fix: 修复Python模块导入错误并优化Chat功能

- 修复 core/agents/api 模块导入问题
- 优化 ChatInput 组件交互体验
- 增强 agent_handler 和 agent_service 功能
- 调整 Chat 页面样式和布局

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

core/agents/agent/context.py

@@ -36,6 +36,22 @@ Your workspace is at: {workspace_path}
 - Be helpful and concise
 - Think step by step when needed
 - Ask for clarification when the request is ambiguous
+
+## Tool Usage Guidelines
+**IMPORTANT**: Only use tools when explicitly requested by the user:
+
+**Use tools for**:
+- Searching the web for current information
+- Executing code or commands
+- Reading or writing files
+- Performing calculations
+
+**DO NOT use tools for**:
+- Simple questions and greetings (e.g., "介绍一下武汉", "你好", "什么是AI")
+- General knowledge that you already know
+- Conversational responses
+
+For simple informational questions, respond directly from your knowledge without calling any tools.
 """
 
     def build_messages(

core/agents/agent/intent_router.py

@@ -0,0 +1,278 @@
+"""Intent recognition system for routing user requests."""
+
+import json
+import logging
+from enum import Enum
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+
+class IntentType(Enum):
+    """Types of user intents."""
+    SIMPLE = "simple"  # Simple Q&A, no tools needed
+    TOOL = "tool"  # Needs tools (search, code, files, etc.)
+    SKILL = "skill"  # Needs specific domain skill
+    TEAM = "team"  # Needs multi-agent collaboration
+    UNKNOWN = "unknown"  # Cannot determine
+
+
+# Intent recognition prompt template
+INTENT_PROMPT = """Analyze the user's message and classify their intent.
+
+Intent Types:
+- simple: General knowledge questions, greetings, casual conversation, simple Q&A
+  Examples: "你好", "介绍一下武汉", "什么是AI", "今天天气怎么样"
+- tool: Requires external tools - web search, code execution, file operations, calculations
+  Examples: "搜索最新的AI新闻", "帮我运行这段代码", "读取文件内容", "计算这个表达式"
+- skill: Requires specific domain skill (coding, design, analysis, etc.)
+  Examples: "用Python写一个排序算法", "分析这段代码的性能", "创建一个网页"
+- team: Requires multiple agents working together
+  Examples: "让设计agent和开发agent一起完成这个任务", "创建一个团队来完成这个项目"
+
+Guidelines:
+- For greetings and simple questions, prefer "simple"
+- Only use "tool" when user explicitly asks for search, execution, or file operations
+- "introduce Wuhan" in Chinese is general knowledge - prefer "simple" unless user specifically asks for latest/current information
+- If ambiguous, prefer "simple" to avoid unnecessary tool calls
+
+User message: {message}
+
+Respond with only the intent type (simple/tool/skill/team), no explanation:"""
+
+
+class IntentRecognizer:
+    """Recognizes user intent to route requests appropriately."""
+
+    def __init__(self, llm_provider=None):
+        """Initialize intent recognizer.
+
+        Args:
+            llm_provider: LLM provider for intent recognition
+        """
+        self._llm_provider = llm_provider
+        self._cache = {}  # Simple cache for recent intents
+
+    def recognize(
+        self,
+        message: str,
+        available_tools: list[str] | None = None,
+        available_skills: list[str] | None = None,
+    ) -> IntentType:
+        """Recognize user intent.
+
+        Args:
+            message: User message
+            available_tools: List of available tool names
+            available_skills: List of available skill names
+
+        Returns:
+            Recognized intent type
+        """
+        # Simple heuristics for common cases (fast path)
+        intent = self._heuristic_recognition(message)
+        if intent != IntentType.UNKNOWN:
+            logger.info(f"Intent recognized (heuristic): {intent.value} for message: {message[:50]}...")
+            return intent
+
+        # Use LLM for complex cases
+        if self._llm_provider:
+            return self._llm_recognition(message)
+
+        # Default to simple if no LLM
+        return IntentType.SIMPLE
+
+    def _heuristic_recognition(self, message: str) -> IntentType:
+        """Fast heuristic-based intent recognition.
+
+        Args:
+            message: User message
+
+        Returns:
+            Recognized intent or UNKNOWN
+        """
+        if not message:
+            return IntentType.UNKNOWN
+
+        message_lower = message.lower().strip()
+
+        # Greetings
+        greetings = ["你好", "hello", "hi", "嗨", "您好", "hey"]
+        if any(g in message_lower for g in greetings) and len(message_lower) < 20:
+            return IntentType.SIMPLE
+
+        # Simple questions patterns
+        simple_patterns = [
+            "什么是", "什么叫", "什么是",
+            "介绍一下", "请介绍",
+            "解释一下", "解释",
+            "怎么样", "好不好",
+            "是什么意思",
+            "who are", "what is", "what's",
+            "tell me about",
+        ]
+
+        # Check for simple patterns that don't require tools
+        for pattern in simple_patterns:
+            if pattern in message_lower:
+                # But exclude if explicitly asking for current/latest/real-time
+                if any(kw in message_lower for kw in ["最新", "现在", "current", "latest", "实时"]):
+                    return IntentType.UNKNOWN  # Might need web search
+                return IntentType.SIMPLE
+
+        # Explicit tool request patterns
+        tool_patterns = [
+            "搜索", "查找", "search",
+            "执行", "运行", "run",
+            "计算", "calculate",
+            "帮我写代码", "write code",
+            "读取", "读取", "read file",
+            "创建文件", "write file",
+        ]
+
+        for pattern in tool_patterns:
+            if pattern in message_lower:
+                return IntentType.TOOL
+
+        # Skill patterns
+        skill_patterns = [
+            "用python", "用java", "用js",
+            "写一个算法", "实现",
+            "创建一个", "开发",
+            "分析", "优化",
+        ]
+
+        for pattern in skill_patterns:
+            if pattern in message_lower:
+                return IntentType.SKILL
+
+        # Team patterns
+        team_patterns = [
+            "团队", "协作", "多个agent",
+            "team", "collaborate", "一起",
+        ]
+
+        for pattern in team_patterns:
+            if pattern in message_lower:
+                return IntentType.TEAM
+
+        return IntentType.UNKNOWN
+
+    def _llm_recognition(self, message: str) -> IntentType:
+        """LLM-based intent recognition.
+
+        Args:
+            message: User message
+
+        Returns:
+            Recognized intent type
+        """
+        try:
+            prompt = INTENT_PROMPT.format(message=message)
+
+            # Use the LLM to classify intent
+            response = self._llm_provider.chat(
+                messages=[{"role": "user", "content": prompt}],
+                max_tokens=50,
+            )
+
+            content = response.content.strip().lower()
+
+            # Parse the response
+            if "simple" in content:
+                return IntentType.SIMPLE
+            elif "tool" in content:
+                return IntentType.TOOL
+            elif "skill" in content:
+                return IntentType.SKILL
+            elif "team" in content:
+                return IntentType.TEAM
+            else:
+                logger.warning(f"Unexpected intent response: {content}")
+                return IntentType.SIMPLE  # Default to simple
+
+        except Exception as e:
+            logger.error(f"LLM intent recognition failed: {e}")
+            return IntentType.SIMPLE  # Default to simple on error
+
+
+class IntentRouter:
+    """Routes requests based on recognized intent."""
+
+    def __init__(
+        self,
+        intent_recognizer: IntentRecognizer | None = None,
+        use_llm_recognition: bool = True,
+    ):
+        """Initialize intent router.
+
+        Args:
+            intent_recognizer: Intent recognizer instance
+            use_llm_recognition: Whether to use LLM for complex cases
+        """
+        self._recognizer = intent_recognizer
+        self._use_llm = use_llm_recognition
+
+    def route(
+        self,
+        message: str,
+        available_tools: list[str] | None = None,
+        available_skills: list[str] | None = None,
+    ) -> dict[str, Any]:
+        """Route the user message based on intent.
+
+        Args:
+            message: User message
+            available_tools: List of available tool names
+            available_skills: List of available skill names
+
+        Returns:
+            Routing decision with intent type and suggested action
+        """
+        # Recognize intent
+        intent = self._recognizer.recognize(
+            message,
+            available_tools,
+            available_skills,
+        )
+
+        # Build routing decision
+        decision = {
+            "intent": intent.value,
+            "action": self._get_action(intent),
+            "message": message,
+        }
+
+        logger.info(f"Routed message to {intent.value}: {message[:50]}...")
+
+        return decision
+
+    def _get_action(self, intent: IntentType) -> str:
+        """Get the action to take based on intent.
+
+        Args:
+            intent: Recognized intent type
+
+        Returns:
+            Action name
+        """
+        return {
+            IntentType.SIMPLE: "direct_response",
+            IntentType.TOOL: "execute_tools",
+            IntentType.SKILL: "execute_skill",
+            IntentType.TEAM: "team_collaboration",
+            IntentType.UNKNOWN: "direct_response",  # Default to direct response
+        }.get(intent, "direct_response")
+
+
+def create_intent_router(llm_provider=None) -> IntentRouter:
+    """Create an intent router with default settings.
+
+    Args:
+        llm_provider: LLM provider for intent recognition
+
+    Returns:
+        Configured IntentRouter instance
+    """
+    recognizer = IntentRecognizer(llm_provider=llm_provider)
+    return IntentRouter(intent_recognizer=recognizer)

core/agents/agent/loop.py

@@ -10,6 +10,7 @@ from typing import Any, Callable, Awaitable, AsyncGenerator
 
 from agents.agent.context import ContextBuilder
 from agents.agent.memory import AgentMemory
+from agents.agent.intent_router import IntentRouter, create_intent_router, IntentType
 from agents.llm import LLMProvider, LLMResponse, ProviderFactory
 from agents.tools import ToolRegistry
 
@@ -28,6 +29,7 @@ class AgentLoop:
         workspace: Path | None = None,
         max_iterations: int = 10,
         tools: ToolRegistry | None = None,
+        enable_intent_routing: bool = True,
     ):
         """Initialize the agent loop.
 
@@ -37,16 +39,24 @@ class AgentLoop:
             workspace: Workspace directory for memory and configs
             max_iterations: Maximum tool call iterations
             tools: Tool registry (creates default if None)
+            enable_intent_routing: Enable intent recognition and routing
         """
         self.provider = provider
         self.model = model
         self.workspace = workspace or Path.cwd()
         self.max_iterations = max_iterations
         self.tools = tools
+        self.enable_intent_routing = enable_intent_routing
 
         self.context = ContextBuilder(self.workspace)
         self.memory = AgentMemory(self.workspace)
 
+        # Initialize intent router
+        if enable_intent_routing:
+            self.intent_router = create_intent_router(llm_provider=provider)
+        else:
+            self.intent_router = None
+
     async def chat(
         self,
         message: str,
@@ -79,10 +89,43 @@ class AgentLoop:
         """
         history = history or []
 
+        # Intent recognition and routing
+        intent_decision = None
+        if self.intent_router and not history:  # Only for first message in conversation
+            try:
+                tool_names = self.tools.tool_names if self.tools else []
+                intent_decision = self.intent_router.route(
+                    message=message,
+                    available_tools=tool_names,
+                )
+                logger.info(f"Intent recognized: {intent_decision['intent']} -> {intent_decision['action']}")
+
+                # For simple intent, respond directly without tool loop
+                if intent_decision["intent"] == IntentType.SIMPLE.value:
+                    # Build messages for direct response
+                    messages = self.context.build_messages(
+                        history=history,
+                        current_message=message,
+                    )
+                    # Call LLM without tools
+                    response = await self.provider.chat_with_retry(
+                        messages=messages,
+                        tools=None,  # No tools for simple requests
+                        model=self.model,
+                    )
+                    content = self._strip_think(response.content) or "好的，让我来回答这个问题。"
+                    # Save to history
+                    self._save_history(session_key, messages, len(history))
+                    return content
+            except Exception as e:
+                logger.warning(f"Intent routing failed: {e}, continuing with normal flow")
+
         # Load history from session if session_key is provided
         if session_key and session_key != "default":
             loaded_history = self.memory.get_history(session_key, max_messages=20)
             if loaded_history:
+                # Merge any split assistant messages
+                loaded_history = self._merge_history_messages(loaded_history)
                 logger.info(f"Loaded {len(loaded_history)} messages from session history")
                 # Merge loaded history with provided history (loaded takes precedence if empty)
                 if not history:
@@ -155,10 +198,43 @@ class AgentLoop:
         """
         history = history or []
 
+        # Intent recognition and routing
+        intent_decision = None
+        if self.intent_router and not history:  # Only for first message in conversation
+            try:
+                tool_names = self.tools.tool_names if self.tools else []
+                intent_decision = self.intent_router.route(
+                    message=message,
+                    available_tools=tool_names,
+                )
+                logger.info(f"Intent recognized: {intent_decision['intent']} -> {intent_decision['action']}")
+
+                # For simple intent, respond directly without tool loop
+                if intent_decision["intent"] == IntentType.SIMPLE.value:
+                    # Build messages for direct response
+                    messages = self.context.build_messages(
+                        history=history,
+                        current_message=message,
+                    )
+                    # Call LLM without tools
+                    response = await self.provider.chat_with_retry(
+                        messages=messages,
+                        tools=None,  # No tools for simple requests
+                        model=self.model,
+                    )
+                    content = self._strip_think(response.content) or "好的，让我来回答这个问题。"
+                    # Save to history
+                    self._save_history(session_key, messages, len(history))
+                    return content
+            except Exception as e:
+                logger.warning(f"Intent routing failed: {e}, continuing with normal flow")
+
         # Load history from session if session_key is provided
         if session_key and session_key != "default":
             loaded_history = self.memory.get_history(session_key, max_messages=20)
             if loaded_history:
+                # Merge any split assistant messages
+                loaded_history = self._merge_history_messages(loaded_history)
                 logger.info(f"Loaded {len(loaded_history)} messages from session history")
                 # Merge loaded history with provided history (loaded takes precedence if empty)
                 if not history:
@@ -334,6 +410,28 @@ class AgentLoop:
 
         tool_defs = self.tools.get_definitions() if self.tools else []
 
+        # Intent recognition - determine if tools are needed before first LLM call
+        user_message = ""
+        for msg in messages:
+            if msg.get("role") == "user":
+                user_message = msg.get("content", "")
+                break
+
+        # Apply intent recognition on first iteration
+        if self.enable_intent_routing and self.intent_router and user_message:
+            available_tools = [t.get("function", {}).get("name", "") for t in tool_defs] if tool_defs else []
+            routing_decision = self.intent_router.route(
+                user_message,
+                available_tools=available_tools,
+            )
+            intent = routing_decision.get("intent", "simple")
+            logger.info(f"Intent recognized: {intent} for message: {user_message[:50]}...")
+
+            # If simple intent, don't pass tools to reduce unnecessary tool calls
+            if intent == "simple":
+                tool_defs = []
+                logger.info("Simple intent detected - disabling tool definitions for this request")
+
         while iteration < self.max_iterations:
             iteration += 1
 
@@ -423,6 +521,28 @@ class AgentLoop:
         model = model or self.model
         tool_defs = self.tools.get_definitions() if self.tools else []
 
+        # Intent recognition - determine if tools are needed before first LLM call
+        user_message = ""
+        for msg in initial_messages:
+            if msg.get("role") == "user":
+                user_message = msg.get("content", "")
+                break
+
+        # Apply intent recognition
+        if self.enable_intent_routing and self.intent_router and user_message:
+            available_tools = [t.get("function", {}).get("name", "") for t in tool_defs] if tool_defs else []
+            routing_decision = self.intent_router.route(
+                user_message,
+                available_tools=available_tools,
+            )
+            intent = routing_decision.get("intent", "simple")
+            logger.info(f"[stream] Intent recognized: {intent} for message: {user_message[:50]}...")
+
+            # If simple intent, don't pass tools to reduce unnecessary tool calls
+            if intent == "simple":
+                tool_defs = []
+                logger.info("[stream] Simple intent detected - disabling tool definitions")
+
         # First call to check for tool calls
         response = await provider.chat_with_retry(
             messages=initial_messages,
@@ -490,6 +610,55 @@ class AgentLoop:
             return f'{tc.name}("{val[:40]}...")' if len(val) > 40 else f'{tc.name}("{val}")'
         return ", ".join(_fmt(tc) for tc in tool_calls)
 
+    @staticmethod
+    def _merge_history_messages(messages: list[dict]) -> list[dict]:
+        """Merge adjacent assistant messages that have content and tool_calls separately.
+
+        When saving/loading history, assistant messages with both content and tool_calls
+        might be split into multiple entries. This method merges them back together.
+
+        Args:
+            messages: List of message dictionaries
+
+        Returns:
+            Merged list of messages
+        """
+        if not messages:
+            return messages
+
+        merged = []
+        i = 0
+        while i < len(messages):
+            current = messages[i].copy()
+
+            # If current is an assistant message with tool_calls, check if next is
+            # an assistant message with content (or vice versa)
+            if current.get("role") == "assistant" and current.get("tool_calls"):
+                # Look ahead for another assistant message to merge with
+                j = i + 1
+                while j < len(messages):
+                    next_msg = messages[j]
+                    if next_msg.get("role") == "assistant":
+                        # Merge content
+                        if next_msg.get("content") and not current.get("content"):
+                            current["content"] = next_msg.get("content")
+                        # Merge tool_calls (should already be in current)
+                        if next_msg.get("tool_calls") and not current.get("tool_calls"):
+                            current["tool_calls"] = next_msg.get("tool_calls")
+                        j += 1
+                    else:
+                        break
+
+                # If we merged multiple messages, skip them
+                if j > i + 1:
+                    logger.debug(f"Merged {j - i} assistant messages")
+                i = j
+            else:
+                merged.append(current)
+                i += 1
+
+        return merged
+
     def _save_history(
         self,
         session_key: str,
@@ -510,13 +679,18 @@ class AgentLoop:
             if role == "user" and content:
                 self.memory.add_to_history("user", str(content)[:1000], session_key)
             elif role == "assistant":
-                # Save assistant message content
+                # Build a combined message with content and tool_calls
+                msg_data = {}
                 if content:
-                    self.memory.add_to_history("assistant", str(content)[:1000], session_key)
-                # Save tool_calls if present (needed for multi-turn tool calls)
+                    msg_data["content"] = str(content)[:1000]
                 if m.get("tool_calls"):
-                    tool_calls_str = json.dumps(m.get("tool_calls", []))
-                    self.memory.add_to_history("assistant", f"[tool_calls]{tool_calls_str}", session_key)
+                    msg_data["tool_calls"] = m.get("tool_calls", [])
+
+                # Save as a single JSON message with all data
+                if msg_data:
+                    msg_str = json.dumps(msg_data)
+                    self.memory.add_to_history("assistant", msg_str, session_key)
+
             # Save tool results (needed for multi-turn conversations)
             elif role == "tool":
                 tool_call_id = m.get("tool_call_id", "")

core/agents/agent/memory.py

@@ -537,7 +537,7 @@ class AgentMemory:
             except:
                 pass
 
-        # Check if content contains tool_calls or tool_result markers
+        # Check if content contains tool_calls or tool_result markers, or is JSON
         # Format as Markdown (产品经理指定格式)
         entry_lines = [
             f"## 消息 {msg_count}",
@@ -553,6 +553,19 @@ class AgentMemory:
             entry_lines.append(f"工具结果: {content[len('[tool_result]'):]}")
             entry_lines.append(f"内容: ")
         else:
+            # Check if it's a JSON object (new format with content + tool_calls)
+            try:
+                data = json.loads(content)
+                if isinstance(data, dict):
+                    # New JSON format: might have content and/or tool_calls
+                    if "content" in data:
+                        entry_lines.append(f"内容: {data['content']}")
+                    if "tool_calls" in data:
+                        entry_lines.append(f"工具调用: {json.dumps(data['tool_calls'])}")
+                else:
+                    entry_lines.append(f"内容: {content}")
+            except (json.JSONDecodeError, TypeError):
+                # Not JSON, treat as regular content
                 entry_lines.append(f"内容: {content}")
 
         entry = "\n".join(entry_lines) + "\n\n"
@@ -631,6 +644,9 @@ class AgentMemory:
                 if line.startswith("工具调用:") and current_message is not None:
                     tool_calls_json = line.split(":", 1)[1].strip()
                     try:
+                        # Set role if not already set
+                        if not current_message.get("role"):
+                            current_message["role"] = "assistant"
                         current_message["tool_calls"] = json.loads(tool_calls_json)
                     except json.JSONDecodeError:
                         pass
@@ -641,6 +657,7 @@ class AgentMemory:
                     tool_result_json = line.split(":", 1)[1].strip()
                     try:
                         tool_result = json.loads(tool_result_json)
+                        current_message["role"] = "tool"  # Set role to tool
                         current_message["tool_call_id"] = tool_result.get("tool_call_id", "")
                         current_message["name"] = tool_result.get("name", "")
                         current_message["content"] = tool_result.get("content", "")

core/agents/api/__init__.py

@@ -1,5 +1,5 @@
 """X-Agents API Module."""
 
-from agents.api.routes import router
+from .routes import router
 
 __all__ = ["router"]

core/agents/api/server.py

@@ -0,0 +1,26 @@
+"""X-Agents API Server."""
+
+import sys
+sys.path.insert(0, 'D:/Code/Project/X-Agents/core')
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from .routes import router
+
+app = FastAPI(title="X-Agents API")
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Include the router
+app.include_router(router)
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8001)

core/agents/tools/builtin.py

@@ -275,7 +275,7 @@ class WebSearchTool(Tool):
 
     @property
     def description(self) -> str:
-        return "Search the web for information using a search engine."
+        return "Search the web for current information, real-time data, or information that is not in your training data. **Only use this when the user explicitly asks for** latest news, current events, real-time information, or specifically requests a web search. **DO NOT use for simple questions** like '介绍一下武汉', '什么是AI' - answer from your knowledge instead."
 
     @property
     def parameters(self) -> dict[str, Any]:

server/cmd/api/main.go

@@ -387,7 +387,14 @@ func main() {
 		log.Println("Default tools initialized")
 	}
 
-	// 4.3 初始化 skills（已禁用自动加载，如需启用请调用 /skill/sync 接口）
+	// 4.3 初始化团队成员智能体
+	if err := agentService.InitTeamMembers(); err != nil {
+		log.Printf("Warning: Failed to init team members: %v", err)
+	} else {
+		log.Println("Team members initialized")
+	}
+
+	// 4.4 初始化 skills（已禁用自动加载，如需启用请调用 /skill/sync 接口）
 	// if err := skillService.InitSkills(); err != nil {
 	// 	log.Printf("Warning: Failed to init skills: %v", err)
 	// } else {
@@ -405,7 +412,7 @@ func main() {
 	toolHandler := handler.NewToolHandler(toolService)
 	mcpHandler := handler.NewMCPHandler(mcpService)
 	skillHandler := handler.NewSkillHandler(skillService)
-	agentHandler := handler.NewAgentHandler(agentService)
+	agentHandler := handler.NewAgentHandler(agentService, agentRepo)
 	memoryHandler := handler.NewMemoryHandler(memoryService)
 	sessionHandler := handler.NewSessionHandler(chatRepo, agentService)
 
@@ -590,6 +597,7 @@ func main() {
 	{
 		agentGroup.GET("/list", agentHandler.ListAgents)
 		agentGroup.POST("/create", agentHandler.CreateAgent)
+		agentGroup.POST("/init-team", agentHandler.InitTeamMembers)
 		agentGroup.PUT("/:id/status", agentHandler.UpdateAgentStatus)
 		agentGroup.PUT("/:id", agentHandler.UpdateAgent)
 		agentGroup.DELETE("/:id", agentHandler.DeleteAgent)

server/config/config.yaml

@@ -1,5 +1,5 @@
 # 本地开发配置
-port: "8082"
+port: "8080"
 jwt_secret: "dev-secret-key"
 
 # 数据库配置 (类型: mysql 或 sqlite)

server/internal/handler/agent_handler.go

@@ -1,10 +1,15 @@
 package handler
 
 import (
+	"log"
 	"net/http"
 	"strconv"
+	"strings"
 
+	"x-agents/server/internal/model"
+	"x-agents/server/internal/repository"
 	"x-agents/server/internal/service"
+	"x-agents/server/internal/utils"
 
 	"github.com/gin-gonic/gin"
 )
@@ -12,22 +17,25 @@ import (
 // AgentHandler Agent 处理器
 type AgentHandler struct {
 	agentService *service.AgentService
+	agentRepo    *repository.AgentRepository
 }
 
 // NewAgentHandler 创建 Agent 处理器
-func NewAgentHandler(agentService *service.AgentService) *AgentHandler {
+func NewAgentHandler(agentService *service.AgentService, agentRepo *repository.AgentRepository) *AgentHandler {
 	return &AgentHandler{
 		agentService: agentService,
+		agentRepo:    agentRepo,
 	}
 }
 
 // ChatRequest 对话请求
 type ChatRequest struct {
-	AgentID   string `json:"agent_id" binding:"required"` // 字符串类型
+	AgentID           string   `json:"agent_id"` // 字符串类型，支持 UUID，可为空（当使用 mentioned_agent_ids 时）
 	Message           string   `json:"message" binding:"required"`
 	SessionID         string   `json:"session_id"`
 	ModelID           string   `json:"model_id"`
 	UseXBot           bool     `json:"use_xbot"`
+	MentionedAgentIDs []string `json:"mentioned_agent_ids"` // @ 提及的智能体 ID 列表
 }
 
 // ChatResponse 对话响应
@@ -131,6 +139,29 @@ func (h *AgentHandler) ChatStream(c *gin.Context) {
 	// 直接使用字符串类型的 agent_id，支持 UUID
 	agentID := req.AgentID
 
+	// 优先使用前端传递的 mentioned_agent_ids
+	if len(req.MentionedAgentIDs) > 0 {
+		// 如果有多个 @ 提及，使用第一个
+		mentionedAgentID := req.MentionedAgentIDs[0]
+		log.Printf("[ChatStream] Using mentioned_agent_ids: %v", req.MentionedAgentIDs)
+		agentID = mentionedAgentID
+		// 清理消息，移除 @ 提及
+		mentionParser := utils.NewMentionParser()
+		req.Message = mentionParser.RemoveMentions(req.Message)
+	} else if agentID == "" {
+		// 兼容：解析消息中的 @ 提及（备用方案）
+		mentionParser := utils.NewMentionParser()
+		mentions := mentionParser.ParseMentions(req.Message)
+		if len(mentions) > 0 {
+			mentionedAgent := h.findAgentByName(mentions[0])
+			if mentionedAgent != nil {
+				log.Printf("[ChatStream] Detected @mention: %s, routing to agent: %s", mentions[0], mentionedAgent.ID)
+				agentID = mentionedAgent.ID
+			}
+			req.Message = mentionParser.RemoveMentions(req.Message)
+		}
+	}
+
 	// 构建 SSE 流
 	c.Header("Content-Type", "text/event-stream")
 	c.Header("Cache-Control", "no-cache")
@@ -144,6 +175,37 @@ func (h *AgentHandler) ChatStream(c *gin.Context) {
 	}
 }
 
+// findAgentByName 根据用户名查找智能体
+func (h *AgentHandler) findAgentByName(name string) *model.Agent {
+	log.Printf("[findAgentByName] Searching for agent: %s, agentRepo: %v", name, h.agentRepo)
+
+	if h.agentRepo == nil {
+		log.Printf("[findAgentByName] ERROR: agentRepo is nil!")
+		return nil
+	}
+
+	// 先尝试精确匹配
+	agents, err := h.agentRepo.FindAll()
+	if err != nil {
+		return nil
+	}
+
+	for _, agent := range agents {
+		if agent.Name == name {
+			return &agent
+		}
+	}
+
+	// 再尝试模糊匹配（忽略大小写）
+	for _, agent := range agents {
+		if strings.Contains(strings.ToLower(agent.Name), strings.ToLower(name)) {
+			return &agent
+		}
+	}
+
+	return nil
+}
+
 // TeamChatRequest 多智能体群聊请求
 type TeamChatRequest struct {
 	SupervisorAgentID int      `json:"supervisor_agent_id" binding:"required"`
@@ -236,6 +298,30 @@ func (h *AgentHandler) CreateAgent(c *gin.Context) {
 	c.JSON(http.StatusOK, result)
 }
 
+// InitTeamMembersResponse 初始化团队成员响应
+type InitTeamMembersResponse struct {
+	Message string `json:"message"`
+	Count   int    `json:"count"`
+}
+
+// InitTeamMembers 初始化团队成员智能体
+// @Summary 初始化团队成员智能体
+// @Tags 智能体管理
+// @Produce json
+// @Success 200 {object} InitTeamMembersResponse
+// @Router /api/agent/init-team [post]
+func (h *AgentHandler) InitTeamMembers(c *gin.Context) {
+	if err := h.agentService.InitTeamMembers(); err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+		return
+	}
+
+	c.JSON(http.StatusOK, InitTeamMembersResponse{
+		Message: "Team members initialized successfully",
+		Count:   1, // 小荣
+	})
+}
+
 // ListAgents 获取智能体列表
 // @Summary 获取智能体列表
 // @Tags 智能体管理

server/internal/service/agent_service.go

@@ -117,7 +117,7 @@ func (s *AgentService) Chat(req AgentChatRequest) (*AgentChatResponse, error) {
 	log.Printf("[AgentService] Sending to Python: model_id=%s, api_key=%s, base_url=%s, provider=%s, model=%s",
 		req.ModelID, apiKeyPreview, req.BaseURL, req.ModelProvider, req.ModelName)
 
-	url := fmt.Sprintf("%s/api/v1/agent/chat", s.pythonURL)
+	url := fmt.Sprintf("%s/agent/chat", s.pythonURL)
 
 	jsonData, err := json.Marshal(req)
 	if err != nil {
@@ -155,7 +155,7 @@ func (s *AgentService) Chat(req AgentChatRequest) (*AgentChatResponse, error) {
 
 // TeamChat 多智能体群聊
 func (s *AgentService) TeamChat(req TeamChatRequest) (*TeamChatResponse, error) {
-	url := fmt.Sprintf("%s/api/v1/agent/team/chat", s.pythonURL)
+	url := fmt.Sprintf("%s/agent/team/chat", s.pythonURL)
 
 	// 设置默认策略
 	if req.Strategy == "" {
@@ -233,7 +233,7 @@ func (s *AgentService) ChatStream(c interface{}, agentID string, message, sessio
 		log.Printf("[ChatStream] modelID is empty or modelRepo is nil: modelID=%s, modelRepo=%v", modelID, s.modelRepo != nil)
 	}
 
-	streamURL := fmt.Sprintf("%s/api/v1/agent/chat/stream", s.pythonURL)
+	streamURL := fmt.Sprintf("%s/agent/chat/stream", s.pythonURL)
 
 	jsonData, err := json.Marshal(reqBody)
 	if err != nil {
@@ -365,6 +365,89 @@ func (s *AgentService) CreateAgent(req CreateAgentRequest, userID int) (*CreateA
 	}, nil
 }
 
+// TeamMemberInitRequest 团队成员初始化请求
+type TeamMemberInitRequest struct {
+	Name            string
+	Description     string
+	Avatar          string
+	Skills          []string
+	RoleDescription string
+}
+
+// InitTeamMembers 初始化团队成员智能体
+func (s *AgentService) InitTeamMembers() error {
+	if s.agentRepo == nil {
+		log.Printf("[AgentService] InitTeamMembers: agentRepo is nil!")
+		return fmt.Errorf("agent repository not initialized")
+	}
+
+	// 骚人开发组团队成员配置
+	teamMembers := []TeamMemberInitRequest{
+		{
+			Name:        "小荣",
+			Description: "前端开发工程师 - 骚人开发组成员",
+			Avatar:      "👨‍💻",
+			Skills:      []string{"Vue 3", "TypeScript", "Element Plus", "Tailwind CSS"},
+			RoleDescription: `你叫小荣，是骚人开发组的前端开发工程师。你细心认真，善于沟通。
+
+技能专长：
+- Vue 3 框架开发
+- TypeScript 类型系统
+- Element Plus 组件库
+- Tailwind CSS 样式框架
+
+性格特点：
+- 细心认真，注重代码质量
+- 善于与团队成员沟通协作
+- 积极解决前端技术难题`,
+		},
+	}
+
+	// 检查是否已存在同名智能体
+	for _, member := range teamMembers {
+		existingAgents, err := s.agentRepo.FindAll()
+		if err != nil {
+			log.Printf("[AgentService] InitTeamMembers: failed to list agents: %v", err)
+			continue
+		}
+
+		exists := false
+		for _, a := range existingAgents {
+			if a.Name == member.Name {
+				exists = true
+				log.Printf("[AgentService] InitTeamMembers: agent %s already exists, skipping", member.Name)
+				break
+			}
+		}
+
+		if !exists {
+			// 创建智能体
+			agent := &model.Agent{
+				ID:              uuid.New().String(),
+				Name:            member.Name,
+				Description:     member.Description,
+				OwnerID:         "1", // 系统管理员
+				Avatar:          member.Avatar,
+				Skills:          member.Skills,
+				RoleDescription: member.RoleDescription,
+				ModelProvider:   "anthropic",
+				ModelName:       "claude-sonnet-4-20250514",
+				IsActive:        true,
+				CreatedAt:       time.Now(),
+				UpdatedAt:       time.Now(),
+			}
+
+			if err := s.agentRepo.Create(agent); err != nil {
+				log.Printf("[AgentService] InitTeamMembers: failed to create agent %s: %v", member.Name, err)
+				continue
+			}
+			log.Printf("[AgentService] InitTeamMembers: created agent %s (ID: %s)", member.Name, agent.ID)
+		}
+	}
+
+	return nil
+}
+
 // ListAgentsResponse 获取智能体列表响应
 type ListAgentsResponse struct {
 	Agents []interface{} `json:"agents"`

web/src/components/chat/ChatInput.vue

@@ -1,15 +1,137 @@
 <script setup lang="ts">
+import { ref, watch, computed } from 'vue'
+
 const props = defineProps<{
   modelValue: string
   loading: boolean
+  agents?: { id: string | number; name: string; avatar: string }[]
+  mentionedAgents?: { id: string | number; name: string; avatar: string }[]
 }>()
 
 const emit = defineEmits<{
   (e: 'update:modelValue', value: string): void
   (e: 'send'): void
+  (e: 'triggerMention'): void
+  (e: 'removeMention', agentId: string | number): void
 }>()
 
+const showMentionPopup = ref(false)
+const lastAtPosition = ref(-1)
+const selectedIndex = ref(0)
+
+// 过滤后的智能体列表（排除已提及的）
+const filteredAgents = computed(() => {
+  if (!props.agents) return []
+  const mentionedIds = props.mentionedAgents?.map(a => a.id) || []
+  return props.agents.filter(a => !mentionedIds.includes(a.id))
+})
+
+// 解析消息中的 @ 提及
+const parseMentions = (text: string) => {
+  const mentions: { id: string | number; name: string; avatar: string }[] = []
+  const regex = /@(\S+)/g
+  let match
+
+  while ((match = regex.exec(text)) !== null) {
+    const name = match[1]
+    const agent = props.agents?.find(a => a.name === name)
+    if (agent && !mentions.find(m => m.id === agent.id)) {
+      mentions.push(agent)
+    }
+  }
+
+  return mentions
+}
+
+// 监听输入
+const handleInput = (e: Event) => {
+  const target = e.target as HTMLTextAreaElement
+  const value = target.value
+  const cursorPos = target.selectionStart
+
+  emit('update:modelValue', value)
+  autoResize(e)
+
+  // 检测是否输入了 @
+  const textBeforeCursor = value.slice(0, cursorPos)
+  const lastAtIndex = textBeforeCursor.lastIndexOf('@')
+
+  if (lastAtIndex !== -1) {
+    // 检查 @ 后面是否有空格或是否在单词中间
+    const textAfterAt = textBeforeCursor.slice(lastAtIndex + 1)
+    if (!textAfterAt.includes(' ') && !textAfterAt.includes('\n')) {
+      showMentionPopup.value = true
+      lastAtPosition.value = lastAtIndex
+      selectedIndex.value = 0
+      emit('triggerMention')
+    } else {
+      showMentionPopup.value = false
+    }
+  } else {
+    showMentionPopup.value = false
+  }
+}
+
+// 选择智能体
+const selectAgent = (agent: { id: string | number; name: string; avatar: string }) => {
+  if (!props.modelValue || lastAtPosition.value === -1) return
+
+  // 获取光标位置前的文本和后的文本
+  const beforeAt = props.modelValue.slice(0, lastAtPosition.value)
+  const afterCursor = props.modelValue.slice((document.querySelector('.chat-input-textarea') as HTMLTextAreaElement)?.selectionStart || 0)
+
+  // 替换 @xxx 为 @智能体名
+  const newValue = beforeAt + '@' + agent.name + ' ' + afterCursor
+
+  emit('update:modelValue', newValue)
+  showMentionPopup.value = false
+  lastAtPosition.value = -1
+  selectedIndex.value = 0
+
+  // 聚焦输入框
+  setTimeout(() => {
+    const textarea = document.querySelector('.chat-input-textarea') as HTMLTextAreaElement
+    if (textarea) {
+      textarea.focus()
+    }
+  }, 50)
+}
+
+// 移除提及
+const removeMention = (agentId: string | number) => {
+  emit('removeMention', agentId)
+}
+
 const handleKeydown = (e: KeyboardEvent) => {
+  // @ 提及弹窗打开时处理方向键
+  if (showMentionPopup.value && filteredAgents.value.length > 0) {
+    if (e.key === 'ArrowDown') {
+      e.preventDefault()
+      selectedIndex.value = (selectedIndex.value + 1) % filteredAgents.value.length
+      return
+    }
+    if (e.key === 'ArrowUp') {
+      e.preventDefault()
+      selectedIndex.value = selectedIndex.value === 0
+        ? filteredAgents.value.length - 1
+        : selectedIndex.value - 1
+      return
+    }
+    if (e.key === 'Enter') {
+      e.preventDefault()
+      const agent = filteredAgents.value[selectedIndex.value]
+      if (agent) {
+        selectAgent(agent)
+      }
+      return
+    }
+    if (e.key === 'Escape') {
+      e.preventDefault()
+      showMentionPopup.value = false
+      return
+    }
+  }
+
   if (e.key === 'Enter' && !e.shiftKey) {
     e.preventDefault()
     emit('send')
@@ -26,6 +148,26 @@ const autoResize = (e: Event) => {
 <template>
   <div class="p-5 border-t border-white/[0.06] bg-[#0c0c0f]/60 backdrop-blur-xl">
     <div class="max-w-3xl mx-auto">
+      <!-- 已提及的智能体显示 -->
+      <div v-if="mentionedAgents && mentionedAgents.length > 0" class="flex flex-wrap gap-2 mb-3">
+        <div
+          v-for="agent in mentionedAgents"
+          :key="agent.id"
+          class="inline-flex items-center gap-1.5 px-2.5 py-1.5 bg-orange-500/20 border border-orange-500/30 rounded-lg text-sm"
+        >
+          <span>{{ agent.avatar }}</span>
+          <span class="text-orange-400">@{{ agent.name }}</span>
+          <button
+            @click="removeMention(agent.id)"
+            class="ml-1 text-orange-400/60 hover:text-orange-400"
+          >
+            <svg class="w-3 h-3" fill="none" stroke="currentColor" viewBox="0 0 24 24">
+              <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M6 18L18 6M6 6l12 12"/>
+            </svg>
+          </button>
+        </div>
+      </div>
+
       <div class="relative bg-[#12121a] rounded-2xl border border-white/[0.08] focus-within:border-orange-500/40 focus-within:shadow-[0_0_30px_rgba(249,115,22,0.08)] transition-all duration-300">
         <!-- 附件按钮 -->
         <button class="absolute left-4 top-1/2 -translate-y-1/2 text-white/25 hover:text-orange-400 transition-colors p-1">
@@ -37,13 +179,33 @@ const autoResize = (e: Event) => {
         <!-- 输入框 -->
         <textarea
           :value="modelValue"
-          @input="emit('update:modelValue', ($event.target as HTMLTextAreaElement).value); autoResize($event)"
+          @input="handleInput"
           @keydown="handleKeydown"
-          placeholder="发送消息..."
+          placeholder="输入 @ 提及智能体..."
           rows="1"
           class="chat-input-textarea w-full bg-transparent text-white placeholder-white/25 py-4 pl-12 pr-28 resize-none focus:outline-none text-[15px]"
         ></textarea>
 
+        <!-- @ 提及弹窗 -->
+        <div
+          v-if="showMentionPopup && filteredAgents.length > 0"
+          class="absolute left-0 bottom-full mb-2 w-64 bg-[#1a1a24] border border-white/10 rounded-xl shadow-xl overflow-hidden z-50"
+        >
+          <div class="p-2">
+            <div class="text-xs text-white/40 px-2 py-1">选择智能体</div>
+            <div
+              v-for="(agent, index) in filteredAgents"
+              :key="agent.id"
+              @click="selectAgent(agent)"
+              class="flex items-center gap-2 px-2 py-2 rounded-lg cursor-pointer transition-colors"
+              :class="index === selectedIndex ? 'bg-orange-500/20' : 'hover:bg-white/5'"
+            >
+              <span class="text-lg">{{ agent.avatar }}</span>
+              <span class="text-white text-sm">{{ agent.name }}</span>
+            </div>
+          </div>
+        </div>
+
         <!-- 发送按钮 -->
         <button
           @click="emit('send')"

web/src/views/Chat.vue

@@ -1,5 +1,6 @@
 <script setup lang="ts">
 import { ref, nextTick, watch, onMounted, onUnmounted } from 'vue'
+import { ElMessage } from 'element-plus'
 import { useChat } from './chat/chat'
 import ChatHeader from '@/components/chat/ChatHeader.vue'
 import ChatMessage from '@/components/chat/ChatMessage.vue'
@@ -45,6 +46,27 @@ const {
 
 const messagesContainer = ref<HTMLElement | null>(null)
 
+// @ 提及的智能体
+const mentionedAgents = ref<{ id: string | number; name: string; avatar: string }[]>([])
+
+// 触发 @ 提及
+const onTriggerMention = () => {
+  // 可以在这里打开智能体选择弹窗，或显示提示
+}
+
+// 移除 @ 提及
+const onRemoveMention = (agentId: string | number) => {
+  const index = mentionedAgents.value.findIndex(a => a.id === agentId)
+  if (index > -1) {
+    mentionedAgents.value.splice(index, 1)
+  }
+  // 从输入框中移除 @ 提及
+  const agent = chatAgents.value.find(a => a.id === agentId)
+  if (agent) {
+    inputMessage.value = inputMessage.value.replace(`@${agent.name}`, '')
+  }
+}
+
 // 构建 API 请求体
 const buildRequestBody = (userContent: string) => {
   const requestBody: any = {
@@ -60,26 +82,56 @@ const buildRequestBody = (userContent: string) => {
     requestBody.session_id = currentSessionId.value
   }
 
+  // 添加 @ 提及的智能体 ID
+  if (mentionedAgents.value.length > 0) {
+    requestBody.mentioned_agent_ids = mentionedAgents.value.map(a => String(a.id))
+  }
+
   return requestBody
 }
 
 // 解析流式响应数据
+// 支持格式: data: "content" (JSON字符串) 或 data: {"content": "xxx"} (JSON对象)
 const parseStreamData = (rawData: string): string => {
+  console.log('[Chat] parseStreamData 原始数据:', rawData)
+
   if (!rawData || rawData === '[DONE]') return ''
 
   try {
     const parsed = JSON.parse(rawData)
+    console.log('[Chat] parseStreamData 解析结果:', parsed, '类型:', typeof parsed)
+
+    // 如果解析结果是字符串（JSON字符串形式），直接返回
     if (typeof parsed === 'string') {
       return parsed
     }
-    return parsed.content || parsed.delta?.content || ''
-  } catch {
+
+    // 如果是对象，尝试获取 content 或 delta.content
+    if (parsed && typeof parsed === 'object') {
+      // 兼容多种格式: content, delta.content, text, message.content
+      return parsed.content || parsed.delta?.content || parsed.text || parsed.message?.content || ''
+    }
+
+    return ''
+  } catch (e) {
+    console.error('[Chat] parseStreamData 解析错误:', e)
+    // 解析失败时，尝试直接返回原始数据（可能是未转义的纯文本）
+    if (rawData && rawData.length > 0) {
+      // 尝试移除首尾空格和引号
+      const trimmed = rawData.trim()
+      if ((trimmed.startsWith('"') && trimmed.endsWith('"')) ||
+          (trimmed.startsWith("'") && trimmed.endsWith("'"))) {
+        return trimmed.slice(1, -1)
+      }
+      return trimmed
+    }
     return ''
   }
 }
 
 // 处理流式响应
 const handleStreamResponse = async (response: Response) => {
+  console.log('[Chat] handleStreamResponse 开始处理流式响应, status:', response.status)
   const reader = response.body.getReader()
   const decoder = new TextDecoder('utf-8')
   let buffer = ''
@@ -97,7 +149,11 @@ const handleStreamResponse = async (response: Response) => {
 
     for (const line of lines) {
       if (line.startsWith('data: ')) {
-        const content = parseStreamData(line.slice(6).trim())
+        const dataPart = line.slice(6).trim()
+        console.log('[Chat] 流式数据行:', line)
+        console.log('[Chat] 流式数据部分:', dataPart)
+        const content = parseStreamData(dataPart)
+        console.log('[Chat] 解析后内容:', content)
         if (content) {
           messages.value[aiMessageIndex].content += content
           await nextTick()
@@ -188,6 +244,11 @@ const handleSelectModel = (model: any) => {
   showModelDropdown.value = false
 }
 
+// 打开群聊选择器
+const openGroupChat = () => {
+  openAgentSelector('group')
+}
+
 // 删除会话
 const handleDeleteSession = async (session: any) => {
   try {
@@ -236,15 +297,17 @@ const generateSessionTitle = async () => {
 const sendMessage = async () => {
   if (!inputMessage.value.trim() || isLoading.value) return
 
+  // 如果没有会话，提示用户先选择智能体
+  if (!currentSessionId.value) {
+    ElMessage.warning('请先选择或创建一个会话')
+    return
+  }
+
   const userContent = inputMessage.value.trim()
   inputMessage.value = ''
+  mentionedAgents.value = []
   resetInputHeight()
 
-  if (!currentSessionId.value) {
-    const session = await createSession()
-    if (!session) return
-  }
-
   const userMessage = createUserMessage(userContent)
   messages.value.push(userMessage)
   await saveMessage('user', userContent)
@@ -287,6 +350,7 @@ onUnmounted(() => {
     <div class="flex-1 flex flex-col bg-[#09090b]">
       <!-- 顶部栏 -->
       <ChatHeader
+        v-if="currentSessionId"
         :selected-agent="selectedAgent"
         :chat-models="chatModels"
         :selected-model="selectedModel"
@@ -301,8 +365,26 @@ onUnmounted(() => {
 
       <!-- 消息区域 -->
       <div ref="messagesContainer" class="flex-1 overflow-y-auto py-4">
-        <!-- 空状态欢迎提示 -->
-        <div v-if="messages.length === 0" class="h-full flex items-center justify-center">
+        <!-- 无会话时显示引导界面 -->
+        <div v-if="!currentSessionId" class="h-full flex items-center justify-center empty-chat">
+          <div class="text-center" style="position: relative; z-index: 1;">
+            <div class="empty-logo">🧠</div>
+            <h2 class="empty-title">欢迎使用 X-Agents</h2>
+            <p class="empty-desc">与智能 AI 助手对话，获取专业解答与创意灵感</p>
+            <div class="flex gap-4 justify-center">
+              <button @click="newChat" class="empty-btn">
+                <i class="fa-solid fa-plus mr-2"></i>
+                开始新对话
+              </button>
+              <button @click="openGroupChat" class="empty-btn empty-btn-secondary">
+                <i class="fa-solid fa-users mr-2"></i>
+                开始群聊
+              </button>
+            </div>
+          </div>
+        </div>
+        <!-- 有会话但无消息时显示欢迎提示 -->
+        <div v-else-if="messages.length === 0" class="h-full flex items-center justify-center">
           <div class="text-center">
             <div class="text-5xl mb-4">{{ selectedAgent?.avatar || '🧠' }}</div>
             <h2 class="text-xl font-semibold text-white mb-2">和 {{ selectedAgent?.name || 'AI' }} 开始对话</h2>
@@ -320,16 +402,22 @@ onUnmounted(() => {
         </div>
       </div>
 
-      <!-- 输入区域 -->
+      <!-- 输入区域 - 仅在有会话时显示 -->
       <ChatInput
+        v-if="currentSessionId"
         v-model="inputMessage"
         :loading="isLoading"
+        :agents="chatAgents"
+        :mentioned-agents="mentionedAgents"
         @send="sendMessage"
+        @trigger-mention="onTriggerMention"
+        @remove-mention="onRemoveMention"
       />
     </div>
 
-    <!-- 右侧边栏 -->
+    <!-- 右侧边栏 - 仅在有会话时显示 -->
     <ChatSidebar
+      v-if="currentSessionId"
       :collapsed="sidebarCollapsed"
       :chat-agents="chatAgents"
       :selected-agent="selectedAgent"

web/src/views/chat/chat.css

@@ -49,3 +49,159 @@
 .agent-glow {
   animation: pulse-glow 2s ease-in-out infinite;
 }
+
+/* 空会话页面样式 */
+.empty-chat {
+  position: relative;
+  overflow: hidden;
+}
+
+.empty-chat::before {
+  content: '';
+  position: absolute;
+  top: -50%;
+  left: -50%;
+  width: 200%;
+  height: 200%;
+  background: radial-gradient(circle at 30% 30%, rgba(249, 115, 22, 0.08) 0%, transparent 50%),
+              radial-gradient(circle at 70% 70%, rgba(239, 68, 68, 0.06) 0%, transparent 50%);
+  animation: bgFloat 20s ease-in-out infinite;
+  pointer-events: none;
+}
+
+@keyframes bgFloat {
+  0%, 100% { transform: translate(0, 0) rotate(0deg); }
+  50% { transform: translate(-2%, -2%) rotate(1deg); }
+}
+
+.empty-logo {
+  width: 100px;
+  height: 100px;
+  margin: 0 auto 24px;
+  background: linear-gradient(135deg, rgba(249, 115, 22, 0.2), rgba(239, 68, 68, 0.1));
+  border-radius: 28px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  font-size: 48px;
+  animation: logoFloat 3s ease-in-out infinite;
+  box-shadow: 0 20px 40px rgba(0, 0, 0, 0.3),
+              inset 0 1px 0 rgba(255, 255, 255, 0.1);
+}
+
+@keyframes logoFloat {
+  0%, 100% { transform: translateY(0); }
+  50% { transform: translateY(-8px); }
+}
+
+.empty-title {
+  font-size: 28px;
+  font-weight: 600;
+  background: linear-gradient(135deg, #fff 0%, rgba(255, 255, 255, 0.7) 100%);
+  -webkit-background-clip: text;
+  -webkit-text-fill-color: transparent;
+  background-clip: text;
+  margin-bottom: 12px;
+}
+
+.empty-desc {
+  color: rgba(255, 255, 255, 0.5);
+  font-size: 15px;
+  margin-bottom: 32px;
+  max-width: 360px;
+}
+
+.empty-btn {
+  padding: 14px 32px;
+  font-size: 15px;
+  font-weight: 500;
+  border-radius: 12px;
+  background: linear-gradient(135deg, #f97316 0%, #ef4444 100%);
+  color: white;
+  border: none;
+  cursor: pointer;
+  transition: all 0.3s ease;
+  box-shadow: 0 8px 24px rgba(249, 115, 22, 0.3);
+}
+
+.empty-btn:hover {
+  transform: translateY(-2px);
+  box-shadow: 0 12px 32px rgba(249, 115, 22, 0.4);
+}
+
+.empty-btn:active {
+  transform: translateY(0);
+}
+
+.empty-btn-secondary {
+  background: rgba(255, 255, 255, 0.08);
+  border: 1px solid rgba(255, 255, 255, 0.12);
+  box-shadow: none;
+}
+
+.empty-btn-secondary:hover {
+  background: rgba(255, 255, 255, 0.12);
+  border-color: rgba(255, 255, 255, 0.2);
+  box-shadow: none;
+}
+
+/* 推荐智能体卡片 */
+.recommend-section {
+  margin-top: 48px;
+}
+
+.recommend-title {
+  font-size: 13px;
+  color: rgba(255, 255, 255, 0.4);
+  text-transform: uppercase;
+  letter-spacing: 1.5px;
+  margin-bottom: 16px;
+}
+
+.recommend-cards {
+  display: flex;
+  gap: 16px;
+  justify-content: center;
+  flex-wrap: wrap;
+}
+
+.recommend-card {
+  width: 160px;
+  padding: 20px 16px;
+  background: rgba(255, 255, 255, 0.03);
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  border-radius: 16px;
+  cursor: pointer;
+  transition: all 0.3s ease;
+  text-align: center;
+}
+
+.recommend-card:hover {
+  background: rgba(255, 255, 255, 0.06);
+  border-color: rgba(249, 115, 22, 0.3);
+  transform: translateY(-4px);
+}
+
+.recommend-avatar {
+  width: 52px;
+  height: 52px;
+  margin: 0 auto 12px;
+  border-radius: 14px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  font-size: 26px;
+}
+
+.recommend-name {
+  font-size: 14px;
+  font-weight: 500;
+  color: white;
+  margin-bottom: 4px;
+}
+
+.recommend-desc {
+  font-size: 11px;
+  color: rgba(255, 255, 255, 0.4);
+  line-height: 1.4;
+}

web/src/views/chat/chat.ts

@@ -309,7 +309,7 @@ export function useChat() {
         timestamp: new Date()
       })
 
-      currentSessionId.value = session.id
+      saveSessionId(session.id)
       return session
     } catch {
       return null
@@ -445,7 +445,24 @@ export function useChat() {
       // 调用后端 API 创建群聊
       const group = await createGroup(name, agentIds)
 
-      if (!group) {
+      if (group) {
+        // 创建成功，刷新群聊列表
+        await fetchGroups()
+
+        // 创建群聊会话
+        const session = await createSession(name)
+        if (session) {
+          saveSessionId(session.id)
+
+          // 显示群聊欢迎消息
+          messages.value = [
+            { id: Date.now(), role: 'assistant', content: `你好！欢迎进入群聊 "${name}"，${selectedAgents.value.length} 位智能体已加入。`, timestamp: new Date() }
+          ]
+
+          // 保存欢迎消息
+          await saveMessage('assistant', messages.value[0].content)
+        }
+      } else {
         // 如果 API 调用失败，使用本地数据
         groupChats.value.unshift({
           id: Date.now(),
@@ -508,18 +525,38 @@ export function useChat() {
   }
 
   // 选择历史对话
+  // 保存会话 ID 到 localStorage
+  const saveSessionId = (sessionId: string) => {
+    localStorage.setItem('current_session_id', sessionId)
+    currentSessionId.value = sessionId
+  }
+
+  // 从 localStorage 恢复会话
+  const restoreSession = async () => {
+    const savedSessionId = localStorage.getItem('current_session_id')
+    if (!savedSessionId) return
+
+    const session = chatSessions.value.find(s => s.id === savedSessionId)
+    if (session) {
+      await selectSession(session)
+    }
+  }
+
   const selectSession = async (session: ChatSession) => {
     const agent = chatAgents.value.find(a => a.id === session.agent_id)
     if (agent) {
       selectedAgent.value = agent
     }
 
-    currentSessionId.value = session.id
+    saveSessionId(session.id)
     await fetchSessionMessages(session.id)
   }
 
   // 新建聊天 - 先打开智能体选择器
   const newChat = () => {
+    // 清除当前会话 ID（新建会话时会重新设置）
+    currentSessionId.value = null
+    localStorage.removeItem('current_session_id')
     // 打开智能体选择器，让用户选择智能体
     openAgentSelector('single')
   }
@@ -561,12 +598,15 @@ export function useChat() {
   }
 
   // 初始化
-  const init = () => {
+  const init = async () => {
     fetchModels()
     fetchAgents()
-    fetchSessions()
+    await fetchSessions()
     fetchGroups()
     document.addEventListener('click', handleClickOutside)
+
+    // 恢复之前选中的会话
+    await restoreSession()
   }
 
   // 清理