feat(agent): implement streaming support for agent events and enhance UI components

- Introduce streaming capabilities for agent events, allowing real-time updates during audits.
- Add new hooks for managing agent stream events in React components.
- Enhance the AgentAudit page to display LLM thinking processes and tool call details in real-time.
- Update API endpoints to support streaming event data and improve error handling.
- Refactor UI components for better organization and user experience during audits.

backend/Dockerfile

@@ -59,3 +59,5 @@ CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
 
 
 
+
+

backend/alembic.ini

@@ -103,3 +103,5 @@ datefmt = %H:%M:%S
 
 
 
+
+

backend/alembic/env.py

@@ -90,3 +90,5 @@ else:
 
 
 
+
+

backend/alembic/script.py.mako

@@ -25,3 +25,5 @@ def downgrade() -> None:
 
 
 
+
+

backend/app/api/v1/endpoints/agent_tasks.py

@@ -29,6 +29,7 @@ from app.models.agent_task import (
 from app.models.project import Project
 from app.models.user import User
 from app.services.agent import AgentRunner, EventManager, run_agent_task
+from app.services.agent.streaming import StreamHandler, StreamEvent, StreamEventType
 
 logger = logging.getLogger(__name__)
 router = APIRouter()
@@ -432,6 +433,171 @@ async def stream_agent_events(
     )
 
 
+@router.get("/{task_id}/stream")
+async def stream_agent_with_thinking(
+    task_id: str,
+    include_thinking: bool = Query(True, description="是否包含 LLM 思考过程"),
+    include_tool_calls: bool = Query(True, description="是否包含工具调用详情"),
+    after_sequence: int = Query(0, ge=0, description="从哪个序号之后开始"),
+    db: AsyncSession = Depends(get_db),
+    current_user: User = Depends(deps.get_current_user),
+):
+    """
+    增强版事件流 (SSE)
+    
+    支持:
+    - LLM 思考过程的 Token 级流式输出
+    - 工具调用的详细输入/输出
+    - 节点执行状态
+    - 发现事件
+    
+    事件类型:
+    - thinking_start: LLM 开始思考
+    - thinking_token: LLM 输出 Token
+    - thinking_end: LLM 思考结束
+    - tool_call_start: 工具调用开始
+    - tool_call_end: 工具调用结束
+    - node_start: 节点开始
+    - node_end: 节点结束
+    - finding_new: 新发现
+    - finding_verified: 验证通过
+    - progress: 进度更新
+    - task_complete: 任务完成
+    - task_error: 任务错误
+    - heartbeat: 心跳
+    """
+    task = await db.get(AgentTask, task_id)
+    if not task:
+        raise HTTPException(status_code=404, detail="任务不存在")
+    
+    project = await db.get(Project, task.project_id)
+    if not project or project.owner_id != current_user.id:
+        raise HTTPException(status_code=403, detail="无权访问此任务")
+    
+    async def enhanced_event_generator():
+        """生成增强版 SSE 事件流"""
+        last_sequence = after_sequence
+        poll_interval = 0.3  # 更短的轮询间隔以支持流式
+        heartbeat_interval = 15  # 心跳间隔
+        max_idle = 600  # 10 分钟无事件后关闭
+        idle_time = 0
+        last_heartbeat = 0
+        
+        # 事件类型过滤
+        skip_types = set()
+        if not include_thinking:
+            skip_types.update(["thinking_start", "thinking_token", "thinking_end"])
+        if not include_tool_calls:
+            skip_types.update(["tool_call_start", "tool_call_input", "tool_call_output", "tool_call_end"])
+        
+        while True:
+            try:
+                async with async_session_factory() as session:
+                    # 查询新事件
+                    result = await session.execute(
+                        select(AgentEvent)
+                        .where(AgentEvent.task_id == task_id)
+                        .where(AgentEvent.sequence > last_sequence)
+                        .order_by(AgentEvent.sequence)
+                        .limit(100)
+                    )
+                    events = result.scalars().all()
+                    
+                    # 获取任务状态
+                    current_task = await session.get(AgentTask, task_id)
+                    task_status = current_task.status if current_task else None
+                
+                if events:
+                    idle_time = 0
+                    for event in events:
+                        last_sequence = event.sequence
+                        
+                        # 获取事件类型字符串
+                        event_type = event.event_type.value if hasattr(event.event_type, 'value') else str(event.event_type)
+                        
+                        # 过滤事件
+                        if event_type in skip_types:
+                            continue
+                        
+                        # 构建事件数据
+                        data = {
+                            "id": event.id,
+                            "type": event_type,
+                            "phase": event.phase.value if event.phase and hasattr(event.phase, 'value') else event.phase,
+                            "message": event.message,
+                            "sequence": event.sequence,
+                            "timestamp": event.created_at.isoformat() if event.created_at else None,
+                        }
+                        
+                        # 添加工具调用详情
+                        if include_tool_calls and event.tool_name:
+                            data["tool"] = {
+                                "name": event.tool_name,
+                                "input": event.tool_input,
+                                "output": event.tool_output,
+                                "duration_ms": event.tool_duration_ms,
+                            }
+                        
+                        # 添加元数据
+                        if event.event_metadata:
+                            data["metadata"] = event.event_metadata
+                        
+                        # 添加 Token 使用
+                        if event.tokens_used:
+                            data["tokens_used"] = event.tokens_used
+                        
+                        # 使用标准 SSE 格式
+                        yield f"event: {event_type}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
+                else:
+                    idle_time += poll_interval
+                
+                # 检查任务是否结束
+                if task_status in [AgentTaskStatus.COMPLETED, AgentTaskStatus.FAILED, AgentTaskStatus.CANCELLED]:
+                    end_data = {
+                        "type": "task_end",
+                        "status": task_status.value,
+                        "message": f"任务{'完成' if task_status == AgentTaskStatus.COMPLETED else '结束'}",
+                    }
+                    yield f"event: task_end\ndata: {json.dumps(end_data, ensure_ascii=False)}\n\n"
+                    break
+                
+                # 发送心跳
+                last_heartbeat += poll_interval
+                if last_heartbeat >= heartbeat_interval:
+                    last_heartbeat = 0
+                    heartbeat_data = {
+                        "type": "heartbeat",
+                        "timestamp": datetime.now(timezone.utc).isoformat(),
+                        "last_sequence": last_sequence,
+                    }
+                    yield f"event: heartbeat\ndata: {json.dumps(heartbeat_data)}\n\n"
+                
+                # 检查空闲超时
+                if idle_time >= max_idle:
+                    timeout_data = {"type": "timeout", "message": "连接超时"}
+                    yield f"event: timeout\ndata: {json.dumps(timeout_data)}\n\n"
+                    break
+                
+                await asyncio.sleep(poll_interval)
+                
+            except Exception as e:
+                logger.error(f"Stream error: {e}")
+                error_data = {"type": "error", "message": str(e)}
+                yield f"event: error\ndata: {json.dumps(error_data)}\n\n"
+                break
+    
+    return StreamingResponse(
+        enhanced_event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no",
+            "Content-Type": "text/event-stream; charset=utf-8",
+        }
+    )
+
+
 @router.get("/{task_id}/events/list", response_model=List[AgentEventResponse])
 async def list_agent_events(
     task_id: str,

backend/app/api/v1/endpoints/members.py

@@ -210,3 +210,5 @@ async def remove_project_member(
 
 
 
+
+

backend/app/api/v1/endpoints/users.py

@@ -225,3 +225,5 @@ async def toggle_user_status(
 
 
 
+
+

backend/app/core/security.py

@@ -29,3 +29,5 @@ def get_password_hash(password: str) -> str:
 
 
 
+
+

backend/app/db/base.py

@@ -12,3 +12,5 @@ class Base:
 
 
 
+
+

backend/app/db/session.py

@@ -28,3 +28,5 @@ async def async_session_factory():
 
 
 
+
+

backend/app/models/analysis.py

@@ -24,3 +24,5 @@ class InstantAnalysis(Base):
 
 
 
+
+

backend/app/models/user.py

@@ -25,3 +25,5 @@ class User(Base):
 
 
 
+
+

backend/app/models/user_config.py

@@ -30,3 +30,5 @@ class UserConfig(Base):
 
 
 
+
+

backend/app/schemas/token.py

@@ -10,3 +10,5 @@ class TokenPayload(BaseModel):
 
 
 
+
+

backend/app/schemas/user.py

@@ -41,3 +41,5 @@ class UserListResponse(BaseModel):
 
 
 
+
+

backend/app/services/agent/agents/analysis.py

@@ -147,11 +147,11 @@ class AnalysisAgent(BaseAgent):
                 deep_findings = await self._analyze_entry_points(entry_points)
                 all_findings.extend(deep_findings)
                 
-                # 分析高风险区域
+                # 分析高风险区域（现在会调用 LLM）
                 risk_findings = await self._analyze_high_risk_areas(high_risk_areas)
                 all_findings.extend(risk_findings)
                 
-                # 语义搜索常见漏洞
+                # 语义搜索常见漏洞（现在会调用 LLM）
                 vuln_types = config.get("target_vulnerabilities", [
                     "sql_injection", "xss", "command_injection",
                     "path_traversal", "ssrf", "hardcoded_secret",
@@ -165,6 +165,12 @@ class AnalysisAgent(BaseAgent):
                     vuln_findings = await self._search_vulnerability_pattern(vuln_type)
                     all_findings.extend(vuln_findings)
                 
+                # 🔥 3. 如果还没有发现，使用 LLM 进行全面扫描
+                if len(all_findings) < 3:
+                    await self.emit_thinking("执行 LLM 全面代码扫描...")
+                    llm_findings = await self._llm_comprehensive_scan(tech_stack)
+                    all_findings.extend(llm_findings)
+            
             # 去重
             all_findings = self._deduplicate_findings(all_findings)
             
@@ -292,12 +298,12 @@ class AnalysisAgent(BaseAgent):
         return findings
     
     async def _analyze_high_risk_areas(self, high_risk_areas: List[str]) -> List[Dict]:
-        """分析高风险区域"""
+        """分析高风险区域 - 使用 LLM 深度分析"""
         findings = []
         
-        pattern_tool = self.tools.get("pattern_match")
         read_tool = self.tools.get("read_file")
         search_tool = self.tools.get("search_code")
+        code_analysis_tool = self.tools.get("code_analysis")
         
         if not search_tool:
             return findings
@@ -305,36 +311,92 @@ class AnalysisAgent(BaseAgent):
         # 在高风险区域搜索危险模式
         dangerous_patterns = [
             ("execute(", "sql_injection"),
+            ("query(", "sql_injection"),
             ("eval(", "code_injection"),
             ("system(", "command_injection"),
             ("exec(", "command_injection"),
+            ("subprocess", "command_injection"),
             ("innerHTML", "xss"),
             ("document.write", "xss"),
+            ("open(", "path_traversal"),
+            ("requests.get", "ssrf"),
         ]
         
-        for pattern, vuln_type in dangerous_patterns[:5]:
+        analyzed_files = set()
+        
+        for pattern, vuln_type in dangerous_patterns[:8]:
             if self.is_cancelled:
                 break
             
             result = await search_tool.execute(keyword=pattern, max_results=10)
             
             if result.success and result.metadata.get("matches", 0) > 0:
-                for match in result.metadata.get("results", [])[:3]:
+                for match in result.metadata.get("results", [])[:5]:
                     file_path = match.get("file", "")
+                    line = match.get("line", 0)
                     
-                    # 检查是否在高风险区域
-                    in_high_risk = any(
-                        area in file_path for area in high_risk_areas
+                    # 避免重复分析同一个文件的同一区域
+                    file_key = f"{file_path}:{line // 50}"
+                    if file_key in analyzed_files:
+                        continue
+                    analyzed_files.add(file_key)
+                    
+                    # 🔥 使用 LLM 深度分析找到的代码
+                    if read_tool and code_analysis_tool:
+                        await self.emit_thinking(f"LLM 分析 {file_path}:{line} 的 {vuln_type} 风险...")
+                        
+                        # 读取代码上下文
+                        read_result = await read_tool.execute(
+                            file_path=file_path,
+                            start_line=max(1, line - 15),
+                            end_line=line + 25,
                         )
                         
-                    if in_high_risk or True:  # 暂时包含所有
+                        if read_result.success:
+                            # 调用 LLM 分析
+                            analysis_result = await code_analysis_tool.execute(
+                                code=read_result.data,
+                                file_path=file_path,
+                                focus=vuln_type,
+                            )
+                            
+                            if analysis_result.success and analysis_result.metadata.get("issues"):
+                                for issue in analysis_result.metadata["issues"]:
+                                    findings.append({
+                                        "vulnerability_type": issue.get("type", vuln_type),
+                                        "severity": issue.get("severity", "medium"),
+                                        "title": issue.get("title", f"LLM 发现: {vuln_type}"),
+                                        "description": issue.get("description", ""),
+                                        "file_path": file_path,
+                                        "line_start": issue.get("line", line),
+                                        "code_snippet": issue.get("code_snippet", match.get("match", "")),
+                                        "suggestion": issue.get("suggestion", ""),
+                                        "ai_explanation": issue.get("ai_explanation", ""),
+                                        "source": "llm_analysis",
+                                        "needs_verification": True,
+                                    })
+                            elif analysis_result.success:
+                                # LLM 分析了但没发现问题，仍记录原始发现
                                 findings.append({
                                     "vulnerability_type": vuln_type,
-                            "severity": "high" if in_high_risk else "medium",
+                                    "severity": "low",
+                                    "title": f"疑似 {vuln_type}: {pattern}",
+                                    "description": f"在 {file_path} 中发现危险模式，但 LLM 分析未确认",
+                                    "file_path": file_path,
+                                    "line_start": line,
+                                    "code_snippet": match.get("match", ""),
+                                    "source": "pattern_search",
+                                    "needs_verification": True,
+                                })
+                    else:
+                        # 没有 LLM 工具，使用基础模式匹配
+                        findings.append({
+                            "vulnerability_type": vuln_type,
+                            "severity": "medium",
                             "title": f"疑似 {vuln_type}: {pattern}",
                             "description": f"在 {file_path} 中发现危险模式 {pattern}",
                             "file_path": file_path,
-                            "line_start": match.get("line", 0),
+                            "line_start": line,
                             "code_snippet": match.get("match", ""),
                             "source": "pattern_search",
                             "needs_verification": True,
@@ -343,10 +405,13 @@ class AnalysisAgent(BaseAgent):
         return findings
     
     async def _search_vulnerability_pattern(self, vuln_type: str) -> List[Dict]:
-        """搜索特定漏洞模式"""
+        """搜索特定漏洞模式 - 使用 RAG + LLM"""
         findings = []
         
         security_tool = self.tools.get("security_search")
+        code_analysis_tool = self.tools.get("code_analysis")
+        read_tool = self.tools.get("read_file")
+        
         if not security_tool:
             return findings
         
@@ -357,20 +422,176 @@ class AnalysisAgent(BaseAgent):
         
         if result.success and result.metadata.get("results_count", 0) > 0:
             for item in result.metadata.get("results", [])[:5]:
+                file_path = item.get("file_path", "")
+                line_start = item.get("line_start", 0)
+                content = item.get("content", "")[:2000]
+                
+                # 🔥 使用 LLM 验证 RAG 搜索结果
+                if code_analysis_tool and content:
+                    await self.emit_thinking(f"LLM 验证 RAG 发现的 {vuln_type}...")
+                    
+                    analysis_result = await code_analysis_tool.execute(
+                        code=content,
+                        file_path=file_path,
+                        focus=vuln_type,
+                    )
+                    
+                    if analysis_result.success and analysis_result.metadata.get("issues"):
+                        for issue in analysis_result.metadata["issues"]:
+                            findings.append({
+                                "vulnerability_type": issue.get("type", vuln_type),
+                                "severity": issue.get("severity", "medium"),
+                                "title": issue.get("title", f"LLM 确认: {vuln_type}"),
+                                "description": issue.get("description", ""),
+                                "file_path": file_path,
+                                "line_start": issue.get("line", line_start),
+                                "code_snippet": issue.get("code_snippet", content[:500]),
+                                "suggestion": issue.get("suggestion", ""),
+                                "ai_explanation": issue.get("ai_explanation", ""),
+                                "source": "rag_llm_analysis",
+                                "needs_verification": True,
+                            })
+                    else:
+                        # RAG 找到但 LLM 未确认
+                        findings.append({
+                            "vulnerability_type": vuln_type,
+                            "severity": "low",
+                            "title": f"疑似 {vuln_type} (待确认)",
+                            "description": f"RAG 搜索发现可能存在 {vuln_type}，但 LLM 未确认",
+                            "file_path": file_path,
+                            "line_start": line_start,
+                            "code_snippet": content[:500],
+                            "source": "rag_search",
+                            "needs_verification": True,
+                        })
+                else:
                     findings.append({
                         "vulnerability_type": vuln_type,
                         "severity": "medium",
                         "title": f"疑似 {vuln_type}",
                         "description": f"通过语义搜索发现可能存在 {vuln_type}",
-                    "file_path": item.get("file_path", ""),
-                    "line_start": item.get("line_start", 0),
-                    "code_snippet": item.get("content", "")[:500],
+                        "file_path": file_path,
+                        "line_start": line_start,
+                        "code_snippet": content[:500],
                         "source": "rag_search",
                         "needs_verification": True,
                     })
         
         return findings
     
+    async def _llm_comprehensive_scan(self, tech_stack: Dict) -> List[Dict]:
+        """
+        LLM 全面代码扫描
+        当其他方法没有发现足够的问题时，使用 LLM 直接分析关键文件
+        """
+        findings = []
+        
+        list_tool = self.tools.get("list_files")
+        read_tool = self.tools.get("read_file")
+        code_analysis_tool = self.tools.get("code_analysis")
+        
+        if not all([list_tool, read_tool, code_analysis_tool]):
+            return findings
+        
+        await self.emit_thinking("LLM 全面扫描关键代码文件...")
+        
+        # 确定要扫描的文件类型
+        languages = tech_stack.get("languages", [])
+        file_patterns = []
+        
+        if "Python" in languages:
+            file_patterns.extend(["*.py"])
+        if "JavaScript" in languages or "TypeScript" in languages:
+            file_patterns.extend(["*.js", "*.ts"])
+        if "Go" in languages:
+            file_patterns.extend(["*.go"])
+        if "Java" in languages:
+            file_patterns.extend(["*.java"])
+        if "PHP" in languages:
+            file_patterns.extend(["*.php"])
+        
+        if not file_patterns:
+            file_patterns = ["*.py", "*.js", "*.ts", "*.go", "*.java", "*.php"]
+        
+        # 扫描关键目录
+        key_dirs = ["src", "app", "api", "routes", "controllers", "handlers", "lib", "utils", "."]
+        scanned_files = 0
+        max_files_to_scan = 10
+        
+        for key_dir in key_dirs:
+            if scanned_files >= max_files_to_scan or self.is_cancelled:
+                break
+            
+            for pattern in file_patterns[:3]:
+                if scanned_files >= max_files_to_scan or self.is_cancelled:
+                    break
+                
+                # 列出文件
+                list_result = await list_tool.execute(
+                    directory=key_dir,
+                    pattern=pattern,
+                    recursive=True,
+                    max_files=20,
+                )
+                
+                if not list_result.success:
+                    continue
+                
+                # 从输出中提取文件路径
+                output = list_result.data
+                file_paths = []
+                for line in output.split('\n'):
+                    line = line.strip()
+                    if line.startswith('📄 '):
+                        file_paths.append(line[2:].strip())
+                
+                # 分析每个文件
+                for file_path in file_paths[:5]:
+                    if scanned_files >= max_files_to_scan or self.is_cancelled:
+                        break
+                    
+                    # 跳过测试文件和配置文件
+                    if any(skip in file_path.lower() for skip in ['test', 'spec', 'mock', '__pycache__', 'node_modules']):
+                        continue
+                    
+                    await self.emit_thinking(f"LLM 分析文件: {file_path}")
+                    
+                    # 读取文件
+                    read_result = await read_tool.execute(
+                        file_path=file_path,
+                        max_lines=200,
+                    )
+                    
+                    if not read_result.success:
+                        continue
+                    
+                    scanned_files += 1
+                    
+                    # 🔥 LLM 深度分析
+                    analysis_result = await code_analysis_tool.execute(
+                        code=read_result.data,
+                        file_path=file_path,
+                    )
+                    
+                    if analysis_result.success and analysis_result.metadata.get("issues"):
+                        for issue in analysis_result.metadata["issues"]:
+                            findings.append({
+                                "vulnerability_type": issue.get("type", "other"),
+                                "severity": issue.get("severity", "medium"),
+                                "title": issue.get("title", "LLM 发现的安全问题"),
+                                "description": issue.get("description", ""),
+                                "file_path": file_path,
+                                "line_start": issue.get("line", 0),
+                                "code_snippet": issue.get("code_snippet", ""),
+                                "suggestion": issue.get("suggestion", ""),
+                                "ai_explanation": issue.get("ai_explanation", ""),
+                                "source": "llm_comprehensive_scan",
+                                "needs_verification": True,
+                            })
+        
+        await self.emit_thinking(f"LLM 全面扫描完成，分析了 {scanned_files} 个文件")
+        return findings
+
     def _deduplicate_findings(self, findings: List[Dict]) -> List[Dict]:
         """去重发现"""
         seen = set()

backend/app/services/agent/agents/react_agent.py

@@ -0,0 +1,375 @@
+"""
+真正的 ReAct Agent 实现
+LLM 是大脑，全程参与决策！
+
+ReAct 循环:
+1. Thought: LLM 思考当前状态和下一步
+2. Action: LLM 决定调用哪个工具
+3. Observation: 执行工具，获取结果
+4. 重复直到 LLM 决定完成
+"""
+
+import json
+import logging
+import re
+from typing import List, Dict, Any, Optional, Tuple
+from dataclasses import dataclass
+
+from .base import BaseAgent, AgentConfig, AgentResult, AgentType, AgentPattern
+
+logger = logging.getLogger(__name__)
+
+
+REACT_SYSTEM_PROMPT = """你是 DeepAudit 安全审计 Agent，一个专业的代码安全分析专家。
+
+## 你的任务
+对目标项目进行全面的安全审计，发现潜在的安全漏洞。
+
+## 你的工具
+{tools_description}
+
+## 工作方式
+你需要通过 **思考-行动-观察** 循环来完成任务：
+
+1. **Thought**: 分析当前情况，思考下一步应该做什么
+2. **Action**: 选择一个工具并执行
+3. **Observation**: 观察工具返回的结果
+4. 重复上述过程直到你认为审计完成
+
+## 输出格式
+每一步必须严格按照以下格式输出：
+
+```
+Thought: [你的思考过程，分析当前状态，决定下一步]
+Action: [工具名称]
+Action Input: [工具参数，JSON 格式]
+```
+
+当你完成分析后，输出：
+```
+Thought: [总结分析结果]
+Final Answer: [JSON 格式的最终发现]
+```
+
+## Final Answer 格式
+```json
+{{
+    "findings": [
+        {{
+            "vulnerability_type": "sql_injection",
+            "severity": "high",
+            "title": "SQL 注入漏洞",
+            "description": "详细描述",
+            "file_path": "path/to/file.py",
+            "line_start": 42,
+            "code_snippet": "危险代码片段",
+            "suggestion": "修复建议"
+        }}
+    ],
+    "summary": "审计总结"
+}}
+```
+
+## 审计策略建议
+1. 先用 list_files 了解项目结构
+2. 识别关键文件（路由、控制器、数据库操作）
+3. 使用 search_code 搜索危险模式（eval, exec, query, innerHTML 等）
+4. 读取可疑文件进行深度分析
+5. 如果有 semgrep，用它进行全面扫描
+
+## 重点关注的漏洞类型
+- SQL 注入 (query, execute, raw SQL)
+- XSS (innerHTML, document.write, v-html)
+- 命令注入 (exec, system, subprocess, child_process)
+- 路径遍历 (open, readFile, path concatenation)
+- SSRF (requests, fetch, http client)
+- 硬编码密钥 (password, secret, api_key, token)
+- 不安全的反序列化 (pickle, yaml.load, eval)
+
+现在开始审计！"""
+
+
+@dataclass
+class AgentStep:
+    """Agent 执行步骤"""
+    thought: str
+    action: Optional[str] = None
+    action_input: Optional[Dict] = None
+    observation: Optional[str] = None
+    is_final: bool = False
+    final_answer: Optional[Dict] = None
+
+
+class ReActAgent(BaseAgent):
+    """
+    真正的 ReAct Agent
+    
+    LLM 全程参与决策，自主选择工具和分析策略
+    """
+    
+    def __init__(
+        self,
+        llm_service,
+        tools: Dict[str, Any],
+        event_emitter=None,
+        agent_type: AgentType = AgentType.ANALYSIS,
+        max_iterations: int = 30,
+    ):
+        config = AgentConfig(
+            name="ReActAgent",
+            agent_type=agent_type,
+            pattern=AgentPattern.REACT,
+            max_iterations=max_iterations,
+            system_prompt=REACT_SYSTEM_PROMPT,
+        )
+        super().__init__(config, llm_service, tools, event_emitter)
+        
+        self._conversation_history: List[Dict[str, str]] = []
+        self._steps: List[AgentStep] = []
+    
+    def _get_tools_description(self) -> str:
+        """生成工具描述"""
+        descriptions = []
+        
+        for name, tool in self.tools.items():
+            if name.startswith("_"):
+                continue
+            
+            desc = f"### {name}\n"
+            desc += f"{tool.description}\n"
+            
+            # 添加参数说明
+            if hasattr(tool, 'args_schema') and tool.args_schema:
+                schema = tool.args_schema.schema()
+                properties = schema.get("properties", {})
+                if properties:
+                    desc += "参数:\n"
+                    for param_name, param_info in properties.items():
+                        param_desc = param_info.get("description", "")
+                        param_type = param_info.get("type", "string")
+                        desc += f"  - {param_name} ({param_type}): {param_desc}\n"
+            
+            descriptions.append(desc)
+        
+        return "\n".join(descriptions)
+    
+    def _build_system_prompt(self, project_info: Dict, task_context: str = "") -> str:
+        """构建系统提示词"""
+        tools_desc = self._get_tools_description()
+        prompt = self.config.system_prompt.format(tools_description=tools_desc)
+        
+        if project_info:
+            prompt += f"\n\n## 项目信息\n"
+            prompt += f"- 名称: {project_info.get('name', 'unknown')}\n"
+            prompt += f"- 语言: {', '.join(project_info.get('languages', ['unknown']))}\n"
+            prompt += f"- 文件数: {project_info.get('file_count', 'unknown')}\n"
+        
+        if task_context:
+            prompt += f"\n\n## 任务上下文\n{task_context}"
+        
+        return prompt
+    
+    def _parse_llm_response(self, response: str) -> AgentStep:
+        """解析 LLM 响应"""
+        step = AgentStep(thought="")
+        
+        # 提取 Thought
+        thought_match = re.search(r'Thought:\s*(.*?)(?=Action:|Final Answer:|$)', response, re.DOTALL)
+        if thought_match:
+            step.thought = thought_match.group(1).strip()
+        
+        # 检查是否是最终答案
+        final_match = re.search(r'Final Answer:\s*(.*?)$', response, re.DOTALL)
+        if final_match:
+            step.is_final = True
+            try:
+                # 尝试提取 JSON
+                answer_text = final_match.group(1).strip()
+                # 移除 markdown 代码块
+                answer_text = re.sub(r'```json\s*', '', answer_text)
+                answer_text = re.sub(r'```\s*', '', answer_text)
+                step.final_answer = json.loads(answer_text)
+            except json.JSONDecodeError:
+                step.final_answer = {"raw_answer": final_match.group(1).strip()}
+            return step
+        
+        # 提取 Action
+        action_match = re.search(r'Action:\s*(\w+)', response)
+        if action_match:
+            step.action = action_match.group(1).strip()
+        
+        # 提取 Action Input
+        input_match = re.search(r'Action Input:\s*(.*?)(?=Thought:|Action:|Observation:|$)', response, re.DOTALL)
+        if input_match:
+            input_text = input_match.group(1).strip()
+            # 移除 markdown 代码块
+            input_text = re.sub(r'```json\s*', '', input_text)
+            input_text = re.sub(r'```\s*', '', input_text)
+            try:
+                step.action_input = json.loads(input_text)
+            except json.JSONDecodeError:
+                # 尝试简单解析
+                step.action_input = {"raw_input": input_text}
+        
+        return step
+    
+    async def _execute_tool(self, tool_name: str, tool_input: Dict) -> str:
+        """执行工具"""
+        tool = self.tools.get(tool_name)
+        
+        if not tool:
+            return f"错误: 工具 '{tool_name}' 不存在。可用工具: {list(self.tools.keys())}"
+        
+        try:
+            self._tool_calls += 1
+            await self.emit_tool_call(tool_name, tool_input)
+            
+            import time
+            start = time.time()
+            
+            result = await tool.execute(**tool_input)
+            
+            duration_ms = int((time.time() - start) * 1000)
+            await self.emit_tool_result(tool_name, str(result.data)[:200], duration_ms)
+            
+            if result.success:
+                # 截断过长的输出
+                output = str(result.data)
+                if len(output) > 4000:
+                    output = output[:4000] + "\n\n... [输出已截断，共 {} 字符]".format(len(str(result.data)))
+                return output
+            else:
+                return f"工具执行失败: {result.error}"
+                
+        except Exception as e:
+            logger.error(f"Tool execution error: {e}")
+            return f"工具执行错误: {str(e)}"
+    
+    async def run(self, input_data: Dict[str, Any]) -> AgentResult:
+        """
+        执行 ReAct Agent
+        
+        LLM 全程参与，自主决策！
+        """
+        import time
+        start_time = time.time()
+        
+        project_info = input_data.get("project_info", {})
+        task_context = input_data.get("task_context", "")
+        config = input_data.get("config", {})
+        
+        # 构建系统提示词
+        system_prompt = self._build_system_prompt(project_info, task_context)
+        
+        # 初始化对话历史
+        self._conversation_history = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": "请开始对项目进行安全审计。首先了解项目结构，然后系统性地搜索和分析潜在的安全漏洞。"},
+        ]
+        
+        self._steps = []
+        all_findings = []
+        
+        await self.emit_thinking("🤖 ReAct Agent 启动，LLM 开始自主分析...")
+        
+        try:
+            for iteration in range(self.config.max_iterations):
+                if self.is_cancelled:
+                    break
+                
+                self._iteration = iteration + 1
+                
+                await self.emit_thinking(f"💭 第 {iteration + 1} 轮思考...")
+                
+                # 🔥 调用 LLM 进行思考和决策
+                response = await self.llm_service.chat_completion_raw(
+                    messages=self._conversation_history,
+                    temperature=0.1,
+                    max_tokens=2048,
+                )
+                
+                llm_output = response.get("content", "")
+                self._total_tokens += response.get("usage", {}).get("total_tokens", 0)
+                
+                # 发射思考事件
+                await self.emit_event("thinking", f"LLM: {llm_output[:500]}...")
+                
+                # 解析 LLM 响应
+                step = self._parse_llm_response(llm_output)
+                self._steps.append(step)
+                
+                # 添加 LLM 响应到历史
+                self._conversation_history.append({
+                    "role": "assistant",
+                    "content": llm_output,
+                })
+                
+                # 检查是否完成
+                if step.is_final:
+                    await self.emit_thinking("✅ LLM 完成分析，生成最终报告")
+                    
+                    if step.final_answer and "findings" in step.final_answer:
+                        all_findings = step.final_answer["findings"]
+                    break
+                
+                # 执行工具
+                if step.action:
+                    await self.emit_thinking(f"🔧 LLM 决定调用工具: {step.action}")
+                    
+                    observation = await self._execute_tool(
+                        step.action,
+                        step.action_input or {}
+                    )
+                    
+                    step.observation = observation
+                    
+                    # 添加观察结果到历史
+                    self._conversation_history.append({
+                        "role": "user",
+                        "content": f"Observation: {observation}",
+                    })
+                else:
+                    # LLM 没有选择工具，提示它继续
+                    self._conversation_history.append({
+                        "role": "user",
+                        "content": "请继续分析，选择一个工具执行，或者如果分析完成，输出 Final Answer。",
+                    })
+            
+            duration_ms = int((time.time() - start_time) * 1000)
+            
+            await self.emit_event(
+                "info",
+                f"🎯 ReAct Agent 完成: {len(all_findings)} 个发现, {self._iteration} 轮迭代, {self._tool_calls} 次工具调用"
+            )
+            
+            return AgentResult(
+                success=True,
+                data={
+                    "findings": all_findings,
+                    "steps": [
+                        {
+                            "thought": s.thought,
+                            "action": s.action,
+                            "action_input": s.action_input,
+                            "observation": s.observation[:500] if s.observation else None,
+                        }
+                        for s in self._steps
+                    ],
+                },
+                iterations=self._iteration,
+                tool_calls=self._tool_calls,
+                tokens_used=self._total_tokens,
+                duration_ms=duration_ms,
+            )
+            
+        except Exception as e:
+            logger.error(f"ReAct Agent failed: {e}", exc_info=True)
+            return AgentResult(success=False, error=str(e))
+    
+    def get_conversation_history(self) -> List[Dict[str, str]]:
+        """获取对话历史"""
+        return self._conversation_history
+    
+    def get_steps(self) -> List[AgentStep]:
+        """获取执行步骤"""
+        return self._steps

backend/app/services/agent/event_manager.py

@@ -193,6 +193,37 @@ class AgentEventEmitter:
             },
         ))
     
+    async def emit_task_complete(
+        self,
+        findings_count: int,
+        duration_ms: int,
+        message: Optional[str] = None,
+    ):
+        """发射任务完成事件"""
+        await self.emit(AgentEventData(
+            event_type="task_complete",
+            message=message or f"✅ 审计完成！发现 {findings_count} 个漏洞，耗时 {duration_ms/1000:.1f}秒",
+            metadata={
+                "findings_count": findings_count,
+                "duration_ms": duration_ms,
+            },
+        ))
+    
+    async def emit_task_error(self, error: str, message: Optional[str] = None):
+        """发射任务错误事件"""
+        await self.emit(AgentEventData(
+            event_type="task_error",
+            message=message or f"❌ 任务失败: {error}",
+            metadata={"error": error},
+        ))
+    
+    async def emit_task_cancelled(self, message: Optional[str] = None):
+        """发射任务取消事件"""
+        await self.emit(AgentEventData(
+            event_type="task_cancel",
+            message=message or "⚠️ 任务已取消",
+        ))
+
 
 class EventManager:
     """
@@ -369,3 +400,14 @@ class EventManager:
         """创建事件发射器"""
         return AgentEventEmitter(task_id, self)
     
+    async def close(self):
+        """关闭事件管理器，清理资源"""
+        # 清理所有队列
+        for task_id in list(self._event_queues.keys()):
+            self.remove_queue(task_id)
+        
+        # 清理所有回调
+        self._event_callbacks.clear()
+        
+        logger.debug("EventManager closed")
+

backend/app/services/agent/graph/runner.py

@@ -15,6 +15,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from langgraph.graph import StateGraph, END
 from langgraph.checkpoint.memory import MemorySaver
 
+from app.services.agent.streaming import StreamHandler, StreamEvent, StreamEventType
 from app.models.agent_task import (
     AgentTask, AgentEvent, AgentFinding,
     AgentTaskStatus, AgentTaskPhase, AgentEventType,
@@ -39,11 +40,15 @@ logger = logging.getLogger(__name__)
 
 
 class LLMService:
-    """LLM 服务封装"""
+    """
+    LLM 服务封装
+    提供代码分析、漏洞检测等 AI 功能
+    """
     
     def __init__(self, model: Optional[str] = None, api_key: Optional[str] = None):
         self.model = model or settings.LLM_MODEL or "gpt-4o-mini"
         self.api_key = api_key or settings.LLM_API_KEY
+        self.base_url = settings.LLM_BASE_URL
     
     async def chat_completion_raw(
         self,
@@ -61,6 +66,7 @@ class LLMService:
                 temperature=temperature,
                 max_tokens=max_tokens,
                 api_key=self.api_key,
+                base_url=self.base_url,
             )
             
             return {
@@ -76,6 +82,125 @@ class LLMService:
             logger.error(f"LLM call failed: {e}")
             raise
     
+    async def analyze_code(self, code: str, language: str) -> Dict[str, Any]:
+        """
+        分析代码安全问题
+        
+        Args:
+            code: 代码内容
+            language: 编程语言
+            
+        Returns:
+            分析结果，包含 issues 列表
+        """
+        prompt = f"""请分析以下 {language} 代码的安全问题。
+
+代码:
+```{language}
+{code[:8000]}
+```
+
+请识别所有潜在的安全漏洞，包括但不限于:
+- SQL 注入
+- XSS (跨站脚本)
+- 命令注入
+- 路径遍历
+- 不安全的反序列化
+- 硬编码密钥/密码
+- 不安全的加密
+- SSRF
+- 认证/授权问题
+
+对于每个发现的问题，请提供:
+1. 漏洞类型
+2. 严重程度 (critical/high/medium/low)
+3. 问题描述
+4. 具体行号
+5. 修复建议
+
+请以 JSON 格式返回结果:
+{{
+    "issues": [
+        {{
+            "type": "漏洞类型",
+            "severity": "严重程度",
+            "title": "问题标题",
+            "description": "详细描述",
+            "line": 行号,
+            "code_snippet": "相关代码片段",
+            "suggestion": "修复建议"
+        }}
+    ],
+    "quality_score": 0-100
+}}
+
+如果没有发现安全问题，返回空的 issues 数组和较高的 quality_score。"""
+
+        try:
+            result = await self.chat_completion_raw(
+                messages=[
+                    {"role": "system", "content": "你是一位专业的代码安全审计专家，擅长发现代码中的安全漏洞。请只返回 JSON 格式的结果，不要包含其他内容。"},
+                    {"role": "user", "content": prompt},
+                ],
+                temperature=0.1,
+                max_tokens=4096,
+            )
+            
+            content = result.get("content", "{}")
+            
+            # 尝试提取 JSON
+            import json
+            import re
+            
+            # 尝试直接解析
+            try:
+                return json.loads(content)
+            except json.JSONDecodeError:
+                pass
+            
+            # 尝试从 markdown 代码块提取
+            json_match = re.search(r'```(?:json)?\s*([\s\S]*?)\s*```', content)
+            if json_match:
+                try:
+                    return json.loads(json_match.group(1))
+                except json.JSONDecodeError:
+                    pass
+            
+            # 返回空结果
+            return {"issues": [], "quality_score": 80}
+            
+        except Exception as e:
+            logger.error(f"Code analysis failed: {e}")
+            return {"issues": [], "quality_score": 0, "error": str(e)}
+    
+    async def analyze_code_with_custom_prompt(
+        self,
+        code: str,
+        language: str,
+        prompt: str,
+        **kwargs
+    ) -> Dict[str, Any]:
+        """使用自定义提示词分析代码"""
+        full_prompt = prompt.replace("{code}", code).replace("{language}", language)
+        
+        try:
+            result = await self.chat_completion_raw(
+                messages=[
+                    {"role": "system", "content": "你是一位专业的代码安全审计专家。"},
+                    {"role": "user", "content": full_prompt},
+                ],
+                temperature=0.1,
+            )
+            
+            return {
+                "analysis": result.get("content", ""),
+                "usage": result.get("usage", {}),
+            }
+            
+        except Exception as e:
+            logger.error(f"Custom analysis failed: {e}")
+            return {"analysis": "", "error": str(e)}
+
 
 class AgentRunner:
     """
@@ -97,8 +222,9 @@ class AgentRunner:
         self.task = task
         self.project_root = project_root
         
-        # 事件管理
-        self.event_manager = EventManager()
+        # 事件管理 - 传入 db_session_factory 以持久化事件
+        from app.db.session import async_session_factory
+        self.event_manager = EventManager(db_session_factory=async_session_factory)
         self.event_emitter = AgentEventEmitter(task.id, self.event_manager)
         
         # LLM 服务
@@ -120,6 +246,22 @@ class AgentRunner:
         
         # 状态
         self._cancelled = False
+        self._running_task: Optional[asyncio.Task] = None
+        
+        # 流式处理器
+        self.stream_handler = StreamHandler(task.id)
+    
+    def cancel(self):
+        """取消任务"""
+        self._cancelled = True
+        if self._running_task and not self._running_task.done():
+            self._running_task.cancel()
+        logger.info(f"Task {self.task.id} cancellation requested")
+    
+    @property
+    def is_cancelled(self) -> bool:
+        """检查是否已取消"""
+        return self._cancelled
     
     async def initialize(self):
         """初始化 Runner"""
@@ -149,15 +291,15 @@ class AgentRunner:
             )
             
             self.indexer = CodeIndexer(
-                embedding_service=embedding_service,
-                vector_db_path=settings.VECTOR_DB_PATH,
                 collection_name=f"project_{self.task.project_id}",
+                embedding_service=embedding_service,
+                persist_directory=settings.VECTOR_DB_PATH,
             )
             
             self.retriever = CodeRetriever(
-                embedding_service=embedding_service,
-                vector_db_path=settings.VECTOR_DB_PATH,
                 collection_name=f"project_{self.task.project_id}",
+                embedding_service=embedding_service,
+                persist_directory=settings.VECTOR_DB_PATH,
             )
             
         except Exception as e:
@@ -261,6 +403,18 @@ class AgentRunner:
         Returns:
             最终状态
         """
+        result = {}
+        async for _ in self.run_with_streaming():
+            pass  # 消费所有事件
+        return result
+    
+    async def run_with_streaming(self) -> AsyncGenerator[StreamEvent, None]:
+        """
+        带流式输出的审计执行
+        
+        Yields:
+            StreamEvent: 流式事件（包含 LLM 思考、工具调用等）
+        """
         import time
         start_time = time.time()
         
@@ -271,17 +425,28 @@ class AgentRunner:
             # 更新任务状态
             await self._update_task_status(AgentTaskStatus.RUNNING)
             
+            # 发射任务开始事件
+            yield StreamEvent(
+                event_type=StreamEventType.TASK_START,
+                sequence=self.stream_handler._next_sequence(),
+                data={"task_id": self.task.id, "message": "🚀 审计任务开始"},
+            )
+            
             # 1. 索引代码
             await self._index_code()
             
             if self._cancelled:
-                return {"success": False, "error": "任务已取消"}
+                yield StreamEvent(
+                    event_type=StreamEventType.TASK_CANCEL,
+                    sequence=self.stream_handler._next_sequence(),
+                    data={"message": "任务已取消"},
+                )
+                return
             
             # 2. 收集项目信息
             project_info = await self._collect_project_info()
             
             # 3. 构建初始状态
-            # 从任务字段构建配置
             task_config = {
                 "target_vulnerabilities": self.task.target_vulnerabilities or [],
                 "verification_level": self.task.verification_level or "sandbox",
@@ -314,7 +479,7 @@ class AgentRunner:
                 "error": None,
             }
             
-            # 4. 执行 LangGraph
+            # 4. 执行 LangGraph with astream_events
             await self.event_emitter.emit_phase_start("langgraph", "🔄 启动 LangGraph 工作流")
             
             run_config = {
@@ -325,16 +490,47 @@ class AgentRunner:
             
             final_state = None
             
-            # 流式执行并发射事件
+            # 使用 astream_events 获取详细事件流
+            try:
+                async for event in self.graph.astream_events(
+                    initial_state,
+                    config=run_config,
+                    version="v2",
+                ):
+                    if self._cancelled:
+                        break
+                    
+                    # 处理 LangGraph 事件
+                    stream_event = await self.stream_handler.process_langgraph_event(event)
+                    if stream_event:
+                        # 同步到 event_emitter 以持久化
+                        await self._sync_stream_event_to_db(stream_event)
+                        yield stream_event
+                    
+                    # 更新最终状态
+                    if event.get("event") == "on_chain_end":
+                        output = event.get("data", {}).get("output")
+                        if isinstance(output, dict):
+                            final_state = output
+                            
+            except Exception as e:
+                # 如果 astream_events 不可用，回退到 astream
+                logger.warning(f"astream_events not available, falling back to astream: {e}")
                 async for event in self.graph.astream(initial_state, config=run_config):
                     if self._cancelled:
                         break
                     
-                # 处理每个节点的输出
                     for node_name, node_output in event.items():
                         await self._handle_node_output(node_name, node_output)
                         
-                    # 更新阶段
+                        # 发射节点事件
+                        yield StreamEvent(
+                            event_type=StreamEventType.NODE_END,
+                            sequence=self.stream_handler._next_sequence(),
+                            node_name=node_name,
+                            data={"message": f"节点 {node_name} 完成"},
+                        )
+                        
                         phase_map = {
                             "recon": AgentTaskPhase.RECONNAISSANCE,
                             "analysis": AgentTaskPhase.ANALYSIS,
@@ -355,6 +551,13 @@ class AgentRunner:
             findings = final_state.get("findings", [])
             await self._save_findings(findings)
             
+            # 发射发现事件
+            for finding in findings[:10]:  # 限制数量
+                yield self.stream_handler.create_finding_event(
+                    finding,
+                    is_verified=finding.get("is_verified", False),
+                )
+            
             # 7. 更新任务摘要
             summary = final_state.get("summary", {})
             security_score = final_state.get("security_score", 100)
@@ -374,30 +577,59 @@ class AgentRunner:
                 duration_ms=duration_ms,
             )
             
-            return {
-                "success": True,
-                "data": {
-                    "findings": findings,
-                    "verified_findings": final_state.get("verified_findings", []),
-                    "summary": summary,
+            yield StreamEvent(
+                event_type=StreamEventType.TASK_COMPLETE,
+                sequence=self.stream_handler._next_sequence(),
+                data={
+                    "findings_count": len(findings),
+                    "verified_count": len(final_state.get("verified_findings", [])),
                     "security_score": security_score,
-                },
                     "duration_ms": duration_ms,
-            }
+                    "message": f"✅ 审计完成！发现 {len(findings)} 个漏洞",
+                },
+            )
             
         except asyncio.CancelledError:
             await self._update_task_status(AgentTaskStatus.CANCELLED)
-            return {"success": False, "error": "任务已取消"}
+            yield StreamEvent(
+                event_type=StreamEventType.TASK_CANCEL,
+                sequence=self.stream_handler._next_sequence(),
+                data={"message": "任务已取消"},
+            )
             
         except Exception as e:
             logger.error(f"LangGraph run failed: {e}", exc_info=True)
             await self._update_task_status(AgentTaskStatus.FAILED, str(e))
             await self.event_emitter.emit_error(str(e))
-            return {"success": False, "error": str(e)}
+            
+            yield StreamEvent(
+                event_type=StreamEventType.TASK_ERROR,
+                sequence=self.stream_handler._next_sequence(),
+                data={"error": str(e), "message": f"❌ 审计失败: {e}"},
+            )
             
         finally:
             await self._cleanup()
     
+    async def _sync_stream_event_to_db(self, event: StreamEvent):
+        """同步流式事件到数据库"""
+        try:
+            # 将 StreamEvent 转换为 AgentEventData
+            await self.event_manager.add_event(
+                task_id=self.task.id,
+                event_type=event.event_type.value,
+                sequence=event.sequence,
+                phase=event.phase,
+                message=event.data.get("message"),
+                tool_name=event.tool_name,
+                tool_input=event.data.get("input") or event.data.get("input_params"),
+                tool_output=event.data.get("output") or event.data.get("output_data"),
+                tool_duration_ms=event.data.get("duration_ms"),
+                metadata=event.data,
+            )
+        except Exception as e:
+            logger.warning(f"Failed to sync stream event to db: {e}")
+    
     async def _handle_node_output(self, node_name: str, output: Dict[str, Any]):
         """处理节点输出"""
         # 发射节点事件
@@ -445,7 +677,8 @@ class AgentRunner:
                     return
                 
                 await self.event_emitter.emit_progress(
-                    progress.processed / max(progress.total, 1) * 100,
+                    progress.processed_files,
+                    progress.total_files,
                     f"正在索引: {progress.current_file or 'N/A'}"
                 )
             
@@ -502,13 +735,23 @@ class AgentRunner:
         
         type_map = {
             "sql_injection": VulnerabilityType.SQL_INJECTION,
+            "nosql_injection": VulnerabilityType.NOSQL_INJECTION,
             "xss": VulnerabilityType.XSS,
             "command_injection": VulnerabilityType.COMMAND_INJECTION,
+            "code_injection": VulnerabilityType.CODE_INJECTION,
             "path_traversal": VulnerabilityType.PATH_TRAVERSAL,
+            "file_inclusion": VulnerabilityType.FILE_INCLUSION,
             "ssrf": VulnerabilityType.SSRF,
+            "xxe": VulnerabilityType.XXE,
+            "deserialization": VulnerabilityType.DESERIALIZATION,
+            "auth_bypass": VulnerabilityType.AUTH_BYPASS,
+            "idor": VulnerabilityType.IDOR,
+            "sensitive_data_exposure": VulnerabilityType.SENSITIVE_DATA_EXPOSURE,
             "hardcoded_secret": VulnerabilityType.HARDCODED_SECRET,
-            "deserialization": VulnerabilityType.INSECURE_DESERIALIZATION,
             "weak_crypto": VulnerabilityType.WEAK_CRYPTO,
+            "race_condition": VulnerabilityType.RACE_CONDITION,
+            "business_logic": VulnerabilityType.BUSINESS_LOGIC,
+            "memory_corruption": VulnerabilityType.MEMORY_CORRUPTION,
         }
         
         for finding in findings:
@@ -536,7 +779,7 @@ class AgentRunner:
                     is_verified=finding.get("is_verified", False),
                     confidence=finding.get("confidence", 0.5),
                     poc=finding.get("poc"),
-                    status=FindingStatus.VERIFIED if finding.get("is_verified") else FindingStatus.OPEN,
+                    status=FindingStatus.VERIFIED if finding.get("is_verified") else FindingStatus.NEW,
                 )
                 
                 self.db.add(db_finding)
@@ -604,10 +847,6 @@ class AgentRunner:
         except Exception as e:
             logger.warning(f"Cleanup error: {e}")
 
-    def cancel(self):
-        """取消任务"""
-        self._cancelled = True
-
 
 # 便捷函数
 async def run_agent_task(

backend/app/services/agent/streaming/__init__.py

@@ -0,0 +1,17 @@
+"""
+Agent 流式输出模块
+支持 LLM Token 流式输出、工具调用展示、思考过程展示
+"""
+
+from .stream_handler import StreamHandler, StreamEvent, StreamEventType
+from .token_streamer import TokenStreamer
+from .tool_stream import ToolStreamHandler
+
+__all__ = [
+    "StreamHandler",
+    "StreamEvent",
+    "StreamEventType",
+    "TokenStreamer",
+    "ToolStreamHandler",
+]
+

backend/app/services/agent/streaming/stream_handler.py

@@ -0,0 +1,453 @@
+"""
+流式事件处理器
+处理 LangGraph 的各种流式事件并转换为前端可消费的格式
+"""
+
+import json
+import logging
+from enum import Enum
+from typing import Any, Dict, Optional, AsyncGenerator, List
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+
+logger = logging.getLogger(__name__)
+
+
+class StreamEventType(str, Enum):
+    """流式事件类型"""
+    # LLM 相关
+    THINKING_START = "thinking_start"      # 开始思考
+    THINKING_TOKEN = "thinking_token"      # 思考 Token
+    THINKING_END = "thinking_end"          # 思考结束
+    
+    # 工具调用相关
+    TOOL_CALL_START = "tool_call_start"    # 工具调用开始
+    TOOL_CALL_INPUT = "tool_call_input"    # 工具输入参数
+    TOOL_CALL_OUTPUT = "tool_call_output"  # 工具输出结果
+    TOOL_CALL_END = "tool_call_end"        # 工具调用结束
+    TOOL_CALL_ERROR = "tool_call_error"    # 工具调用错误
+    
+    # 节点相关
+    NODE_START = "node_start"              # 节点开始
+    NODE_END = "node_end"                  # 节点结束
+    
+    # 阶段相关
+    PHASE_START = "phase_start"
+    PHASE_END = "phase_end"
+    
+    # 发现相关
+    FINDING_NEW = "finding_new"            # 新发现
+    FINDING_VERIFIED = "finding_verified"  # 验证通过
+    
+    # 状态相关
+    PROGRESS = "progress"
+    INFO = "info"
+    WARNING = "warning"
+    ERROR = "error"
+    
+    # 任务相关
+    TASK_START = "task_start"
+    TASK_COMPLETE = "task_complete"
+    TASK_ERROR = "task_error"
+    TASK_CANCEL = "task_cancel"
+    
+    # 心跳
+    HEARTBEAT = "heartbeat"
+
+
+@dataclass
+class StreamEvent:
+    """流式事件"""
+    event_type: StreamEventType
+    data: Dict[str, Any] = field(default_factory=dict)
+    timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
+    sequence: int = 0
+    
+    # 可选字段
+    node_name: Optional[str] = None
+    phase: Optional[str] = None
+    tool_name: Optional[str] = None
+    
+    def to_sse(self) -> str:
+        """转换为 SSE 格式"""
+        event_data = {
+            "type": self.event_type.value,
+            "data": self.data,
+            "timestamp": self.timestamp,
+            "sequence": self.sequence,
+        }
+        
+        if self.node_name:
+            event_data["node"] = self.node_name
+        if self.phase:
+            event_data["phase"] = self.phase
+        if self.tool_name:
+            event_data["tool"] = self.tool_name
+        
+        return f"event: {self.event_type.value}\ndata: {json.dumps(event_data, ensure_ascii=False)}\n\n"
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """转换为字典"""
+        return {
+            "event_type": self.event_type.value,
+            "data": self.data,
+            "timestamp": self.timestamp,
+            "sequence": self.sequence,
+            "node_name": self.node_name,
+            "phase": self.phase,
+            "tool_name": self.tool_name,
+        }
+
+
+class StreamHandler:
+    """
+    流式事件处理器
+    
+    最佳实践:
+    1. 使用 astream_events 捕获所有 LangGraph 事件
+    2. 将内部事件转换为前端友好的格式
+    3. 支持多种事件类型的分发
+    """
+    
+    def __init__(self, task_id: str):
+        self.task_id = task_id
+        self._sequence = 0
+        self._current_phase = None
+        self._current_node = None
+        self._thinking_buffer = []
+        self._tool_states: Dict[str, Dict] = {}
+    
+    def _next_sequence(self) -> int:
+        """获取下一个序列号"""
+        self._sequence += 1
+        return self._sequence
+    
+    async def process_langgraph_event(self, event: Dict[str, Any]) -> Optional[StreamEvent]:
+        """
+        处理 LangGraph 事件
+        
+        支持的事件类型:
+        - on_chain_start: 链/节点开始
+        - on_chain_end: 链/节点结束
+        - on_chain_stream: LLM Token 流
+        - on_chat_model_start: 模型开始
+        - on_chat_model_stream: 模型 Token 流
+        - on_chat_model_end: 模型结束
+        - on_tool_start: 工具开始
+        - on_tool_end: 工具结束
+        - on_custom_event: 自定义事件
+        """
+        event_kind = event.get("event", "")
+        event_name = event.get("name", "")
+        event_data = event.get("data", {})
+        
+        # LLM Token 流
+        if event_kind == "on_chat_model_stream":
+            return await self._handle_llm_stream(event_data, event_name)
+        
+        # LLM 开始
+        elif event_kind == "on_chat_model_start":
+            return await self._handle_llm_start(event_data, event_name)
+        
+        # LLM 结束
+        elif event_kind == "on_chat_model_end":
+            return await self._handle_llm_end(event_data, event_name)
+        
+        # 工具开始
+        elif event_kind == "on_tool_start":
+            return await self._handle_tool_start(event_name, event_data)
+        
+        # 工具结束
+        elif event_kind == "on_tool_end":
+            return await self._handle_tool_end(event_name, event_data)
+        
+        # 节点开始
+        elif event_kind == "on_chain_start" and self._is_node_event(event_name):
+            return await self._handle_node_start(event_name, event_data)
+        
+        # 节点结束
+        elif event_kind == "on_chain_end" and self._is_node_event(event_name):
+            return await self._handle_node_end(event_name, event_data)
+        
+        # 自定义事件
+        elif event_kind == "on_custom_event":
+            return await self._handle_custom_event(event_name, event_data)
+        
+        return None
+    
+    def _is_node_event(self, name: str) -> bool:
+        """判断是否是节点事件"""
+        node_names = ["recon", "analysis", "verification", "report", "ReconNode", "AnalysisNode", "VerificationNode", "ReportNode"]
+        return any(n.lower() in name.lower() for n in node_names)
+    
+    async def _handle_llm_start(self, data: Dict, name: str) -> StreamEvent:
+        """处理 LLM 开始事件"""
+        self._thinking_buffer = []
+        
+        return StreamEvent(
+            event_type=StreamEventType.THINKING_START,
+            sequence=self._next_sequence(),
+            node_name=self._current_node,
+            phase=self._current_phase,
+            data={
+                "model": name,
+                "message": "🤔 正在思考...",
+            },
+        )
+    
+    async def _handle_llm_stream(self, data: Dict, name: str) -> Optional[StreamEvent]:
+        """处理 LLM Token 流事件"""
+        chunk = data.get("chunk")
+        if not chunk:
+            return None
+        
+        # 提取 Token 内容
+        content = ""
+        if hasattr(chunk, "content"):
+            content = chunk.content
+        elif isinstance(chunk, dict):
+            content = chunk.get("content", "")
+        
+        if not content:
+            return None
+        
+        # 添加到缓冲区
+        self._thinking_buffer.append(content)
+        
+        return StreamEvent(
+            event_type=StreamEventType.THINKING_TOKEN,
+            sequence=self._next_sequence(),
+            node_name=self._current_node,
+            phase=self._current_phase,
+            data={
+                "token": content,
+                "accumulated": "".join(self._thinking_buffer),
+            },
+        )
+    
+    async def _handle_llm_end(self, data: Dict, name: str) -> StreamEvent:
+        """处理 LLM 结束事件"""
+        full_response = "".join(self._thinking_buffer)
+        self._thinking_buffer = []
+        
+        # 提取使用的 Token 数
+        usage = {}
+        output = data.get("output")
+        if output and hasattr(output, "usage_metadata"):
+            usage = {
+                "input_tokens": getattr(output.usage_metadata, "input_tokens", 0),
+                "output_tokens": getattr(output.usage_metadata, "output_tokens", 0),
+            }
+        
+        return StreamEvent(
+            event_type=StreamEventType.THINKING_END,
+            sequence=self._next_sequence(),
+            node_name=self._current_node,
+            phase=self._current_phase,
+            data={
+                "response": full_response[:2000],  # 截断长响应
+                "usage": usage,
+                "message": "💡 思考完成",
+            },
+        )
+    
+    async def _handle_tool_start(self, tool_name: str, data: Dict) -> StreamEvent:
+        """处理工具开始事件"""
+        import time
+        
+        tool_input = data.get("input", {})
+        
+        # 记录工具状态
+        self._tool_states[tool_name] = {
+            "start_time": time.time(),
+            "input": tool_input,
+        }
+        
+        return StreamEvent(
+            event_type=StreamEventType.TOOL_CALL_START,
+            sequence=self._next_sequence(),
+            node_name=self._current_node,
+            phase=self._current_phase,
+            tool_name=tool_name,
+            data={
+                "tool_name": tool_name,
+                "input": self._truncate_data(tool_input),
+                "message": f"🔧 调用工具: {tool_name}",
+            },
+        )
+    
+    async def _handle_tool_end(self, tool_name: str, data: Dict) -> StreamEvent:
+        """处理工具结束事件"""
+        import time
+        
+        # 计算执行时间
+        duration_ms = 0
+        if tool_name in self._tool_states:
+            start_time = self._tool_states[tool_name].get("start_time", time.time())
+            duration_ms = int((time.time() - start_time) * 1000)
+            del self._tool_states[tool_name]
+        
+        # 提取输出
+        output = data.get("output", "")
+        if hasattr(output, "content"):
+            output = output.content
+        
+        return StreamEvent(
+            event_type=StreamEventType.TOOL_CALL_END,
+            sequence=self._next_sequence(),
+            node_name=self._current_node,
+            phase=self._current_phase,
+            tool_name=tool_name,
+            data={
+                "tool_name": tool_name,
+                "output": self._truncate_data(output),
+                "duration_ms": duration_ms,
+                "message": f"✅ 工具 {tool_name} 完成 ({duration_ms}ms)",
+            },
+        )
+    
+    async def _handle_node_start(self, node_name: str, data: Dict) -> StreamEvent:
+        """处理节点开始事件"""
+        self._current_node = node_name
+        
+        # 映射节点到阶段
+        phase_map = {
+            "recon": "reconnaissance",
+            "analysis": "analysis",
+            "verification": "verification",
+            "report": "reporting",
+        }
+        
+        for key, phase in phase_map.items():
+            if key in node_name.lower():
+                self._current_phase = phase
+                break
+        
+        return StreamEvent(
+            event_type=StreamEventType.NODE_START,
+            sequence=self._next_sequence(),
+            node_name=node_name,
+            phase=self._current_phase,
+            data={
+                "node": node_name,
+                "phase": self._current_phase,
+                "message": f"▶️ 开始节点: {node_name}",
+            },
+        )
+    
+    async def _handle_node_end(self, node_name: str, data: Dict) -> StreamEvent:
+        """处理节点结束事件"""
+        # 提取输出信息
+        output = data.get("output", {})
+        
+        summary = {}
+        if isinstance(output, dict):
+            # 提取关键信息
+            if "findings" in output:
+                summary["findings_count"] = len(output["findings"])
+            if "entry_points" in output:
+                summary["entry_points_count"] = len(output["entry_points"])
+            if "high_risk_areas" in output:
+                summary["high_risk_areas_count"] = len(output["high_risk_areas"])
+            if "verified_findings" in output:
+                summary["verified_count"] = len(output["verified_findings"])
+        
+        return StreamEvent(
+            event_type=StreamEventType.NODE_END,
+            sequence=self._next_sequence(),
+            node_name=node_name,
+            phase=self._current_phase,
+            data={
+                "node": node_name,
+                "phase": self._current_phase,
+                "summary": summary,
+                "message": f"⏹️ 节点完成: {node_name}",
+            },
+        )
+    
+    async def _handle_custom_event(self, event_name: str, data: Dict) -> StreamEvent:
+        """处理自定义事件"""
+        # 映射自定义事件名到事件类型
+        event_type_map = {
+            "finding": StreamEventType.FINDING_NEW,
+            "finding_verified": StreamEventType.FINDING_VERIFIED,
+            "progress": StreamEventType.PROGRESS,
+            "warning": StreamEventType.WARNING,
+            "error": StreamEventType.ERROR,
+        }
+        
+        event_type = event_type_map.get(event_name, StreamEventType.INFO)
+        
+        return StreamEvent(
+            event_type=event_type,
+            sequence=self._next_sequence(),
+            node_name=self._current_node,
+            phase=self._current_phase,
+            data=data,
+        )
+    
+    def _truncate_data(self, data: Any, max_length: int = 1000) -> Any:
+        """截断数据"""
+        if isinstance(data, str):
+            return data[:max_length] + "..." if len(data) > max_length else data
+        elif isinstance(data, dict):
+            return {k: self._truncate_data(v, max_length // 2) for k, v in list(data.items())[:10]}
+        elif isinstance(data, list):
+            return [self._truncate_data(item, max_length // len(data)) for item in data[:10]]
+        else:
+            return str(data)[:max_length]
+    
+    def create_progress_event(
+        self,
+        current: int,
+        total: int,
+        message: Optional[str] = None,
+    ) -> StreamEvent:
+        """创建进度事件"""
+        percentage = (current / total * 100) if total > 0 else 0
+        
+        return StreamEvent(
+            event_type=StreamEventType.PROGRESS,
+            sequence=self._next_sequence(),
+            node_name=self._current_node,
+            phase=self._current_phase,
+            data={
+                "current": current,
+                "total": total,
+                "percentage": round(percentage, 1),
+                "message": message or f"进度: {current}/{total}",
+            },
+        )
+    
+    def create_finding_event(
+        self,
+        finding: Dict[str, Any],
+        is_verified: bool = False,
+    ) -> StreamEvent:
+        """创建发现事件"""
+        event_type = StreamEventType.FINDING_VERIFIED if is_verified else StreamEventType.FINDING_NEW
+        
+        return StreamEvent(
+            event_type=event_type,
+            sequence=self._next_sequence(),
+            node_name=self._current_node,
+            phase=self._current_phase,
+            data={
+                "title": finding.get("title", "Unknown"),
+                "severity": finding.get("severity", "medium"),
+                "vulnerability_type": finding.get("vulnerability_type", "other"),
+                "file_path": finding.get("file_path"),
+                "line_start": finding.get("line_start"),
+                "is_verified": is_verified,
+                "message": f"{'✅ 已验证' if is_verified else '🔍 新发现'}: [{finding.get('severity', 'medium').upper()}] {finding.get('title', 'Unknown')}",
+            },
+        )
+    
+    def create_heartbeat(self) -> StreamEvent:
+        """创建心跳事件"""
+        return StreamEvent(
+            event_type=StreamEventType.HEARTBEAT,
+            sequence=self._sequence,  # 心跳不增加序列号
+            data={"message": "ping"},
+        )
+

backend/app/services/agent/streaming/token_streamer.py

@@ -0,0 +1,261 @@
+"""
+LLM Token 流式输出处理器
+支持多种 LLM 提供商的流式输出
+"""
+
+import asyncio
+import logging
+from typing import Any, Dict, Optional, AsyncGenerator, Callable
+from dataclasses import dataclass
+from datetime import datetime, timezone
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class TokenChunk:
+    """Token 块"""
+    content: str
+    token_count: int = 1
+    finish_reason: Optional[str] = None
+    model: Optional[str] = None
+    
+    # 统计信息
+    accumulated_content: str = ""
+    total_tokens: int = 0
+
+
+class TokenStreamer:
+    """
+    LLM Token 流式输出处理器
+    
+    最佳实践:
+    1. 使用 LiteLLM 的流式 API
+    2. 实时发送每个 Token
+    3. 跟踪累积内容和 Token 使用
+    4. 支持中断和超时
+    """
+    
+    def __init__(
+        self,
+        model: str,
+        api_key: Optional[str] = None,
+        base_url: Optional[str] = None,
+        on_token: Optional[Callable[[TokenChunk], None]] = None,
+    ):
+        self.model = model
+        self.api_key = api_key
+        self.base_url = base_url
+        self.on_token = on_token
+        
+        self._cancelled = False
+        self._accumulated_content = ""
+        self._total_tokens = 0
+    
+    def cancel(self):
+        """取消流式输出"""
+        self._cancelled = True
+    
+    async def stream_completion(
+        self,
+        messages: list[Dict[str, str]],
+        temperature: float = 0.1,
+        max_tokens: int = 4096,
+    ) -> AsyncGenerator[TokenChunk, None]:
+        """
+        流式调用 LLM
+        
+        Args:
+            messages: 消息列表
+            temperature: 温度
+            max_tokens: 最大 Token 数
+            
+        Yields:
+            TokenChunk: Token 块
+        """
+        try:
+            import litellm
+            
+            response = await litellm.acompletion(
+                model=self.model,
+                messages=messages,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                api_key=self.api_key,
+                base_url=self.base_url,
+                stream=True,  # 启用流式输出
+            )
+            
+            async for chunk in response:
+                if self._cancelled:
+                    break
+                
+                # 提取内容
+                content = ""
+                finish_reason = None
+                
+                if hasattr(chunk, "choices") and chunk.choices:
+                    choice = chunk.choices[0]
+                    if hasattr(choice, "delta") and choice.delta:
+                        content = getattr(choice.delta, "content", "") or ""
+                    finish_reason = getattr(choice, "finish_reason", None)
+                
+                if content:
+                    self._accumulated_content += content
+                    self._total_tokens += 1
+                    
+                    token_chunk = TokenChunk(
+                        content=content,
+                        token_count=1,
+                        finish_reason=finish_reason,
+                        model=self.model,
+                        accumulated_content=self._accumulated_content,
+                        total_tokens=self._total_tokens,
+                    )
+                    
+                    # 回调
+                    if self.on_token:
+                        self.on_token(token_chunk)
+                    
+                    yield token_chunk
+                
+                # 检查是否完成
+                if finish_reason:
+                    break
+                    
+        except asyncio.CancelledError:
+            logger.info("Token streaming cancelled")
+            raise
+            
+        except Exception as e:
+            logger.error(f"Token streaming error: {e}")
+            raise
+    
+    async def stream_with_tools(
+        self,
+        messages: list[Dict[str, str]],
+        tools: list[Dict[str, Any]],
+        temperature: float = 0.1,
+        max_tokens: int = 4096,
+    ) -> AsyncGenerator[Dict[str, Any], None]:
+        """
+        带工具调用的流式输出
+        
+        Args:
+            messages: 消息列表
+            tools: 工具定义列表
+            temperature: 温度
+            max_tokens: 最大 Token 数
+            
+        Yields:
+            包含 token 或 tool_call 的字典
+        """
+        try:
+            import litellm
+            
+            response = await litellm.acompletion(
+                model=self.model,
+                messages=messages,
+                tools=tools,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                api_key=self.api_key,
+                base_url=self.base_url,
+                stream=True,
+            )
+            
+            # 工具调用累积器
+            tool_calls_accumulator: Dict[int, Dict] = {}
+            
+            async for chunk in response:
+                if self._cancelled:
+                    break
+                
+                if not hasattr(chunk, "choices") or not chunk.choices:
+                    continue
+                
+                choice = chunk.choices[0]
+                delta = getattr(choice, "delta", None)
+                finish_reason = getattr(choice, "finish_reason", None)
+                
+                if delta:
+                    # 处理文本内容
+                    content = getattr(delta, "content", "") or ""
+                    if content:
+                        self._accumulated_content += content
+                        self._total_tokens += 1
+                        
+                        yield {
+                            "type": "token",
+                            "content": content,
+                            "accumulated": self._accumulated_content,
+                            "total_tokens": self._total_tokens,
+                        }
+                    
+                    # 处理工具调用
+                    tool_calls = getattr(delta, "tool_calls", None) or []
+                    for tool_call in tool_calls:
+                        idx = tool_call.index
+                        
+                        if idx not in tool_calls_accumulator:
+                            tool_calls_accumulator[idx] = {
+                                "id": tool_call.id or "",
+                                "name": "",
+                                "arguments": "",
+                            }
+                        
+                        if tool_call.function:
+                            if tool_call.function.name:
+                                tool_calls_accumulator[idx]["name"] = tool_call.function.name
+                            if tool_call.function.arguments:
+                                tool_calls_accumulator[idx]["arguments"] += tool_call.function.arguments
+                        
+                        yield {
+                            "type": "tool_call_chunk",
+                            "index": idx,
+                            "tool_call": tool_calls_accumulator[idx],
+                        }
+                
+                # 完成时发送最终工具调用
+                if finish_reason == "tool_calls":
+                    for idx, tool_call in tool_calls_accumulator.items():
+                        yield {
+                            "type": "tool_call_complete",
+                            "index": idx,
+                            "tool_call": tool_call,
+                        }
+                
+                if finish_reason:
+                    yield {
+                        "type": "finish",
+                        "reason": finish_reason,
+                        "accumulated": self._accumulated_content,
+                        "total_tokens": self._total_tokens,
+                    }
+                    break
+                    
+        except asyncio.CancelledError:
+            logger.info("Tool streaming cancelled")
+            raise
+            
+        except Exception as e:
+            logger.error(f"Tool streaming error: {e}")
+            yield {
+                "type": "error",
+                "error": str(e),
+            }
+    
+    def get_accumulated_content(self) -> str:
+        """获取累积内容"""
+        return self._accumulated_content
+    
+    def get_total_tokens(self) -> int:
+        """获取总 Token 数"""
+        return self._total_tokens
+    
+    def reset(self):
+        """重置状态"""
+        self._cancelled = False
+        self._accumulated_content = ""
+        self._total_tokens = 0
+

backend/app/services/agent/streaming/tool_stream.py

@@ -0,0 +1,319 @@
+"""
+工具调用流式处理器
+展示工具调用的输入、执行过程和输出
+"""
+
+import asyncio
+import time
+import logging
+from typing import Any, Dict, Optional, AsyncGenerator, List, Callable
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from enum import Enum
+
+logger = logging.getLogger(__name__)
+
+
+class ToolCallState(str, Enum):
+    """工具调用状态"""
+    PENDING = "pending"       # 等待执行
+    RUNNING = "running"       # 执行中
+    SUCCESS = "success"       # 成功
+    ERROR = "error"          # 错误
+    TIMEOUT = "timeout"      # 超时
+
+
+@dataclass
+class ToolCallEvent:
+    """工具调用事件"""
+    tool_name: str
+    state: ToolCallState
+    
+    # 输入输出
+    input_params: Dict[str, Any] = field(default_factory=dict)
+    output_data: Optional[Any] = None
+    error_message: Optional[str] = None
+    
+    # 时间
+    start_time: Optional[float] = None
+    end_time: Optional[float] = None
+    duration_ms: int = 0
+    
+    # 元数据
+    call_id: Optional[str] = None
+    sequence: int = 0
+    timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """转换为字典"""
+        return {
+            "tool_name": self.tool_name,
+            "state": self.state.value,
+            "input_params": self._truncate(self.input_params),
+            "output_data": self._truncate(self.output_data),
+            "error_message": self.error_message,
+            "duration_ms": self.duration_ms,
+            "call_id": self.call_id,
+            "sequence": self.sequence,
+            "timestamp": self.timestamp,
+        }
+    
+    def _truncate(self, data: Any, max_length: int = 500) -> Any:
+        """截断数据"""
+        if data is None:
+            return None
+        if isinstance(data, str):
+            return data[:max_length] + "..." if len(data) > max_length else data
+        elif isinstance(data, dict):
+            return {k: self._truncate(v, max_length // 2) for k, v in list(data.items())[:20]}
+        elif isinstance(data, list):
+            max_items = min(20, len(data))
+            return [self._truncate(item, max_length // max_items) for item in data[:max_items]]
+        else:
+            s = str(data)
+            return s[:max_length] + "..." if len(s) > max_length else s
+
+
+class ToolStreamHandler:
+    """
+    工具调用流式处理器
+    
+    功能:
+    1. 跟踪工具调用状态
+    2. 记录输入参数
+    3. 流式输出执行过程
+    4. 记录输出和执行时间
+    """
+    
+    def __init__(
+        self,
+        on_event: Optional[Callable[[ToolCallEvent], None]] = None,
+    ):
+        self.on_event = on_event
+        self._sequence = 0
+        self._active_calls: Dict[str, ToolCallEvent] = {}
+        self._history: List[ToolCallEvent] = []
+    
+    def _next_sequence(self) -> int:
+        """获取下一个序列号"""
+        self._sequence += 1
+        return self._sequence
+    
+    def _generate_call_id(self) -> str:
+        """生成调用 ID"""
+        import uuid
+        return str(uuid.uuid4())[:8]
+    
+    async def emit_tool_start(
+        self,
+        tool_name: str,
+        input_params: Dict[str, Any],
+        call_id: Optional[str] = None,
+    ) -> ToolCallEvent:
+        """
+        发射工具开始事件
+        
+        Args:
+            tool_name: 工具名称
+            input_params: 输入参数
+            call_id: 调用 ID
+            
+        Returns:
+            工具调用事件
+        """
+        call_id = call_id or self._generate_call_id()
+        
+        event = ToolCallEvent(
+            tool_name=tool_name,
+            state=ToolCallState.RUNNING,
+            input_params=input_params,
+            start_time=time.time(),
+            call_id=call_id,
+            sequence=self._next_sequence(),
+        )
+        
+        self._active_calls[call_id] = event
+        
+        if self.on_event:
+            self.on_event(event)
+        
+        return event
+    
+    async def emit_tool_end(
+        self,
+        call_id: str,
+        output_data: Any,
+        is_error: bool = False,
+        error_message: Optional[str] = None,
+    ) -> ToolCallEvent:
+        """
+        发射工具结束事件
+        
+        Args:
+            call_id: 调用 ID
+            output_data: 输出数据
+            is_error: 是否错误
+            error_message: 错误消息
+            
+        Returns:
+            工具调用事件
+        """
+        if call_id not in self._active_calls:
+            logger.warning(f"Unknown tool call: {call_id}")
+            return None
+        
+        event = self._active_calls[call_id]
+        event.end_time = time.time()
+        event.duration_ms = int((event.end_time - event.start_time) * 1000) if event.start_time else 0
+        event.output_data = output_data
+        event.sequence = self._next_sequence()
+        
+        if is_error:
+            event.state = ToolCallState.ERROR
+            event.error_message = error_message or str(output_data)
+        else:
+            event.state = ToolCallState.SUCCESS
+        
+        # 移动到历史记录
+        del self._active_calls[call_id]
+        self._history.append(event)
+        
+        if self.on_event:
+            self.on_event(event)
+        
+        return event
+    
+    async def emit_tool_timeout(self, call_id: str, timeout_seconds: int) -> ToolCallEvent:
+        """发射工具超时事件"""
+        if call_id not in self._active_calls:
+            return None
+        
+        event = self._active_calls[call_id]
+        event.end_time = time.time()
+        event.duration_ms = int((event.end_time - event.start_time) * 1000) if event.start_time else 0
+        event.state = ToolCallState.TIMEOUT
+        event.error_message = f"Tool execution timed out after {timeout_seconds}s"
+        event.sequence = self._next_sequence()
+        
+        del self._active_calls[call_id]
+        self._history.append(event)
+        
+        if self.on_event:
+            self.on_event(event)
+        
+        return event
+    
+    def wrap_tool(
+        self,
+        tool_func: Callable,
+        tool_name: str,
+        timeout: Optional[int] = None,
+    ) -> Callable:
+        """
+        包装工具函数以自动跟踪
+        
+        Args:
+            tool_func: 工具函数
+            tool_name: 工具名称
+            timeout: 超时时间（秒）
+            
+        Returns:
+            包装后的函数
+        """
+        async def wrapped(*args, **kwargs):
+            call_id = self._generate_call_id()
+            
+            # 发射开始事件
+            await self.emit_tool_start(
+                tool_name=tool_name,
+                input_params={"args": args, "kwargs": kwargs},
+                call_id=call_id,
+            )
+            
+            try:
+                # 执行工具
+                if asyncio.iscoroutinefunction(tool_func):
+                    if timeout:
+                        result = await asyncio.wait_for(
+                            tool_func(*args, **kwargs),
+                            timeout=timeout,
+                        )
+                    else:
+                        result = await tool_func(*args, **kwargs)
+                else:
+                    if timeout:
+                        result = await asyncio.wait_for(
+                            asyncio.to_thread(tool_func, *args, **kwargs),
+                            timeout=timeout,
+                        )
+                    else:
+                        result = tool_func(*args, **kwargs)
+                
+                # 发射结束事件
+                await self.emit_tool_end(call_id, result)
+                
+                return result
+                
+            except asyncio.TimeoutError:
+                await self.emit_tool_timeout(call_id, timeout or 0)
+                raise
+                
+            except Exception as e:
+                await self.emit_tool_end(call_id, None, is_error=True, error_message=str(e))
+                raise
+        
+        return wrapped
+    
+    def get_active_calls(self) -> List[ToolCallEvent]:
+        """获取活跃的调用"""
+        return list(self._active_calls.values())
+    
+    def get_history(self, limit: int = 100) -> List[ToolCallEvent]:
+        """获取历史记录"""
+        return self._history[-limit:]
+    
+    def get_stats(self) -> Dict[str, Any]:
+        """获取统计信息"""
+        total_calls = len(self._history)
+        success_calls = sum(1 for e in self._history if e.state == ToolCallState.SUCCESS)
+        error_calls = sum(1 for e in self._history if e.state == ToolCallState.ERROR)
+        timeout_calls = sum(1 for e in self._history if e.state == ToolCallState.TIMEOUT)
+        
+        total_duration = sum(e.duration_ms for e in self._history)
+        avg_duration = total_duration / total_calls if total_calls > 0 else 0
+        
+        # 按工具统计
+        tool_stats = {}
+        for event in self._history:
+            if event.tool_name not in tool_stats:
+                tool_stats[event.tool_name] = {
+                    "calls": 0,
+                    "success": 0,
+                    "errors": 0,
+                    "total_duration_ms": 0,
+                }
+            tool_stats[event.tool_name]["calls"] += 1
+            if event.state == ToolCallState.SUCCESS:
+                tool_stats[event.tool_name]["success"] += 1
+            elif event.state in [ToolCallState.ERROR, ToolCallState.TIMEOUT]:
+                tool_stats[event.tool_name]["errors"] += 1
+            tool_stats[event.tool_name]["total_duration_ms"] += event.duration_ms
+        
+        return {
+            "total_calls": total_calls,
+            "success_calls": success_calls,
+            "error_calls": error_calls,
+            "timeout_calls": timeout_calls,
+            "success_rate": success_calls / total_calls if total_calls > 0 else 0,
+            "total_duration_ms": total_duration,
+            "avg_duration_ms": round(avg_duration, 2),
+            "active_calls": len(self._active_calls),
+            "by_tool": tool_stats,
+        }
+    
+    def clear(self):
+        """清空记录"""
+        self._active_calls.clear()
+        self._history.clear()
+        self._sequence = 0
+

backend/app/services/agent/tools/pattern_tool.py

@@ -41,6 +41,16 @@ class PatternMatchTool(AgentTool):
     使用正则表达式快速扫描代码中的危险模式
     """
     
+    def __init__(self, project_root: str = None):
+        """
+        初始化模式匹配工具
+        
+        Args:
+            project_root: 项目根目录（可选，用于上下文）
+        """
+        super().__init__()
+        self.project_root = project_root
+    
     # 危险模式定义
     PATTERNS: Dict[str, Dict[str, Any]] = {
         # SQL 注入模式

backend/app/services/llm/adapters/baidu_adapter.py

@@ -145,3 +145,5 @@ class BaiduAdapter(BaseLLMAdapter):
 
 
 
+
+

backend/app/services/llm/adapters/doubao_adapter.py

@@ -83,3 +83,5 @@ class DoubaoAdapter(BaseLLMAdapter):
 
 
 
+
+

backend/app/services/llm/adapters/minimax_adapter.py

@@ -86,3 +86,5 @@ class MinimaxAdapter(BaseLLMAdapter):
 
 
 
+
+

backend/app/services/llm/base_adapter.py

@@ -134,3 +134,5 @@ class BaseLLMAdapter(ABC):
 
 
 
+
+

backend/app/services/llm/types.py

@@ -120,3 +120,5 @@ DEFAULT_BASE_URLS: Dict[LLMProvider, str] = {
 
 
 
+
+

backend/tests/agent/__init__.py

@@ -0,0 +1,5 @@
+"""
+DeepAudit Agent 测试套件
+企业级测试框架，覆盖工具、Agent、节点和完整流程
+"""
+

backend/tests/agent/conftest.py

@@ -0,0 +1,295 @@
+"""
+Agent 测试配置和 Fixtures
+提供测试所需的公共设施
+"""
+
+import pytest
+import asyncio
+import tempfile
+import shutil
+import os
+from typing import Dict, Any, Optional
+from unittest.mock import AsyncMock, MagicMock, patch
+from dataclasses import dataclass
+
+
+# ============ 测试配置 ============
+
+@pytest.fixture(scope="session")
+def event_loop():
+    """创建事件循环"""
+    loop = asyncio.new_event_loop()
+    yield loop
+    loop.close()
+
+
+@pytest.fixture
+def temp_project_dir():
+    """创建临时项目目录，包含测试代码"""
+    temp_dir = tempfile.mkdtemp(prefix="deepaudit_test_")
+    
+    # 创建测试项目结构
+    os.makedirs(os.path.join(temp_dir, "src"), exist_ok=True)
+    os.makedirs(os.path.join(temp_dir, "config"), exist_ok=True)
+    
+    # 创建有漏洞的测试代码 - SQL 注入
+    sql_vuln_code = '''
+import sqlite3
+
+def get_user(user_id):
+    """危险：SQL 注入漏洞"""
+    conn = sqlite3.connect("users.db")
+    cursor = conn.cursor()
+    # 直接拼接用户输入，存在 SQL 注入风险
+    query = f"SELECT * FROM users WHERE id = '{user_id}'"
+    cursor.execute(query)
+    return cursor.fetchone()
+
+def search_users(name):
+    """危险：SQL 注入漏洞"""
+    conn = sqlite3.connect("users.db")
+    cursor = conn.cursor()
+    cursor.execute("SELECT * FROM users WHERE name LIKE '%" + name + "%'")
+    return cursor.fetchall()
+'''
+    
+    # 创建有漏洞的测试代码 - 命令注入
+    cmd_vuln_code = '''
+import os
+import subprocess
+
+def run_command(user_input):
+    """危险：命令注入漏洞"""
+    # 直接执行用户输入
+    os.system(f"echo {user_input}")
+    
+def execute_script(script_name):
+    """危险：命令注入漏洞"""
+    subprocess.call(f"bash {script_name}", shell=True)
+'''
+    
+    # 创建有漏洞的测试代码 - XSS
+    xss_vuln_code = '''
+from flask import Flask, request, render_template_string
+
+app = Flask(__name__)
+
+@app.route("/greet")
+def greet():
+    """危险：XSS 漏洞"""
+    name = request.args.get("name", "")
+    # 直接将用户输入嵌入 HTML，存在 XSS 风险
+    return f"<h1>Hello, {name}!</h1>"
+
+@app.route("/search")
+def search():
+    """危险：XSS 漏洞"""
+    query = request.args.get("q", "")
+    html = f"<p>搜索结果: {query}</p>"
+    return render_template_string(html)
+'''
+    
+    # 创建有漏洞的测试代码 - 路径遍历
+    path_vuln_code = '''
+import os
+
+def read_file(filename):
+    """危险：路径遍历漏洞"""
+    # 没有验证文件路径
+    filepath = os.path.join("/app/data", filename)
+    with open(filepath, "r") as f:
+        return f.read()
+
+def download_file(user_path):
+    """危险：路径遍历漏洞"""
+    # 直接使用用户输入作为文件路径
+    with open(user_path, "rb") as f:
+        return f.read()
+'''
+    
+    # 创建有漏洞的测试代码 - 硬编码密钥
+    secret_vuln_code = '''
+# 配置文件
+DATABASE_URL = "postgresql://user:password123@localhost/db"
+API_KEY = "sk-1234567890abcdef1234567890abcdef"
+SECRET_KEY = "super_secret_key_dont_share"
+AWS_SECRET_ACCESS_KEY = "wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY"
+
+def connect_database():
+    password = "admin123"  # 硬编码密码
+    return f"mysql://root:{password}@localhost/mydb"
+'''
+    
+    # 创建安全的代码（用于对比）
+    safe_code = '''
+import sqlite3
+from typing import Optional
+
+def get_user_safe(user_id: int) -> Optional[dict]:
+    """安全：使用参数化查询"""
+    conn = sqlite3.connect("users.db")
+    cursor = conn.cursor()
+    cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))
+    return cursor.fetchone()
+
+def validate_input(user_input: str) -> str:
+    """输入验证"""
+    import re
+    if not re.match(r'^[a-zA-Z0-9_]+$', user_input):
+        raise ValueError("Invalid input")
+    return user_input
+'''
+    
+    # 创建配置文件
+    config_code = '''
+import os
+
+class Config:
+    """安全配置"""
+    DATABASE_URL = os.environ.get("DATABASE_URL")
+    SECRET_KEY = os.environ.get("SECRET_KEY")
+    DEBUG = False
+'''
+    
+    # 创建 requirements.txt
+    requirements = '''
+flask>=2.0.0
+sqlalchemy>=2.0.0
+requests>=2.28.0
+'''
+    
+    # 写入文件
+    with open(os.path.join(temp_dir, "src", "sql_vuln.py"), "w") as f:
+        f.write(sql_vuln_code)
+    
+    with open(os.path.join(temp_dir, "src", "cmd_vuln.py"), "w") as f:
+        f.write(cmd_vuln_code)
+    
+    with open(os.path.join(temp_dir, "src", "xss_vuln.py"), "w") as f:
+        f.write(xss_vuln_code)
+    
+    with open(os.path.join(temp_dir, "src", "path_vuln.py"), "w") as f:
+        f.write(path_vuln_code)
+    
+    with open(os.path.join(temp_dir, "src", "secrets.py"), "w") as f:
+        f.write(secret_vuln_code)
+    
+    with open(os.path.join(temp_dir, "src", "safe_code.py"), "w") as f:
+        f.write(safe_code)
+    
+    with open(os.path.join(temp_dir, "config", "settings.py"), "w") as f:
+        f.write(config_code)
+    
+    with open(os.path.join(temp_dir, "requirements.txt"), "w") as f:
+        f.write(requirements)
+    
+    yield temp_dir
+    
+    # 清理
+    shutil.rmtree(temp_dir, ignore_errors=True)
+
+
+@pytest.fixture
+def mock_llm_service():
+    """模拟 LLM 服务"""
+    service = MagicMock()
+    service.chat_completion_raw = AsyncMock(return_value={
+        "content": "测试响应",
+        "usage": {"total_tokens": 100},
+    })
+    return service
+
+
+@pytest.fixture
+def mock_event_emitter():
+    """模拟事件发射器"""
+    emitter = MagicMock()
+    emitter.emit_info = AsyncMock()
+    emitter.emit_warning = AsyncMock()
+    emitter.emit_error = AsyncMock()
+    emitter.emit_thinking = AsyncMock()
+    emitter.emit_tool_call = AsyncMock()
+    emitter.emit_tool_result = AsyncMock()
+    emitter.emit_finding = AsyncMock()
+    emitter.emit_progress = AsyncMock()
+    emitter.emit_phase_start = AsyncMock()
+    emitter.emit_phase_complete = AsyncMock()
+    emitter.emit_task_complete = AsyncMock()
+    emitter.emit = AsyncMock()
+    return emitter
+
+
+@pytest.fixture
+def mock_db_session():
+    """模拟数据库会话"""
+    session = AsyncMock()
+    session.add = MagicMock()
+    session.commit = AsyncMock()
+    session.rollback = AsyncMock()
+    session.get = AsyncMock(return_value=None)
+    session.execute = AsyncMock()
+    return session
+
+
+@dataclass
+class MockProject:
+    """模拟项目"""
+    id: str = "test-project-id"
+    name: str = "Test Project"
+    description: str = "Test project for unit tests"
+
+
+@dataclass
+class MockAgentTask:
+    """模拟 Agent 任务"""
+    id: str = "test-task-id"
+    project_id: str = "test-project-id"
+    project: MockProject = None
+    name: str = "Test Agent Task"
+    status: str = "pending"
+    current_phase: str = "planning"
+    target_vulnerabilities: list = None
+    verification_level: str = "sandbox"
+    exclude_patterns: list = None
+    target_files: list = None
+    max_iterations: int = 50
+    timeout_seconds: int = 1800
+    
+    def __post_init__(self):
+        if self.project is None:
+            self.project = MockProject()
+        if self.target_vulnerabilities is None:
+            self.target_vulnerabilities = []
+        if self.exclude_patterns is None:
+            self.exclude_patterns = []
+        if self.target_files is None:
+            self.target_files = []
+
+
+@pytest.fixture
+def mock_task():
+    """创建模拟任务"""
+    return MockAgentTask()
+
+
+# ============ 测试辅助函数 ============
+
+def assert_finding_valid(finding: Dict[str, Any]):
+    """验证漏洞发现的格式"""
+    required_fields = ["title", "severity", "vulnerability_type"]
+    for field in required_fields:
+        assert field in finding, f"Missing required field: {field}"
+    
+    valid_severities = ["critical", "high", "medium", "low", "info"]
+    assert finding["severity"] in valid_severities, f"Invalid severity: {finding['severity']}"
+
+
+def count_findings_by_type(findings: list, vuln_type: str) -> int:
+    """统计特定类型的漏洞数量"""
+    return sum(1 for f in findings if f.get("vulnerability_type") == vuln_type)
+
+
+def count_findings_by_severity(findings: list, severity: str) -> int:
+    """统计特定严重程度的漏洞数量"""
+    return sum(1 for f in findings if f.get("severity") == severity)
+

backend/tests/agent/run_tests.py

@@ -0,0 +1,96 @@
+#!/usr/bin/env python
+"""
+Agent 测试运行器
+运行所有 Agent 相关测试并生成报告
+"""
+
+import subprocess
+import sys
+import os
+from datetime import datetime
+from pathlib import Path
+
+
+def run_tests():
+    """运行测试"""
+    # 获取项目根目录
+    project_root = Path(__file__).parent.parent.parent
+    os.chdir(project_root)
+    
+    print("=" * 60)
+    print("DeepAudit Agent 测试套件")
+    print(f"时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    print("=" * 60)
+    print()
+    
+    # 测试命令
+    cmd = [
+        sys.executable, "-m", "pytest",
+        "tests/agent/",
+        "-v",
+        "--tb=short",
+        "-x",  # 遇到第一个失败就停止
+        "--color=yes",
+    ]
+    
+    print(f"运行命令: {' '.join(cmd)}")
+    print()
+    
+    # 运行测试
+    result = subprocess.run(cmd, cwd=project_root)
+    
+    print()
+    print("=" * 60)
+    if result.returncode == 0:
+        print("✅ 所有测试通过!")
+    else:
+        print(f"❌ 测试失败 (退出码: {result.returncode})")
+    print("=" * 60)
+    
+    return result.returncode
+
+
+def run_tests_with_coverage():
+    """运行测试并生成覆盖率报告"""
+    project_root = Path(__file__).parent.parent.parent
+    os.chdir(project_root)
+    
+    cmd = [
+        sys.executable, "-m", "pytest",
+        "tests/agent/",
+        "-v",
+        "--cov=app/services/agent",
+        "--cov-report=term-missing",
+        "--cov-report=html:coverage_agent",
+    ]
+    
+    result = subprocess.run(cmd, cwd=project_root)
+    return result.returncode
+
+
+def run_specific_test(test_name: str):
+    """运行特定测试"""
+    project_root = Path(__file__).parent.parent.parent
+    os.chdir(project_root)
+    
+    cmd = [
+        sys.executable, "-m", "pytest",
+        f"tests/agent/{test_name}",
+        "-v",
+        "--tb=long",
+        "-s",  # 显示 print 输出
+    ]
+    
+    result = subprocess.run(cmd, cwd=project_root)
+    return result.returncode
+
+
+if __name__ == "__main__":
+    if len(sys.argv) > 1:
+        if sys.argv[1] == "--coverage":
+            sys.exit(run_tests_with_coverage())
+        else:
+            sys.exit(run_specific_test(sys.argv[1]))
+    else:
+        sys.exit(run_tests())
+

backend/tests/agent/test_agents.py

@@ -0,0 +1,213 @@
+"""
+Agent 单元测试
+测试各个 Agent 的功能
+"""
+
+import pytest
+import asyncio
+import os
+from unittest.mock import MagicMock, AsyncMock, patch
+
+from app.services.agent.agents.base import BaseAgent, AgentConfig, AgentResult, AgentType, AgentPattern
+from app.services.agent.agents.recon import ReconAgent
+from app.services.agent.agents.analysis import AnalysisAgent
+from app.services.agent.agents.verification import VerificationAgent
+
+
+class TestReconAgent:
+    """Recon Agent 测试"""
+    
+    @pytest.fixture
+    def recon_agent(self, temp_project_dir, mock_llm_service, mock_event_emitter):
+        """创建 Recon Agent 实例"""
+        from app.services.agent.tools import (
+            FileReadTool, FileSearchTool, ListFilesTool,
+        )
+        
+        tools = {
+            "list_files": ListFilesTool(temp_project_dir),
+            "read_file": FileReadTool(temp_project_dir),
+            "search_code": FileSearchTool(temp_project_dir),
+        }
+        
+        return ReconAgent(
+            llm_service=mock_llm_service,
+            tools=tools,
+            event_emitter=mock_event_emitter,
+        )
+    
+    @pytest.mark.asyncio
+    async def test_recon_agent_run(self, recon_agent, temp_project_dir):
+        """测试 Recon Agent 运行"""
+        result = await recon_agent.run({
+            "project_info": {
+                "name": "Test Project",
+                "root": temp_project_dir,
+            },
+            "config": {},
+        })
+        
+        assert result.success is True
+        assert result.data is not None
+        
+        # 验证返回数据结构
+        data = result.data
+        assert "tech_stack" in data
+        assert "entry_points" in data or "high_risk_areas" in data
+    
+    @pytest.mark.asyncio
+    async def test_recon_agent_identifies_python(self, recon_agent, temp_project_dir):
+        """测试 Recon Agent 识别 Python 技术栈"""
+        result = await recon_agent.run({
+            "project_info": {"root": temp_project_dir},
+            "config": {},
+        })
+        
+        assert result.success is True
+        tech_stack = result.data.get("tech_stack", {})
+        languages = tech_stack.get("languages", [])
+        
+        # 应该识别出 Python
+        assert "Python" in languages or len(languages) > 0
+    
+    @pytest.mark.asyncio
+    async def test_recon_agent_finds_high_risk_areas(self, recon_agent, temp_project_dir):
+        """测试 Recon Agent 发现高风险区域"""
+        result = await recon_agent.run({
+            "project_info": {"root": temp_project_dir},
+            "config": {},
+        })
+        
+        assert result.success is True
+        high_risk_areas = result.data.get("high_risk_areas", [])
+        
+        # 应该发现高风险区域
+        assert len(high_risk_areas) > 0
+
+
+class TestAnalysisAgent:
+    """Analysis Agent 测试"""
+    
+    @pytest.fixture
+    def analysis_agent(self, temp_project_dir, mock_llm_service, mock_event_emitter):
+        """创建 Analysis Agent 实例"""
+        from app.services.agent.tools import (
+            FileReadTool, FileSearchTool, PatternMatchTool,
+        )
+        
+        tools = {
+            "read_file": FileReadTool(temp_project_dir),
+            "search_code": FileSearchTool(temp_project_dir),
+            "pattern_match": PatternMatchTool(temp_project_dir),
+        }
+        
+        return AnalysisAgent(
+            llm_service=mock_llm_service,
+            tools=tools,
+            event_emitter=mock_event_emitter,
+        )
+    
+    @pytest.mark.asyncio
+    async def test_analysis_agent_run(self, analysis_agent, temp_project_dir):
+        """测试 Analysis Agent 运行"""
+        result = await analysis_agent.run({
+            "tech_stack": {"languages": ["Python"]},
+            "entry_points": [],
+            "high_risk_areas": ["src/sql_vuln.py", "src/cmd_vuln.py"],
+            "config": {},
+        })
+        
+        assert result.success is True
+        assert result.data is not None
+    
+    @pytest.mark.asyncio
+    async def test_analysis_agent_finds_vulnerabilities(self, analysis_agent, temp_project_dir):
+        """测试 Analysis Agent 发现漏洞"""
+        result = await analysis_agent.run({
+            "tech_stack": {"languages": ["Python"]},
+            "entry_points": [],
+            "high_risk_areas": [
+                "src/sql_vuln.py",
+                "src/cmd_vuln.py",
+                "src/xss_vuln.py",
+                "src/secrets.py",
+            ],
+            "config": {},
+        })
+        
+        assert result.success is True
+        findings = result.data.get("findings", [])
+        
+        # 应该发现一些漏洞
+        # 注意：具体数量取决于分析逻辑
+        assert isinstance(findings, list)
+
+
+class TestAgentResult:
+    """Agent 结果测试"""
+    
+    def test_agent_result_success(self):
+        """测试成功的 Agent 结果"""
+        result = AgentResult(
+            success=True,
+            data={"findings": []},
+            iterations=5,
+            tool_calls=10,
+        )
+        
+        assert result.success is True
+        assert result.iterations == 5
+        assert result.tool_calls == 10
+    
+    def test_agent_result_failure(self):
+        """测试失败的 Agent 结果"""
+        result = AgentResult(
+            success=False,
+            error="Test error",
+        )
+        
+        assert result.success is False
+        assert result.error == "Test error"
+    
+    def test_agent_result_to_dict(self):
+        """测试 Agent 结果转字典"""
+        result = AgentResult(
+            success=True,
+            data={"key": "value"},
+            iterations=3,
+        )
+        
+        d = result.to_dict()
+        
+        assert d["success"] is True
+        assert d["iterations"] == 3
+
+
+class TestAgentConfig:
+    """Agent 配置测试"""
+    
+    def test_agent_config_defaults(self):
+        """测试 Agent 配置默认值"""
+        config = AgentConfig(
+            name="Test",
+            agent_type=AgentType.RECON,
+        )
+        
+        assert config.pattern == AgentPattern.REACT
+        assert config.max_iterations == 20
+        assert config.temperature == 0.1
+    
+    def test_agent_config_custom(self):
+        """测试自定义 Agent 配置"""
+        config = AgentConfig(
+            name="Custom",
+            agent_type=AgentType.ANALYSIS,
+            pattern=AgentPattern.PLAN_AND_EXECUTE,
+            max_iterations=50,
+            temperature=0.5,
+        )
+        
+        assert config.pattern == AgentPattern.PLAN_AND_EXECUTE
+        assert config.max_iterations == 50
+        assert config.temperature == 0.5
+

backend/tests/agent/test_integration.py

@@ -0,0 +1,355 @@
+"""
+Agent 集成测试
+测试完整的审计流程
+"""
+
+import pytest
+import asyncio
+import os
+from unittest.mock import MagicMock, AsyncMock, patch
+from datetime import datetime
+
+from app.services.agent.graph.runner import AgentRunner, LLMService
+from app.services.agent.graph.audit_graph import AuditState, create_audit_graph
+from app.services.agent.graph.nodes import ReconNode, AnalysisNode, VerificationNode, ReportNode
+from app.services.agent.event_manager import EventManager, AgentEventEmitter
+
+
+class TestLLMService:
+    """LLM 服务测试"""
+    
+    @pytest.mark.asyncio
+    async def test_llm_service_initialization(self):
+        """测试 LLM 服务初始化"""
+        with patch("app.core.config.settings") as mock_settings:
+            mock_settings.LLM_MODEL = "gpt-4o-mini"
+            mock_settings.LLM_API_KEY = "test-key"
+            
+            service = LLMService()
+            
+            assert service.model == "gpt-4o-mini"
+
+
+class TestEventManager:
+    """事件管理器测试"""
+    
+    def test_event_manager_initialization(self):
+        """测试事件管理器初始化"""
+        manager = EventManager()
+        
+        assert manager._event_queues == {}
+        assert manager._event_callbacks == {}
+    
+    @pytest.mark.asyncio
+    async def test_event_emitter(self):
+        """测试事件发射器"""
+        manager = EventManager()
+        emitter = AgentEventEmitter("test-task-id", manager)
+        
+        await emitter.emit_info("Test message")
+        
+        assert emitter._sequence == 1
+    
+    @pytest.mark.asyncio
+    async def test_event_emitter_phase_tracking(self):
+        """测试事件发射器阶段跟踪"""
+        manager = EventManager()
+        emitter = AgentEventEmitter("test-task-id", manager)
+        
+        await emitter.emit_phase_start("recon", "开始信息收集")
+        
+        assert emitter._current_phase == "recon"
+    
+    @pytest.mark.asyncio
+    async def test_event_emitter_task_complete(self):
+        """测试任务完成事件"""
+        manager = EventManager()
+        emitter = AgentEventEmitter("test-task-id", manager)
+        
+        await emitter.emit_task_complete(findings_count=5, duration_ms=1000)
+        
+        assert emitter._sequence == 1
+
+
+class TestAuditGraph:
+    """审计图测试"""
+    
+    def test_create_audit_graph(self, mock_event_emitter):
+        """测试创建审计图"""
+        # 创建模拟节点
+        recon_node = MagicMock()
+        analysis_node = MagicMock()
+        verification_node = MagicMock()
+        report_node = MagicMock()
+        
+        graph = create_audit_graph(
+            recon_node=recon_node,
+            analysis_node=analysis_node,
+            verification_node=verification_node,
+            report_node=report_node,
+        )
+        
+        assert graph is not None
+
+
+class TestReconNode:
+    """Recon 节点测试"""
+    
+    @pytest.fixture
+    def recon_node_with_mock_agent(self, mock_event_emitter):
+        """创建带模拟 Agent 的 Recon 节点"""
+        mock_agent = MagicMock()
+        mock_agent.run = AsyncMock(return_value=MagicMock(
+            success=True,
+            data={
+                "tech_stack": {"languages": ["Python"]},
+                "entry_points": [{"path": "src/app.py", "type": "api"}],
+                "high_risk_areas": ["src/sql_vuln.py"],
+                "dependencies": {},
+                "initial_findings": [],
+            }
+        ))
+        
+        return ReconNode(mock_agent, mock_event_emitter)
+    
+    @pytest.mark.asyncio
+    async def test_recon_node_success(self, recon_node_with_mock_agent):
+        """测试 Recon 节点成功执行"""
+        state = {
+            "project_info": {"name": "Test"},
+            "config": {},
+        }
+        
+        result = await recon_node_with_mock_agent(state)
+        
+        assert "tech_stack" in result
+        assert "entry_points" in result
+        assert result["current_phase"] == "recon_complete"
+    
+    @pytest.mark.asyncio
+    async def test_recon_node_failure(self, mock_event_emitter):
+        """测试 Recon 节点失败处理"""
+        mock_agent = MagicMock()
+        mock_agent.run = AsyncMock(return_value=MagicMock(
+            success=False,
+            error="Test error",
+            data=None,
+        ))
+        
+        node = ReconNode(mock_agent, mock_event_emitter)
+        
+        result = await node({
+            "project_info": {},
+            "config": {},
+        })
+        
+        assert "error" in result
+        assert result["current_phase"] == "error"
+
+
+class TestAnalysisNode:
+    """Analysis 节点测试"""
+    
+    @pytest.fixture
+    def analysis_node_with_mock_agent(self, mock_event_emitter):
+        """创建带模拟 Agent 的 Analysis 节点"""
+        mock_agent = MagicMock()
+        mock_agent.run = AsyncMock(return_value=MagicMock(
+            success=True,
+            data={
+                "findings": [
+                    {
+                        "id": "finding-1",
+                        "title": "SQL Injection",
+                        "severity": "high",
+                        "vulnerability_type": "sql_injection",
+                        "file_path": "src/sql_vuln.py",
+                        "line_start": 10,
+                        "description": "SQL injection vulnerability",
+                    }
+                ],
+                "should_continue": False,
+            }
+        ))
+        
+        return AnalysisNode(mock_agent, mock_event_emitter)
+    
+    @pytest.mark.asyncio
+    async def test_analysis_node_success(self, analysis_node_with_mock_agent):
+        """测试 Analysis 节点成功执行"""
+        state = {
+            "project_info": {"name": "Test"},
+            "tech_stack": {"languages": ["Python"]},
+            "entry_points": [],
+            "high_risk_areas": ["src/sql_vuln.py"],
+            "config": {},
+            "iteration": 0,
+            "findings": [],
+        }
+        
+        result = await analysis_node_with_mock_agent(state)
+        
+        assert "findings" in result
+        assert len(result["findings"]) > 0
+        assert result["iteration"] == 1
+
+
+class TestIntegrationFlow:
+    """完整流程集成测试"""
+    
+    @pytest.mark.asyncio
+    async def test_full_audit_flow_mock(self, temp_project_dir, mock_db_session, mock_task):
+        """测试完整审计流程（使用模拟）"""
+        # 这个测试验证整个流程的连接性
+        
+        # 创建事件管理器
+        event_manager = EventManager()
+        emitter = AgentEventEmitter(mock_task.id, event_manager)
+        
+        # 模拟 LLM 服务
+        mock_llm = MagicMock()
+        mock_llm.chat_completion_raw = AsyncMock(return_value={
+            "content": "Analysis complete",
+            "usage": {"total_tokens": 100},
+        })
+        
+        # 验证事件发射
+        await emitter.emit_phase_start("init", "初始化")
+        await emitter.emit_info("测试消息")
+        await emitter.emit_phase_complete("init", "初始化完成")
+        
+        assert emitter._sequence == 3
+    
+    @pytest.mark.asyncio
+    async def test_audit_state_typing(self):
+        """测试审计状态类型定义"""
+        state: AuditState = {
+            "project_root": "/tmp/test",
+            "project_info": {"name": "Test"},
+            "config": {},
+            "task_id": "test-id",
+            "tech_stack": {},
+            "entry_points": [],
+            "high_risk_areas": [],
+            "dependencies": {},
+            "findings": [],
+            "verified_findings": [],
+            "false_positives": [],
+            "current_phase": "start",
+            "iteration": 0,
+            "max_iterations": 50,
+            "should_continue_analysis": False,
+            "messages": [],
+            "events": [],
+            "summary": None,
+            "security_score": None,
+            "error": None,
+        }
+        
+        assert state["current_phase"] == "start"
+        assert state["max_iterations"] == 50
+
+
+class TestToolIntegration:
+    """工具集成测试"""
+    
+    @pytest.mark.asyncio
+    async def test_tools_work_together(self, temp_project_dir):
+        """测试工具协同工作"""
+        from app.services.agent.tools import (
+            FileReadTool, FileSearchTool, ListFilesTool, PatternMatchTool,
+        )
+        
+        # 1. 列出文件
+        list_tool = ListFilesTool(temp_project_dir)
+        list_result = await list_tool.execute(directory="src", recursive=False)
+        assert list_result.success is True
+        
+        # 2. 搜索关键代码
+        search_tool = FileSearchTool(temp_project_dir)
+        search_result = await search_tool.execute(keyword="execute")
+        assert search_result.success is True
+        
+        # 3. 读取文件内容
+        read_tool = FileReadTool(temp_project_dir)
+        read_result = await read_tool.execute(file_path="src/sql_vuln.py")
+        assert read_result.success is True
+        
+        # 4. 模式匹配
+        pattern_tool = PatternMatchTool(temp_project_dir)
+        pattern_result = await pattern_tool.execute(
+            code=read_result.data,
+            file_path="src/sql_vuln.py",
+            language="python"
+        )
+        assert pattern_result.success is True
+
+
+class TestErrorHandling:
+    """错误处理测试"""
+    
+    @pytest.mark.asyncio
+    async def test_tool_error_handling(self, temp_project_dir):
+        """测试工具错误处理"""
+        from app.services.agent.tools import FileReadTool
+        
+        tool = FileReadTool(temp_project_dir)
+        
+        # 尝试读取不存在的文件
+        result = await tool.execute(file_path="nonexistent/file.py")
+        
+        assert result.success is False
+        assert result.error is not None
+    
+    @pytest.mark.asyncio
+    async def test_agent_graceful_degradation(self, mock_event_emitter):
+        """测试 Agent 优雅降级"""
+        # 创建一个会失败的 Agent
+        mock_agent = MagicMock()
+        mock_agent.run = AsyncMock(side_effect=Exception("Simulated error"))
+        
+        node = ReconNode(mock_agent, mock_event_emitter)
+        
+        result = await node({
+            "project_info": {},
+            "config": {},
+        })
+        
+        # 应该返回错误状态而不是崩溃
+        assert "error" in result
+        assert result["current_phase"] == "error"
+
+
+class TestPerformance:
+    """性能测试"""
+    
+    @pytest.mark.asyncio
+    async def test_tool_response_time(self, temp_project_dir):
+        """测试工具响应时间"""
+        from app.services.agent.tools import ListFilesTool
+        import time
+        
+        tool = ListFilesTool(temp_project_dir)
+        
+        start = time.time()
+        await tool.execute(directory=".", recursive=True)
+        duration = time.time() - start
+        
+        # 工具应该在合理时间内响应
+        assert duration < 5.0  # 5 秒内
+    
+    @pytest.mark.asyncio
+    async def test_multiple_tool_calls(self, temp_project_dir):
+        """测试多次工具调用"""
+        from app.services.agent.tools import FileSearchTool
+        
+        tool = FileSearchTool(temp_project_dir)
+        
+        # 执行多次调用
+        for _ in range(5):
+            result = await tool.execute(keyword="def")
+            assert result.success is True
+        
+        # 验证调用计数
+        assert tool._call_count == 5
+

backend/tests/agent/test_tools.py

@@ -0,0 +1,248 @@
+"""
+Agent 工具单元测试
+测试各种安全分析工具的功能
+"""
+
+import pytest
+import asyncio
+import os
+from unittest.mock import MagicMock, AsyncMock, patch
+
+# 导入工具
+from app.services.agent.tools import (
+    FileReadTool, FileSearchTool, ListFilesTool,
+    PatternMatchTool,
+)
+from app.services.agent.tools.base import ToolResult
+
+
+class TestFileTools:
+    """文件操作工具测试"""
+    
+    @pytest.mark.asyncio
+    async def test_file_read_tool_success(self, temp_project_dir):
+        """测试文件读取工具 - 成功读取"""
+        tool = FileReadTool(temp_project_dir)
+        
+        result = await tool.execute(file_path="src/sql_vuln.py")
+        
+        assert result.success is True
+        assert "SELECT * FROM users" in result.data
+        assert "sql_injection" in result.data.lower() or "cursor.execute" in result.data
+    
+    @pytest.mark.asyncio
+    async def test_file_read_tool_not_found(self, temp_project_dir):
+        """测试文件读取工具 - 文件不存在"""
+        tool = FileReadTool(temp_project_dir)
+        
+        result = await tool.execute(file_path="nonexistent.py")
+        
+        assert result.success is False
+        assert "不存在" in result.error or "not found" in result.error.lower()
+    
+    @pytest.mark.asyncio
+    async def test_file_read_tool_path_traversal_blocked(self, temp_project_dir):
+        """测试文件读取工具 - 路径遍历被阻止"""
+        tool = FileReadTool(temp_project_dir)
+        
+        result = await tool.execute(file_path="../../../etc/passwd")
+        
+        assert result.success is False
+        assert "安全" in result.error or "security" in result.error.lower()
+    
+    @pytest.mark.asyncio
+    async def test_file_search_tool(self, temp_project_dir):
+        """测试文件搜索工具"""
+        tool = FileSearchTool(temp_project_dir)
+        
+        result = await tool.execute(keyword="cursor.execute")
+        
+        assert result.success is True
+        assert "sql_vuln.py" in result.data
+    
+    @pytest.mark.asyncio
+    async def test_list_files_tool(self, temp_project_dir):
+        """测试文件列表工具"""
+        tool = ListFilesTool(temp_project_dir)
+        
+        result = await tool.execute(directory=".", recursive=True)
+        
+        assert result.success is True
+        assert "sql_vuln.py" in result.data
+        assert "requirements.txt" in result.data
+    
+    @pytest.mark.asyncio
+    async def test_list_files_tool_pattern(self, temp_project_dir):
+        """测试文件列表工具 - 文件模式过滤"""
+        tool = ListFilesTool(temp_project_dir)
+        
+        result = await tool.execute(directory="src", pattern="*.py")
+        
+        assert result.success is True
+        assert "sql_vuln.py" in result.data
+
+
+class TestPatternMatchTool:
+    """模式匹配工具测试"""
+    
+    @pytest.mark.asyncio
+    async def test_pattern_match_sql_injection(self, temp_project_dir):
+        """测试模式匹配 - SQL 注入检测"""
+        tool = PatternMatchTool(temp_project_dir)
+        
+        # 读取有漏洞的代码
+        with open(os.path.join(temp_project_dir, "src", "sql_vuln.py")) as f:
+            code = f.read()
+        
+        result = await tool.execute(
+            code=code,
+            file_path="src/sql_vuln.py",
+            pattern_types=["sql_injection"],
+            language="python"
+        )
+        
+        assert result.success is True
+        # 应该检测到 SQL 注入模式
+        if result.data:
+            assert "sql" in str(result.data).lower() or len(result.metadata.get("matches", [])) > 0
+    
+    @pytest.mark.asyncio
+    async def test_pattern_match_command_injection(self, temp_project_dir):
+        """测试模式匹配 - 命令注入检测"""
+        tool = PatternMatchTool(temp_project_dir)
+        
+        with open(os.path.join(temp_project_dir, "src", "cmd_vuln.py")) as f:
+            code = f.read()
+        
+        result = await tool.execute(
+            code=code,
+            file_path="src/cmd_vuln.py",
+            pattern_types=["command_injection"],
+            language="python"
+        )
+        
+        assert result.success is True
+    
+    @pytest.mark.asyncio
+    async def test_pattern_match_xss(self, temp_project_dir):
+        """测试模式匹配 - XSS 检测"""
+        tool = PatternMatchTool(temp_project_dir)
+        
+        with open(os.path.join(temp_project_dir, "src", "xss_vuln.py")) as f:
+            code = f.read()
+        
+        result = await tool.execute(
+            code=code,
+            file_path="src/xss_vuln.py",
+            pattern_types=["xss"],
+            language="python"
+        )
+        
+        assert result.success is True
+    
+    @pytest.mark.asyncio
+    async def test_pattern_match_hardcoded_secrets(self, temp_project_dir):
+        """测试模式匹配 - 硬编码密钥检测"""
+        tool = PatternMatchTool(temp_project_dir)
+        
+        with open(os.path.join(temp_project_dir, "src", "secrets.py")) as f:
+            code = f.read()
+        
+        result = await tool.execute(
+            code=code,
+            file_path="src/secrets.py",
+            pattern_types=["hardcoded_secret"],
+        )
+        
+        assert result.success is True
+    
+    @pytest.mark.asyncio
+    async def test_pattern_match_safe_code(self, temp_project_dir):
+        """测试模式匹配 - 安全代码应该没有问题"""
+        tool = PatternMatchTool(temp_project_dir)
+        
+        with open(os.path.join(temp_project_dir, "src", "safe_code.py")) as f:
+            code = f.read()
+        
+        result = await tool.execute(
+            code=code,
+            file_path="src/safe_code.py",
+            pattern_types=["sql_injection"],
+            language="python"
+        )
+        
+        assert result.success is True
+        # 安全代码使用参数化查询，不应该有 SQL 注入漏洞
+        # 检查结果数据，如果有 matches 字段
+        matches = result.metadata.get("matches", [])
+        if isinstance(matches, list):
+            # 参数化查询不应该被误报为 SQL 注入
+            sql_injection_count = sum(
+                1 for m in matches 
+                if isinstance(m, dict) and "sql" in m.get("pattern_type", "").lower()
+            )
+            # 安全代码的 SQL 注入匹配应该很少或没有
+            assert sql_injection_count <= 1  # 允许少量误报
+
+
+class TestToolResult:
+    """工具结果测试"""
+    
+    def test_tool_result_success(self):
+        """测试成功的工具结果"""
+        result = ToolResult(success=True, data="test data")
+        
+        assert result.success is True
+        assert result.data == "test data"
+        assert result.error is None
+    
+    def test_tool_result_failure(self):
+        """测试失败的工具结果"""
+        result = ToolResult(success=False, error="test error")
+        
+        assert result.success is False
+        assert result.error == "test error"
+    
+    def test_tool_result_to_string(self):
+        """测试工具结果转字符串"""
+        result = ToolResult(success=True, data={"key": "value"})
+        
+        string = result.to_string()
+        
+        assert "key" in string
+        assert "value" in string
+    
+    def test_tool_result_to_string_truncate(self):
+        """测试工具结果字符串截断"""
+        long_data = "x" * 10000
+        result = ToolResult(success=True, data=long_data)
+        
+        string = result.to_string(max_length=100)
+        
+        assert len(string) < len(long_data)
+        assert "truncated" in string.lower()
+
+
+class TestToolMetadata:
+    """工具元数据测试"""
+    
+    @pytest.mark.asyncio
+    async def test_tool_call_count(self, temp_project_dir):
+        """测试工具调用计数"""
+        tool = ListFilesTool(temp_project_dir)
+        
+        await tool.execute(directory=".")
+        await tool.execute(directory="src")
+        
+        assert tool._call_count == 2
+    
+    @pytest.mark.asyncio
+    async def test_tool_duration_tracking(self, temp_project_dir):
+        """测试工具执行时间跟踪"""
+        tool = ListFilesTool(temp_project_dir)
+        
+        result = await tool.execute(directory=".")
+        
+        assert result.duration_ms >= 0
+        assert tool._total_duration_ms >= 0
+

docs/AGENT_DEPLOYMENT_CHECKLIST.md

@@ -0,0 +1,229 @@
+# DeepAudit Agent 审计功能部署清单
+
+## 📋 生产部署前必须完成的检查
+
+### 1. 环境依赖 ✅
+
+```bash
+# 后端依赖
+cd backend
+uv pip install chromadb litellm langchain langgraph
+
+# 外部安全工具（可选但推荐）
+pip install semgrep bandit safety
+
+# 或者使用系统包管理器
+brew install semgrep  # macOS
+apt install semgrep   # Ubuntu
+```
+
+### 2. LLM 配置 ✅
+
+在 `.env` 文件中配置：
+
+```env
+# LLM 配置（必须）
+LLM_PROVIDER=openai      # 或 azure, anthropic, ollama 等
+LLM_MODEL=gpt-4o-mini    # 推荐使用 gpt-4 系列
+LLM_API_KEY=sk-xxx       # 你的 API Key
+LLM_BASE_URL=            # 可选，自定义端点
+
+# 嵌入模型配置（RAG 需要）
+EMBEDDING_PROVIDER=openai
+EMBEDDING_MODEL=text-embedding-3-small
+```
+
+### 3. 数据库迁移 ✅
+
+```bash
+cd backend
+alembic upgrade head
+```
+
+确保以下表已创建：
+- `agent_tasks`
+- `agent_events`
+- `agent_findings`
+
+### 4. 向量数据库 ✅
+
+```bash
+# 创建向量数据库目录
+mkdir -p /var/data/deepaudit/vector_db
+
+# 在 .env 中配置
+VECTOR_DB_PATH=/var/data/deepaudit/vector_db
+```
+
+### 5. Docker 沙箱（可选）
+
+如果需要漏洞验证功能：
+
+```bash
+# 拉取沙箱镜像
+docker pull python:3.11-slim
+
+# 配置沙箱参数
+SANDBOX_IMAGE=python:3.11-slim
+SANDBOX_MEMORY_LIMIT=256m
+SANDBOX_CPU_LIMIT=0.5
+```
+
+---
+
+## 🔬 功能测试检查
+
+### 测试 1: 基础流程
+
+```bash
+cd backend
+PYTHONPATH=. uv run pytest tests/agent/ -v
+```
+
+预期结果：43 个测试全部通过
+
+### 测试 2: LLM 连接
+
+```bash
+cd backend
+PYTHONPATH=. uv run python -c "
+import asyncio
+from app.services.agent.graph.runner import LLMService
+
+async def test():
+    llm = LLMService()
+    result = await llm.analyze_code('print(\"hello\")', 'python')
+    print('LLM 连接成功:', 'issues' in result)
+
+asyncio.run(test())
+"
+```
+
+### 测试 3: 外部工具
+
+```bash
+# 测试 Semgrep
+semgrep --version
+
+# 测试 Bandit
+bandit --version
+```
+
+### 测试 4: 端到端测试
+
+1. 启动后端：`cd backend && uv run uvicorn app.main:app --reload`
+2. 启动前端：`cd frontend && npm run dev`
+3. 创建一个项目并上传代码
+4. 选择 "Agent 审计模式" 创建任务
+5. 观察执行日志和发现
+
+---
+
+## ⚠️ 已知限制
+
+| 限制 | 影响 | 解决方案 |
+|------|------|---------|
+| **LLM 成本** | 每次审计消耗 Token | 使用 gpt-4o-mini 降低成本 |
+| **扫描时间** | 大项目需要较长时间 | 设置合理的超时时间 |
+| **误报率** | AI 可能产生误报 | 启用验证阶段过滤 |
+| **外部工具依赖** | 需要手动安装 | 提供 Docker 镜像 |
+
+---
+
+## 🚀 生产环境建议
+
+### 1. 资源配置
+
+```yaml
+# Kubernetes 部署示例
+resources:
+  limits:
+    memory: "2Gi"
+    cpu: "2"
+  requests:
+    memory: "1Gi"
+    cpu: "1"
+```
+
+### 2. 并发控制
+
+```env
+# 限制同时运行的任务数
+MAX_CONCURRENT_AGENT_TASKS=3
+AGENT_TASK_TIMEOUT=1800  # 30 分钟
+```
+
+### 3. 日志监控
+
+```python
+# 配置日志级别
+LOG_LEVEL=INFO
+# 启用 SQLAlchemy 日志（调试用）
+SQLALCHEMY_ECHO=false
+```
+
+### 4. 安全考虑
+
+- [ ] 限制上传文件大小
+- [ ] 限制扫描目录范围
+- [ ] 启用沙箱隔离
+- [ ] 配置 API 速率限制
+
+---
+
+## ✅ 部署状态检查
+
+运行以下命令验证部署状态：
+
+```bash
+cd backend
+PYTHONPATH=. uv run python -c "
+print('检查部署状态...')
+
+# 1. 检查数据库连接
+try:
+    from app.db.session import async_session_factory
+    print('✅ 数据库配置正确')
+except Exception as e:
+    print(f'❌ 数据库错误: {e}')
+
+# 2. 检查 LLM 配置
+from app.core.config import settings
+if settings.LLM_API_KEY:
+    print('✅ LLM API Key 已配置')
+else:
+    print('⚠️ LLM API Key 未配置')
+
+# 3. 检查向量数据库
+import os
+if os.path.exists(settings.VECTOR_DB_PATH or '/tmp'):
+    print('✅ 向量数据库路径存在')
+else:
+    print('⚠️ 向量数据库路径不存在')
+
+# 4. 检查外部工具
+import shutil
+tools = ['semgrep', 'bandit']
+for tool in tools:
+    if shutil.which(tool):
+        print(f'✅ {tool} 已安装')
+    else:
+        print(f'⚠️ {tool} 未安装（可选）')
+
+print()
+print('部署检查完成！')
+"
+```
+
+---
+
+## 📝 结论
+
+Agent 审计功能已经具备**基本的生产能力**，但建议：
+
+1. **先在测试环境验证** - 用一个小项目测试完整流程
+2. **监控 LLM 成本** - 观察 Token 消耗情况
+3. **逐步开放** - 先给少数用户使用，收集反馈
+4. **持续优化** - 根据实际效果调整 prompt 和阈值
+
+如有问题，请查看日志或联系开发团队。

frontend/Dockerfile

@@ -55,3 +55,5 @@ EXPOSE 3000
 ENTRYPOINT ["/docker-entrypoint.sh"]
 CMD ["serve", "-s", "dist", "-l", "3000"]
 
+
+

frontend/src/app/ProtectedRoute.tsx

@@ -18,3 +18,5 @@ export const ProtectedRoute = () => {
 
 
 
+
+

frontend/src/hooks/useAgentStream.ts

@@ -0,0 +1,280 @@
+/**
+ * Agent 流式事件 React Hook
+ * 
+ * 用于在 React 组件中消费 Agent 审计的实时事件流
+ */
+
+import { useState, useEffect, useCallback, useRef } from 'react';
+import {
+  AgentStreamHandler,
+  StreamEventData,
+  StreamOptions,
+  AgentStreamState,
+} from '../shared/api/agentStream';
+
+export interface UseAgentStreamOptions extends Omit<StreamOptions, 'onEvent'> {
+  autoConnect?: boolean;
+  maxEvents?: number;
+}
+
+export interface UseAgentStreamReturn extends AgentStreamState {
+  connect: () => void;
+  disconnect: () => void;
+  isConnected: boolean;
+  clearEvents: () => void;
+}
+
+/**
+ * Agent 流式事件 Hook
+ * 
+ * @example
+ * ```tsx
+ * function AgentAuditPanel({ taskId }: { taskId: string }) {
+ *   const {
+ *     events,
+ *     thinking,
+ *     isThinking,
+ *     toolCalls,
+ *     currentPhase,
+ *     progress,
+ *     findings,
+ *     isComplete,
+ *     error,
+ *     connect,
+ *     disconnect,
+ *     isConnected,
+ *   } = useAgentStream(taskId);
+ * 
+ *   useEffect(() => {
+ *     connect();
+ *     return () => disconnect();
+ *   }, [taskId]);
+ * 
+ *   return (
+ *     <div>
+ *       {isThinking && <ThinkingIndicator text={thinking} />}
+ *       {toolCalls.map(tc => <ToolCallCard key={tc.name} {...tc} />)}
+ *       {findings.map(f => <FindingCard key={f.id} {...f} />)}
+ *     </div>
+ *   );
+ * }
+ * ```
+ */
+export function useAgentStream(
+  taskId: string | null,
+  options: UseAgentStreamOptions = {}
+): UseAgentStreamReturn {
+  const {
+    autoConnect = false,
+    maxEvents = 500,
+    includeThinking = true,
+    includeToolCalls = true,
+    afterSequence = 0,
+    ...callbackOptions
+  } = options;
+
+  // 状态
+  const [events, setEvents] = useState<StreamEventData[]>([]);
+  const [thinking, setThinking] = useState('');
+  const [isThinking, setIsThinking] = useState(false);
+  const [toolCalls, setToolCalls] = useState<AgentStreamState['toolCalls']>([]);
+  const [currentPhase, setCurrentPhase] = useState('');
+  const [progress, setProgress] = useState({ current: 0, total: 100, percentage: 0 });
+  const [findings, setFindings] = useState<Record<string, unknown>[]>([]);
+  const [isComplete, setIsComplete] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+  const [isConnected, setIsConnected] = useState(false);
+
+  // Handler ref
+  const handlerRef = useRef<AgentStreamHandler | null>(null);
+  const thinkingBufferRef = useRef<string[]>([]);
+
+  // 连接
+  const connect = useCallback(() => {
+    if (!taskId) return;
+
+    // 断开现有连接
+    if (handlerRef.current) {
+      handlerRef.current.disconnect();
+    }
+
+    // 重置状态
+    setEvents([]);
+    setThinking('');
+    setIsThinking(false);
+    setToolCalls([]);
+    setCurrentPhase('');
+    setProgress({ current: 0, total: 100, percentage: 0 });
+    setFindings([]);
+    setIsComplete(false);
+    setError(null);
+    thinkingBufferRef.current = [];
+
+    // 创建新的 handler
+    handlerRef.current = new AgentStreamHandler(taskId, {
+      includeThinking,
+      includeToolCalls,
+      afterSequence,
+      
+      onEvent: (event) => {
+        setEvents((prev) => [...prev.slice(-maxEvents + 1), event]);
+      },
+
+      onThinkingStart: () => {
+        thinkingBufferRef.current = [];
+        setIsThinking(true);
+        setThinking('');
+        callbackOptions.onThinkingStart?.();
+      },
+
+      onThinkingToken: (token, accumulated) => {
+        thinkingBufferRef.current.push(token);
+        setThinking(accumulated);
+        callbackOptions.onThinkingToken?.(token, accumulated);
+      },
+
+      onThinkingEnd: (response) => {
+        setIsThinking(false);
+        setThinking(response);
+        thinkingBufferRef.current = [];
+        callbackOptions.onThinkingEnd?.(response);
+      },
+
+      onToolStart: (name, input) => {
+        setToolCalls((prev) => [
+          ...prev,
+          { name, input, status: 'running' as const },
+        ]);
+        callbackOptions.onToolStart?.(name, input);
+      },
+
+      onToolEnd: (name, output, durationMs) => {
+        setToolCalls((prev) =>
+          prev.map((tc) =>
+            tc.name === name && tc.status === 'running'
+              ? { ...tc, output, durationMs, status: 'success' as const }
+              : tc
+          )
+        );
+        callbackOptions.onToolEnd?.(name, output, durationMs);
+      },
+
+      onNodeStart: (nodeName, phase) => {
+        setCurrentPhase(phase);
+        callbackOptions.onNodeStart?.(nodeName, phase);
+      },
+
+      onNodeEnd: (nodeName, summary) => {
+        callbackOptions.onNodeEnd?.(nodeName, summary);
+      },
+
+      onProgress: (current, total, message) => {
+        setProgress({
+          current,
+          total,
+          percentage: total > 0 ? Math.round((current / total) * 100) : 0,
+        });
+        callbackOptions.onProgress?.(current, total, message);
+      },
+
+      onFinding: (finding, isVerified) => {
+        setFindings((prev) => [...prev, finding]);
+        callbackOptions.onFinding?.(finding, isVerified);
+      },
+
+      onComplete: (data) => {
+        setIsComplete(true);
+        setIsConnected(false);
+        callbackOptions.onComplete?.(data);
+      },
+
+      onError: (err) => {
+        setError(err);
+        setIsComplete(true);
+        setIsConnected(false);
+        callbackOptions.onError?.(err);
+      },
+
+      onHeartbeat: () => {
+        callbackOptions.onHeartbeat?.();
+      },
+    });
+
+    handlerRef.current.connect();
+    setIsConnected(true);
+  }, [taskId, includeThinking, includeToolCalls, afterSequence, maxEvents, callbackOptions]);
+
+  // 断开连接
+  const disconnect = useCallback(() => {
+    if (handlerRef.current) {
+      handlerRef.current.disconnect();
+      handlerRef.current = null;
+    }
+    setIsConnected(false);
+  }, []);
+
+  // 清空事件
+  const clearEvents = useCallback(() => {
+    setEvents([]);
+  }, []);
+
+  // 自动连接
+  useEffect(() => {
+    if (autoConnect && taskId) {
+      connect();
+    }
+    return () => {
+      disconnect();
+    };
+  }, [taskId, autoConnect, connect, disconnect]);
+
+  // 清理
+  useEffect(() => {
+    return () => {
+      if (handlerRef.current) {
+        handlerRef.current.disconnect();
+      }
+    };
+  }, []);
+
+  return {
+    events,
+    thinking,
+    isThinking,
+    toolCalls,
+    currentPhase,
+    progress,
+    findings,
+    isComplete,
+    error,
+    connect,
+    disconnect,
+    isConnected,
+    clearEvents,
+  };
+}
+
+/**
+ * 简化版 Hook - 只获取思考过程
+ */
+export function useAgentThinking(taskId: string | null) {
+  const { thinking, isThinking, connect, disconnect } = useAgentStream(taskId, {
+    includeToolCalls: false,
+  });
+  
+  return { thinking, isThinking, connect, disconnect };
+}
+
+/**
+ * 简化版 Hook - 只获取工具调用
+ */
+export function useAgentToolCalls(taskId: string | null) {
+  const { toolCalls, connect, disconnect } = useAgentStream(taskId, {
+    includeThinking: false,
+  });
+  
+  return { toolCalls, connect, disconnect };
+}
+
+export default useAgentStream;
+

frontend/src/pages/AgentAudit.tsx

@@ -1,6 +1,7 @@
 /**
  * Agent 审计页面
  * 机械终端风格的 AI Agent 审计界面
+ * 支持 LLM 思考过程和工具调用的实时流式展示
  */
 
 import { useState, useEffect, useRef, useCallback } from "react";
@@ -9,12 +10,14 @@ import {
   Terminal, Bot, Cpu, Shield, AlertTriangle, CheckCircle2,
   Loader2, Code, Zap, Activity, ChevronRight, XCircle,
   FileCode, Search, Bug, Lock, Play, Square, RefreshCw,
-  ArrowLeft, Download, ExternalLink
+  ArrowLeft, Download, ExternalLink, Brain, Wrench, 
+  ChevronDown, ChevronUp, Clock, Sparkles
 } from "lucide-react";
 import { Button } from "@/components/ui/button";
 import { Badge } from "@/components/ui/badge";
 import { ScrollArea } from "@/components/ui/scroll-area";
 import { toast } from "sonner";
+import { useAgentStream } from "@/hooks/useAgentStream";
 import {
   type AgentTask,
   type AgentEvent,
@@ -91,10 +94,36 @@ export default function AgentAuditPage() {
   const [isLoading, setIsLoading] = useState(true);
   const [isStreaming, setIsStreaming] = useState(false);
   const [currentTime, setCurrentTime] = useState(new Date().toLocaleTimeString("zh-CN", { hour12: false }));
+  const [showThinking, setShowThinking] = useState(true);
+  const [showToolDetails, setShowToolDetails] = useState(true);
   
   const eventsEndRef = useRef<HTMLDivElement>(null);
+  const thinkingEndRef = useRef<HTMLDivElement>(null);
   const abortControllerRef = useRef<AbortController | null>(null);
   
+  // 使用增强版流式 Hook
+  const {
+    thinking,
+    isThinking,
+    toolCalls,
+    currentPhase: streamPhase,
+    progress: streamProgress,
+    connect: connectStream,
+    disconnect: disconnectStream,
+    isConnected: isStreamConnected,
+  } = useAgentStream(taskId || null, {
+    includeThinking: true,
+    includeToolCalls: true,
+    onFinding: () => loadFindings(),
+    onComplete: () => {
+      loadTask();
+      loadFindings();
+    },
+    onError: (err) => {
+      console.error("Stream error:", err);
+    },
+  });
+  
   // 是否完成
   const isComplete = task?.status === "completed" || task?.status === "failed" || task?.status === "cancelled";
   
@@ -146,12 +175,24 @@ export default function AgentAuditPage() {
     init();
   }, [loadTask, loadEvents, loadFindings]);
   
-  // 事件流
+  // 连接增强版流式 API
+  useEffect(() => {
+    if (!taskId || isComplete || isLoading) return;
+    
+    connectStream();
+    setIsStreaming(true);
+    
+    return () => {
+      disconnectStream();
+      setIsStreaming(false);
+    };
+  }, [taskId, isComplete, isLoading, connectStream, disconnectStream]);
+  
+  // 旧版事件流（作为后备）
   useEffect(() => {
     if (!taskId || isComplete || isLoading) return;
     
     const startStreaming = async () => {
-      setIsStreaming(true);
       abortControllerRef.current = new AbortController();
       
       try {
@@ -179,8 +220,6 @@ export default function AgentAuditPage() {
         if ((error as Error).name !== "AbortError") {
           console.error("Event stream error:", error);
         }
-      } finally {
-        setIsStreaming(false);
       }
     };
     
@@ -205,6 +244,30 @@ export default function AgentAuditPage() {
     return () => clearInterval(interval);
   }, []);
   
+  // 定期轮询任务状态（作为 SSE 的后备机制）
+  useEffect(() => {
+    if (!taskId || isComplete || isLoading) return;
+    
+    // 每 3 秒轮询一次任务状态
+    const pollInterval = setInterval(async () => {
+      try {
+        const taskData = await getAgentTask(taskId);
+        setTask(taskData);
+        
+        // 如果任务已完成/失败/取消，刷新其他数据
+        if (taskData.status === "completed" || taskData.status === "failed" || taskData.status === "cancelled") {
+          await loadEvents();
+          await loadFindings();
+          clearInterval(pollInterval);
+        }
+      } catch (error) {
+        console.error("Failed to poll task status:", error);
+      }
+    }, 3000);
+    
+    return () => clearInterval(pollInterval);
+  }, [taskId, isComplete, isLoading, loadEvents, loadFindings]);
+  
   // 取消任务
   const handleCancel = async () => {
     if (!taskId) return;
@@ -291,14 +354,85 @@ export default function AgentAuditPage() {
         </div>
       </div>
       
+      {/* 错误提示 */}
+      {task.status === "failed" && task.error_message && (
+        <div className="mx-4 mt-2 p-3 bg-red-900/30 border border-red-700 rounded-lg">
+          <div className="flex items-start gap-2">
+            <XCircle className="w-5 h-5 text-red-400 flex-shrink-0 mt-0.5" />
+            <div>
+              <p className="text-red-400 font-semibold text-sm">任务执行失败</p>
+              <p className="text-red-300/80 text-xs mt-1 font-mono break-all">{task.error_message}</p>
+            </div>
+          </div>
+        </div>
+      )}
+      
       <div className="flex h-[calc(100vh-56px)]">
         {/* 左侧：执行日志 */}
         <div className="flex-1 p-4 flex flex-col min-w-0">
+          
+          {/* 思考过程展示区域 */}
+          {(isThinking || thinking) && showThinking && (
+            <div className="mb-4 bg-purple-950/30 rounded-lg border border-purple-800/50 overflow-hidden">
+              <div 
+                className="flex items-center justify-between px-3 py-2 bg-purple-900/30 border-b border-purple-800/30 cursor-pointer"
+                onClick={() => setShowThinking(!showThinking)}
+              >
+                <div className="flex items-center gap-2 text-xs text-purple-400">
+                  <Brain className={`w-4 h-4 ${isThinking ? "animate-pulse" : ""}`} />
+                  <span className="uppercase tracking-wider">AI Thinking</span>
+                  {isThinking && (
+                    <span className="flex items-center gap-1 text-purple-300">
+                      <Sparkles className="w-3 h-3 animate-spin" />
+                      <span className="text-[10px]">Processing...</span>
+                    </span>
+                  )}
+                </div>
+                {showThinking ? <ChevronUp className="w-4 h-4 text-purple-400" /> : <ChevronDown className="w-4 h-4 text-purple-400" />}
+              </div>
+              
+              <div className="max-h-40 overflow-y-auto">
+                <div className="p-3 text-sm text-purple-200/80 font-mono whitespace-pre-wrap">
+                  {thinking || "正在思考..."}
+                  {isThinking && <span className="animate-pulse text-purple-400">▌</span>}
+                </div>
+                <div ref={thinkingEndRef} />
+              </div>
+            </div>
+          )}
+          
+          {/* 工具调用展示区域 */}
+          {toolCalls.length > 0 && showToolDetails && (
+            <div className="mb-4 bg-yellow-950/20 rounded-lg border border-yellow-800/30 overflow-hidden">
+              <div 
+                className="flex items-center justify-between px-3 py-2 bg-yellow-900/20 border-b border-yellow-800/20 cursor-pointer"
+                onClick={() => setShowToolDetails(!showToolDetails)}
+              >
+                <div className="flex items-center gap-2 text-xs text-yellow-500">
+                  <Wrench className="w-4 h-4" />
+                  <span className="uppercase tracking-wider">Tool Calls</span>
+                  <Badge variant="outline" className="text-[10px] px-1.5 py-0 bg-yellow-900/30 border-yellow-700 text-yellow-400">
+                    {toolCalls.length}
+                  </Badge>
+                </div>
+                {showToolDetails ? <ChevronUp className="w-4 h-4 text-yellow-500" /> : <ChevronDown className="w-4 h-4 text-yellow-500" />}
+              </div>
+              
+              <div className="max-h-48 overflow-y-auto">
+                <div className="p-2 space-y-2">
+                  {toolCalls.slice(-5).map((tc, idx) => (
+                    <ToolCallCard key={`${tc.name}-${idx}`} toolCall={tc} />
+                  ))}
+                </div>
+              </div>
+            </div>
+          )}
+          
           <div className="flex items-center justify-between mb-3">
             <div className="flex items-center gap-2 text-xs text-cyan-400">
               <Terminal className="w-4 h-4" />
               <span className="uppercase tracking-wider">Execution Log</span>
-              {isStreaming && (
+              {(isStreaming || isStreamConnected) && (
                 <span className="flex items-center gap-1 text-green-400">
                   <span className="w-2 h-2 bg-green-400 rounded-full animate-pulse" />
                   LIVE
@@ -540,6 +674,94 @@ function EventLine({ event }: { event: AgentEvent }) {
   );
 }
 
+// 工具调用卡片组件
+interface ToolCallProps {
+  toolCall: {
+    name: string;
+    input: Record<string, unknown>;
+    output?: unknown;
+    durationMs?: number;
+    status: 'running' | 'success' | 'error';
+  };
+}
+
+function ToolCallCard({ toolCall }: ToolCallProps) {
+  const [expanded, setExpanded] = useState(false);
+  
+  const statusConfig = {
+    running: {
+      icon: <Loader2 className="w-3 h-3 animate-spin text-yellow-400" />,
+      badge: "bg-yellow-900/30 border-yellow-700 text-yellow-400",
+      text: "Running",
+    },
+    success: {
+      icon: <CheckCircle2 className="w-3 h-3 text-green-400" />,
+      badge: "bg-green-900/30 border-green-700 text-green-400",
+      text: "Done",
+    },
+    error: {
+      icon: <XCircle className="w-3 h-3 text-red-400" />,
+      badge: "bg-red-900/30 border-red-700 text-red-400",
+      text: "Error",
+    },
+  };
+  
+  const config = statusConfig[toolCall.status];
+  
+  return (
+    <div className="bg-gray-900/50 rounded border border-gray-700/50 overflow-hidden">
+      <div 
+        className="flex items-center justify-between px-2 py-1.5 cursor-pointer hover:bg-gray-800/50"
+        onClick={() => setExpanded(!expanded)}
+      >
+        <div className="flex items-center gap-2">
+          {config.icon}
+          <span className="text-xs font-mono text-gray-300">{toolCall.name}</span>
+        </div>
+        <div className="flex items-center gap-2">
+          {toolCall.durationMs && (
+            <span className="text-[10px] text-gray-500">
+              <Clock className="w-2.5 h-2.5 inline mr-0.5" />
+              {toolCall.durationMs}ms
+            </span>
+          )}
+          <Badge variant="outline" className={`text-[10px] px-1 py-0 ${config.badge}`}>
+            {config.text}
+          </Badge>
+          {expanded ? <ChevronUp className="w-3 h-3 text-gray-500" /> : <ChevronDown className="w-3 h-3 text-gray-500" />}
+        </div>
+      </div>
+      
+      {expanded && (
+        <div className="border-t border-gray-700/50 text-[11px] font-mono">
+          {/* 输入 */}
+          {toolCall.input && Object.keys(toolCall.input).length > 0 && (
+            <div className="p-2 border-b border-gray-800/50">
+              <span className="text-gray-500 text-[10px] uppercase">Input:</span>
+              <pre className="mt-1 text-cyan-300/80 whitespace-pre-wrap break-all max-h-24 overflow-y-auto">
+                {JSON.stringify(toolCall.input, null, 2).slice(0, 500)}
+              </pre>
+            </div>
+          )}
+          
+          {/* 输出 */}
+          {toolCall.output && (
+            <div className="p-2">
+              <span className="text-gray-500 text-[10px] uppercase">Output:</span>
+              <pre className="mt-1 text-green-300/80 whitespace-pre-wrap break-all max-h-24 overflow-y-auto">
+                {typeof toolCall.output === 'string' 
+                  ? toolCall.output.slice(0, 500)
+                  : JSON.stringify(toolCall.output, null, 2).slice(0, 500)
+                }
+              </pre>
+            </div>
+          )}
+        </div>
+      )}
+    </div>
+  );
+}
+
 // 发现卡片组件
 function FindingCard({ finding }: { finding: AgentFinding }) {
   const colorClass = severityColors[finding.severity] || severityColors.info;

frontend/src/shared/api/agentStream.ts

@@ -0,0 +1,496 @@
+/**
+ * Agent 流式事件处理
+ * 
+ * 最佳实践:
+ * 1. 使用 EventSource API 或 fetch + ReadableStream
+ * 2. 支持重连机制
+ * 3. 分类处理不同事件类型
+ */
+
+// 事件类型定义
+export type StreamEventType =
+  // LLM 相关
+  | 'thinking_start'
+  | 'thinking_token'
+  | 'thinking_end'
+  // 工具调用相关
+  | 'tool_call_start'
+  | 'tool_call_input'
+  | 'tool_call_output'
+  | 'tool_call_end'
+  | 'tool_call_error'
+  // 节点相关
+  | 'node_start'
+  | 'node_end'
+  // 阶段相关
+  | 'phase_start'
+  | 'phase_end'
+  | 'phase_complete'
+  // 发现相关
+  | 'finding_new'
+  | 'finding_verified'
+  // 状态相关
+  | 'progress'
+  | 'info'
+  | 'warning'
+  | 'error'
+  // 任务相关
+  | 'task_start'
+  | 'task_complete'
+  | 'task_error'
+  | 'task_cancel'
+  | 'task_end'
+  // 心跳
+  | 'heartbeat';
+
+// 工具调用详情
+export interface ToolCallDetail {
+  name: string;
+  input?: Record<string, unknown>;
+  output?: unknown;
+  duration_ms?: number;
+}
+
+// 流式事件数据
+export interface StreamEventData {
+  id?: string;
+  type: StreamEventType;
+  phase?: string;
+  message?: string;
+  sequence?: number;
+  timestamp?: string;
+  tool?: ToolCallDetail;
+  metadata?: Record<string, unknown>;
+  tokens_used?: number;
+  // 特定类型数据
+  token?: string;           // thinking_token
+  accumulated?: string;     // thinking_token/thinking_end
+  status?: string;          // task_end
+  error?: string;           // task_error
+  findings_count?: number;  // task_complete
+  security_score?: number;  // task_complete
+}
+
+// 事件回调类型
+export type StreamEventCallback = (event: StreamEventData) => void;
+
+// 流式选项
+export interface StreamOptions {
+  includeThinking?: boolean;
+  includeToolCalls?: boolean;
+  afterSequence?: number;
+  onThinkingStart?: () => void;
+  onThinkingToken?: (token: string, accumulated: string) => void;
+  onThinkingEnd?: (fullResponse: string) => void;
+  onToolStart?: (toolName: string, input: Record<string, unknown>) => void;
+  onToolEnd?: (toolName: string, output: unknown, durationMs: number) => void;
+  onNodeStart?: (nodeName: string, phase: string) => void;
+  onNodeEnd?: (nodeName: string, summary: Record<string, unknown>) => void;
+  onFinding?: (finding: Record<string, unknown>, isVerified: boolean) => void;
+  onProgress?: (current: number, total: number, message: string) => void;
+  onComplete?: (data: { findingsCount: number; securityScore: number }) => void;
+  onError?: (error: string) => void;
+  onHeartbeat?: () => void;
+  onEvent?: StreamEventCallback;  // 通用事件回调
+}
+
+/**
+ * Agent 流式事件处理器
+ */
+export class AgentStreamHandler {
+  private taskId: string;
+  private eventSource: EventSource | null = null;
+  private options: StreamOptions;
+  private reconnectAttempts = 0;
+  private maxReconnectAttempts = 5;
+  private reconnectDelay = 1000;
+  private isConnected = false;
+  private thinkingBuffer: string[] = [];
+
+  constructor(taskId: string, options: StreamOptions = {}) {
+    this.taskId = taskId;
+    this.options = {
+      includeThinking: true,
+      includeToolCalls: true,
+      afterSequence: 0,
+      ...options,
+    };
+  }
+
+  /**
+   * 开始监听事件流
+   */
+  connect(): void {
+    const token = localStorage.getItem('access_token');
+    if (!token) {
+      this.options.onError?.('未登录');
+      return;
+    }
+
+    const params = new URLSearchParams({
+      include_thinking: String(this.options.includeThinking),
+      include_tool_calls: String(this.options.includeToolCalls),
+      after_sequence: String(this.options.afterSequence),
+    });
+
+    // 使用 EventSource (不支持自定义 headers，需要通过 URL 传递 token)
+    // 或者使用 fetch + ReadableStream
+    this.connectWithFetch(token, params);
+  }
+
+  /**
+   * 使用 fetch 连接（支持自定义 headers）
+   */
+  private async connectWithFetch(token: string, params: URLSearchParams): Promise<void> {
+    const url = `/api/v1/agent-tasks/${this.taskId}/stream?${params}`;
+
+    try {
+      const response = await fetch(url, {
+        headers: {
+          'Authorization': `Bearer ${token}`,
+          'Accept': 'text/event-stream',
+        },
+      });
+
+      if (!response.ok) {
+        throw new Error(`HTTP ${response.status}: ${response.statusText}`);
+      }
+
+      this.isConnected = true;
+      this.reconnectAttempts = 0;
+
+      const reader = response.body?.getReader();
+      if (!reader) {
+        throw new Error('无法获取响应流');
+      }
+
+      const decoder = new TextDecoder();
+      let buffer = '';
+
+      while (true) {
+        const { done, value } = await reader.read();
+        
+        if (done) {
+          break;
+        }
+
+        buffer += decoder.decode(value, { stream: true });
+        
+        // 解析 SSE 事件
+        const events = this.parseSSE(buffer);
+        buffer = events.remaining;
+        
+        for (const event of events.parsed) {
+          this.handleEvent(event);
+        }
+      }
+    } catch (error) {
+      this.isConnected = false;
+      console.error('Stream connection error:', error);
+      
+      // 尝试重连
+      if (this.reconnectAttempts < this.maxReconnectAttempts) {
+        this.reconnectAttempts++;
+        setTimeout(() => this.connect(), this.reconnectDelay * this.reconnectAttempts);
+      } else {
+        this.options.onError?.(`连接失败: ${error}`);
+      }
+    }
+  }
+
+  /**
+   * 解析 SSE 格式
+   */
+  private parseSSE(buffer: string): { parsed: StreamEventData[]; remaining: string } {
+    const parsed: StreamEventData[] = [];
+    const lines = buffer.split('\n');
+    let remaining = '';
+    let currentEvent: Partial<StreamEventData> = {};
+    
+    for (let i = 0; i < lines.length; i++) {
+      const line = lines[i];
+      
+      // 空行表示事件结束
+      if (line === '') {
+        if (currentEvent.type) {
+          parsed.push(currentEvent as StreamEventData);
+          currentEvent = {};
+        }
+        continue;
+      }
+      
+      // 检查是否是最后一行（可能不完整）
+      if (i === lines.length - 1 && !buffer.endsWith('\n')) {
+        remaining = line;
+        break;
+      }
+      
+      // 解析 event: 行
+      if (line.startsWith('event:')) {
+        currentEvent.type = line.slice(6).trim() as StreamEventType;
+      }
+      // 解析 data: 行
+      else if (line.startsWith('data:')) {
+        try {
+          const data = JSON.parse(line.slice(5).trim());
+          currentEvent = { ...currentEvent, ...data };
+        } catch {
+          // 忽略解析错误
+        }
+      }
+    }
+    
+    return { parsed, remaining };
+  }
+
+  /**
+   * 处理事件
+   */
+  private handleEvent(event: StreamEventData): void {
+    // 通用回调
+    this.options.onEvent?.(event);
+
+    // 分类处理
+    switch (event.type) {
+      // LLM 思考
+      case 'thinking_start':
+        this.thinkingBuffer = [];
+        this.options.onThinkingStart?.();
+        break;
+        
+      case 'thinking_token':
+        if (event.token) {
+          this.thinkingBuffer.push(event.token);
+          this.options.onThinkingToken?.(
+            event.token,
+            event.accumulated || this.thinkingBuffer.join('')
+          );
+        }
+        break;
+        
+      case 'thinking_end':
+        const fullResponse = event.accumulated || this.thinkingBuffer.join('');
+        this.thinkingBuffer = [];
+        this.options.onThinkingEnd?.(fullResponse);
+        break;
+
+      // 工具调用
+      case 'tool_call_start':
+        if (event.tool) {
+          this.options.onToolStart?.(
+            event.tool.name,
+            event.tool.input || {}
+          );
+        }
+        break;
+        
+      case 'tool_call_end':
+        if (event.tool) {
+          this.options.onToolEnd?.(
+            event.tool.name,
+            event.tool.output,
+            event.tool.duration_ms || 0
+          );
+        }
+        break;
+
+      // 节点
+      case 'node_start':
+        this.options.onNodeStart?.(
+          event.metadata?.node as string || 'unknown',
+          event.phase || ''
+        );
+        break;
+        
+      case 'node_end':
+        this.options.onNodeEnd?.(
+          event.metadata?.node as string || 'unknown',
+          event.metadata?.summary as Record<string, unknown> || {}
+        );
+        break;
+
+      // 发现
+      case 'finding_new':
+      case 'finding_verified':
+        this.options.onFinding?.(
+          event.metadata || {},
+          event.type === 'finding_verified'
+        );
+        break;
+
+      // 进度
+      case 'progress':
+        this.options.onProgress?.(
+          event.metadata?.current as number || 0,
+          event.metadata?.total as number || 100,
+          event.message || ''
+        );
+        break;
+
+      // 任务完成
+      case 'task_complete':
+      case 'task_end':
+        if (event.status !== 'cancelled' && event.status !== 'failed') {
+          this.options.onComplete?.({
+            findingsCount: event.findings_count || event.metadata?.findings_count as number || 0,
+            securityScore: event.security_score || event.metadata?.security_score as number || 100,
+          });
+        }
+        this.disconnect();
+        break;
+
+      // 错误
+      case 'task_error':
+      case 'error':
+        this.options.onError?.(event.error || event.message || '未知错误');
+        this.disconnect();
+        break;
+
+      // 心跳
+      case 'heartbeat':
+        this.options.onHeartbeat?.();
+        break;
+    }
+  }
+
+  /**
+   * 断开连接
+   */
+  disconnect(): void {
+    this.isConnected = false;
+    if (this.eventSource) {
+      this.eventSource.close();
+      this.eventSource = null;
+    }
+  }
+
+  /**
+   * 检查是否已连接
+   */
+  get connected(): boolean {
+    return this.isConnected;
+  }
+}
+
+/**
+ * 创建流式事件处理器的便捷函数
+ */
+export function createAgentStream(
+  taskId: string,
+  options: StreamOptions = {}
+): AgentStreamHandler {
+  return new AgentStreamHandler(taskId, options);
+}
+
+/**
+ * React Hook 风格的使用示例
+ * 
+ * ```tsx
+ * const { events, thinking, toolCalls, connect, disconnect } = useAgentStream(taskId);
+ * 
+ * useEffect(() => {
+ *   connect();
+ *   return () => disconnect();
+ * }, [taskId]);
+ * ```
+ */
+export interface AgentStreamState {
+  events: StreamEventData[];
+  thinking: string;
+  isThinking: boolean;
+  toolCalls: Array<{
+    name: string;
+    input: Record<string, unknown>;
+    output?: unknown;
+    durationMs?: number;
+    status: 'running' | 'success' | 'error';
+  }>;
+  currentPhase: string;
+  progress: { current: number; total: number; percentage: number };
+  findings: Array<Record<string, unknown>>;
+  isComplete: boolean;
+  error: string | null;
+}
+
+/**
+ * 创建用于 React 状态管理的流式处理器
+ */
+export function createAgentStreamWithState(
+  taskId: string,
+  onStateChange: (state: AgentStreamState) => void
+): AgentStreamHandler {
+  const state: AgentStreamState = {
+    events: [],
+    thinking: '',
+    isThinking: false,
+    toolCalls: [],
+    currentPhase: '',
+    progress: { current: 0, total: 100, percentage: 0 },
+    findings: [],
+    isComplete: false,
+    error: null,
+  };
+
+  const updateState = (updates: Partial<AgentStreamState>) => {
+    Object.assign(state, updates);
+    onStateChange({ ...state });
+  };
+
+  return new AgentStreamHandler(taskId, {
+    onEvent: (event) => {
+      updateState({
+        events: [...state.events, event].slice(-500), // 保留最近 500 条
+      });
+    },
+    onThinkingStart: () => {
+      updateState({ isThinking: true, thinking: '' });
+    },
+    onThinkingToken: (_, accumulated) => {
+      updateState({ thinking: accumulated });
+    },
+    onThinkingEnd: (response) => {
+      updateState({ isThinking: false, thinking: response });
+    },
+    onToolStart: (name, input) => {
+      updateState({
+        toolCalls: [
+          ...state.toolCalls,
+          { name, input, status: 'running' },
+        ],
+      });
+    },
+    onToolEnd: (name, output, durationMs) => {
+      updateState({
+        toolCalls: state.toolCalls.map((tc) =>
+          tc.name === name && tc.status === 'running'
+            ? { ...tc, output, durationMs, status: 'success' as const }
+            : tc
+        ),
+      });
+    },
+    onNodeStart: (_, phase) => {
+      updateState({ currentPhase: phase });
+    },
+    onProgress: (current, total, _) => {
+      updateState({
+        progress: {
+          current,
+          total,
+          percentage: total > 0 ? Math.round((current / total) * 100) : 0,
+        },
+      });
+    },
+    onFinding: (finding, _) => {
+      updateState({
+        findings: [...state.findings, finding],
+      });
+    },
+    onComplete: () => {
+      updateState({ isComplete: true });
+    },
+    onError: (error) => {
+      updateState({ error, isComplete: true });
+    },
+  });
+}
+

frontend/src/shared/api/agentTasks.ts

@@ -43,6 +43,9 @@ export interface AgentTask {
   
   // 进度
   progress_percentage: number;
+  
+  // 错误信息
+  error_message: string | null;
 }
 
 export interface AgentFinding {
@@ -249,7 +252,7 @@ export async function* streamAgentEvents(
   afterSequence = 0,
   signal?: AbortSignal
 ): AsyncGenerator<AgentEvent, void, unknown> {
-  const token = localStorage.getItem("auth_token");
+  const token = localStorage.getItem("access_token");
   const baseUrl = import.meta.env.VITE_API_URL || "";
   const url = `${baseUrl}/api/v1/agent-tasks/${taskId}/events?after_sequence=${afterSequence}`;