, "message": f"unknown action: {action}", "payload": None}
+            return {"action": action, "code": 200, "message": "success", "payload": None}
+        except Exception as e:
+            logger.error(f"[SkillService] dispatch error: action={action}, error={e}")
+            return {"action": action, "code": 500, "message": str(e), "payload": None}
+
+    # ------------------------------------------------------------------
+    # internal helpers
+    # ------------------------------------------------------------------
+    @staticmethod
+    def _download_file(url: str, dest: str):
+        """
+        Download a file from *url* and save to *dest*.
+
+        :param url: remote file URL
+        :param dest: local destination path
+        """
+        if requests is None:
+            raise RuntimeError("requests library is required for downloading skill files")
+
+        dest_dir = os.path.dirname(dest)
+        if dest_dir:
+            os.makedirs(dest_dir, exist_ok=True)
+
+        resp = requests.get(url, timeout=60)
+        resp.raise_for_status()
+        with open(dest, "wb") as f:
+            f.write(resp.content)
+        logger.debug(f"[SkillService] downloaded {url} -> {dest}")
--- a/agent/skills/types.py
+++ b/agent/skills/types.py
@@ -45,7 +45,7 @@ class Skill:
    description: str
    file_path: str
    base_dir: str
-    source: str  # managed, workspace, bundled, etc.
+    source: str  # builtin or custom
    content: str  # Full markdown content
    disable_model_invocation: bool = False
    frontmatter: Dict[str, Any] = field(default_factory=dict)
--- a/app.py
+++ b/app.py
@@ -7,11 +7,186 @@ import time

 from channel import channel_factory
 from common import const
-from config import load_config
+from common.log import logger
+from config import load_config, conf
 from plugins import *
 import threading


+_channel_mgr = None
+
+
+def get_channel_manager():
+    return _channel_mgr
+
+
+def _parse_channel_type(raw) -> list:
+    """
+    Parse channel_type config value into a list of channel names.
+    Supports:
+      - single string: "feishu"
+      - comma-separated string: "feishu, dingtalk"
+      - list: ["feishu", "dingtalk"]
+    """
+    if isinstance(raw, list):
+        return [ch.strip() for ch in raw if ch.strip()]
+    if isinstance(raw, str):
+        return [ch.strip() for ch in raw.split(",") if ch.strip()]
+    return []
+
+
+class ChannelManager:
+    """
+    Manage the lifecycle of multiple channels running concurrently.
+    Each channel.startup() runs in its own daemon thread.
+    The web channel is started as default console unless explicitly disabled.
+    """
+
+    def __init__(self):
+        self._channels = {}        # channel_name -> channel instance
+        self._threads = {}         # channel_name -> thread
+        self._primary_channel = None
+        self._lock = threading.Lock()
+
+    @property
+    def channel(self):
+        """Return the primary (first non-web) channel for backward compatibility."""
+        return self._primary_channel
+
+    def get_channel(self, channel_name: str):
+        return self._channels.get(channel_name)
+
+    def start(self, channel_names: list, first_start: bool = False):
+        """
+        Create and start one or more channels in sub-threads.
+        If first_start is True, plugins and linkai client will also be initialized.
+        """
+        with self._lock:
+            channels = []
+            for name in channel_names:
+                ch = channel_factory.create_channel(name)
+                self._channels[name] = ch
+                channels.append((name, ch))
+                if self._primary_channel is None and name != "web":
+                    self._primary_channel = ch
+
+            if self._primary_channel is None and channels:
+                self._primary_channel = channels[0][1]
+
+            if first_start:
+                PluginManager().load_plugins()
+
+                if conf().get("use_linkai"):
+                    try:
+                        from common import cloud_client
+                        threading.Thread(
+                            target=cloud_client.start,
+                            args=(self._primary_channel, self),
+                            daemon=True,
+                        ).start()
+                    except Exception:
+                        pass
+
+            # Start web console first so its logs print cleanly,
+            # then start remaining channels after a brief pause.
+            web_entry = None
+            other_entries = []
+            for entry in channels:
+                if entry[0] == "web":
+                    web_entry = entry
+                else:
+                    other_entries.append(entry)
+
+            ordered = ([web_entry] if web_entry else []) + other_entries
+            for i, (name, ch) in enumerate(ordered):
+                if i > 0 and name != "web":
+                    time.sleep(0.1)
+                t = threading.Thread(target=self._run_channel, args=(name, ch), daemon=True)
+                self._threads[name] = t
+                t.start()
+                logger.debug(f"[ChannelManager] Channel '{name}' started in sub-thread")
+
+    def _run_channel(self, name: str, channel):
+        try:
+            channel.startup()
+        except Exception as e:
+            logger.error(f"[ChannelManager] Channel '{name}' startup error: {e}")
+            logger.exception(e)
+
+    def stop(self, channel_name: str = None):
+        """
+        Stop channel(s). If channel_name is given, stop only that channel;
+        otherwise stop all channels.
+        """
+        with self._lock:
+            names = [channel_name] if channel_name else list(self._channels.keys())
+            for name in names:
+                ch = self._channels.pop(name, None)
+                self._threads.pop(name, None)
+                if ch is None:
+                    continue
+                logger.info(f"[ChannelManager] Stopping channel '{name}'...")
+                try:
+                    if hasattr(ch, 'stop'):
+                        ch.stop()
+                except Exception as e:
+                    logger.warning(f"[ChannelManager] Error during channel '{name}' stop: {e}")
+            if channel_name and self._primary_channel is self._channels.get(channel_name):
+                self._primary_channel = None
+
+    def restart(self, new_channel_name: str):
+        """
+        Restart a single channel with a new channel type.
+        Can be called from any thread (e.g. linkai config callback).
+        """
+        logger.info(f"[ChannelManager] Restarting channel to '{new_channel_name}'...")
+        self.stop(new_channel_name)
+        _clear_singleton_cache(new_channel_name)
+        time.sleep(1)
+        self.start([new_channel_name], first_start=False)
+        logger.info(f"[ChannelManager] Channel restarted to '{new_channel_name}' successfully")
+
+
+def _clear_singleton_cache(channel_name: str):
+    """
+    Clear the singleton cache for the channel class so that
+    a new instance can be created with updated config.
+    """
+    cls_map = {
+        "wx": "channel.wechat.wechat_channel.WechatChannel",
+        "wxy": "channel.wechat.wechaty_channel.WechatyChannel",
+        "wcf": "channel.wechat.wcf_channel.WechatfChannel",
+        "web": "channel.web.web_channel.WebChannel",
+        "wechatmp": "channel.wechatmp.wechatmp_channel.WechatMPChannel",
+        "wechatmp_service": "channel.wechatmp.wechatmp_channel.WechatMPChannel",
+        "wechatcom_app": "channel.wechatcom.wechatcomapp_channel.WechatComAppChannel",
+        "wework": "channel.wework.wework_channel.WeworkChannel",
+        const.FEISHU: "channel.feishu.feishu_channel.FeiShuChanel",
+        const.DINGTALK: "channel.dingtalk.dingtalk_channel.DingTalkChanel",
+    }
+    module_path = cls_map.get(channel_name)
+    if not module_path:
+        return
+    try:
+        parts = module_path.rsplit(".", 1)
+        module_name, class_name = parts[0], parts[1]
+        import importlib
+        module = importlib.import_module(module_name)
+        wrapper = getattr(module, class_name, None)
+        if wrapper and hasattr(wrapper, '__closure__') and wrapper.__closure__:
+            for cell in wrapper.__closure__:
+                try:
+                    cell_contents = cell.cell_contents
+                    if isinstance(cell_contents, dict):
+                        cell_contents.clear()
+                        logger.debug(f"[ChannelManager] Cleared singleton cache for {class_name}")
+                        break
+                except ValueError:
+                    pass
+    except Exception as e:
+        logger.warning(f"[ChannelManager] Failed to clear singleton cache: {e}")
+
+
 def sigterm_handler_wrap(_signo):
    old_handler = signal.getsignal(_signo)

@@ -25,22 +200,8 @@ def sigterm_handler_wrap(_signo):
    signal.signal(_signo, func)


-def start_channel(channel_name: str):
-    channel = channel_factory.create_channel(channel_name)
-    if channel_name in ["wx", "wxy", "terminal", "wechatmp", "web", "wechatmp_service", "wechatcom_app", "wework",
-                        const.FEISHU, const.DINGTALK]:
-        PluginManager().load_plugins()
-
-    if conf().get("use_linkai"):
-        try:
-            from common import linkai_client
-            threading.Thread(target=linkai_client.start, args=(channel,)).start()
-        except Exception as e:
-            pass
-    channel.startup()
-
-
 def run():
+    global _channel_mgr
    try:
        # load config
        load_config()
@@ -49,16 +210,28 @@ def run():
        # kill signal
        sigterm_handler_wrap(signal.SIGTERM)

-        # create channel
-        channel_name = conf().get("channel_type", "wx")
+        # Parse channel_type into a list
+        raw_channel = conf().get("channel_type", "wx")

        if "--cmd" in sys.argv:
-            channel_name = "terminal"
+            channel_names = ["terminal"]
+        else:
+            channel_names = _parse_channel_type(raw_channel)
+            if not channel_names:
+                channel_names = ["wx"]

-        if channel_name == "wxy":
+        if "wxy" in channel_names:
            os.environ["WECHATY_LOG"] = "warn"

-        start_channel(channel_name)
+        # Auto-start web console unless explicitly disabled
+        web_console_enabled = conf().get("web_console", True)
+        if web_console_enabled and "web" not in channel_names:
+            channel_names.append("web")
+
+        logger.info(f"[App] Starting channels: {channel_names}")
+
+        _channel_mgr = ChannelManager()
+        _channel_mgr.start(channel_names, first_start=True)

        while True:
            time.sleep(1)
--- a/bridge/agent_bridge.py
+++ b/bridge/agent_bridge.py
@@ -28,7 +28,7 @@ def add_openai_compatible_support(bot_instance):
    """
    if hasattr(bot_instance, 'call_with_tools'):
        # Bot already has tool calling support (e.g., ZHIPUAIBot)
-        logger.info(f"[AgentBridge] {type(bot_instance).__name__} already has native tool calling support")
+        logger.debug(f"[AgentBridge] {type(bot_instance).__name__} already has native tool calling support")
        return bot_instance

    # Create a temporary mixin class that combines the bot with OpenAI compatibility
@@ -325,6 +325,10 @@ class AgentBridge:
                                logger.warning(f"[AgentBridge] Failed to attach context to scheduler: {e}")
                            break
            
+            # Record message count before execution so we can diff new messages
+            with agent.messages_lock:
+                pre_run_len = len(agent.messages)
+
            try:
                # Use agent's run_stream method with event handler
                response = agent.run_stream(
@@ -336,9 +340,16 @@ class AgentBridge:
                # Restore original tools
                if context and context.get("is_scheduled_task"):
                    agent.tools = original_tools
-                
+
                # Log execution summary
                event_handler.log_summary()
+
+            # Persist new messages generated during this run
+            if session_id:
+                channel_type = (context.get("channel_type") or "") if context else ""
+                with agent.messages_lock:
+                    new_messages = agent.messages[pre_run_len:]
+                self._persist_messages(session_id, list(new_messages), channel_type)
            
            # Check if there are files to send (from read tool)
            if hasattr(agent, 'stream_executor') and hasattr(agent.stream_executor, 'files_to_send'):
@@ -475,6 +486,32 @@ class AgentBridge:
            except Exception as e:
                logger.warning(f"[AgentBridge] Failed to migrate API keys: {e}")
    
+    def _persist_messages(
+        self, session_id: str, new_messages: list, channel_type: str = ""
+    ) -> None:
+        """
+        Persist new messages to the conversation store after each agent run.
+
+        Failures are logged but never propagate — they must not interrupt replies.
+        """
+        if not new_messages:
+            return
+        try:
+            from config import conf
+            if not conf().get("conversation_persistence", True):
+                return
+        except Exception:
+            pass
+        try:
+            from agent.memory import get_conversation_store
+            get_conversation_store().append_messages(
+                session_id, new_messages, channel_type=channel_type
+            )
+        except Exception as e:
+            logger.warning(
+                f"[AgentBridge] Failed to persist messages for session={session_id}: {e}"
+            )
+
    def clear_session(self, session_id: str):
        """
        Clear a specific session's agent and conversation history
--- a/bridge/agent_event_handler.py
+++ b/bridge/agent_event_handler.py
@@ -74,7 +74,7 @@ class AgentEventHandler:
        # Only send thinking process if followed by tool calls
        if tool_calls:
            if self.current_thinking.strip():
-                logger.debug(f"💭 {self.current_thinking.strip()[:200]}{'...' if len(self.current_thinking) > 200 else ''}")
+                logger.info(f"💭 {self.current_thinking.strip()[:200]}{'...' if len(self.current_thinking) > 200 else ''}")
                # Send thinking process to channel
                self._send_to_channel(f"{self.current_thinking.strip()}")
        else:
@@ -94,15 +94,15 @@ class AgentEventHandler:
    
    def _send_to_channel(self, message):
        """
-        Try to send message to channel
-        
-        Args:
-            message: Message to send
+        Try to send intermediate message to channel.
+        Skipped in SSE mode because thinking text is already streamed via on_event.
        """
+        if self.context and self.context.get("on_event"):
+            return
+
        if self.channel:
            try:
                from bridge.reply import Reply, ReplyType
-                # Create a Reply object for the message
                reply = Reply(ReplyType.TEXT, message)
                self.channel._send(reply, self.context)
            except Exception as e:
--- a/bridge/agent_initializer.py
+++ b/bridge/agent_initializer.py
@@ -118,8 +118,47 @@ class AgentInitializer:
        # Attach memory manager
        if memory_manager:
            agent.memory_manager = memory_manager
-        
+
+        # Restore persisted conversation history for this session
+        if session_id:
+            self._restore_conversation_history(agent, session_id)
+
        return agent
+
+    def _restore_conversation_history(self, agent, session_id: str) -> None:
+        """
+        Load persisted conversation messages from SQLite and inject them
+        into the agent's in-memory message list.
+
+        Only runs when conversation persistence is enabled (default: True).
+        Respects agent_max_context_turns to limit how many turns are loaded.
+        """
+        from config import conf
+        if not conf().get("conversation_persistence", True):
+            return
+
+        try:
+            from agent.memory import get_conversation_store
+            store = get_conversation_store()
+            # On restore, load at most min(10, max_turns // 2) turns so that
+            # a long-running session does not immediately fill the context window
+            # after a restart.  The full max_turns budget is reserved for the
+            # live conversation that follows.
+            max_turns = conf().get("agent_max_context_turns", 30)
+            restore_turns = min(6, max(1, max_turns // 3))
+            saved = store.load_messages(session_id, max_turns=restore_turns)
+            if saved:
+                with agent.messages_lock:
+                    agent.messages = saved
+                logger.debug(
+                    f"[AgentInitializer] Restored {len(saved)} messages "
+                    f"({restore_turns} turns cap) for session={session_id}"
+                )
+        except Exception as e:
+            logger.warning(
+                f"[AgentInitializer] Failed to restore conversation history for "
+                f"session={session_id}: {e}"
+            )
    
    def _load_env_file(self):
        """Load environment variables from .env file"""
@@ -291,7 +330,7 @@ class AgentInitializer:
        """Initialize skill manager"""
        try:
            from agent.skills import SkillManager
-            skill_manager = SkillManager(workspace_dir=workspace_root)
+            skill_manager = SkillManager(custom_dir=os.path.join(workspace_root, "skills"))
            return skill_manager
        except Exception as e:
            logger.warning(f"[AgentInitializer] Failed to initialize SkillManager: {e}")
--- a/bridge/bridge.py
+++ b/bridge/bridge.py
@@ -55,6 +55,11 @@ class Bridge(object):

            if model_type in [const.MOONSHOT, "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k"]:
                self.btype["chat"] = const.MOONSHOT
+            if model_type and model_type.startswith("kimi"):
+                self.btype["chat"] = const.MOONSHOT
+
+            if model_type and model_type.startswith("doubao"):
+                self.btype["chat"] = const.DOUBAO

            if model_type in [const.MODELSCOPE]:
                self.btype["chat"] = const.MODELSCOPE
--- a/channel/channel.py
+++ b/channel/channel.py
@@ -19,6 +19,12 @@ class Channel(object):
        """
        raise NotImplementedError

+    def stop(self):
+        """
+        stop channel gracefully, called before restart
+        """
+        pass
+
    def handle_text(self, msg):
        """
        process received msg
@@ -51,11 +57,14 @@ class Channel(object):
                if context and "channel_type" not in context:
                    context["channel_type"] = self.channel_type

+                # Read on_event callback injected by the channel (e.g. web SSE)
+                on_event = context.get("on_event") if context else None
+
                # Use agent bridge to handle the query
                return Bridge().fetch_agent_reply(
                    query=query,
                    context=context,
-                    on_event=None,
+                    on_event=on_event,
                    clear_history=False
                )
            except Exception as e:
--- a/channel/chat_channel.py
+++ b/channel/chat_channel.py
@@ -24,11 +24,16 @@ handler_pool = ThreadPoolExecutor(max_workers=8)  # 处理消息的线程池
 class ChatChannel(Channel):
    name = None  # 登录的用户名
    user_id = None  # 登录的用户id
-    futures = {}  # 记录每个session_id提交到线程池的future对象, 用于重置会话时把没执行的future取消掉，正在执行的不会被取消
-    sessions = {}  # 用于控制并发，每个session_id同时只能有一个context在处理
-    lock = threading.Lock()  # 用于控制对sessions的访问

    def __init__(self):
+        # Instance-level attributes so each channel subclass has its own
+        # independent session queue and lock. Previously these were class-level,
+        # which caused contexts from one channel (e.g. Feishu) to be consumed
+        # by another channel's consume() thread (e.g. Web), leading to errors
+        # like "No request_id found in context".
+        self.futures = {}
+        self.sessions = {}
+        self.lock = threading.Lock()
        _thread = threading.Thread(target=self.consume)
        _thread.setDaemon(True)
        _thread.start()
--- a/channel/dingtalk/dingtalk_channel.py
+++ b/channel/dingtalk/dingtalk_channel.py
@@ -90,13 +90,9 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
    dingtalk_client_secret = conf().get('dingtalk_client_secret')

    def setup_logger(self):
-        logger = logging.getLogger()
-        handler = logging.StreamHandler()
-        handler.setFormatter(
-            logging.Formatter('%(asctime)s %(name)-8s %(levelname)-8s %(message)s [%(filename)s:%(lineno)d]'))
-        logger.addHandler(handler)
-        logger.setLevel(logging.INFO)
-        return logger
+        # Suppress verbose logs from dingtalk_stream SDK
+        logging.getLogger("dingtalk_stream").setLevel(logging.WARNING)
+        return logging.getLogger("DingTalk")

    def __init__(self):
        super().__init__()
@@ -104,6 +100,7 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
        self.logger = self.setup_logger()
        # 历史消息id暂存，用于幂等控制
        self.receivedMsgs = ExpiredDict(conf().get("expires_in_seconds", 3600))
+        self._stream_client = None
        logger.debug("[DingTalk] client_id={}, client_secret={} ".format(
            self.dingtalk_client_id, self.dingtalk_client_secret))
        # 无需群校验和前缀
@@ -119,9 +116,19 @@ class DingTalkChanel(ChatChannel, dingtalk_stream.ChatbotHandler):
    def startup(self):
        credential = dingtalk_stream.Credential(self.dingtalk_client_id, self.dingtalk_client_secret)
        client = dingtalk_stream.DingTalkStreamClient(credential)
+        self._stream_client = client
        client.register_callback_handler(dingtalk_stream.chatbot.ChatbotMessage.TOPIC, self)
        logger.info("[DingTalk] ✅ Stream connected, ready to receive messages")
        client.start_forever()
+
+    def stop(self):
+        if self._stream_client:
+            try:
+                self._stream_client.stop()
+                logger.info("[DingTalk] Stream client stopped")
+            except Exception as e:
+                logger.warning(f"[DingTalk] Error stopping stream client: {e}")
+            self._stream_client = None
    
    def get_access_token(self):
        """
--- a/channel/feishu/feishu_channel.py
+++ b/channel/feishu/feishu_channel.py
@@ -12,6 +12,7 @@
 """

 import json
+import logging
 import os
 import ssl
 import threading
@@ -32,6 +33,9 @@ from common.log import logger
 from common.singleton import singleton
 from config import conf

+# Suppress verbose logs from Lark SDK
+logging.getLogger("Lark").setLevel(logging.WARNING)
+
 URL_VERIFICATION = "url_verification"

 # 尝试导入飞书SDK,如果未安装则websocket模式不可用
@@ -56,6 +60,7 @@ class FeiShuChanel(ChatChannel):
        super().__init__()
        # 历史消息id暂存，用于幂等控制
        self.receivedMsgs = ExpiredDict(60 * 60 * 7.1)
+        self._http_server = None
        logger.debug("[FeiShu] app_id={}, app_secret={}, verification_token={}, event_mode={}".format(
            self.feishu_app_id, self.feishu_app_secret, self.feishu_token, self.feishu_event_mode))
        # 无需群校验和前缀
@@ -73,6 +78,15 @@ class FeiShuChanel(ChatChannel):
        else:
            self._startup_webhook()

+    def stop(self):
+        if self._http_server:
+            try:
+                self._http_server.stop()
+                logger.info("[FeiShu] HTTP server stopped")
+            except Exception as e:
+                logger.warning(f"[FeiShu] Error stopping HTTP server: {e}")
+            self._http_server = None
+
    def _startup_webhook(self):
        """启动HTTP服务器接收事件(webhook模式)"""
        logger.debug("[FeiShu] Starting in webhook mode...")
@@ -81,7 +95,14 @@ class FeiShuChanel(ChatChannel):
        )
        app = web.application(urls, globals(), autoreload=False)
        port = conf().get("feishu_port", 9891)
-        web.httpserver.runsimple(app.wsgifunc(), ("0.0.0.0", port))
+        func = web.httpserver.StaticMiddleware(app.wsgifunc())
+        func = web.httpserver.LogMiddleware(func)
+        server = web.httpserver.WSGIServer(("0.0.0.0", port), func)
+        self._http_server = server
+        try:
+            server.start()
+        except (KeyboardInterrupt, SystemExit):
+            server.stop()

    def _startup_websocket(self):
        """启动长连接接收事件(websocket模式)"""
@@ -138,7 +159,7 @@ class FeiShuChanel(ChatChannel):
                        self.feishu_app_id,
                        self.feishu_app_secret,
                        event_handler=event_handler,
-                        log_level=lark.LogLevel.DEBUG if conf().get("debug") else lark.LogLevel.INFO
+                        log_level=lark.LogLevel.DEBUG if conf().get("debug") else lark.LogLevel.WARNING
                    )

                    logger.debug("[FeiShu] Websocket client starting...")
--- a/channel/web/chat.html
+++ b/channel/web/chat.html
--- a/channel/web/static/css/console.css
+++ b/channel/web/static/css/console.css
@@ -0,0 +1,239 @@
+/* =====================================================================
+   CowAgent Console Styles
+   ===================================================================== */
+
+/* Animations */
+@keyframes pulseDot {
+    0%, 80%, 100% { transform: scale(0.6); opacity: 0.4; }
+    40% { transform: scale(1); opacity: 1; }
+}
+
+/* Scrollbar */
+* { scrollbar-width: thin; scrollbar-color: #94a3b8 transparent; }
+::-webkit-scrollbar { width: 6px; height: 6px; }
+::-webkit-scrollbar-track { background: transparent; }
+::-webkit-scrollbar-thumb { background: #94a3b8; border-radius: 3px; }
+::-webkit-scrollbar-thumb:hover { background: #64748b; }
+.dark ::-webkit-scrollbar-thumb { background: #475569; }
+.dark ::-webkit-scrollbar-thumb:hover { background: #64748b; }
+
+/* Sidebar */
+.sidebar-item.active {
+    background: rgba(255, 255, 255, 0.08);
+    color: #FFFFFF;
+}
+.sidebar-item.active .item-icon { color: #4ABE6E; }
+
+/* Menu Groups */
+.menu-group-items { max-height: 0; overflow: hidden; transition: max-height 0.25s ease-out; }
+.menu-group.open .menu-group-items { max-height: 500px; transition: max-height 0.35s ease-in; }
+.menu-group .chevron { transition: transform 0.25s ease; }
+.menu-group.open .chevron { transform: rotate(90deg); }
+
+/* View Switching */
+.view { display: none; height: 100%; }
+.view.active { display: flex; flex-direction: column; }
+
+/* Markdown Content */
+.msg-content p { margin: 0.5em 0; line-height: 1.7; }
+.msg-content p:first-child { margin-top: 0; }
+.msg-content p:last-child { margin-bottom: 0; }
+.msg-content h1, .msg-content h2, .msg-content h3,
+.msg-content h4, .msg-content h5, .msg-content h6 {
+    margin-top: 1.2em; margin-bottom: 0.6em; font-weight: 600; line-height: 1.3;
+}
+.msg-content h1 { font-size: 1.4em; }
+.msg-content h2 { font-size: 1.25em; }
+.msg-content h3 { font-size: 1.1em; }
+.msg-content ul, .msg-content ol { margin: 0.5em 0; padding-left: 1.8em; }
+.msg-content li { margin: 0.25em 0; }
+.msg-content pre {
+    border-radius: 8px; overflow-x: auto; margin: 0.8em 0;
+    background: #f1f5f9; padding: 1em;
+}
+.dark .msg-content pre { background: #111111; }
+.msg-content code {
+    font-family: 'JetBrains Mono', 'Fira Code', Consolas, monospace;
+    font-size: 0.875em;
+}
+.msg-content :not(pre) > code {
+    background: rgba(74, 190, 110, 0.1); color: #1C6B3B;
+    padding: 2px 6px; border-radius: 4px;
+}
+.dark .msg-content :not(pre) > code {
+    background: rgba(74, 190, 110, 0.15); color: #74E9A4;
+}
+.msg-content pre code { background: transparent; padding: 0; color: inherit; }
+.msg-content blockquote {
+    border-left: 3px solid #4ABE6E; padding: 0.5em 1em;
+    margin: 0.8em 0; background: rgba(74, 190, 110, 0.05); border-radius: 0 6px 6px 0;
+}
+.dark .msg-content blockquote { background: rgba(74, 190, 110, 0.08); }
+.msg-content table { border-collapse: collapse; width: 100%; margin: 0.8em 0; }
+.msg-content th, .msg-content td {
+    border: 1px solid #e2e8f0; padding: 8px 12px; text-align: left;
+}
+.dark .msg-content th, .dark .msg-content td { border-color: rgba(255,255,255,0.1); }
+.msg-content th { background: #f1f5f9; font-weight: 600; }
+.dark .msg-content th { background: #111111; }
+.msg-content img { max-width: 100%; height: auto; border-radius: 8px; margin: 0.5em 0; }
+.msg-content a { color: #35A85B; text-decoration: underline; }
+.msg-content a:hover { color: #228547; }
+.msg-content hr { border: none; height: 1px; background: #e2e8f0; margin: 1.2em 0; }
+.dark .msg-content hr { background: rgba(255,255,255,0.1); }
+
+/* SSE Streaming cursor */
+@keyframes blink { 0%, 100% { opacity: 1; } 50% { opacity: 0; } }
+.sse-streaming::after {
+    content: '▋';
+    display: inline-block;
+    margin-left: 2px;
+    color: #4ABE6E;
+    animation: blink 0.9s step-end infinite;
+    font-size: 0.85em;
+    vertical-align: middle;
+}
+
+/* Agent steps (thinking summaries + tool indicators) */
+.agent-steps:empty { display: none; }
+.agent-steps:not(:empty) {
+    margin-bottom: 0.625rem;
+    padding-bottom: 0.5rem;
+    border-bottom: 1px dashed rgba(0, 0, 0, 0.08);
+}
+.dark .agent-steps:not(:empty) { border-bottom-color: rgba(255, 255, 255, 0.08); }
+
+.agent-step {
+    font-size: 0.75rem;
+    line-height: 1.4;
+    color: #94a3b8;
+    margin-bottom: 0.25rem;
+}
+.agent-step:last-child { margin-bottom: 0; }
+
+/* Thinking step - collapsible */
+.agent-thinking-step .thinking-header {
+    display: flex;
+    align-items: center;
+    gap: 0.375rem;
+    cursor: pointer;
+    user-select: none;
+}
+.agent-thinking-step .thinking-header.no-toggle { cursor: default; }
+.agent-thinking-step .thinking-header:not(.no-toggle):hover { color: #64748b; }
+.dark .agent-thinking-step .thinking-header:not(.no-toggle):hover { color: #cbd5e1; }
+.agent-thinking-step .thinking-header i:first-child { font-size: 0.625rem; margin-top: 1px; }
+.agent-thinking-step .thinking-chevron {
+    font-size: 0.5rem;
+    margin-left: auto;
+    transition: transform 0.2s ease;
+    opacity: 0.5;
+}
+.agent-thinking-step.expanded .thinking-chevron { transform: rotate(90deg); }
+.agent-thinking-step .thinking-full {
+    display: none;
+    margin-top: 0.375rem;
+    margin-left: 1rem;
+    padding: 0.5rem;
+    background: rgba(0, 0, 0, 0.02);
+    border-radius: 6px;
+    border: 1px solid rgba(0, 0, 0, 0.04);
+    font-size: 0.75rem;
+    line-height: 1.5;
+    color: #94a3b8;
+    max-height: 200px;
+    overflow-y: auto;
+}
+.dark .agent-thinking-step .thinking-full {
+    background: rgba(255, 255, 255, 0.02);
+    border-color: rgba(255, 255, 255, 0.04);
+}
+.agent-thinking-step.expanded .thinking-full { display: block; }
+.agent-thinking-step .thinking-full p { margin: 0.25em 0; }
+.agent-thinking-step .thinking-full p:first-child { margin-top: 0; }
+.agent-thinking-step .thinking-full p:last-child { margin-bottom: 0; }
+
+/* Tool step - collapsible */
+.agent-tool-step .tool-header {
+    display: flex;
+    align-items: center;
+    gap: 0.375rem;
+    cursor: pointer;
+    user-select: none;
+    padding: 1px 0;
+    border-radius: 4px;
+}
+.agent-tool-step .tool-header:hover { color: #64748b; }
+.dark .agent-tool-step .tool-header:hover { color: #cbd5e1; }
+.agent-tool-step .tool-icon { font-size: 0.625rem; }
+.agent-tool-step .tool-chevron {
+    font-size: 0.5rem;
+    margin-left: auto;
+    transition: transform 0.2s ease;
+    opacity: 0.5;
+}
+.agent-tool-step.expanded .tool-chevron { transform: rotate(90deg); }
+.agent-tool-step .tool-time {
+    font-size: 0.65rem;
+    opacity: 0.6;
+    margin-left: 0.25rem;
+}
+
+/* Tool detail panel */
+.agent-tool-step .tool-detail {
+    display: none;
+    margin-top: 0.375rem;
+    margin-left: 1rem;
+    padding: 0.5rem;
+    background: rgba(0, 0, 0, 0.02);
+    border-radius: 6px;
+    border: 1px solid rgba(0, 0, 0, 0.04);
+}
+.dark .agent-tool-step .tool-detail {
+    background: rgba(255, 255, 255, 0.02);
+    border-color: rgba(255, 255, 255, 0.04);
+}
+.agent-tool-step.expanded .tool-detail { display: block; }
+.tool-detail-section { margin-bottom: 0.375rem; }
+.tool-detail-section:last-child { margin-bottom: 0; }
+.tool-detail-label {
+    font-size: 0.625rem;
+    font-weight: 600;
+    text-transform: uppercase;
+    letter-spacing: 0.05em;
+    opacity: 0.6;
+    margin-bottom: 0.125rem;
+}
+.tool-detail-content {
+    font-family: 'JetBrains Mono', 'Fira Code', Consolas, monospace;
+    font-size: 0.7rem;
+    line-height: 1.5;
+    white-space: pre-wrap;
+    word-break: break-all;
+    max-height: 200px;
+    overflow-y: auto;
+    margin: 0;
+    padding: 0.25rem 0;
+    background: transparent;
+    color: inherit;
+}
+.tool-error-text { color: #f87171; }
+
+/* Tool failed state */
+.agent-tool-step.tool-failed .tool-name { color: #f87171; }
+
+/* Chat Input */
+#chat-input {
+    resize: none; height: 42px; max-height: 180px;
+    overflow-y: hidden;
+    transition: border-color 0.2s ease;
+}
+
+/* Placeholder Cards */
+.placeholder-card {
+    transition: transform 0.2s ease, box-shadow 0.2s ease;
+}
+.placeholder-card:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 8px 25px -5px rgba(0, 0, 0, 0.1);
+}
--- a/channel/web/static/js/console.js
+++ b/channel/web/static/js/console.js
--- a/channel/web/web_channel.py
+++ b/channel/web/web_channel.py
@@ -1,10 +1,15 @@
-import sys
 import time
-import web
 import json
+import logging
+import mimetypes
+import os
+import threading
+import time
 import uuid
-import io
 from queue import Queue, Empty
+
+import web
+
 from bridge.context import *
 from bridge.reply import Reply, ReplyType
 from channel.chat_channel import ChatChannel, check_prefix
@@ -12,20 +17,17 @@ from channel.chat_message import ChatMessage
 from common.log import logger
 from common.singleton import singleton
 from config import conf
-import os
-import mimetypes  # 添加这行来处理MIME类型
-import threading
-import logging
+

 class WebMessage(ChatMessage):
    def __init__(
-        self,
-        msg_id,
-        content,
-        ctype=ContextType.TEXT,
-        from_user_id="User",
-        to_user_id="Chatgpt",
-        other_user_id="Chatgpt",
+            self,
+            msg_id,
+            content,
+            ctype=ContextType.TEXT,
+            from_user_id="User",
+            to_user_id="Chatgpt",
+            other_user_id="Chatgpt",
    ):
        self.msg_id = msg_id
        self.ctype = ctype
@@ -39,7 +41,7 @@ class WebMessage(ChatMessage):
 class WebChannel(ChatChannel):
    NOT_SUPPORT_REPLYTYPE = [ReplyType.VOICE]
    _instance = None
-    
+
    # def __new__(cls):
    #     if cls._instance is None:
    #         cls._instance = super(WebChannel, cls).__new__(cls)
@@ -47,10 +49,11 @@ class WebChannel(ChatChannel):

    def __init__(self):
        super().__init__()
-        self.msg_id_counter = 0  # 添加消息ID计数器
-        self.session_queues = {}  # 存储session_id到队列的映射
-        self.request_to_session = {}  # 存储request_id到session_id的映射
-
+        self.msg_id_counter = 0
+        self.session_queues = {}  # session_id -> Queue (fallback polling)
+        self.request_to_session = {}  # request_id -> session_id
+        self.sse_queues = {}  # request_id -> Queue (SSE streaming)
+        self._http_server = None

    def _generate_msg_id(self):
        """生成唯一的消息ID"""
@@ -70,22 +73,30 @@ class WebChannel(ChatChannel):
            if reply.type == ReplyType.IMAGE_URL:
                time.sleep(0.5)

-            # 获取请求ID和会话ID
            request_id = context.get("request_id", None)
-            
            if not request_id:
                logger.error("No request_id found in context, cannot send message")
                return
-                
-            # 通过request_id获取session_id
+
            session_id = self.request_to_session.get(request_id)
            if not session_id:
                logger.error(f"No session_id found for request {request_id}")
                return
-            
-            # 检查是否有会话队列
+
+            # SSE mode: push done event to SSE queue
+            if request_id in self.sse_queues:
+                content = reply.content if reply.content is not None else ""
+                self.sse_queues[request_id].put({
+                    "type": "done",
+                    "content": content,
+                    "request_id": request_id,
+                    "timestamp": time.time()
+                })
+                logger.debug(f"SSE done sent for request {request_id}")
+                return
+
+            # Fallback: polling mode
            if session_id in self.session_queues:
-                # 创建响应数据，包含请求ID以区分不同请求的响应
                response_data = {
                    "type": str(reply.type),
                    "content": reply.content,
@@ -93,69 +104,134 @@ class WebChannel(ChatChannel):
                    "request_id": request_id
                }
                self.session_queues[session_id].put(response_data)
-                logger.debug(f"Response sent to queue for session {session_id}, request {request_id}")
+                logger.debug(f"Response sent to poll queue for session {session_id}, request {request_id}")
            else:
                logger.warning(f"No response queue found for session {session_id}, response dropped")
-            
+
        except Exception as e:
            logger.error(f"Error in send method: {e}")

+    def _make_sse_callback(self, request_id: str):
+        """Build an on_event callback that pushes agent stream events into the SSE queue."""
+
+        def on_event(event: dict):
+            if request_id not in self.sse_queues:
+                return
+            q = self.sse_queues[request_id]
+            event_type = event.get("type")
+            data = event.get("data", {})
+
+            if event_type == "message_update":
+                delta = data.get("delta", "")
+                if delta:
+                    q.put({"type": "delta", "content": delta})
+
+            elif event_type == "tool_execution_start":
+                tool_name = data.get("tool_name", "tool")
+                arguments = data.get("arguments", {})
+                q.put({"type": "tool_start", "tool": tool_name, "arguments": arguments})
+
+            elif event_type == "tool_execution_end":
+                tool_name = data.get("tool_name", "tool")
+                status = data.get("status", "success")
+                result = data.get("result", "")
+                exec_time = data.get("execution_time", 0)
+                # Truncate long results to avoid huge SSE payloads
+                result_str = str(result)
+                if len(result_str) > 2000:
+                    result_str = result_str[:2000] + "…"
+                q.put({
+                    "type": "tool_end",
+                    "tool": tool_name,
+                    "status": status,
+                    "result": result_str,
+                    "execution_time": round(exec_time, 2)
+                })
+
+        return on_event
+
    def post_message(self):
        """
        Handle incoming messages from users via POST request.
        Returns a request_id for tracking this specific request.
        """
        try:
-            data = web.data()  # 获取原始POST数据
+            data = web.data()
            json_data = json.loads(data)
            session_id = json_data.get('session_id', f'session_{int(time.time())}')
            prompt = json_data.get('message', '')
-            
-            # 生成请求ID
+            use_sse = json_data.get('stream', True)
+
            request_id = self._generate_request_id()
-            
-            # 将请求ID与会话ID关联
            self.request_to_session[request_id] = session_id
-            
-            # 确保会话队列存在
+
            if session_id not in self.session_queues:
                self.session_queues[session_id] = Queue()
-            
-            # Web channel 不需要前缀，确保消息能通过前缀检查
+
+            if use_sse:
+                self.sse_queues[request_id] = Queue()
+
            trigger_prefixs = conf().get("single_chat_prefix", [""])
            if check_prefix(prompt, trigger_prefixs) is None:
-                # 如果没有匹配到前缀，给消息加上第一个前缀
                if trigger_prefixs:
                    prompt = trigger_prefixs[0] + prompt
                    logger.debug(f"[WebChannel] Added prefix to message: {prompt}")
-            
-            # 创建消息对象
+
            msg = WebMessage(self._generate_msg_id(), prompt)
-            msg.from_user_id = session_id  # 使用会话ID作为用户ID
-            
-            # 创建上下文，明确指定 isgroup=False
+            msg.from_user_id = session_id
+
            context = self._compose_context(ContextType.TEXT, prompt, msg=msg, isgroup=False)
-            
-            # 检查 context 是否为 None（可能被插件过滤等）
+
            if context is None:
                logger.warning(f"[WebChannel] Context is None for session {session_id}, message may be filtered")
+                if request_id in self.sse_queues:
+                    del self.sse_queues[request_id]
                return json.dumps({"status": "error", "message": "Message was filtered"})

-            # 覆盖必要的字段（_compose_context 会设置默认值，但我们需要使用实际的 session_id）
            context["session_id"] = session_id
            context["receiver"] = session_id
            context["request_id"] = request_id
-            
-            # 异步处理消息 - 只传递上下文
+
+            if use_sse:
+                context["on_event"] = self._make_sse_callback(request_id)
+
            threading.Thread(target=self.produce, args=(context,)).start()
-            
-            # 返回请求ID
-            return json.dumps({"status": "success", "request_id": request_id})
-            
+
+            return json.dumps({"status": "success", "request_id": request_id, "stream": use_sse})
+
        except Exception as e:
            logger.error(f"Error processing message: {e}")
            return json.dumps({"status": "error", "message": str(e)})

+    def stream_response(self, request_id: str):
+        """
+        SSE generator for a given request_id.
+        Yields UTF-8 encoded bytes to avoid WSGI Latin-1 mangling.
+        """
+        if request_id not in self.sse_queues:
+            yield b"data: {\"type\": \"error\", \"message\": \"invalid request_id\"}\n\n"
+            return
+
+        q = self.sse_queues[request_id]
+        timeout = 300  # 5 minutes max
+        deadline = time.time() + timeout
+
+        try:
+            while time.time() < deadline:
+                try:
+                    item = q.get(timeout=1)
+                except Empty:
+                    yield b": keepalive\n\n"
+                    continue
+
+                payload = json.dumps(item, ensure_ascii=False)
+                yield f"data: {payload}\n\n".encode("utf-8")
+
+                if item.get("type") == "done":
+                    break
+        finally:
+            self.sse_queues.pop(request_id, None)
+
    def poll_response(self):
        """
        Poll for responses using the session_id.
@@ -164,28 +240,28 @@ class WebChannel(ChatChannel):
            data = web.data()
            json_data = json.loads(data)
            session_id = json_data.get('session_id')
-            
+
            if not session_id or session_id not in self.session_queues:
                return json.dumps({"status": "error", "message": "Invalid session ID"})
-            
+
            # 尝试从队列获取响应，不等待
            try:
                # 使用peek而不是get，这样如果前端没有成功处理，下次还能获取到
                response = self.session_queues[session_id].get(block=False)
-                
+
                # 返回响应，包含请求ID以区分不同请求
                return json.dumps({
-                    "status": "success", 
+                    "status": "success",
                    "has_content": True,
                    "content": response["content"],
                    "request_id": response["request_id"],
                    "timestamp": response["timestamp"]
                })
-                
+
            except Empty:
                # 没有新响应
                return json.dumps({"status": "success", "has_content": False})
-                
+
        except Exception as e:
            logger.error(f"Error polling response: {e}")
            return json.dumps({"status": "error", "message": str(e)})
@@ -198,9 +274,10 @@ class WebChannel(ChatChannel):

    def startup(self):
        port = conf().get("web_port", 9899)
-        
+
        # 打印可用渠道类型提示
-        logger.info("[WebChannel] 当前channel为web，可修改 config.json 配置文件中的 channel_type 字段进行切换。全部可用类型为：")
+        logger.info(
+            "[WebChannel] 全部可用通道如下，可修改 config.json 配置文件中的 channel_type 字段进行切换，多个通道用逗号分隔：")
        logger.info("[WebChannel]   1. web              - 网页")
        logger.info("[WebChannel]   2. terminal         - 终端")
        logger.info("[WebChannel]   3. feishu           - 飞书")
@@ -208,40 +285,58 @@ class WebChannel(ChatChannel):
        logger.info("[WebChannel]   5. wechatcom_app    - 企微自建应用")
        logger.info("[WebChannel]   6. wechatmp         - 个人公众号")
        logger.info("[WebChannel]   7. wechatmp_service - 企业公众号")
-        logger.info(f"[WebChannel] 🌐 本地访问: http://localhost:{port}/chat")
-        logger.info(f"[WebChannel] 🌍 服务器访问: http://YOUR_IP:{port}/chat (请将YOUR_IP替换为服务器IP)")
-        logger.info("[WebChannel] ✅ Web对话网页已运行")
-        
+        logger.info("[WebChannel] ✅ Web控制台已运行")
+        logger.info(f"[WebChannel] 🌐 本地访问: http://localhost:{port}")
+        logger.info(f"[WebChannel] 🌍 服务器访问: http://YOUR_IP:{port} (请将YOUR_IP替换为服务器IP)")
+
        # 确保静态文件目录存在
        static_dir = os.path.join(os.path.dirname(__file__), 'static')
        if not os.path.exists(static_dir):
            os.makedirs(static_dir)
            logger.debug(f"[WebChannel] Created static directory: {static_dir}")
-        
+
        urls = (
            '/', 'RootHandler',
            '/message', 'MessageHandler',
            '/poll', 'PollHandler',
+            '/stream', 'StreamHandler',
            '/chat', 'ChatHandler',
            '/config', 'ConfigHandler',
+            '/api/skills', 'SkillsHandler',
+            '/api/memory', 'MemoryHandler',
+            '/api/memory/content', 'MemoryContentHandler',
+            '/api/scheduler', 'SchedulerHandler',
+            '/api/history', 'HistoryHandler',
+            '/api/logs', 'LogsHandler',
            '/assets/(.*)', 'AssetsHandler',
        )
        app = web.application(urls, globals(), autoreload=False)
-        
+
        # 完全禁用web.py的HTTP日志输出
        web.httpserver.LogMiddleware.log = lambda self, status, environ: None
-        
+
        # 配置web.py的日志级别为ERROR
        logging.getLogger("web").setLevel(logging.ERROR)
        logging.getLogger("web.httpserver").setLevel(logging.ERROR)
-        
-        # 抑制 web.py 默认的服务器启动消息
-        old_stdout = sys.stdout
-        sys.stdout = io.StringIO()
+
+        # Build WSGI app with middleware (same as runsimple but without print)
+        func = web.httpserver.StaticMiddleware(app.wsgifunc())
+        func = web.httpserver.LogMiddleware(func)
+        server = web.httpserver.WSGIServer(("0.0.0.0", port), func)
+        self._http_server = server
        try:
-            web.httpserver.runsimple(app.wsgifunc(), ("0.0.0.0", port))
-        finally:
-            sys.stdout = old_stdout
+            server.start()
+        except (KeyboardInterrupt, SystemExit):
+            server.stop()
+
+    def stop(self):
+        if self._http_server:
+            try:
+                self._http_server.stop()
+                logger.info("[WebChannel] HTTP server stopped")
+            except Exception as e:
+                logger.warning(f"[WebChannel] Error stopping HTTP server: {e}")
+            self._http_server = None


 class RootHandler:
@@ -260,6 +355,21 @@ class PollHandler:
        return WebChannel().poll_response()


+class StreamHandler:
+    def GET(self):
+        params = web.input(request_id='')
+        request_id = params.request_id
+        if not request_id:
+            raise web.badrequest()
+
+        web.header('Content-Type', 'text/event-stream; charset=utf-8')
+        web.header('Cache-Control', 'no-cache')
+        web.header('X-Accel-Buffering', 'no')
+        web.header('Access-Control-Allow-Origin', '*')
+
+        return WebChannel().stream_response(request_id)
+
+
 class ChatHandler:
    def GET(self):
        # 正常返回聊天页面
@@ -270,28 +380,181 @@ class ChatHandler:

 class ConfigHandler:
    def GET(self):
-        """返回前端需要的配置信息"""
+        """Return configuration info for the web console."""
        try:
-            use_agent = conf().get("agent", False)
-            
+            local_config = conf()
+            use_agent = local_config.get("agent", False)
+
            if use_agent:
                title = "CowAgent"
-                subtitle = "我可以帮你解答问题、管理计算机、创造和执行技能，并通过长期记忆不断成长"
            else:
-                title = "AI 助手"
-                subtitle = "我可以回答问题、提供信息或者帮助您完成各种任务"
-            
+                title = "AI Assistant"
+
            return json.dumps({
                "status": "success",
                "use_agent": use_agent,
                "title": title,
-                "subtitle": subtitle
+                "model": local_config.get("model", ""),
+                "channel_type": local_config.get("channel_type", ""),
+                "agent_max_context_tokens": local_config.get("agent_max_context_tokens", ""),
+                "agent_max_context_turns": local_config.get("agent_max_context_turns", ""),
+                "agent_max_steps": local_config.get("agent_max_steps", ""),
            })
        except Exception as e:
            logger.error(f"Error getting config: {e}")
            return json.dumps({"status": "error", "message": str(e)})


+def _get_workspace_root():
+    """Resolve the agent workspace directory."""
+    from common.utils import expand_path
+    return expand_path(conf().get("agent_workspace", "~/cow"))
+
+
+class SkillsHandler:
+    def GET(self):
+        web.header('Content-Type', 'application/json; charset=utf-8')
+        try:
+            from agent.skills.service import SkillService
+            from agent.skills.manager import SkillManager
+            workspace_root = _get_workspace_root()
+            manager = SkillManager(custom_dir=os.path.join(workspace_root, "skills"))
+            service = SkillService(manager)
+            skills = service.query()
+            return json.dumps({"status": "success", "skills": skills}, ensure_ascii=False)
+        except Exception as e:
+            logger.error(f"[WebChannel] Skills API error: {e}")
+            return json.dumps({"status": "error", "message": str(e)})
+
+
+class MemoryHandler:
+    def GET(self):
+        web.header('Content-Type', 'application/json; charset=utf-8')
+        try:
+            from agent.memory.service import MemoryService
+            params = web.input(page='1', page_size='20')
+            workspace_root = _get_workspace_root()
+            service = MemoryService(workspace_root)
+            result = service.list_files(page=int(params.page), page_size=int(params.page_size))
+            return json.dumps({"status": "success", **result}, ensure_ascii=False)
+        except Exception as e:
+            logger.error(f"[WebChannel] Memory API error: {e}")
+            return json.dumps({"status": "error", "message": str(e)})
+
+
+class MemoryContentHandler:
+    def GET(self):
+        web.header('Content-Type', 'application/json; charset=utf-8')
+        try:
+            from agent.memory.service import MemoryService
+            params = web.input(filename='')
+            if not params.filename:
+                return json.dumps({"status": "error", "message": "filename required"})
+            workspace_root = _get_workspace_root()
+            service = MemoryService(workspace_root)
+            result = service.get_content(params.filename)
+            return json.dumps({"status": "success", **result}, ensure_ascii=False)
+        except FileNotFoundError:
+            return json.dumps({"status": "error", "message": "file not found"})
+        except Exception as e:
+            logger.error(f"[WebChannel] Memory content API error: {e}")
+            return json.dumps({"status": "error", "message": str(e)})
+
+
+class SchedulerHandler:
+    def GET(self):
+        web.header('Content-Type', 'application/json; charset=utf-8')
+        try:
+            from agent.tools.scheduler.task_store import TaskStore
+            workspace_root = _get_workspace_root()
+            store_path = os.path.join(workspace_root, "scheduler", "tasks.json")
+            store = TaskStore(store_path)
+            tasks = store.list_tasks()
+            return json.dumps({"status": "success", "tasks": tasks}, ensure_ascii=False)
+        except Exception as e:
+            logger.error(f"[WebChannel] Scheduler API error: {e}")
+            return json.dumps({"status": "error", "message": str(e)})
+
+
+class HistoryHandler:
+    def GET(self):
+        """
+        Return paginated conversation history for a session.
+
+        Query params:
+            session_id  (required)
+            page        int, default 1  (1 = most recent messages)
+            page_size   int, default 20
+        """
+        web.header('Content-Type', 'application/json; charset=utf-8')
+        web.header('Access-Control-Allow-Origin', '*')
+        try:
+            params = web.input(session_id='', page='1', page_size='20')
+            session_id = params.session_id.strip()
+            if not session_id:
+                return json.dumps({"status": "error", "message": "session_id required"})
+
+            from agent.memory import get_conversation_store
+            store = get_conversation_store()
+            result = store.load_history_page(
+                session_id=session_id,
+                page=int(params.page),
+                page_size=int(params.page_size),
+            )
+            return json.dumps({"status": "success", **result}, ensure_ascii=False)
+        except Exception as e:
+            logger.error(f"[WebChannel] History API error: {e}")
+            return json.dumps({"status": "error", "message": str(e)})
+
+
+class LogsHandler:
+    def GET(self):
+        """Stream the last N lines of run.log as SSE, then tail new lines."""
+        web.header('Content-Type', 'text/event-stream; charset=utf-8')
+        web.header('Cache-Control', 'no-cache')
+        web.header('X-Accel-Buffering', 'no')
+
+        from config import get_root
+        log_path = os.path.join(get_root(), "run.log")
+
+        def generate():
+            if not os.path.isfile(log_path):
+                yield b"data: {\"type\": \"error\", \"message\": \"run.log not found\"}\n\n"
+                return
+
+            # Read last 200 lines for initial display
+            try:
+                with open(log_path, 'r', encoding='utf-8', errors='replace') as f:
+                    lines = f.readlines()
+                tail_lines = lines[-200:]
+                chunk = ''.join(tail_lines)
+                payload = json.dumps({"type": "init", "content": chunk}, ensure_ascii=False)
+                yield f"data: {payload}\n\n".encode('utf-8')
+            except Exception as e:
+                yield f"data: {{\"type\": \"error\", \"message\": \"{e}\"}}\n\n".encode('utf-8')
+                return
+
+            # Tail new lines
+            try:
+                with open(log_path, 'r', encoding='utf-8', errors='replace') as f:
+                    f.seek(0, 2)  # seek to end
+                    deadline = time.time() + 600  # 10 min max
+                    while time.time() < deadline:
+                        line = f.readline()
+                        if line:
+                            payload = json.dumps({"type": "line", "content": line}, ensure_ascii=False)
+                            yield f"data: {payload}\n\n".encode('utf-8')
+                        else:
+                            yield b": keepalive\n\n"
+                            time.sleep(1)
+            except GeneratorExit:
+                return
+            except Exception:
+                return
+
+        return generate()
+
+
 class AssetsHandler:
    def GET(self, file_path):  # 修改默认参数
        try:
--- a/channel/wechat/wechat_channel.py
+++ b/channel/wechat/wechat_channel.py
@@ -151,7 +151,7 @@ class WechatChannel(ChatChannel):

    def exitCallback(self):
        try:
-            from common.linkai_client import chat_client
+            from common.cloud_client import chat_client
            if chat_client.client_id and conf().get("use_linkai"):
                _send_logout()
                time.sleep(2)
@@ -283,7 +283,7 @@ class WechatChannel(ChatChannel):

 def _send_login_success():
    try:
-        from common.linkai_client import chat_client
+        from common.cloud_client import chat_client
        if chat_client.client_id:
            chat_client.send_login_success()
    except Exception as e:
@@ -292,7 +292,7 @@ def _send_login_success():

 def _send_logout():
    try:
-        from common.linkai_client import chat_client
+        from common.cloud_client import chat_client
        if chat_client.client_id:
            chat_client.send_logout()
    except Exception as e:
@@ -301,7 +301,7 @@ def _send_logout():

 def _send_qr_code(qrcode_list: list):
    try:
-        from common.linkai_client import chat_client
+        from common.cloud_client import chat_client
        if chat_client.client_id:
            chat_client.send_qrcode(qrcode_list)
    except Exception as e:
--- a/channel/wechatcom/wechatcomapp_channel.py
+++ b/channel/wechatcom/wechatcomapp_channel.py
@@ -36,6 +36,7 @@ class WechatComAppChannel(ChatChannel):
        self.agent_id = conf().get("wechatcomapp_agent_id")
        self.token = conf().get("wechatcomapp_token")
        self.aes_key = conf().get("wechatcomapp_aes_key")
+        self._http_server = None
        logger.info(
            "[wechatcom] Initializing WeCom app channel, corp_id: {}, agent_id: {}".format(self.corp_id, self.agent_id)
        )
@@ -51,13 +52,24 @@ class WechatComAppChannel(ChatChannel):
        logger.info("[wechatcom] 📡 Listening on http://0.0.0.0:{}/wxcomapp/".format(port))
        logger.info("[wechatcom] 🤖 Ready to receive messages")
        
-        # Suppress web.py's default server startup message
-        old_stdout = sys.stdout
-        sys.stdout = io.StringIO()
+        # Build WSGI app with middleware (same as runsimple but without print)
+        func = web.httpserver.StaticMiddleware(app.wsgifunc())
+        func = web.httpserver.LogMiddleware(func)
+        server = web.httpserver.WSGIServer(("0.0.0.0", port), func)
+        self._http_server = server
        try:
-            web.httpserver.runsimple(app.wsgifunc(), ("0.0.0.0", port))
-        finally:
-            sys.stdout = old_stdout
+            server.start()
+        except (KeyboardInterrupt, SystemExit):
+            server.stop()
+
+    def stop(self):
+        if self._http_server:
+            try:
+                self._http_server.stop()
+                logger.info("[wechatcom] HTTP server stopped")
+            except Exception as e:
+                logger.warning(f"[wechatcom] Error stopping HTTP server: {e}")
+            self._http_server = None

    def send(self, reply: Reply, context: Context):
        receiver = context["receiver"]
--- a/channel/wechatmp/wechatmp_channel.py
+++ b/channel/wechatmp/wechatmp_channel.py
@@ -41,6 +41,7 @@ class WechatMPChannel(ChatChannel):
        super().__init__()
        self.passive_reply = passive_reply
        self.NOT_SUPPORT_REPLYTYPE = []
+        self._http_server = None
        appid = conf().get("wechatmp_app_id")
        secret = conf().get("wechatmp_app_secret")
        token = conf().get("wechatmp_token")
@@ -69,7 +70,23 @@ class WechatMPChannel(ChatChannel):
            urls = ("/wx", "channel.wechatmp.active_reply.Query")
        app = web.application(urls, globals(), autoreload=False)
        port = conf().get("wechatmp_port", 8080)
-        web.httpserver.runsimple(app.wsgifunc(), ("0.0.0.0", port))
+        func = web.httpserver.StaticMiddleware(app.wsgifunc())
+        func = web.httpserver.LogMiddleware(func)
+        server = web.httpserver.WSGIServer(("0.0.0.0", port), func)
+        self._http_server = server
+        try:
+            server.start()
+        except (KeyboardInterrupt, SystemExit):
+            server.stop()
+
+    def stop(self):
+        if self._http_server:
+            try:
+                self._http_server.stop()
+                logger.info("[wechatmp] HTTP server stopped")
+            except Exception as e:
+                logger.warning(f"[wechatmp] Error stopping HTTP server: {e}")
+            self._http_server = None

    def start_loop(self, loop):
        asyncio.set_event_loop(loop)
--- a/channel/wework/wework_channel.py
+++ b/channel/wework/wework_channel.py
@@ -20,7 +20,6 @@ from common.utils import compress_imgfile, fsize
 from config import conf
 from channel.wework.run import wework
 from channel.wework import run
-from PIL import Image


 def get_wxid_by_name(room_members, group_wxid, name):
@@ -55,6 +54,7 @@ def download_and_compress_image(url, filename, quality=30):
    image_storage.seek(0)

    # 读取并保存图片
+    from PIL import Image
    image = Image.open(image_storage)
    image_path = os.path.join(directory, f"{filename}.png")
    image.save(image_path, "png")
--- a/common/cloud_client.py
+++ b/common/cloud_client.py
@@ -0,0 +1,375 @@
+"""
+Cloud management client for connecting to the LinkAI control console.
+
+Handles remote configuration sync, message push, and skill management
+via the LinkAI socket protocol.
+"""
+
+from bridge.context import Context, ContextType
+from bridge.reply import Reply, ReplyType
+from common.log import logger
+from linkai import LinkAIClient, PushMsg
+from config import conf, pconf, plugin_config, available_setting, write_plugin_config, get_root
+from plugins import PluginManager
+import threading
+import time
+import json
+import os
+
+
+chat_client: LinkAIClient
+
+
+class CloudClient(LinkAIClient):
+    def __init__(self, api_key: str, channel, host: str = ""):
+        super().__init__(api_key, host)
+        self.channel = channel
+        self.client_type = channel.channel_type
+        self.channel_mgr = None
+        self._skill_service = None
+        self._memory_service = None
+        self._chat_service = None
+
+    @property
+    def skill_service(self):
+        """Lazy-init SkillService so it is available once SkillManager exists."""
+        if self._skill_service is None:
+            try:
+                from agent.skills.manager import SkillManager
+                from agent.skills.service import SkillService
+                from config import conf
+                from common.utils import expand_path
+                workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
+                manager = SkillManager(custom_dir=os.path.join(workspace_root, "skills"))
+                self._skill_service = SkillService(manager)
+                logger.debug("[CloudClient] SkillService initialised")
+            except Exception as e:
+                logger.error(f"[CloudClient] Failed to init SkillService: {e}")
+        return self._skill_service
+
+    @property
+    def memory_service(self):
+        """Lazy-init MemoryService."""
+        if self._memory_service is None:
+            try:
+                from agent.memory.service import MemoryService
+                from config import conf
+                from common.utils import expand_path
+                workspace_root = expand_path(conf().get("agent_workspace", "~/cow"))
+                self._memory_service = MemoryService(workspace_root)
+                logger.debug("[CloudClient] MemoryService initialised")
+            except Exception as e:
+                logger.error(f"[CloudClient] Failed to init MemoryService: {e}")
+        return self._memory_service
+
+    @property
+    def chat_service(self):
+        """Lazy-init ChatService (requires AgentBridge via Bridge singleton)."""
+        if self._chat_service is None:
+            try:
+                from agent.chat.service import ChatService
+                from bridge.bridge import Bridge
+                agent_bridge = Bridge().get_agent_bridge()
+                self._chat_service = ChatService(agent_bridge)
+                logger.debug("[CloudClient] ChatService initialised")
+            except Exception as e:
+                logger.error(f"[CloudClient] Failed to init ChatService: {e}")
+        return self._chat_service
+
+    # ------------------------------------------------------------------
+    # message push callback
+    # ------------------------------------------------------------------
+    def on_message(self, push_msg: PushMsg):
+        session_id = push_msg.session_id
+        msg_content = push_msg.msg_content
+        logger.info(f"receive msg push, session_id={session_id}, msg_content={msg_content}")
+        context = Context()
+        context.type = ContextType.TEXT
+        context["receiver"] = session_id
+        context["isgroup"] = push_msg.is_group
+        self.channel.send(Reply(ReplyType.TEXT, content=msg_content), context)
+
+    # ------------------------------------------------------------------
+    # config callback
+    # ------------------------------------------------------------------
+    def on_config(self, config: dict):
+        if not self.client_id:
+            return
+        logger.info(f"[CloudClient] Loading remote config: {config}")
+        if config.get("enabled") != "Y":
+            return
+
+        local_config = conf()
+        need_restart_channel = False
+
+        for key in config.keys():
+            if key in available_setting and config.get(key) is not None:
+                local_config[key] = config.get(key)
+
+        # Voice settings
+        reply_voice_mode = config.get("reply_voice_mode")
+        if reply_voice_mode:
+            if reply_voice_mode == "voice_reply_voice":
+                local_config["voice_reply_voice"] = True
+                local_config["always_reply_voice"] = False
+            elif reply_voice_mode == "always_reply_voice":
+                local_config["always_reply_voice"] = True
+                local_config["voice_reply_voice"] = True
+            elif reply_voice_mode == "no_reply_voice":
+                local_config["always_reply_voice"] = False
+                local_config["voice_reply_voice"] = False
+
+        # Model configuration
+        if config.get("model"):
+            local_config["model"] = config.get("model")
+
+        # Channel configuration
+        if config.get("channelType"):
+            if local_config.get("channel_type") != config.get("channelType"):
+                local_config["channel_type"] = config.get("channelType")
+                need_restart_channel = True
+
+        # Channel-specific app credentials
+        current_channel_type = local_config.get("channel_type", "")
+
+        if config.get("app_id") is not None:
+            if current_channel_type == "feishu":
+                if local_config.get("feishu_app_id") != config.get("app_id"):
+                    local_config["feishu_app_id"] = config.get("app_id")
+                    need_restart_channel = True
+            elif current_channel_type == "dingtalk":
+                if local_config.get("dingtalk_client_id") != config.get("app_id"):
+                    local_config["dingtalk_client_id"] = config.get("app_id")
+                    need_restart_channel = True
+            elif current_channel_type in ("wechatmp", "wechatmp_service"):
+                if local_config.get("wechatmp_app_id") != config.get("app_id"):
+                    local_config["wechatmp_app_id"] = config.get("app_id")
+                    need_restart_channel = True
+            elif current_channel_type == "wechatcom_app":
+                if local_config.get("wechatcomapp_agent_id") != config.get("app_id"):
+                    local_config["wechatcomapp_agent_id"] = config.get("app_id")
+                    need_restart_channel = True
+
+        if config.get("app_secret"):
+            if current_channel_type == "feishu":
+                if local_config.get("feishu_app_secret") != config.get("app_secret"):
+                    local_config["feishu_app_secret"] = config.get("app_secret")
+                    need_restart_channel = True
+            elif current_channel_type == "dingtalk":
+                if local_config.get("dingtalk_client_secret") != config.get("app_secret"):
+                    local_config["dingtalk_client_secret"] = config.get("app_secret")
+                    need_restart_channel = True
+            elif current_channel_type in ("wechatmp", "wechatmp_service"):
+                if local_config.get("wechatmp_app_secret") != config.get("app_secret"):
+                    local_config["wechatmp_app_secret"] = config.get("app_secret")
+                    need_restart_channel = True
+            elif current_channel_type == "wechatcom_app":
+                if local_config.get("wechatcomapp_secret") != config.get("app_secret"):
+                    local_config["wechatcomapp_secret"] = config.get("app_secret")
+                    need_restart_channel = True
+
+        if config.get("admin_password"):
+            if not pconf("Godcmd"):
+                write_plugin_config({"Godcmd": {"password": config.get("admin_password"), "admin_users": []}})
+            else:
+                pconf("Godcmd")["password"] = config.get("admin_password")
+            PluginManager().instances["GODCMD"].reload()
+
+        if config.get("group_app_map") and pconf("linkai"):
+            local_group_map = {}
+            for mapping in config.get("group_app_map"):
+                local_group_map[mapping.get("group_name")] = mapping.get("app_code")
+            pconf("linkai")["group_app_map"] = local_group_map
+            PluginManager().instances["LINKAI"].reload()
+
+        if config.get("text_to_image") and config.get("text_to_image") == "midjourney" and pconf("linkai"):
+            if pconf("linkai")["midjourney"]:
+                pconf("linkai")["midjourney"]["enabled"] = True
+                pconf("linkai")["midjourney"]["use_image_create_prefix"] = True
+        elif config.get("text_to_image") and config.get("text_to_image") in ["dall-e-2", "dall-e-3"]:
+            if pconf("linkai")["midjourney"]:
+                pconf("linkai")["midjourney"]["use_image_create_prefix"] = False
+
+        # Save configuration to config.json file
+        self._save_config_to_file(local_config)
+
+        if need_restart_channel:
+            self._restart_channel(local_config.get("channel_type", ""))
+
+    # ------------------------------------------------------------------
+    # skill callback
+    # ------------------------------------------------------------------
+    def on_skill(self, data: dict) -> dict:
+        """
+        Handle SKILL messages from the cloud console.
+        Delegates to SkillService.dispatch for the actual operations.
+
+        :param data: message data with 'action', 'clientId', 'payload'
+        :return: response dict
+        """
+        action = data.get("action", "")
+        payload = data.get("payload")
+        logger.info(f"[CloudClient] on_skill: action={action}")
+
+        svc = self.skill_service
+        if svc is None:
+            return {"action": action, "code": 500, "message": "SkillService not available", "payload": None}
+
+        return svc.dispatch(action, payload)
+
+    # ------------------------------------------------------------------
+    # memory callback
+    # ------------------------------------------------------------------
+    def on_memory(self, data: dict) -> dict:
+        """
+        Handle MEMORY messages from the cloud console.
+        Delegates to MemoryService.dispatch for the actual operations.
+
+        :param data: message data with 'action', 'clientId', 'payload'
+        :return: response dict
+        """
+        action = data.get("action", "")
+        payload = data.get("payload")
+        logger.info(f"[CloudClient] on_memory: action={action}")
+
+        svc = self.memory_service
+        if svc is None:
+            return {"action": action, "code": 500, "message": "MemoryService not available", "payload": None}
+
+        return svc.dispatch(action, payload)
+
+    # ------------------------------------------------------------------
+    # chat callback
+    # ------------------------------------------------------------------
+    def on_chat(self, data: dict, send_chunk_fn):
+        """
+        Handle CHAT messages from the cloud console.
+        Runs the agent in streaming mode and sends chunks back via send_chunk_fn.
+
+        :param data: message data with 'action' and 'payload' (query, session_id)
+        :param send_chunk_fn: callable(chunk_data: dict) to send one streaming chunk
+        """
+        payload = data.get("payload", {})
+        query = payload.get("query", "")
+        session_id = payload.get("session_id", "cloud_console")
+        logger.info(f"[CloudClient] on_chat: session={session_id}, query={query[:80]}")
+
+        svc = self.chat_service
+        if svc is None:
+            raise RuntimeError("ChatService not available")
+
+        svc.run(query=query, session_id=session_id, send_chunk_fn=send_chunk_fn)
+
+    # ------------------------------------------------------------------
+    # channel restart helpers
+    # ------------------------------------------------------------------
+    def _restart_channel(self, new_channel_type: str):
+        """
+        Restart the channel via ChannelManager when channel type changes.
+        """
+        if self.channel_mgr:
+            logger.info(f"[CloudClient] Restarting channel to '{new_channel_type}'...")
+            threading.Thread(target=self._do_restart_channel, args=(self.channel_mgr, new_channel_type), daemon=True).start()
+        else:
+            logger.warning("[CloudClient] ChannelManager not available, please restart the application manually")
+
+    def _do_restart_channel(self, mgr, new_channel_type: str):
+        """
+        Perform the channel restart in a separate thread to avoid blocking the config callback.
+        """
+        try:
+            mgr.restart(new_channel_type)
+            # Update the client's channel reference
+            if mgr.channel:
+                self.channel = mgr.channel
+                self.client_type = mgr.channel.channel_type
+                logger.info(f"[CloudClient] Channel reference updated to '{new_channel_type}'")
+        except Exception as e:
+            logger.error(f"[CloudClient] Channel restart failed: {e}")
+
+    # ------------------------------------------------------------------
+    # config persistence
+    # ------------------------------------------------------------------
+    def _save_config_to_file(self, local_config: dict):
+        """
+        Save configuration to config.json file.
+        """
+        try:
+            config_path = os.path.join(get_root(), "config.json")
+            if not os.path.exists(config_path):
+                logger.warning(f"[CloudClient] config.json not found at {config_path}, skip saving")
+                return
+
+            with open(config_path, "r", encoding="utf-8") as f:
+                file_config = json.load(f)
+
+            file_config.update(dict(local_config))
+
+            with open(config_path, "w", encoding="utf-8") as f:
+                json.dump(file_config, f, indent=4, ensure_ascii=False)
+
+            logger.info("[CloudClient] Configuration saved to config.json successfully")
+        except Exception as e:
+            logger.error(f"[CloudClient] Failed to save configuration to config.json: {e}")
+
+
+def start(channel, channel_mgr=None):
+    global chat_client
+    chat_client = CloudClient(api_key=conf().get("linkai_api_key"), host=conf().get("cloud_host", ""), channel=channel)
+    chat_client.channel_mgr = channel_mgr
+    chat_client.config = _build_config()
+    chat_client.start()
+    time.sleep(1.5)
+    if chat_client.client_id:
+        logger.info("[CloudClient] Console: https://link-ai.tech/console/clients")
+
+
+def _build_config():
+    local_conf = conf()
+    config = {
+        "linkai_app_code": local_conf.get("linkai_app_code"),
+        "single_chat_prefix": local_conf.get("single_chat_prefix"),
+        "single_chat_reply_prefix": local_conf.get("single_chat_reply_prefix"),
+        "single_chat_reply_suffix": local_conf.get("single_chat_reply_suffix"),
+        "group_chat_prefix": local_conf.get("group_chat_prefix"),
+        "group_chat_reply_prefix": local_conf.get("group_chat_reply_prefix"),
+        "group_chat_reply_suffix": local_conf.get("group_chat_reply_suffix"),
+        "group_name_white_list": local_conf.get("group_name_white_list"),
+        "nick_name_black_list": local_conf.get("nick_name_black_list"),
+        "speech_recognition": "Y" if local_conf.get("speech_recognition") else "N",
+        "text_to_image": local_conf.get("text_to_image"),
+        "image_create_prefix": local_conf.get("image_create_prefix"),
+        "model": local_conf.get("model"),
+        "agent_max_context_turns": local_conf.get("agent_max_context_turns"),
+        "agent_max_context_tokens": local_conf.get("agent_max_context_tokens"),
+        "agent_max_steps": local_conf.get("agent_max_steps"),
+        "channelType": local_conf.get("channel_type"),
+    }
+
+    if local_conf.get("always_reply_voice"):
+        config["reply_voice_mode"] = "always_reply_voice"
+    elif local_conf.get("voice_reply_voice"):
+        config["reply_voice_mode"] = "voice_reply_voice"
+
+    if pconf("linkai"):
+        config["group_app_map"] = pconf("linkai").get("group_app_map")
+
+    if plugin_config.get("Godcmd"):
+        config["admin_password"] = plugin_config.get("Godcmd").get("password")
+
+    # Add channel-specific app credentials
+    current_channel_type = local_conf.get("channel_type", "")
+    if current_channel_type == "feishu":
+        config["app_id"] = local_conf.get("feishu_app_id")
+        config["app_secret"] = local_conf.get("feishu_app_secret")
+    elif current_channel_type == "dingtalk":
+        config["app_id"] = local_conf.get("dingtalk_client_id")
+        config["app_secret"] = local_conf.get("dingtalk_client_secret")
+    elif current_channel_type in ("wechatmp", "wechatmp_service"):
+        config["app_id"] = local_conf.get("wechatmp_app_id")
+        config["app_secret"] = local_conf.get("wechatmp_app_secret")
+    elif current_channel_type == "wechatcom_app":
+        config["app_id"] = local_conf.get("wechatcomapp_agent_id")
+        config["app_secret"] = local_conf.get("wechatcomapp_secret")
+
+    return config
--- a/common/const.py
+++ b/common/const.py
@@ -26,8 +26,9 @@ CLAUDE_35_SONNET_1022 = "claude-3-5-sonnet-20241022"  # 带具体日期的模型
 CLAUDE_35_SONNET_0620 = "claude-3-5-sonnet-20240620"
 CLAUDE_4_OPUS = "claude-opus-4-0"
 CLAUDE_4_6_OPUS = "claude-opus-4-6"      # Claude Opus 4.6 - Agent推荐模型
-CLAUDE_4_SONNET = "claude-sonnet-4-0"    # Claude Sonnet 4.0 - Agent推荐模型
+CLAUDE_4_SONNET = "claude-sonnet-4-0"    # Claude Sonnet 4.0
 CLAUDE_4_5_SONNET = "claude-sonnet-4-5"  # Claude Sonnet 4.5 - Agent推荐模型
+CLAUDE_4_6_SONNET = "claude-sonnet-4-6"  # Claude Sonnet 4.6 - Agent推荐模型

 # Gemini (Google)
 GEMINI_PRO = "gemini-1.0-pro"
@@ -35,10 +36,11 @@ GEMINI_15_flash = "gemini-1.5-flash"
 GEMINI_15_PRO = "gemini-1.5-pro"
 GEMINI_20_flash_exp = "gemini-2.0-flash-exp"  # exp结尾为实验模型，会逐步不再支持
 GEMINI_20_FLASH = "gemini-2.0-flash"  # 正式版模型
-GEMINI_25_FLASH_PRE = "gemini-2.5-flash-preview-05-20"  # preview为预览版模型，主要是新能力体验
+GEMINI_25_FLASH_PRE = "gemini-2.5-flash-preview-05-20"
 GEMINI_25_PRO_PRE = "gemini-2.5-pro-preview-05-06"
 GEMINI_3_FLASH_PRE = "gemini-3-flash-preview"  # Gemini 3 Flash Preview - Agent推荐模型
-GEMINI_3_PRO_PRE = "gemini-3-pro-preview"  # Gemini 3 Pro Preview - Agent推荐模型
+GEMINI_3_PRO_PRE = "gemini-3-pro-preview"  # Gemini 3 Pro Preview
+GEMINI_31_PRO_PRE = "gemini-3.1-pro-preview"  # Gemini 3.1 Pro Preview - Agent推荐模型

 # OpenAI
 GPT35 = "gpt-3.5-turbo"
@@ -80,15 +82,18 @@ QWEN_PLUS = "qwen-plus"
 QWEN_MAX = "qwen-max"
 QWEN_LONG = "qwen-long"
 QWEN3_MAX = "qwen3-max"  # Qwen3 Max - Agent推荐模型
+QWEN35_PLUS = "qwen3.5-plus"  # Qwen3.5 Plus - Omni model (MultiModalConversation)
 QWQ_PLUS = "qwq-plus"

 # MiniMax
+MINIMAX_M2_5 = "MiniMax-M2.5"  # MiniMax M2.5 - Latest
 MINIMAX_M2_1 = "MiniMax-M2.1"  # MiniMax M2.1 - Agent推荐模型
 MINIMAX_M2_1_LIGHTNING = "MiniMax-M2.1-lightning"  # MiniMax M2.1 极速版
 MINIMAX_M2 = "MiniMax-M2"  # MiniMax M2
 MINIMAX_ABAB6_5 = "abab6.5-chat"  # MiniMax abab6.5

 # GLM (智谱AI)
+GLM_5 = "glm-5"  # 智谱 GLM-5 - Latest
 GLM_4 = "glm-4"
 GLM_4_PLUS = "glm-4-plus"
 GLM_4_flash = "glm-4-flash"
@@ -101,6 +106,15 @@ GLM_4_7 = "glm-4.7"  # 智谱 GLM-4.7 - Agent推荐模型

 # Kimi (Moonshot)
 MOONSHOT = "moonshot"
+KIMI_K2 = "kimi-k2"
+KIMI_K2_5 = "kimi-k2.5"
+
+# Doubao (Volcengine Ark)
+DOUBAO = "doubao"
+DOUBAO_SEED_2_CODE = "doubao-seed-2-0-code-preview-260215"
+DOUBAO_SEED_2_PRO = "doubao-seed-2-0-pro-260215"
+DOUBAO_SEED_2_LITE = "doubao-seed-2-0-lite-260215"
+DOUBAO_SEED_2_MINI = "doubao-seed-2-0-mini-260215"

 # 其他模型
 WEN_XIN = "wenxin"
@@ -121,12 +135,12 @@ MODELSCOPE_MODEL_LIST = ["LLM-Research/c4ai-command-r-plus-08-2024","mistralai/M

 MODEL_LIST = [
              # Claude
-              CLAUDE3, CLAUDE_4_6_OPUS, CLAUDE_4_OPUS, CLAUDE_4_5_SONNET, CLAUDE_4_SONNET, CLAUDE_3_OPUS, CLAUDE_3_OPUS_0229, 
+              CLAUDE3, CLAUDE_4_6_SONNET, CLAUDE_4_6_OPUS, CLAUDE_4_OPUS, CLAUDE_4_5_SONNET, CLAUDE_4_SONNET, CLAUDE_3_OPUS, CLAUDE_3_OPUS_0229, 
              CLAUDE_35_SONNET, CLAUDE_35_SONNET_1022, CLAUDE_35_SONNET_0620, CLAUDE_3_SONNET, CLAUDE_3_HAIKU, 
              "claude", "claude-3-haiku", "claude-3-sonnet", "claude-3-opus", "claude-3.5-sonnet",
              
              # Gemini
-              GEMINI_3_PRO_PRE, GEMINI_3_FLASH_PRE, GEMINI_25_PRO_PRE, GEMINI_25_FLASH_PRE, 
+              GEMINI_31_PRO_PRE, GEMINI_3_PRO_PRE, GEMINI_3_FLASH_PRE, GEMINI_25_PRO_PRE, GEMINI_25_FLASH_PRE, 
              GEMINI_20_FLASH, GEMINI_20_flash_exp, GEMINI_15_PRO, GEMINI_15_flash, GEMINI_PRO, GEMINI,
              
              # OpenAI
@@ -142,18 +156,22 @@ MODEL_LIST = [
              DEEPSEEK_CHAT, DEEPSEEK_REASONER,
              
              # Qwen
-              QWEN, QWEN_TURBO, QWEN_PLUS, QWEN_MAX, QWEN_LONG, QWEN3_MAX,
+              QWEN, QWEN_TURBO, QWEN_PLUS, QWEN_MAX, QWEN_LONG, QWEN3_MAX, QWEN35_PLUS,
              
              # MiniMax
-              MiniMax, MINIMAX_M2_1, MINIMAX_M2_1_LIGHTNING, MINIMAX_M2, MINIMAX_ABAB6_5,
-              
+              MiniMax, MINIMAX_M2_5, MINIMAX_M2_1, MINIMAX_M2_1_LIGHTNING, MINIMAX_M2, MINIMAX_ABAB6_5,
+
              # GLM
-              ZHIPU_AI, GLM_4, GLM_4_PLUS, GLM_4_flash, GLM_4_LONG, GLM_4_ALLTOOLS, 
+              ZHIPU_AI, GLM_5, GLM_4, GLM_4_PLUS, GLM_4_flash, GLM_4_LONG, GLM_4_ALLTOOLS,
              GLM_4_0520, GLM_4_AIR, GLM_4_AIRX, GLM_4_7,
-              
+
              # Kimi
              MOONSHOT, "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k",
-              
+              KIMI_K2, KIMI_K2_5,
+
+              # Doubao
+              DOUBAO, DOUBAO_SEED_2_CODE, DOUBAO_SEED_2_PRO, DOUBAO_SEED_2_LITE, DOUBAO_SEED_2_MINI,
+
              # 其他模型
              WEN_XIN, WEN_XIN_4, XUNFEI,
              LINKAI_35, LINKAI_4_TURBO, LINKAI_4o,
--- a/common/linkai_client.py
+++ b/common/linkai_client.py
@@ -1,110 +0,0 @@
-from bridge.context import Context, ContextType
-from bridge.reply import Reply, ReplyType
-from common.log import logger
-from linkai import LinkAIClient, PushMsg
-from config import conf, pconf, plugin_config, available_setting, write_plugin_config
-from plugins import PluginManager
-import time
-
-
-chat_client: LinkAIClient
-
-
-class ChatClient(LinkAIClient):
-    def __init__(self, api_key, host, channel):
-        super().__init__(api_key, host)
-        self.channel = channel
-        self.client_type = channel.channel_type
-
-    def on_message(self, push_msg: PushMsg):
-        session_id = push_msg.session_id
-        msg_content = push_msg.msg_content
-        logger.info(f"receive msg push, session_id={session_id}, msg_content={msg_content}")
-        context = Context()
-        context.type = ContextType.TEXT
-        context["receiver"] = session_id
-        context["isgroup"] = push_msg.is_group
-        self.channel.send(Reply(ReplyType.TEXT, content=msg_content), context)
-
-    def on_config(self, config: dict):
-        if not self.client_id:
-            return
-        logger.info(f"[LinkAI] 从客户端管理加载远程配置: {config}")
-        if config.get("enabled") != "Y":
-            return
-
-        local_config = conf()
-        for key in config.keys():
-            if key in available_setting and config.get(key) is not None:
-                local_config[key] = config.get(key)
-        # 语音配置
-        reply_voice_mode = config.get("reply_voice_mode")
-        if reply_voice_mode:
-            if reply_voice_mode == "voice_reply_voice":
-                local_config["voice_reply_voice"] = True
-                local_config["always_reply_voice"] = False
-            elif reply_voice_mode == "always_reply_voice":
-                local_config["always_reply_voice"] = True
-                local_config["voice_reply_voice"] = True
-            elif reply_voice_mode == "no_reply_voice":
-                local_config["always_reply_voice"] = False
-                local_config["voice_reply_voice"] = False
-
-        if config.get("admin_password"):
-            if not pconf("Godcmd"):
-                write_plugin_config({"Godcmd": {"password": config.get("admin_password"), "admin_users": []} })
-            else:
-                pconf("Godcmd")["password"] = config.get("admin_password")
-            PluginManager().instances["GODCMD"].reload()
-
-        if config.get("group_app_map") and pconf("linkai"):
-            local_group_map = {}
-            for mapping in config.get("group_app_map"):
-                local_group_map[mapping.get("group_name")] = mapping.get("app_code")
-            pconf("linkai")["group_app_map"] = local_group_map
-            PluginManager().instances["LINKAI"].reload()
-
-        if config.get("text_to_image") and config.get("text_to_image") == "midjourney" and pconf("linkai"):
-            if pconf("linkai")["midjourney"]:
-                pconf("linkai")["midjourney"]["enabled"] = True
-                pconf("linkai")["midjourney"]["use_image_create_prefix"] = True
-        elif config.get("text_to_image") and config.get("text_to_image") in ["dall-e-2", "dall-e-3"]:
-            if pconf("linkai")["midjourney"]:
-                pconf("linkai")["midjourney"]["use_image_create_prefix"] = False
-
-
-def start(channel):
-    global chat_client
-    chat_client = ChatClient(api_key=conf().get("linkai_api_key"), host="", channel=channel)
-    chat_client.config = _build_config()
-    chat_client.start()
-    time.sleep(1.5)
-    if chat_client.client_id:
-        logger.info("[LinkAI] 可前往控制台进行线上登录和配置：https://link-ai.tech/console/clients")
-
-
-def _build_config():
-    local_conf = conf()
-    config = {
-        "linkai_app_code": local_conf.get("linkai_app_code"),
-        "single_chat_prefix": local_conf.get("single_chat_prefix"),
-        "single_chat_reply_prefix": local_conf.get("single_chat_reply_prefix"),
-        "single_chat_reply_suffix": local_conf.get("single_chat_reply_suffix"),
-        "group_chat_prefix": local_conf.get("group_chat_prefix"),
-        "group_chat_reply_prefix": local_conf.get("group_chat_reply_prefix"),
-        "group_chat_reply_suffix": local_conf.get("group_chat_reply_suffix"),
-        "group_name_white_list": local_conf.get("group_name_white_list"),
-        "nick_name_black_list": local_conf.get("nick_name_black_list"),
-        "speech_recognition": "Y" if local_conf.get("speech_recognition") else "N",
-        "text_to_image": local_conf.get("text_to_image"),
-        "image_create_prefix": local_conf.get("image_create_prefix")
-    }
-    if local_conf.get("always_reply_voice"):
-        config["reply_voice_mode"] = "always_reply_voice"
-    elif local_conf.get("voice_reply_voice"):
-        config["reply_voice_mode"] = "voice_reply_voice"
-    if pconf("linkai"):
-        config["group_app_map"] = pconf("linkai").get("group_app_map")
-    if plugin_config.get("Godcmd"):
-        config["admin_password"] = plugin_config.get("Godcmd").get("password")
-    return config
--- a/common/utils.py
+++ b/common/utils.py
@@ -2,7 +2,6 @@ import io
 import os
 import re
 from urllib.parse import urlparse
-from PIL import Image
 from common.log import logger

 def fsize(file):
@@ -23,6 +22,7 @@ def fsize(file):
 def compress_imgfile(file, max_size):
    if fsize(file) <= max_size:
        return file
+    from PIL import Image
    file.seek(0)
    img = Image.open(file)
    rgb_image = img.convert("RGB")
--- a/config-template.json
+++ b/config-template.json
@@ -1,15 +1,17 @@
 {
  "channel_type": "web",
-  "model": "glm-4.7",
+  "model": "MiniMax-M2.5",
+  "minimax_api_key": "",
+  "zhipu_ai_api_key": "",
+  "ark_api_key": "",
+  "moonshot_api_key": "",
+  "dashscope_api_key": "",
  "claude_api_key": "",
  "claude_api_base": "https://api.anthropic.com/v1",
  "open_ai_api_key": "",
  "open_ai_api_base": "https://api.openai.com/v1",
  "gemini_api_key": "",
  "gemini_api_base": "https://generativelanguage.googleapis.com",
-  "zhipu_ai_api_key": "",
-  "minimax_api_key": "",
-  "dashscope_api_key": "",
  "voice_to_text": "openai",
  "text_to_voice": "openai",
  "voice_reply_voice": false,
--- a/config.py
+++ b/config.py
@@ -160,7 +160,8 @@ available_setting = {
    # chatgpt指令自定义触发词
    "clear_memory_commands": ["#清除记忆"],  # 重置会话指令，必须以#开头
    # channel配置
-    "channel_type": "",  # 通道类型，支持：{wx,wxy,terminal,wechatmp,wechatmp_service,wechatcom_app,dingtalk}
+    "channel_type": "",  # 通道类型，支持多渠道同时运行。单个: "feishu"，多个: "feishu, dingtalk" 或 ["feishu", "dingtalk"]。可选值: web,feishu,dingtalk,wechatmp,wechatmp_service,wechatcom_app
+    "web_console": True,  # 是否自动启动Web控制台（默认启动）。设为False可禁用
    "subscribe_msg": "",  # 订阅消息, 支持: wechatmp, wechatmp_service, wechatcom_app
    "debug": False,  # 是否开启debug模式，开启后会打印更多日志
    "appdata_dir": "",  # 数据目录
@@ -174,7 +175,10 @@ available_setting = {
    "zhipu_ai_api_key": "",
    "zhipu_ai_api_base": "https://open.bigmodel.cn/api/paas/v4",
    "moonshot_api_key": "",
-    "moonshot_base_url": "https://api.moonshot.cn/v1/chat/completions",
+    "moonshot_base_url": "https://api.moonshot.cn/v1",
+    # 豆包(火山方舟) 平台配置
+    "ark_api_key": "",
+    "ark_base_url": "https://ark.cn-beijing.volces.com/api/v3",
    #魔搭社区 平台配置
    "modelscope_api_key": "",
    "modelscope_base_url": "https://api-inference.modelscope.cn/v1/chat/completions",
@@ -183,6 +187,7 @@ available_setting = {
    "linkai_api_key": "",
    "linkai_app_code": "",
    "linkai_api_base": "https://api.link-ai.tech",  # linkAI服务地址
+    "cloud_host": "client.link-ai.tech",
    "minimax_api_key": "",
    "Minimax_group_id": "",
    "Minimax_base_url": "",
--- a/docs/agent.md
+++ b/docs/agent.md
@@ -8,7 +8,7 @@ Cow项目从简单的聊天机器人全面升级为超级智能助理 **CowAgent
 - **工具系统**：内置实现10+种工具，包括文件读写、bash终端、浏览器、定时任务、记忆管理等，通过Agent管理你的计算机或服务器
 - **长期记忆**：自动将对话记忆持久化至本地文件和数据库中，包括全局记忆和天级记忆，支持关键词及向量检索
 - **Skills系统**：新增Skill运行引擎，内置多种技能，并支持通过自然语言对话完成自定义Skills开发
- **多渠道和多模型支持**：支持在Web、飞书、钉钉、企微等多渠道与Agent交互，支持Claude、Gemini、OpenAI、GLM、MiniMax、Qwen 等多种国内外主流模型
+- **多渠道和多模型支持**：支持在Web、飞书、钉钉、企微等多渠道与Agent交互，支持Claude、Gemini、OpenAI、GLM、MiniMax、Qwen、Kimi、Doubao 等多种国内外主流模型
 - **安全和成本**：通过秘钥管理工具、提示词控制、系统权限等手段控制Agent的访问安全；通过最大记忆轮次、最大上下文token、工具执行步数对token成本进行限制


@@ -137,11 +137,13 @@ bash <(curl -sS https://cdn.link-ai.tech/code/cow/run.sh)

 Agent模式推荐使用以下模型，可根据效果及成本综合选择：

- **MiniMax**: `MiniMax-M2.1`
- **GLM**: `glm-4.7`
- **Qwen**: `qwen3-max`
- **Claude**: `claude-sonnet-4-5`、`claude-sonnet-4-0`
- **Gemini**: `gemini-3-flash-preview`、`gemini-3-pro-preview`
+- **MiniMax**: `MiniMax-M2.5`
+- **GLM**: `glm-5`
+- **Kimi**: `kimi-k2.5`
+- **Doubao**: `doubao-seed-2-0-code-preview-260215`
+- **Qwen**: `qwen3.5-plus`
+- **Claude**: `claude-sonnet-4-6`
+- **Gemini**: `gemini-3.1-pro-preview`

 详细模型配置方式参考 [README.md 模型说明](../README.md#模型说明)

--- a/models/bot_factory.py
+++ b/models/bot_factory.py
@@ -69,5 +69,8 @@ def create_bot(bot_type):
        from models.modelscope.modelscope_bot import ModelScopeBot
        return ModelScopeBot()

+    elif bot_type == const.DOUBAO:
+        from models.doubao.doubao_bot import DoubaoBot
+        return DoubaoBot()

    raise RuntimeError
--- a/models/dashscope/dashscope_bot.py
+++ b/models/dashscope/dashscope_bot.py
@@ -10,25 +10,26 @@ from config import conf, load_config
 from .dashscope_session import DashscopeSession
 import os
 import dashscope
+from dashscope import MultiModalConversation
 from http import HTTPStatus



+# Legacy model name mapping for older dashscope SDK constants.
+# New models don't need to be added here — they use their name string directly.
 dashscope_models = {
    "qwen-turbo": dashscope.Generation.Models.qwen_turbo,
    "qwen-plus": dashscope.Generation.Models.qwen_plus,
    "qwen-max": dashscope.Generation.Models.qwen_max,
    "qwen-bailian-v1": dashscope.Generation.Models.bailian_v1,
-    # Qwen3 series models - use string directly as model name
-    "qwen3-max": "qwen3-max",
-    "qwen3-plus": "qwen3-plus", 
-    "qwen3-turbo": "qwen3-turbo",
-    # Other new models
-    "qwen-long": "qwen-long",
-    "qwq-32b-preview": "qwq-32b-preview",
-    "qvq-72b-preview": "qvq-72b-preview"
 }
-# ZhipuAI对话模型API
+
+# Model name prefixes that require MultiModalConversation API instead of Generation API.
+# Qwen3.5+ series are omni models that only support MultiModalConversation.
+MULTIMODAL_MODEL_PREFIXES = ("qwen3.5-",)
+
+
+# Qwen对话模型API
 class DashscopeBot(Bot):
    def __init__(self):
        super().__init__()
@@ -39,6 +40,11 @@ class DashscopeBot(Bot):
            os.environ["DASHSCOPE_API_KEY"] = self.api_key
        self.client = dashscope.Generation

+    @staticmethod
+    def _is_multimodal_model(model_name: str) -> bool:
+        """Check if the model requires MultiModalConversation API"""
+        return model_name.startswith(MULTIMODAL_MODEL_PREFIXES)
+
    def reply(self, query, context=None):
        # acquire reply content
        if context.type == ContextType.TEXT:
@@ -93,16 +99,33 @@ class DashscopeBot(Bot):
        """
        try:
            dashscope.api_key = self.api_key
-            response = self.client.call(
-                dashscope_models[self.model_name],
-                messages=session.messages,
-                result_format="message"
-            )
+            model = dashscope_models.get(self.model_name, self.model_name)
+            if self._is_multimodal_model(self.model_name):
+                mm_messages = self._prepare_messages_for_multimodal(session.messages)
+                response = MultiModalConversation.call(
+                    model=model,
+                    messages=mm_messages,
+                    result_format="message"
+                )
+            else:
+                response = self.client.call(
+                    model,
+                    messages=session.messages,
+                    result_format="message"
+                )
            if response.status_code == HTTPStatus.OK:
-                content = response.output.choices[0]["message"]["content"]
+                resp_dict = self._response_to_dict(response)
+                choice = resp_dict["output"]["choices"][0]
+                content = choice.get("message", {}).get("content", "")
+                # Multimodal models may return content as a list of blocks
+                if isinstance(content, list):
+                    content = "".join(
+                        item.get("text", "") for item in content if isinstance(item, dict)
+                    )
+                usage = resp_dict.get("usage", {})
                return {
-                    "total_tokens": response.usage["total_tokens"],
-                    "completion_tokens": response.usage["output_tokens"],
+                    "total_tokens": usage.get("total_tokens", 0),
+                    "completion_tokens": usage.get("output_tokens", 0),
                    "content": content,
                }
            else:
@@ -232,36 +255,54 @@ class DashscopeBot(Bot):
        try:
            # Set API key before calling
            dashscope.api_key = self.api_key
-            
-            response = dashscope.Generation.call(
-                model=dashscope_models.get(model_name, model_name),
-                messages=messages,
-                **parameters
-            )
-            
+            model = dashscope_models.get(model_name, model_name)
+
+            if self._is_multimodal_model(model_name):
+                messages = self._prepare_messages_for_multimodal(messages)
+                response = MultiModalConversation.call(
+                    model=model,
+                    messages=messages,
+                    **parameters
+                )
+            else:
+                response = dashscope.Generation.call(
+                    model=model,
+                    messages=messages,
+                    **parameters
+                )
+
            if response.status_code == HTTPStatus.OK:
-                # Convert DashScope response to OpenAI-compatible format
-                choice = response.output.choices[0]
+                # Convert response to dict to avoid DashScope object KeyError issues
+                resp_dict = self._response_to_dict(response)
+                choice = resp_dict["output"]["choices"][0]
+                message = choice.get("message", {})
+                content = message.get("content", "")
+                # Multimodal models may return content as a list of blocks
+                if isinstance(content, list):
+                    content = "".join(
+                        item.get("text", "") for item in content if isinstance(item, dict)
+                    )
+                usage = resp_dict.get("usage", {})
                return {
-                    "id": response.request_id,
+                    "id": resp_dict.get("request_id"),
                    "object": "chat.completion",
                    "created": 0,
                    "model": model_name,
                    "choices": [{
                        "index": 0,
                        "message": {
-                            "role": choice.message.role,
-                            "content": choice.message.content,
+                            "role": message.get("role", "assistant"),
+                            "content": content,
                            "tool_calls": self._convert_tool_calls_to_openai_format(
-                                choice.message.get("tool_calls")
+                                message.get("tool_calls")
                            )
                        },
-                        "finish_reason": choice.finish_reason
+                        "finish_reason": choice.get("finish_reason")
                    }],
                    "usage": {
-                        "prompt_tokens": response.usage.input_tokens,
-                        "completion_tokens": response.usage.output_tokens,
-                        "total_tokens": response.usage.total_tokens
+                        "prompt_tokens": usage.get("input_tokens", 0),
+                        "completion_tokens": usage.get("output_tokens", 0),
+                        "total_tokens": usage.get("total_tokens", 0)
                    }
                }
            else:
@@ -271,7 +312,7 @@ class DashscopeBot(Bot):
                    "message": response.message,
                    "status_code": response.status_code
                }
-                
+
        except Exception as e:
            logger.error(f"[DASHSCOPE] sync response error: {e}")
            return {
@@ -285,48 +326,52 @@ class DashscopeBot(Bot):
        try:
            # Set API key before calling
            dashscope.api_key = self.api_key
-            
-            responses = dashscope.Generation.call(
-                model=dashscope_models.get(model_name, model_name),
-                messages=messages,
-                stream=True,
-                **parameters
-            )
+            model = dashscope_models.get(model_name, model_name)
+
+            if self._is_multimodal_model(model_name):
+                messages = self._prepare_messages_for_multimodal(messages)
+                responses = MultiModalConversation.call(
+                    model=model,
+                    messages=messages,
+                    stream=True,
+                    **parameters
+                )
+            else:
+                responses = dashscope.Generation.call(
+                    model=model,
+                    messages=messages,
+                    stream=True,
+                    **parameters
+                )
            
            # Stream chunks to caller, converting to OpenAI format
            for response in responses:
-                if response.status_code != HTTPStatus.OK:
-                    logger.error(f"[DASHSCOPE] Stream error: {response.code} - {response.message}")
+                # Convert to dict first to avoid DashScope proxy object KeyError
+                resp_dict = self._response_to_dict(response)
+                status_code = resp_dict.get("status_code", 200)
+
+                if status_code != HTTPStatus.OK:
+                    err_code = resp_dict.get("code", "")
+                    err_msg = resp_dict.get("message", "Unknown error")
+                    logger.error(f"[DASHSCOPE] Stream error: {err_code} - {err_msg}")
                    yield {
                        "error": True,
-                        "message": response.message,
-                        "status_code": response.status_code
+                        "message": err_msg,
+                        "status_code": status_code
                    }
                    continue
-                
-                # Get choice - use try-except because DashScope raises KeyError on hasattr()
-                try:
-                    if isinstance(response.output, dict):
-                        choice = response.output['choices'][0]
-                    else:
-                        choice = response.output.choices[0]
-                except (KeyError, AttributeError, IndexError) as e:
-                    logger.warning(f"[DASHSCOPE] Cannot get choice: {e}")
+
+                choices = resp_dict.get("output", {}).get("choices", [])
+                if not choices:
                    continue
-                
-                # Get finish_reason safely
-                finish_reason = None
-                try:
-                    if isinstance(choice, dict):
-                        finish_reason = choice.get('finish_reason')
-                    else:
-                        finish_reason = choice.finish_reason
-                except (KeyError, AttributeError):
-                    pass
-                
+
+                choice = choices[0]
+                finish_reason = choice.get("finish_reason")
+                message = choice.get("message", {})
+
                # Convert to OpenAI-compatible format
                openai_chunk = {
-                    "id": response.request_id,
+                    "id": resp_dict.get("request_id"),
                    "object": "chat.completion.chunk",
                    "created": 0,
                    "model": model_name,
@@ -336,66 +381,90 @@ class DashscopeBot(Bot):
                        "finish_reason": finish_reason
                    }]
                }
-                
-                # Get message safely - use try-except
-                message = {}
-                try:
-                    if isinstance(choice, dict):
-                        message = choice.get('message', {})
-                    else:
-                        message = choice.message
-                except (KeyError, AttributeError):
-                    pass
-                
-                # Add role if present
-                role = None
-                try:
-                    if isinstance(message, dict):
-                        role = message.get('role')
-                    else:
-                        role = message.role
-                except (KeyError, AttributeError):
-                    pass
+
+                # Add role
+                role = message.get("role")
                if role:
                    openai_chunk["choices"][0]["delta"]["role"] = role
-                
-                # Add content if present
-                content = None
-                try:
-                    if isinstance(message, dict):
-                        content = message.get('content')
-                    else:
-                        content = message.content
-                except (KeyError, AttributeError):
-                    pass
+
+                # Add reasoning_content (thinking process from models like qwen3.5)
+                reasoning_content = message.get("reasoning_content")
+                if reasoning_content:
+                    openai_chunk["choices"][0]["delta"]["reasoning_content"] = reasoning_content
+
+                # Add content (multimodal models may return list of blocks)
+                content = message.get("content")
+                if isinstance(content, list):
+                    content = "".join(
+                        item.get("text", "") for item in content if isinstance(item, dict)
+                    )
                if content:
                    openai_chunk["choices"][0]["delta"]["content"] = content
-                
-                # Add tool_calls if present
-                # DashScope's response object raises KeyError on hasattr() if attr doesn't exist
-                # So we use try-except instead
-                tool_calls = None
-                try:
-                    if isinstance(message, dict):
-                        tool_calls = message.get('tool_calls')
-                    else:
-                        tool_calls = message.tool_calls
-                except (KeyError, AttributeError):
-                    pass
-                
+
+                # Add tool_calls
+                tool_calls = message.get("tool_calls")
                if tool_calls:
                    openai_chunk["choices"][0]["delta"]["tool_calls"] = self._convert_tool_calls_to_openai_format(tool_calls)
-                
+
                yield openai_chunk
-                
+
        except Exception as e:
-            logger.error(f"[DASHSCOPE] stream response error: {e}")
+            logger.error(f"[DASHSCOPE] stream response error: {e}", exc_info=True)
            yield {
                "error": True,
                "message": str(e),
                "status_code": 500
            }
    
+    @staticmethod
+    def _response_to_dict(response) -> dict:
+        """
+        Convert DashScope response object to a plain dict.
+
+        DashScope SDK wraps responses in proxy objects whose __getattr__
+        delegates to __getitem__, raising KeyError (not AttributeError)
+        when an attribute is missing.  Standard hasattr / getattr only
+        catch AttributeError, so we must use try-except everywhere.
+        """
+        _SENTINEL = object()
+
+        def _safe_getattr(obj, name, default=_SENTINEL):
+            """getattr that also catches KeyError from DashScope proxy objects."""
+            try:
+                return getattr(obj, name)
+            except (AttributeError, KeyError, TypeError):
+                return default
+
+        def _has_attr(obj, name):
+            return _safe_getattr(obj, name) is not _SENTINEL
+
+        def _to_dict(obj):
+            if isinstance(obj, (str, int, float, bool, type(None))):
+                return obj
+            if isinstance(obj, dict):
+                return {k: _to_dict(v) for k, v in obj.items()}
+            if isinstance(obj, (list, tuple)):
+                return [_to_dict(i) for i in obj]
+            # DashScope response objects behave like dicts (have .keys())
+            if _has_attr(obj, "keys"):
+                try:
+                    return {k: _to_dict(obj[k]) for k in obj.keys()}
+                except Exception:
+                    pass
+            return obj
+
+        result = {}
+        # Extract known top-level fields safely
+        for attr in ("request_id", "status_code", "code", "message", "output", "usage"):
+            val = _safe_getattr(response, attr)
+            if val is _SENTINEL:
+                try:
+                    val = response[attr]
+                except (KeyError, TypeError, IndexError):
+                    continue
+            result[attr] = _to_dict(val)
+        return result
+
    def _convert_tools_to_dashscope_format(self, tools):
        """
        Convert tools from Claude format to DashScope format
@@ -424,6 +493,37 @@ class DashscopeBot(Bot):
        
        return dashscope_tools
    
+    @staticmethod
+    def _prepare_messages_for_multimodal(messages: list) -> list:
+        """
+        Ensure messages are compatible with MultiModalConversation API.
+
+        MultiModalConversation._preprocess_messages iterates every message
+        with ``content = message["content"]; for elem in content: ...``,
+        which means:
+          1. Every message MUST have a 'content' key.
+          2. 'content' MUST be an iterable (list), not a plain string.
+             The expected format is [{"text": "..."}, ...].
+
+        Meanwhile the DashScope API requires role='tool' messages to follow
+        assistant tool_calls, so we must NOT convert them to role='user'.
+        We just ensure they have a list-typed 'content'.
+        """
+        result = []
+        for msg in messages:
+            msg = dict(msg)  # shallow copy
+
+            # Normalize content to list format [{"text": "..."}]
+            content = msg.get("content")
+            if content is None or (isinstance(content, str) and content == ""):
+                msg["content"] = [{"text": ""}]
+            elif isinstance(content, str):
+                msg["content"] = [{"text": content}]
+            # If content is already a list, keep as-is (already in multimodal format)
+
+            result.append(msg)
+        return result
+
    def _convert_messages_to_dashscope_format(self, messages):
        """
        Convert messages from Claude format to DashScope format
--- a/models/doubao/init.py
+++ b/models/doubao/init.py
--- a/models/doubao/doubao_bot.py
+++ b/models/doubao/doubao_bot.py
@@ -0,0 +1,520 @@
+# encoding:utf-8
+
+import json
+import time
+
+import requests
+from models.bot import Bot
+from models.session_manager import SessionManager
+from bridge.context import ContextType
+from bridge.reply import Reply, ReplyType
+from common.log import logger
+from config import conf, load_config
+from .doubao_session import DoubaoSession
+
+
+# Doubao (火山方舟 / Volcengine Ark) API Bot
+class DoubaoBot(Bot):
+    def __init__(self):
+        super().__init__()
+        self.sessions = SessionManager(DoubaoSession, model=conf().get("model") or "doubao-seed-2-0-pro-260215")
+        model = conf().get("model") or "doubao-seed-2-0-pro-260215"
+        self.args = {
+            "model": model,
+            "temperature": conf().get("temperature", 0.8),
+            "top_p": conf().get("top_p", 1.0),
+        }
+        self.api_key = conf().get("ark_api_key")
+        self.base_url = conf().get("ark_base_url", "https://ark.cn-beijing.volces.com/api/v3")
+        # Ensure base_url does not end with /chat/completions
+        if self.base_url.endswith("/chat/completions"):
+            self.base_url = self.base_url.rsplit("/chat/completions", 1)[0]
+        if self.base_url.endswith("/"):
+            self.base_url = self.base_url.rstrip("/")
+
+    def reply(self, query, context=None):
+        # acquire reply content
+        if context.type == ContextType.TEXT:
+            logger.info("[DOUBAO] query={}".format(query))
+
+            session_id = context["session_id"]
+            reply = None
+            clear_memory_commands = conf().get("clear_memory_commands", ["#清除记忆"])
+            if query in clear_memory_commands:
+                self.sessions.clear_session(session_id)
+                reply = Reply(ReplyType.INFO, "记忆已清除")
+            elif query == "#清除所有":
+                self.sessions.clear_all_session()
+                reply = Reply(ReplyType.INFO, "所有人记忆已清除")
+            elif query == "#更新配置":
+                load_config()
+                reply = Reply(ReplyType.INFO, "配置已更新")
+            if reply:
+                return reply
+            session = self.sessions.session_query(query, session_id)
+            logger.debug("[DOUBAO] session query={}".format(session.messages))
+
+            model = context.get("doubao_model")
+            new_args = self.args.copy()
+            if model:
+                new_args["model"] = model
+
+            reply_content = self.reply_text(session, args=new_args)
+            logger.debug(
+                "[DOUBAO] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
+                    session.messages,
+                    session_id,
+                    reply_content["content"],
+                    reply_content["completion_tokens"],
+                )
+            )
+            if reply_content["completion_tokens"] == 0 and len(reply_content["content"]) > 0:
+                reply = Reply(ReplyType.ERROR, reply_content["content"])
+            elif reply_content["completion_tokens"] > 0:
+                self.sessions.session_reply(reply_content["content"], session_id, reply_content["total_tokens"])
+                reply = Reply(ReplyType.TEXT, reply_content["content"])
+            else:
+                reply = Reply(ReplyType.ERROR, reply_content["content"])
+                logger.debug("[DOUBAO] reply {} used 0 tokens.".format(reply_content))
+            return reply
+        else:
+            reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type))
+            return reply
+
+    def reply_text(self, session: DoubaoSession, args=None, retry_count: int = 0) -> dict:
+        """
+        Call Doubao chat completion API to get the answer
+        :param session: a conversation session
+        :param args: model args
+        :param retry_count: retry count
+        :return: {}
+        """
+        try:
+            headers = {
+                "Content-Type": "application/json",
+                "Authorization": "Bearer " + self.api_key
+            }
+            body = args.copy()
+            body["messages"] = session.messages
+            # Disable thinking by default for better efficiency
+            body["thinking"] = {"type": "disabled"}
+            res = requests.post(
+                f"{self.base_url}/chat/completions",
+                headers=headers,
+                json=body
+            )
+            if res.status_code == 200:
+                response = res.json()
+                return {
+                    "total_tokens": response["usage"]["total_tokens"],
+                    "completion_tokens": response["usage"]["completion_tokens"],
+                    "content": response["choices"][0]["message"]["content"]
+                }
+            else:
+                response = res.json()
+                error = response.get("error", {})
+                logger.error(f"[DOUBAO] chat failed, status_code={res.status_code}, "
+                             f"msg={error.get('message')}, type={error.get('type')}")
+
+                result = {"completion_tokens": 0, "content": "提问太快啦，请休息一下再问我吧"}
+                need_retry = False
+                if res.status_code >= 500:
+                    logger.warn(f"[DOUBAO] do retry, times={retry_count}")
+                    need_retry = retry_count < 2
+                elif res.status_code == 401:
+                    result["content"] = "授权失败，请检查API Key是否正确"
+                elif res.status_code == 429:
+                    result["content"] = "请求过于频繁，请稍后再试"
+                    need_retry = retry_count < 2
+                else:
+                    need_retry = False
+
+                if need_retry:
+                    time.sleep(3)
+                    return self.reply_text(session, args, retry_count + 1)
+                else:
+                    return result
+        except Exception as e:
+            logger.exception(e)
+            need_retry = retry_count < 2
+            result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
+            if need_retry:
+                return self.reply_text(session, args, retry_count + 1)
+            else:
+                return result
+
+    # ==================== Agent mode support ====================
+
+    def call_with_tools(self, messages, tools=None, stream: bool = False, **kwargs):
+        """
+        Call Doubao API with tool support for agent integration.
+
+        This method handles:
+        1. Format conversion (Claude format -> OpenAI format)
+        2. System prompt injection
+        3. Streaming SSE response with tool_calls
+        4. Thinking (reasoning) is disabled by default for efficiency
+
+        Args:
+            messages: List of messages (may be in Claude format from agent)
+            tools: List of tool definitions (may be in Claude format from agent)
+            stream: Whether to use streaming
+            **kwargs: Additional parameters (max_tokens, temperature, system, model, etc.)
+
+        Returns:
+            Generator yielding OpenAI-format chunks (for streaming)
+        """
+        try:
+            # Convert messages from Claude format to OpenAI format
+            converted_messages = self._convert_messages_to_openai_format(messages)
+
+            # Inject system prompt if provided
+            system_prompt = kwargs.pop("system", None)
+            if system_prompt:
+                if not converted_messages or converted_messages[0].get("role") != "system":
+                    converted_messages.insert(0, {"role": "system", "content": system_prompt})
+                else:
+                    converted_messages[0] = {"role": "system", "content": system_prompt}
+
+            # Convert tools from Claude format to OpenAI format
+            converted_tools = None
+            if tools:
+                converted_tools = self._convert_tools_to_openai_format(tools)
+
+            # Resolve model / temperature
+            model = kwargs.pop("model", None) or self.args["model"]
+            max_tokens = kwargs.pop("max_tokens", None)
+            # Don't pop temperature, just ignore it - let API use default
+            kwargs.pop("temperature", None)
+
+            # Build request body (omit temperature, let the API use its own default)
+            request_body = {
+                "model": model,
+                "messages": converted_messages,
+                "stream": stream,
+            }
+            if max_tokens is not None:
+                request_body["max_tokens"] = max_tokens
+
+            # Add tools
+            if converted_tools:
+                request_body["tools"] = converted_tools
+                request_body["tool_choice"] = "auto"
+
+            # Explicitly disable thinking to avoid reasoning_content issues
+            # in multi-turn tool calls
+            request_body["thinking"] = {"type": "disabled"}
+
+            logger.debug(f"[DOUBAO] API call: model={model}, "
+                         f"tools={len(converted_tools) if converted_tools else 0}, stream={stream}")
+
+            if stream:
+                return self._handle_stream_response(request_body)
+            else:
+                return self._handle_sync_response(request_body)
+
+        except Exception as e:
+            logger.error(f"[DOUBAO] call_with_tools error: {e}")
+            import traceback
+            logger.error(traceback.format_exc())
+
+            def error_generator():
+                yield {"error": True, "message": str(e), "status_code": 500}
+            return error_generator()
+
+    # -------------------- streaming --------------------
+
+    def _handle_stream_response(self, request_body: dict):
+        """Handle streaming SSE response from Doubao API and yield OpenAI-format chunks."""
+        try:
+            headers = {
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {self.api_key}"
+            }
+
+            url = f"{self.base_url}/chat/completions"
+            response = requests.post(url, headers=headers, json=request_body, stream=True, timeout=120)
+
+            if response.status_code != 200:
+                error_msg = response.text
+                logger.error(f"[DOUBAO] API error: status={response.status_code}, msg={error_msg}")
+                yield {"error": True, "message": error_msg, "status_code": response.status_code}
+                return
+
+            current_tool_calls = {}
+            finish_reason = None
+
+            for line in response.iter_lines():
+                if not line:
+                    continue
+
+                line = line.decode("utf-8")
+                if not line.startswith("data: "):
+                    continue
+
+                data_str = line[6:]  # Remove "data: " prefix
+                if data_str.strip() == "[DONE]":
+                    break
+
+                try:
+                    chunk = json.loads(data_str)
+                except json.JSONDecodeError as e:
+                    logger.warning(f"[DOUBAO] JSON decode error: {e}, data: {data_str[:200]}")
+                    continue
+
+                # Check for error in chunk
+                if chunk.get("error"):
+                    error_data = chunk["error"]
+                    error_msg = error_data.get("message", "Unknown error") if isinstance(error_data, dict) else str(error_data)
+                    logger.error(f"[DOUBAO] stream error: {error_msg}")
+                    yield {"error": True, "message": error_msg, "status_code": 500}
+                    return
+
+                if not chunk.get("choices"):
+                    continue
+
+                choice = chunk["choices"][0]
+                delta = choice.get("delta", {})
+
+                # Skip reasoning_content (thinking) - don't log or forward
+                if delta.get("reasoning_content"):
+                    continue
+
+                # Handle text content
+                if "content" in delta and delta["content"]:
+                    yield {
+                        "choices": [{
+                            "index": 0,
+                            "delta": {
+                                "role": "assistant",
+                                "content": delta["content"]
+                            }
+                        }]
+                    }
+
+                # Handle tool_calls (streamed incrementally)
+                if "tool_calls" in delta:
+                    for tool_call_chunk in delta["tool_calls"]:
+                        index = tool_call_chunk.get("index", 0)
+                        if index not in current_tool_calls:
+                            current_tool_calls[index] = {
+                                "id": tool_call_chunk.get("id", ""),
+                                "type": "tool_use",
+                                "name": tool_call_chunk.get("function", {}).get("name", ""),
+                                "input": ""
+                            }
+
+                        # Accumulate arguments
+                        if "function" in tool_call_chunk and "arguments" in tool_call_chunk["function"]:
+                            current_tool_calls[index]["input"] += tool_call_chunk["function"]["arguments"]
+
+                        # Yield OpenAI-format tool call delta
+                        yield {
+                            "choices": [{
+                                "index": 0,
+                                "delta": {
+                                    "tool_calls": [tool_call_chunk]
+                                }
+                            }]
+                        }
+
+                # Capture finish_reason
+                if choice.get("finish_reason"):
+                    finish_reason = choice["finish_reason"]
+
+            # Final chunk with finish_reason
+            yield {
+                "choices": [{
+                    "index": 0,
+                    "delta": {},
+                    "finish_reason": finish_reason
+                }]
+            }
+
+        except requests.exceptions.Timeout:
+            logger.error("[DOUBAO] Request timeout")
+            yield {"error": True, "message": "Request timeout", "status_code": 500}
+        except Exception as e:
+            logger.error(f"[DOUBAO] stream response error: {e}")
+            import traceback
+            logger.error(traceback.format_exc())
+            yield {"error": True, "message": str(e), "status_code": 500}
+
+    # -------------------- sync --------------------
+
+    def _handle_sync_response(self, request_body: dict):
+        """Handle synchronous API response and yield a single result dict."""
+        try:
+            headers = {
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {self.api_key}"
+            }
+
+            request_body.pop("stream", None)
+            url = f"{self.base_url}/chat/completions"
+            response = requests.post(url, headers=headers, json=request_body, timeout=120)
+
+            if response.status_code != 200:
+                error_msg = response.text
+                logger.error(f"[DOUBAO] API error: status={response.status_code}, msg={error_msg}")
+                yield {"error": True, "message": error_msg, "status_code": response.status_code}
+                return
+
+            result = response.json()
+            message = result["choices"][0]["message"]
+            finish_reason = result["choices"][0]["finish_reason"]
+
+            response_data = {"role": "assistant", "content": []}
+
+            # Add text content
+            if message.get("content"):
+                response_data["content"].append({
+                    "type": "text",
+                    "text": message["content"]
+                })
+
+            # Add tool calls
+            if message.get("tool_calls"):
+                for tool_call in message["tool_calls"]:
+                    response_data["content"].append({
+                        "type": "tool_use",
+                        "id": tool_call["id"],
+                        "name": tool_call["function"]["name"],
+                        "input": json.loads(tool_call["function"]["arguments"])
+                    })
+
+            # Map finish_reason
+            if finish_reason == "tool_calls":
+                response_data["stop_reason"] = "tool_use"
+            elif finish_reason == "stop":
+                response_data["stop_reason"] = "end_turn"
+            else:
+                response_data["stop_reason"] = finish_reason
+
+            yield response_data
+
+        except requests.exceptions.Timeout:
+            logger.error("[DOUBAO] Request timeout")
+            yield {"error": True, "message": "Request timeout", "status_code": 500}
+        except Exception as e:
+            logger.error(f"[DOUBAO] sync response error: {e}")
+            import traceback
+            logger.error(traceback.format_exc())
+            yield {"error": True, "message": str(e), "status_code": 500}
+
+    # -------------------- format conversion --------------------
+
+    def _convert_messages_to_openai_format(self, messages):
+        """
+        Convert messages from Claude format to OpenAI format.
+
+        Claude format uses content blocks: tool_use / tool_result / text
+        OpenAI format uses tool_calls in assistant, role=tool for results
+        """
+        if not messages:
+            return []
+
+        converted = []
+
+        for msg in messages:
+            role = msg.get("role")
+            content = msg.get("content")
+
+            # Already a simple string - pass through
+            if isinstance(content, str):
+                converted.append(msg)
+                continue
+
+            if not isinstance(content, list):
+                converted.append(msg)
+                continue
+
+            if role == "user":
+                text_parts = []
+                tool_results = []
+
+                for block in content:
+                    if not isinstance(block, dict):
+                        continue
+                    if block.get("type") == "text":
+                        text_parts.append(block.get("text", ""))
+                    elif block.get("type") == "tool_result":
+                        tool_call_id = block.get("tool_use_id") or ""
+                        result_content = block.get("content", "")
+                        if not isinstance(result_content, str):
+                            result_content = json.dumps(result_content, ensure_ascii=False)
+                        tool_results.append({
+                            "role": "tool",
+                            "tool_call_id": tool_call_id,
+                            "content": result_content
+                        })
+
+                # Tool results first (must come right after assistant with tool_calls)
+                for tr in tool_results:
+                    converted.append(tr)
+
+                if text_parts:
+                    converted.append({"role": "user", "content": "\n".join(text_parts)})
+
+            elif role == "assistant":
+                openai_msg = {"role": "assistant"}
+                text_parts = []
+                tool_calls = []
+
+                for block in content:
+                    if not isinstance(block, dict):
+                        continue
+                    if block.get("type") == "text":
+                        text_parts.append(block.get("text", ""))
+                    elif block.get("type") == "tool_use":
+                        tool_calls.append({
+                            "id": block.get("id"),
+                            "type": "function",
+                            "function": {
+                                "name": block.get("name"),
+                                "arguments": json.dumps(block.get("input", {}))
+                            }
+                        })
+
+                if text_parts:
+                    openai_msg["content"] = "\n".join(text_parts)
+                elif not tool_calls:
+                    openai_msg["content"] = ""
+
+                if tool_calls:
+                    openai_msg["tool_calls"] = tool_calls
+                    if not text_parts:
+                        openai_msg["content"] = None
+
+                converted.append(openai_msg)
+            else:
+                converted.append(msg)
+
+        return converted
+
+    def _convert_tools_to_openai_format(self, tools):
+        """
+        Convert tools from Claude format to OpenAI format.
+
+        Claude: {name, description, input_schema}
+        OpenAI: {type: "function", function: {name, description, parameters}}
+        """
+        if not tools:
+            return None
+
+        converted = []
+        for tool in tools:
+            # Already in OpenAI format
+            if "type" in tool and tool["type"] == "function":
+                converted.append(tool)
+            else:
+                converted.append({
+                    "type": "function",
+                    "function": {
+                        "name": tool.get("name"),
+                        "description": tool.get("description"),
+                        "parameters": tool.get("input_schema", {})
+                    }
+                })
+
+        return converted
--- a/models/doubao/doubao_session.py
+++ b/models/doubao/doubao_session.py
@@ -0,0 +1,51 @@
+from models.session_manager import Session
+from common.log import logger
+
+
+class DoubaoSession(Session):
+    def __init__(self, session_id, system_prompt=None, model="doubao-seed-2-0-pro-260215"):
+        super().__init__(session_id, system_prompt)
+        self.model = model
+        self.reset()
+
+    def discard_exceeding(self, max_tokens, cur_tokens=None):
+        precise = True
+        try:
+            cur_tokens = self.calc_tokens()
+        except Exception as e:
+            precise = False
+            if cur_tokens is None:
+                raise e
+            logger.debug("Exception when counting tokens precisely for query: {}".format(e))
+        while cur_tokens > max_tokens:
+            if len(self.messages) > 2:
+                self.messages.pop(1)
+            elif len(self.messages) == 2 and self.messages[1]["role"] == "assistant":
+                self.messages.pop(1)
+                if precise:
+                    cur_tokens = self.calc_tokens()
+                else:
+                    cur_tokens = cur_tokens - max_tokens
+                break
+            elif len(self.messages) == 2 and self.messages[1]["role"] == "user":
+                logger.warn("user message exceed max_tokens. total_tokens={}".format(cur_tokens))
+                break
+            else:
+                logger.debug("max_tokens={}, total_tokens={}, len(messages)={}".format(
+                    max_tokens, cur_tokens, len(self.messages)))
+                break
+            if precise:
+                cur_tokens = self.calc_tokens()
+            else:
+                cur_tokens = cur_tokens - max_tokens
+        return cur_tokens
+
+    def calc_tokens(self):
+        return num_tokens_from_messages(self.messages, self.model)
+
+
+def num_tokens_from_messages(messages, model):
+    tokens = 0
+    for msg in messages:
+        tokens += len(msg["content"])
+    return tokens
--- a/models/gemini/google_gemini_bot.py
+++ b/models/gemini/google_gemini_bot.py
@@ -6,11 +6,14 @@ Google gemini bot
 """
 # encoding:utf-8

+import base64
 import json
+import mimetypes
+import os
+import re
 import time
 import requests
 from models.bot import Bot
-import google.generativeai as genai
 from models.session_manager import SessionManager
 from bridge.context import ContextType, Context
 from bridge.reply import Reply, ReplyType
@@ -18,7 +21,6 @@ from common.log import logger
 from config import conf
 from models.chatgpt.chat_gpt_session import ChatGPTSession
 from models.baidu.baidu_wenxin_session import BaiduWenxinSession
-from google.generativeai.types import HarmCategory, HarmBlockThreshold


 # OpenAI对话模型API (可用)
@@ -43,6 +45,7 @@ class GoogleGeminiBot(Bot):
            self.api_base = "https://generativelanguage.googleapis.com"

    def reply(self, query, context: Context = None) -> Reply:
+        session_id = None
        try:
            if context.type != ContextType.TEXT:
                logger.warn(f"[Gemini] Unsupported message type, type={context.type}")
@@ -50,43 +53,47 @@ class GoogleGeminiBot(Bot):
            logger.info(f"[Gemini] query={query}")
            session_id = context["session_id"]
            session = self.sessions.session_query(query, session_id)
-            gemini_messages = self._convert_to_gemini_messages(self.filter_messages(session.messages))
-            logger.debug(f"[Gemini] messages={gemini_messages}")
-            genai.configure(api_key=self.api_key)
-            model = genai.GenerativeModel(self.model)
-            
-            # 添加安全设置
-            safety_settings = {
-                HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
-                HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
-                HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
-                HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
-            }
-            
-            # 生成回复，包含安全设置
-            response = model.generate_content(
-                gemini_messages,
-                safety_settings=safety_settings
+            filtered_messages = self.filter_messages(session.messages)
+            logger.debug(f"[Gemini] messages={filtered_messages}")
+
+            response = self.call_with_tools(
+                messages=filtered_messages,
+                tools=None,
+                stream=False,
+                model=self.model
            )
-            if response.candidates and response.candidates[0].content:
-                reply_text = response.candidates[0].content.parts[0].text
-                logger.info(f"[Gemini] reply={reply_text}")
-                self.sessions.session_reply(reply_text, session_id)
-                return Reply(ReplyType.TEXT, reply_text)
-            else:
-                # 没有有效响应内容，可能内容被屏蔽，输出安全评分
-                logger.warning("[Gemini] No valid response generated. Checking safety ratings.")
-                if hasattr(response, 'candidates') and response.candidates:
-                    for rating in response.candidates[0].safety_ratings:
-                        logger.warning(f"Safety rating: {rating.category} - {rating.probability}")
-                error_message = "No valid response generated due to safety constraints."
+
+            if isinstance(response, dict) and response.get("error"):
+                error_message = response.get("message", "Failed to invoke [Gemini] api!")
+                logger.error(f"[Gemini] API error: {error_message}")
                self.sessions.session_reply(error_message, session_id)
                return Reply(ReplyType.ERROR, error_message)
+
+            choices = response.get("choices", []) if isinstance(response, dict) else []
+            if choices and choices[0].get("message"):
+                reply_text = choices[0]["message"].get("content")
+                if reply_text:
+                    logger.info(f"[Gemini] reply={reply_text}")
+                    self.sessions.session_reply(reply_text, session_id)
+                    return Reply(ReplyType.TEXT, reply_text)
+
+            logger.warning("[Gemini] No valid response generated. Checking safety ratings.")
+            safety_ratings = response.get("safety_ratings", []) if isinstance(response, dict) else []
+            if safety_ratings:
+                for rating in safety_ratings:
+                    category = rating.get("category", "UNKNOWN")
+                    probability = rating.get("probability", "UNKNOWN")
+                    logger.warning(f"[Gemini] Safety rating: {category} - {probability}")
+
+            error_message = "No valid response generated due to safety constraints."
+            self.sessions.session_reply(error_message, session_id)
+            return Reply(ReplyType.ERROR, error_message)
                    
        except Exception as e:
            logger.error(f"[Gemini] Error generating response: {str(e)}", exc_info=True)
            error_message = "Failed to invoke [Gemini] api!"
-            self.sessions.session_reply(error_message, session_id)
+            if session_id:
+                self.sessions.session_reply(error_message, session_id)
            return Reply(ReplyType.ERROR, error_message)
            
    def _convert_to_gemini_messages(self, messages: list):
@@ -127,6 +134,93 @@ class GoogleGeminiBot(Bot):
                turn = "user"
        return res

+    @staticmethod
+    def _extract_image_paths_from_text(content: str):
+        if not isinstance(content, str):
+            return "", []
+        pattern = r"\[图片:\s*([^\]]+)\]"
+        image_paths = [m.strip().strip("'\"") for m in re.findall(pattern, content) if m.strip()]
+        cleaned_text = re.sub(pattern, "", content)
+        cleaned_text = re.sub(r"\n{3,}", "\n\n", cleaned_text).strip()
+        return cleaned_text, image_paths
+
+    @staticmethod
+    def _build_image_inline_part(image_path: str):
+        if not image_path:
+            return None
+        try:
+            if image_path.startswith("file://"):
+                image_path = image_path[7:]
+
+            image_path = os.path.expanduser(image_path)
+            if not os.path.exists(image_path):
+                logger.warning(f"[Gemini] Image file not found: {image_path}")
+                return None
+
+            with open(image_path, "rb") as f:
+                image_bytes = f.read()
+
+            mime_type = mimetypes.guess_type(image_path)[0] or "image/png"
+            if not mime_type.startswith("image/"):
+                mime_type = "image/png"
+
+            return {
+                "inlineData": {
+                    "mimeType": mime_type,
+                    "data": base64.b64encode(image_bytes).decode("utf-8")
+                }
+            }
+        except Exception as e:
+            logger.warning(f"[Gemini] Failed to build inline image part from path={image_path}, err={e}")
+            return None
+
+    @staticmethod
+    def _build_inline_part_from_image_url(image_url):
+        if not image_url:
+            return None
+
+        if isinstance(image_url, dict):
+            image_url = image_url.get("url")
+        if not image_url or not isinstance(image_url, str):
+            return None
+
+        if image_url.startswith("data:"):
+            match = re.match(r"^data:([^;]+);base64,(.+)$", image_url, re.DOTALL)
+            if not match:
+                logger.warning("[Gemini] Invalid data URL for image block")
+                return None
+            return {
+                "inlineData": {
+                    "mimeType": match.group(1),
+                    "data": match.group(2).strip()
+                }
+            }
+
+        if image_url.startswith("file://") or os.path.exists(os.path.expanduser(image_url)):
+            return GoogleGeminiBot._build_image_inline_part(image_url)
+
+        if image_url.startswith("http://") or image_url.startswith("https://"):
+            try:
+                response = requests.get(image_url, timeout=20)
+                if response.status_code != 200:
+                    logger.warning(f"[Gemini] Failed to fetch remote image: status={response.status_code}, url={image_url}")
+                    return None
+                mime_type = response.headers.get("Content-Type", "image/png").split(";")[0].strip()
+                if not mime_type.startswith("image/"):
+                    mime_type = "image/png"
+                return {
+                    "inlineData": {
+                        "mimeType": mime_type,
+                        "data": base64.b64encode(response.content).decode("utf-8")
+                    }
+                }
+            except Exception as e:
+                logger.warning(f"[Gemini] Failed to download remote image: url={image_url}, err={e}")
+                return None
+
+        logger.warning(f"[Gemini] Unsupported image URL format: {image_url[:120]}")
+        return None
+
    def call_with_tools(self, messages, tools=None, stream=False, **kwargs):
        """
        Call Gemini API with tool support using REST API (following official docs)
@@ -145,6 +239,15 @@ class GoogleGeminiBot(Bot):
            
            # Build REST API payload
            payload = {"contents": []}
+            inline_image_count = 0
+
+            # Keep legacy behavior: disable Gemini safety blocking like old SDK path.
+            payload["safetySettings"] = [
+                {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"},
+                {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"},
+                {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"},
+                {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"},
+            ]
            
            # Extract and set system instruction
            system_prompt = kwargs.get("system", "")
@@ -174,8 +277,19 @@ class GoogleGeminiBot(Bot):
                parts = []
                
                if isinstance(content, str):
-                    # Simple text content
-                    parts.append({"text": content})
+                    # Text with optional [图片: /path/to/file] markers
+                    cleaned_text, image_paths = self._extract_image_paths_from_text(content)
+                    if cleaned_text:
+                        parts.append({"text": cleaned_text})
+                    image_added = False
+                    for image_path in image_paths:
+                        image_part = self._build_image_inline_part(image_path)
+                        if image_part:
+                            parts.append(image_part)
+                            image_added = True
+                            inline_image_count += 1
+                    if not cleaned_text and not image_added and content:
+                        parts.append({"text": content})
                    
                elif isinstance(content, list):
                    # List of content blocks (Claude format)
@@ -188,8 +302,39 @@ class GoogleGeminiBot(Bot):
                        block_type = block.get("type")
                        
                        if block_type == "text":
-                            # Text block
-                            parts.append({"text": block.get("text", "")})
+                            # Text block with optional image markers
+                            block_text = block.get("text", "")
+                            cleaned_text, image_paths = self._extract_image_paths_from_text(block_text)
+                            if cleaned_text:
+                                parts.append({"text": cleaned_text})
+                            for image_path in image_paths:
+                                image_part = self._build_image_inline_part(image_path)
+                                if image_part:
+                                    parts.append(image_part)
+
+                        elif block_type in ["image", "image_url"]:
+                            # OpenAI format: {"type":"image_url","image_url":{"url":"..."}}
+                            # Claude format: {"type":"image","source":{"type":"base64","media_type":"...","data":"..."}}
+                            image_part = None
+                            if block_type == "image":
+                                source = block.get("source", {})
+                                if isinstance(source, dict) and source.get("type") == "base64" and source.get("data"):
+                                    image_part = {
+                                        "inlineData": {
+                                            "mimeType": source.get("media_type", "image/png"),
+                                            "data": source.get("data")
+                                        }
+                                    }
+                                elif block.get("image_url"):
+                                    image_part = self._build_inline_part_from_image_url(block.get("image_url"))
+                            else:
+                                image_part = self._build_inline_part_from_image_url(block.get("image_url"))
+
+                            if image_part:
+                                parts.append(image_part)
+                                inline_image_count += 1
+                            else:
+                                logger.warning(f"[Gemini] Skip invalid image block: {str(block)[:200]}")
                            
                        elif block_type == "tool_result":
                            # Convert Claude tool_result to Gemini functionResponse
@@ -237,6 +382,9 @@ class GoogleGeminiBot(Bot):
                        "role": gemini_role,
                        "parts": parts
                    })
+
+            if inline_image_count > 0:
+                logger.info(f"[Gemini] Multimodal request includes {inline_image_count} image part(s)")
            
            # Generation config
            gen_config = {}
@@ -363,15 +511,18 @@ class GoogleGeminiBot(Bot):
            candidates = data.get("candidates", [])
            if not candidates:
                logger.warning("[Gemini] No candidates in response")
+                prompt_feedback = data.get("promptFeedback", {})
                return {
                    "error": True,
                    "message": "No candidates in response",
-                    "status_code": 500
+                    "status_code": 500,
+                    "safety_ratings": prompt_feedback.get("safetyRatings", [])
                }
            
            candidate = candidates[0]
            content = candidate.get("content", {})
            parts = content.get("parts", [])
+            safety_ratings = candidate.get("safetyRatings", [])
            
            logger.debug(f"[Gemini] Candidate parts count: {len(parts)}")
            
@@ -419,7 +570,8 @@ class GoogleGeminiBot(Bot):
                    "message": message_dict,
                    "finish_reason": "tool_calls" if tool_calls else "stop"
                }],
-                "usage": data.get("usageMetadata", {})
+                "usage": data.get("usageMetadata", {}),
+                "safety_ratings": safety_ratings
            }
            
        except Exception as e:
--- a/models/moonshot/moonshot_bot.py
+++ b/models/moonshot/moonshot_bot.py
@@ -1,9 +1,9 @@
 # encoding:utf-8

+import json
 import time

-import openai
-import openai.error
+import requests
 from models.bot import Bot
 from models.session_manager import SessionManager
 from bridge.context import ContextType
@@ -11,10 +11,9 @@ from bridge.reply import Reply, ReplyType
 from common.log import logger
 from config import conf, load_config
 from .moonshot_session import MoonshotSession
-import requests


-# ZhipuAI对话模型API
+# Moonshot (Kimi) API Bot
 class MoonshotBot(Bot):
    def __init__(self):
        super().__init__()
@@ -23,17 +22,22 @@ class MoonshotBot(Bot):
        if model == "moonshot":
            model = "moonshot-v1-32k"
        self.args = {
-            "model": model,  # 对话模型的名称
-            "temperature": conf().get("temperature", 0.3),  # 如果设置，值域须为 [0, 1] 我们推荐 0.3，以达到较合适的效果。
-            "top_p": conf().get("top_p", 1.0),  # 使用默认值
+            "model": model,
+            "temperature": conf().get("temperature", 0.3),
+            "top_p": conf().get("top_p", 1.0),
        }
        self.api_key = conf().get("moonshot_api_key")
-        self.base_url = conf().get("moonshot_base_url", "https://api.moonshot.cn/v1/chat/completions")
+        self.base_url = conf().get("moonshot_base_url", "https://api.moonshot.cn/v1")
+        # Ensure base_url does not end with /chat/completions (backward compat)
+        if self.base_url.endswith("/chat/completions"):
+            self.base_url = self.base_url.rsplit("/chat/completions", 1)[0]
+        if self.base_url.endswith("/"):
+            self.base_url = self.base_url.rstrip("/")

    def reply(self, query, context=None):
        # acquire reply content
        if context.type == ContextType.TEXT:
-            logger.info("[MOONSHOT_AI] query={}".format(query))
+            logger.info("[MOONSHOT] query={}".format(query))

            session_id = context["session_id"]
            reply = None
@@ -50,19 +54,16 @@ class MoonshotBot(Bot):
            if reply:
                return reply
            session = self.sessions.session_query(query, session_id)
-            logger.debug("[MOONSHOT_AI] session query={}".format(session.messages))
+            logger.debug("[MOONSHOT] session query={}".format(session.messages))

            model = context.get("moonshot_model")
            new_args = self.args.copy()
            if model:
                new_args["model"] = model
-            # if context.get('stream'):
-            #     # reply in stream
-            #     return self.reply_text_stream(query, new_query, session_id)

            reply_content = self.reply_text(session, args=new_args)
            logger.debug(
-                "[MOONSHOT_AI] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
+                "[MOONSHOT] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
                    session.messages,
                    session_id,
                    reply_content["content"],
@@ -76,17 +77,17 @@ class MoonshotBot(Bot):
                reply = Reply(ReplyType.TEXT, reply_content["content"])
            else:
                reply = Reply(ReplyType.ERROR, reply_content["content"])
-                logger.debug("[MOONSHOT_AI] reply {} used 0 tokens.".format(reply_content))
+                logger.debug("[MOONSHOT] reply {} used 0 tokens.".format(reply_content))
            return reply
        else:
            reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type))
            return reply

-    def reply_text(self, session: MoonshotSession, args=None, retry_count=0) -> dict:
+    def reply_text(self, session: MoonshotSession, args=None, retry_count: int = 0) -> dict:
        """
-        call openai's ChatCompletion to get the answer
+        Call Moonshot chat completion API to get the answer
        :param session: a conversation session
-        :param session_id: session id
+        :param args: model args
        :param retry_count: retry count
        :return: {}
        """
@@ -97,10 +98,8 @@ class MoonshotBot(Bot):
            }
            body = args
            body["messages"] = session.messages
-            # logger.debug("[MOONSHOT_AI] response={}".format(response))
-            # logger.info("[MOONSHOT_AI] reply={}, total_tokens={}".format(response.choices[0]['message']['content'], response["usage"]["total_tokens"]))
            res = requests.post(
-                self.base_url,
+                f"{self.base_url}/chat/completions",
                headers=headers,
                json=body
            )
@@ -114,14 +113,13 @@ class MoonshotBot(Bot):
            else:
                response = res.json()
                error = response.get("error")
-                logger.error(f"[MOONSHOT_AI] chat failed, status_code={res.status_code}, "
+                logger.error(f"[MOONSHOT] chat failed, status_code={res.status_code}, "
                             f"msg={error.get('message')}, type={error.get('type')}")

                result = {"completion_tokens": 0, "content": "提问太快啦，请休息一下再问我吧"}
                need_retry = False
                if res.status_code >= 500:
-                    # server error, need retry
-                    logger.warn(f"[MOONSHOT_AI] do retry, times={retry_count}")
+                    logger.warn(f"[MOONSHOT] do retry, times={retry_count}")
                    need_retry = retry_count < 2
                elif res.status_code == 401:
                    result["content"] = "授权失败，请检查API Key是否正确"
@@ -144,3 +142,380 @@ class MoonshotBot(Bot):
                return self.reply_text(session, args, retry_count + 1)
            else:
                return result
+
+    # ==================== Agent mode support ====================
+
+    def call_with_tools(self, messages, tools=None, stream: bool = False, **kwargs):
+        """
+        Call Moonshot API with tool support for agent integration.
+
+        This method handles:
+        1. Format conversion (Claude format -> OpenAI format)
+        2. System prompt injection
+        3. Streaming SSE response with tool_calls
+        4. Thinking (reasoning) is disabled by default to avoid tool_choice conflicts
+
+        Args:
+            messages: List of messages (may be in Claude format from agent)
+            tools: List of tool definitions (may be in Claude format from agent)
+            stream: Whether to use streaming
+            **kwargs: Additional parameters (max_tokens, temperature, system, model, etc.)
+
+        Returns:
+            Generator yielding OpenAI-format chunks (for streaming)
+        """
+        try:
+            # Convert messages from Claude format to OpenAI format
+            converted_messages = self._convert_messages_to_openai_format(messages)
+
+            # Inject system prompt if provided
+            system_prompt = kwargs.pop("system", None)
+            if system_prompt:
+                if not converted_messages or converted_messages[0].get("role") != "system":
+                    converted_messages.insert(0, {"role": "system", "content": system_prompt})
+                else:
+                    converted_messages[0] = {"role": "system", "content": system_prompt}
+
+            # Convert tools from Claude format to OpenAI format
+            converted_tools = None
+            if tools:
+                converted_tools = self._convert_tools_to_openai_format(tools)
+
+            # Resolve model / temperature
+            model = kwargs.pop("model", None) or self.args["model"]
+            max_tokens = kwargs.pop("max_tokens", None)
+            # Don't pop temperature, just ignore it
+            kwargs.pop("temperature", None)
+
+            # Build request body (omit temperature, let the API use its own default)
+            request_body = {
+                "model": model,
+                "messages": converted_messages,
+                "stream": stream,
+            }
+            if max_tokens is not None:
+                request_body["max_tokens"] = max_tokens
+
+            # Add tools
+            if converted_tools:
+                request_body["tools"] = converted_tools
+                request_body["tool_choice"] = "auto"
+
+            # Explicitly disable thinking to avoid reasoning_content issues in multi-turn tool calls.
+            # kimi-k2.5 may enable thinking by default; without preserving reasoning_content
+            # in conversation history the API will reject subsequent requests.
+            request_body["thinking"] = {"type": "disabled"}
+
+            logger.debug(f"[MOONSHOT] API call: model={model}, "
+                         f"tools={len(converted_tools) if converted_tools else 0}, stream={stream}")
+
+            if stream:
+                return self._handle_stream_response(request_body)
+            else:
+                return self._handle_sync_response(request_body)
+
+        except Exception as e:
+            logger.error(f"[MOONSHOT] call_with_tools error: {e}")
+            import traceback
+            logger.error(traceback.format_exc())
+
+            def error_generator():
+                yield {"error": True, "message": str(e), "status_code": 500}
+            return error_generator()
+
+    # -------------------- streaming --------------------
+
+    def _handle_stream_response(self, request_body: dict):
+        """Handle streaming SSE response from Moonshot API and yield OpenAI-format chunks."""
+        try:
+            headers = {
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {self.api_key}"
+            }
+
+            url = f"{self.base_url}/chat/completions"
+            response = requests.post(url, headers=headers, json=request_body, stream=True, timeout=120)
+
+            if response.status_code != 200:
+                error_msg = response.text
+                logger.error(f"[MOONSHOT] API error: status={response.status_code}, msg={error_msg}")
+                yield {"error": True, "message": error_msg, "status_code": response.status_code}
+                return
+
+            current_tool_calls = {}
+            finish_reason = None
+
+            for line in response.iter_lines():
+                if not line:
+                    continue
+
+                line = line.decode("utf-8")
+                if not line.startswith("data: "):
+                    continue
+
+                data_str = line[6:]  # Remove "data: " prefix
+                if data_str.strip() == "[DONE]":
+                    break
+
+                try:
+                    chunk = json.loads(data_str)
+                except json.JSONDecodeError as e:
+                    logger.warning(f"[MOONSHOT] JSON decode error: {e}, data: {data_str[:200]}")
+                    continue
+
+                # Check for error in chunk
+                if chunk.get("error"):
+                    error_data = chunk["error"]
+                    error_msg = error_data.get("message", "Unknown error") if isinstance(error_data, dict) else str(error_data)
+                    logger.error(f"[MOONSHOT] stream error: {error_msg}")
+                    yield {"error": True, "message": error_msg, "status_code": 500}
+                    return
+
+                if not chunk.get("choices"):
+                    continue
+
+                choice = chunk["choices"][0]
+                delta = choice.get("delta", {})
+
+                # Skip reasoning_content (thinking) – don't log or forward
+                if delta.get("reasoning_content"):
+                    continue
+
+                # Handle text content
+                if "content" in delta and delta["content"]:
+                    yield {
+                        "choices": [{
+                            "index": 0,
+                            "delta": {
+                                "role": "assistant",
+                                "content": delta["content"]
+                            }
+                        }]
+                    }
+
+                # Handle tool_calls (streamed incrementally)
+                if "tool_calls" in delta:
+                    for tool_call_chunk in delta["tool_calls"]:
+                        index = tool_call_chunk.get("index", 0)
+                        if index not in current_tool_calls:
+                            current_tool_calls[index] = {
+                                "id": tool_call_chunk.get("id", ""),
+                                "type": "tool_use",
+                                "name": tool_call_chunk.get("function", {}).get("name", ""),
+                                "input": ""
+                            }
+
+                        # Accumulate arguments
+                        if "function" in tool_call_chunk and "arguments" in tool_call_chunk["function"]:
+                            current_tool_calls[index]["input"] += tool_call_chunk["function"]["arguments"]
+
+                        # Yield OpenAI-format tool call delta
+                        yield {
+                            "choices": [{
+                                "index": 0,
+                                "delta": {
+                                    "tool_calls": [tool_call_chunk]
+                                }
+                            }]
+                        }
+
+                # Capture finish_reason
+                if choice.get("finish_reason"):
+                    finish_reason = choice["finish_reason"]
+
+            # Final chunk with finish_reason
+            yield {
+                "choices": [{
+                    "index": 0,
+                    "delta": {},
+                    "finish_reason": finish_reason
+                }]
+            }
+
+        except requests.exceptions.Timeout:
+            logger.error("[MOONSHOT] Request timeout")
+            yield {"error": True, "message": "Request timeout", "status_code": 500}
+        except Exception as e:
+            logger.error(f"[MOONSHOT] stream response error: {e}")
+            import traceback
+            logger.error(traceback.format_exc())
+            yield {"error": True, "message": str(e), "status_code": 500}
+
+    # -------------------- sync --------------------
+
+    def _handle_sync_response(self, request_body: dict):
+        """Handle synchronous API response and yield a single result dict."""
+        try:
+            headers = {
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {self.api_key}"
+            }
+
+            request_body.pop("stream", None)
+            url = f"{self.base_url}/chat/completions"
+            response = requests.post(url, headers=headers, json=request_body, timeout=120)
+
+            if response.status_code != 200:
+                error_msg = response.text
+                logger.error(f"[MOONSHOT] API error: status={response.status_code}, msg={error_msg}")
+                yield {"error": True, "message": error_msg, "status_code": response.status_code}
+                return
+
+            result = response.json()
+            message = result["choices"][0]["message"]
+            finish_reason = result["choices"][0]["finish_reason"]
+
+            response_data = {"role": "assistant", "content": []}
+
+            # Add text content
+            if message.get("content"):
+                response_data["content"].append({
+                    "type": "text",
+                    "text": message["content"]
+                })
+
+            # Add tool calls
+            if message.get("tool_calls"):
+                for tool_call in message["tool_calls"]:
+                    response_data["content"].append({
+                        "type": "tool_use",
+                        "id": tool_call["id"],
+                        "name": tool_call["function"]["name"],
+                        "input": json.loads(tool_call["function"]["arguments"])
+                    })
+
+            # Map finish_reason
+            if finish_reason == "tool_calls":
+                response_data["stop_reason"] = "tool_use"
+            elif finish_reason == "stop":
+                response_data["stop_reason"] = "end_turn"
+            else:
+                response_data["stop_reason"] = finish_reason
+
+            yield response_data
+
+        except requests.exceptions.Timeout:
+            logger.error("[MOONSHOT] Request timeout")
+            yield {"error": True, "message": "Request timeout", "status_code": 500}
+        except Exception as e:
+            logger.error(f"[MOONSHOT] sync response error: {e}")
+            import traceback
+            logger.error(traceback.format_exc())
+            yield {"error": True, "message": str(e), "status_code": 500}
+
+    # -------------------- format conversion --------------------
+
+    def _convert_messages_to_openai_format(self, messages):
+        """
+        Convert messages from Claude format to OpenAI format.
+
+        Claude format uses content blocks: tool_use / tool_result / text
+        OpenAI format uses tool_calls in assistant, role=tool for results
+        """
+        if not messages:
+            return []
+
+        converted = []
+
+        for msg in messages:
+            role = msg.get("role")
+            content = msg.get("content")
+
+            # Already a simple string – pass through
+            if isinstance(content, str):
+                converted.append(msg)
+                continue
+
+            if not isinstance(content, list):
+                converted.append(msg)
+                continue
+
+            if role == "user":
+                text_parts = []
+                tool_results = []
+
+                for block in content:
+                    if not isinstance(block, dict):
+                        continue
+                    if block.get("type") == "text":
+                        text_parts.append(block.get("text", ""))
+                    elif block.get("type") == "tool_result":
+                        tool_call_id = block.get("tool_use_id") or ""
+                        result_content = block.get("content", "")
+                        if not isinstance(result_content, str):
+                            result_content = json.dumps(result_content, ensure_ascii=False)
+                        tool_results.append({
+                            "role": "tool",
+                            "tool_call_id": tool_call_id,
+                            "content": result_content
+                        })
+
+                # Tool results first (must come right after assistant with tool_calls)
+                for tr in tool_results:
+                    converted.append(tr)
+
+                if text_parts:
+                    converted.append({"role": "user", "content": "\n".join(text_parts)})
+
+            elif role == "assistant":
+                openai_msg = {"role": "assistant"}
+                text_parts = []
+                tool_calls = []
+
+                for block in content:
+                    if not isinstance(block, dict):
+                        continue
+                    if block.get("type") == "text":
+                        text_parts.append(block.get("text", ""))
+                    elif block.get("type") == "tool_use":
+                        tool_calls.append({
+                            "id": block.get("id"),
+                            "type": "function",
+                            "function": {
+                                "name": block.get("name"),
+                                "arguments": json.dumps(block.get("input", {}))
+                            }
+                        })
+
+                if text_parts:
+                    openai_msg["content"] = "\n".join(text_parts)
+                elif not tool_calls:
+                    openai_msg["content"] = ""
+
+                if tool_calls:
+                    openai_msg["tool_calls"] = tool_calls
+                    if not text_parts:
+                        openai_msg["content"] = None
+
+                converted.append(openai_msg)
+            else:
+                converted.append(msg)
+
+        return converted
+
+    def _convert_tools_to_openai_format(self, tools):
+        """
+        Convert tools from Claude format to OpenAI format.
+
+        Claude: {name, description, input_schema}
+        OpenAI: {type: "function", function: {name, description, parameters}}
+        """
+        if not tools:
+            return None
+
+        converted = []
+        for tool in tools:
+            # Already in OpenAI format
+            if "type" in tool and tool["type"] == "function":
+                converted.append(tool)
+            else:
+                converted.append({
+                    "type": "function",
+                    "function": {
+                        "name": tool.get("name"),
+                        "description": tool.get("description"),
+                        "parameters": tool.get("input_schema", {})
+                    }
+                })
+
+        return converted
--- a/models/zhipuai/zhipuai_bot.py
+++ b/models/zhipuai/zhipuai_bot.py
@@ -310,13 +310,9 @@ class ZHIPUAIBot(Bot, ZhipuAIImage):
                if hasattr(delta, 'content') and delta.content:
                    openai_chunk["choices"][0]["delta"]["content"] = delta.content
                
-                # Add reasoning_content if present (GLM-4.7 specific)
+                # Add reasoning_content as separate field if present (GLM-5/GLM-4.7 thinking)
                if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
-                    # Store reasoning in content or metadata
-                    if "content" not in openai_chunk["choices"][0]["delta"]:
-                        openai_chunk["choices"][0]["delta"]["content"] = ""
-                    # Prepend reasoning to content
-                    openai_chunk["choices"][0]["delta"]["content"] = delta.reasoning_content + openai_chunk["choices"][0]["delta"].get("content", "")
+                    openai_chunk["choices"][0]["delta"]["reasoning_content"] = delta.reasoning_content
                
                # Add tool_calls if present
                if hasattr(delta, 'tool_calls') and delta.tool_calls:
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,5 @@
 openai==0.27.8
+aiohttp>=3.8.6,<3.10
 HTMLParser>=0.0.2
 PyQRCode==1.2.1
 qrcode==7.4.2
--- a/run.sh
+++ b/run.sh
@@ -270,24 +270,26 @@ select_model() {
    echo -e "${CYAN}${BOLD}=========================================${NC}"
    echo -e "${CYAN}${BOLD}   Select AI Model${NC}"
    echo -e "${CYAN}${BOLD}=========================================${NC}"
-    echo -e "${YELLOW}1) MiniMax (MiniMax-M2.1, MiniMax-M2.1-lightning, etc.)${NC}"
-    echo -e "${YELLOW}2) Zhipu AI (glm-4.7, glm-4.6, etc.)${NC}"
-    echo -e "${YELLOW}3) Qwen (qwen3-max, qwen-plus, qwq-plus, etc.)${NC}"
-    echo -e "${YELLOW}4) Claude (claude-sonnet-4-5, claude-opus-4-0, etc.)${NC}"
-    echo -e "${YELLOW}5) Gemini (gemini-3-flash-preview, gemini-2.5-pro, etc.)${NC}"
-    echo -e "${YELLOW}6) OpenAI GPT (gpt-5.2, gpt-4.1, etc.)${NC}"
-    echo -e "${YELLOW}7) LinkAI (access multiple models via one API)${NC}"
+    echo -e "${YELLOW}1) MiniMax (MiniMax-M2.5, MiniMax-M2.1, etc.)${NC}"
+    echo -e "${YELLOW}2) Zhipu AI (glm-5, glm-4.7, etc.)${NC}"
+    echo -e "${YELLOW}3) Kimi (kimi-k2.5, kimi-k2, etc.)${NC}"
+    echo -e "${YELLOW}4) Doubao (doubao-seed-2-0-code-preview-260215, etc.)${NC}"
+    echo -e "${YELLOW}5) Qwen (qwen3.5-plus, qwen3-max, qwq-plus, etc.)${NC}"
+    echo -e "${YELLOW}6) Claude (claude-sonnet-4-6, claude-opus-4-6, etc.)${NC}"
+    echo -e "${YELLOW}7) Gemini (gemini-3.1-pro-preview, gemini-3-flash-preview, etc.)${NC}"
+    echo -e "${YELLOW}8) OpenAI GPT (gpt-5.2, gpt-4.1, etc.)${NC}"
+    echo -e "${YELLOW}9) LinkAI (access multiple models via one API)${NC}"
    echo ""
    
    while true; do
        read -p "Enter your choice [press Enter for default: 1 - MiniMax]: " model_choice
        model_choice=${model_choice:-1}
        case "$model_choice" in
-            1|2|3|4|5|6|7)
+            1|2|3|4|5|6|7|8|9)
                break
                ;;
            *)
-                echo -e "${RED}Invalid choice. Please enter 1-7.${NC}"
+                echo -e "${RED}Invalid choice. Please enter 1-9.${NC}"
                ;;
        esac
    done
@@ -300,8 +302,8 @@ configure_model() {
            # MiniMax
            echo -e "${GREEN}Configuring MiniMax...${NC}"
            read -p "Enter MiniMax API Key: " minimax_key
-            read -p "Enter model name [press Enter for default: MiniMax-M2.1]: " model_name
-            model_name=${model_name:-MiniMax-M2.1}
+            read -p "Enter model name [press Enter for default: MiniMax-M2.5]: " model_name
+            model_name=${model_name:-MiniMax-M2.5}
            
            MODEL_NAME="$model_name"
            MINIMAX_KEY="$minimax_key"
@@ -310,28 +312,48 @@ configure_model() {
            # Zhipu AI
            echo -e "${GREEN}Configuring Zhipu AI...${NC}"
            read -p "Enter Zhipu AI API Key: " zhipu_key
-            read -p "Enter model name [press Enter for default: glm-4.7]: " model_name
-            model_name=${model_name:-glm-4.7}
+            read -p "Enter model name [press Enter for default: glm-5]: " model_name
+            model_name=${model_name:-glm-5}
            
            MODEL_NAME="$model_name"
            ZHIPU_KEY="$zhipu_key"
            ;;
        3)
+            # Kimi (Moonshot)
+            echo -e "${GREEN}Configuring Kimi (Moonshot)...${NC}"
+            read -p "Enter Moonshot API Key: " moonshot_key
+            read -p "Enter model name [press Enter for default: kimi-k2.5]: " model_name
+            model_name=${model_name:-kimi-k2.5}
+            
+            MODEL_NAME="$model_name"
+            MOONSHOT_KEY="$moonshot_key"
+            ;;
+        4)
+            # Doubao (Volcengine Ark)
+            echo -e "${GREEN}Configuring Doubao (Volcengine Ark)...${NC}"
+            read -p "Enter Ark API Key: " ark_key
+            read -p "Enter model name [press Enter for default: doubao-seed-2-0-code-preview-260215]: " model_name
+            model_name=${model_name:-doubao-seed-2-0-code-preview-260215}
+            
+            MODEL_NAME="$model_name"
+            ARK_KEY="$ark_key"
+            ;;
+        5)
            # Qwen (DashScope)
            echo -e "${GREEN}Configuring Qwen (DashScope)...${NC}"
            read -p "Enter DashScope API Key: " dashscope_key
-            read -p "Enter model name [press Enter for default: qwen3-max]: " model_name
-            model_name=${model_name:-qwen3-max}
+            read -p "Enter model name [press Enter for default: qwen3.5-plus]: " model_name
+            model_name=${model_name:-qwen3.5-plus}
            
            MODEL_NAME="$model_name"
            DASHSCOPE_KEY="$dashscope_key"
            ;;
-        4)
+        6)
            # Claude
            echo -e "${GREEN}Configuring Claude...${NC}"
            read -p "Enter Claude API Key: " claude_key
-            read -p "Enter model name [press Enter for default: claude-sonnet-4-5]: " model_name
-            model_name=${model_name:-claude-sonnet-4-5}
+            read -p "Enter model name [press Enter for default: claude-sonnet-4-6]: " model_name
+            model_name=${model_name:-claude-sonnet-4-6}
            read -p "Enter API Base URL [press Enter for default: https://api.anthropic.com/v1]: " api_base
            api_base=${api_base:-https://api.anthropic.com/v1}
            
@@ -339,12 +361,12 @@ configure_model() {
            CLAUDE_KEY="$claude_key"
            CLAUDE_BASE="$api_base"
            ;;
-        5)
+        7)
            # Gemini
            echo -e "${GREEN}Configuring Gemini...${NC}"
            read -p "Enter Gemini API Key: " gemini_key
-            read -p "Enter model name [press Enter for default: gemini-3-flash-preview]: " model_name
-            model_name=${model_name:-gemini-3-flash-preview}
+            read -p "Enter model name [press Enter for default: gemini-3.1-pro-preview]: " model_name
+            model_name=${model_name:-gemini-3.1-pro-preview}
            read -p "Enter API Base URL [press Enter for default: https://generativelanguage.googleapis.com]: " api_base
            api_base=${api_base:-https://generativelanguage.googleapis.com}
            
@@ -352,7 +374,7 @@ configure_model() {
            GEMINI_KEY="$gemini_key"
            GEMINI_BASE="$api_base"
            ;;
-        6)
+        8)
            # OpenAI
            echo -e "${GREEN}Configuring OpenAI GPT...${NC}"
            read -p "Enter OpenAI API Key: " openai_key
@@ -365,12 +387,12 @@ configure_model() {
            OPENAI_KEY="$openai_key"
            OPENAI_BASE="$api_base"
            ;;
-        7)
+        9)
            # LinkAI
            echo -e "${GREEN}Configuring LinkAI...${NC}"
            read -p "Enter LinkAI API Key: " linkai_key
-            read -p "Enter model name [press Enter for default: MiniMax-M2.1]: " model_name
-            model_name=${model_name:-MiniMax-M2.1}
+            read -p "Enter model name [press Enter for default: MiniMax-M2.5]: " model_name
+            model_name=${model_name:-MiniMax-M2.5}
            
            MODEL_NAME="$model_name"
            USE_LINKAI="true"
@@ -483,6 +505,8 @@ create_config_file() {
  "gemini_api_key": "${GEMINI_KEY:-}",
  "gemini_api_base": "${GEMINI_BASE:-https://generativelanguage.googleapis.com}",
  "zhipu_ai_api_key": "${ZHIPU_KEY:-}",
+  "moonshot_api_key": "${MOONSHOT_KEY:-}",
+  "ark_api_key": "${ARK_KEY:-}",
  "dashscope_api_key": "${DASHSCOPE_KEY:-}",
  "minimax_api_key": "${MINIMAX_KEY:-}",
  "voice_to_text": "openai",
@@ -518,6 +542,8 @@ EOF
  "gemini_api_key": "${GEMINI_KEY:-}",
  "gemini_api_base": "${GEMINI_BASE:-https://generativelanguage.googleapis.com}",
  "zhipu_ai_api_key": "${ZHIPU_KEY:-}",
+  "moonshot_api_key": "${MOONSHOT_KEY:-}",
+  "ark_api_key": "${ARK_KEY:-}",
  "dashscope_api_key": "${DASHSCOPE_KEY:-}",
  "minimax_api_key": "${MINIMAX_KEY:-}",
  "voice_to_text": "openai",
@@ -552,6 +578,8 @@ EOF
  "gemini_api_key": "${GEMINI_KEY:-}",
  "gemini_api_base": "${GEMINI_BASE:-https://generativelanguage.googleapis.com}",
  "zhipu_ai_api_key": "${ZHIPU_KEY:-}",
+  "moonshot_api_key": "${MOONSHOT_KEY:-}",
+  "ark_api_key": "${ARK_KEY:-}",
  "dashscope_api_key": "${DASHSCOPE_KEY:-}",
  "minimax_api_key": "${MINIMAX_KEY:-}",
  "voice_to_text": "openai",
@@ -592,6 +620,8 @@ EOF
  "gemini_api_key": "${GEMINI_KEY:-}",
  "gemini_api_base": "${GEMINI_BASE:-https://generativelanguage.googleapis.com}",
  "zhipu_ai_api_key": "${ZHIPU_KEY:-}",
+  "moonshot_api_key": "${MOONSHOT_KEY:-}",
+  "ark_api_key": "${ARK_KEY:-}",
  "dashscope_api_key": "${DASHSCOPE_KEY:-}",
  "minimax_api_key": "${MINIMAX_KEY:-}",
  "voice_to_text": "openai",
Author	SHA1	Message	Date
zhayujie	7cce224499	feat: support multi-channel	2026-02-26 18:34:08 +08:00
zhayujie	925d728a86	fix: replace upsert syntax to support SQLite lower version	2026-02-26 10:44:04 +08:00
zhayujie	9917552b4b	fix: improve web UI stability and conversation history restore - Fix dark mode FOUC: apply theme in <head> before first paint, defer transition-colors to post-init to avoid animated flash on load - Fix Safari IME Enter bug: defer compositionend reset via setTimeout(0) - Fix history scroll: use requestAnimationFrame before scrollChatToBottom - Limit restore turns to min(6, max_turns//3) on restart - Fix load_messages cutoff to start at turn boundary, preventing orphaned tool_use/tool_result pairs from being sent to the LLM - Merge all assistant messages within one user turn into a single bubble; render tool_calls in history using same CSS as live SSE view - Handle empty choices list in stream chunks	2026-02-26 10:35:20 +08:00
zhayujie	29bfbecdc9	feat: persistent storage of conversation history	2026-02-25 18:01:39 +08:00
zhayujie	1a7a8c98d9	docs: add scam warning disclaimer	2026-02-25 01:34:16 +08:00
zhayujie	cddb38ac3d	Merge pull request #2673 from zhayujie/feat-web-console feat: web console	2026-02-24 00:06:29 +08:00
zhayujie	394853c0fb	feat: web console module display	2026-02-24 00:04:17 +08:00
zhayujie	c0702c8b36	feat: web channel stream chat	2026-02-23 22:19:50 +08:00
zhayujie	d610608391	feat: add cloud host config	2026-02-23 15:06:31 +08:00
zhayujie	9082eec91d	feat: dark mode is used by default	2026-02-23 14:57:02 +08:00
zhayujie	f1a1413b5f	feat: web console upgrade	2026-02-21 17:56:31 +08:00
zhayujie	c1e7f9af9b	Merge pull request #2672 from zhayujie/feat-config-update feat: cloud config update	2026-02-21 11:34:05 +08:00
zhayujie	1c71c4e38b	feat: agent chat service	2026-02-21 00:39:36 +08:00
zhayujie	5e3eccb3f6	feat: support memory service	2026-02-20 23:44:05 +08:00
zhayujie	e1dc037eb9	feat: cloud skills manage	2026-02-20 23:23:04 +08:00
zhayujie	97e9b4c801	Merge branch 'master' into feat-config-update	2026-02-20 18:58:21 +08:00
zhayujie	52d7cad735	feat: support gemini-3.1-pro-preview and claude-4.6-sonnet	2026-02-20 12:14:59 +08:00
zhayujie	c0b1d270ba	Merge branch 'master' of github.com:zhayujie/chatgpt-on-wechat	2026-02-19 14:18:39 +08:00
zhayujie	e59a2892e4	feat: support qwen3.5-plus	2026-02-19 14:18:16 +08:00
zhayujie	5fa0376a49	Merge pull request #2670 from SgtPepper114/fix/gemini-dingtalk-image-inline fix(gemini): 修复钉钉图片标记未转多模态导致的识图失效	2026-02-19 13:57:04 +08:00
SgtPepper114	05a33042c8	fix(gemini): support dingtalk image markers as multimodal input - parse [图片: path] markers in text and convert to Gemini inlineData parts - unify reply path via call_with_tools to reuse multimodal conversion - keep legacy safety behavior (BLOCK_NONE) and restore safety ratings logging on empty response - add multimodal request image-part count log for debugging	2026-02-16 13:26:57 +00:00
zhayujie	ce58f23cbc	feat: dashscope model name	2026-02-16 20:11:38 +08:00
zhayujie	b6fc9fa370	fix: run script dependency issues	2026-02-15 00:02:50 +08:00
zhayujie	00ae38faae	docs: update models in README	2026-02-14 17:36:36 +08:00
zhayujie	ab28ee58ab	feat: add doubao-2.0-code model and update README	2026-02-14 16:49:44 +08:00
zhayujie	48db538a2e	feat: support Minimax-M2.5, glm-5, kimi-k2.5	2026-02-14 15:27:44 +08:00
zhayujie	46945942e1	feat: support channel start in sub thread	2026-02-13 12:38:52 +08:00