2025-12-12 16:30:57 +08:00
前端页面情况:
16:18:28
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
🔗 Connected to audit stream
2025-12-12 16:30:57 +08:00
16:18:43
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Orchestrator
2025-12-12 16:30:57 +08:00
Thought: 我需要首先了解这个项目的整体结构和功能, 以便确定审计的重点。从目录结构来看, 这是一个复杂的多模块系统, 包含多个引擎( MediaEngine, MindSpider, QueryEngine等) 和相关的报告系统。我应该先使用recon Agent来分析项目结构、技术栈和入口点, 以便后续有针对性地进行安全审计。
16:18:49
📤 调度 recon Agent: 分析项目整体结构、技术栈、入口点和主要功能模块...
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Orchestrator
2025-12-12 16:30:57 +08:00
16:18:57
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 用户指定了审计范围是1个目标文件: InsightEngine/llms/__init__.py。我需要直接读取和分析这个文件, 不要浪费时间遍历其他目录。首先, 我应该读取这个文件的内容来了解其结构和功能。
16:19:00
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Completed: read_file
Recon
0ms
2025-12-12 16:30:57 +08:00
16:19:10
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 我已经读取了指定的文件 InsightEngine/llms/__init__.py。这是一个简单的Python模块初始化文件, 它从.base模块导入了LLMClient类。为了更好地理解这个模块的上下文和依赖关系, 我应该搜索项目中是否有其他相关的文件, 特别是base.py文件, 以及可能使用LLMClient的其他文件。
16:19:13
Completed: search_code
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
13ms
16:19:15
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 搜索结果显示LLMClient只在InsightEngine/llms/__init__.py文件中被引用。为了更全面地了解这个模块, 我应该查找base.py文件, 因为它是LLMClient的定义所在。同时, 我也应该搜索项目中是否有其他使用这个LLMClient的文件, 以了解它的使用场景。
16:19:17
Completed: search_code
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
6ms
16:19:19
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 搜索base.py没有找到结果。让我尝试搜索"from .base"或"import base"来找到base模块的定义位置。同时, 我也应该搜索整个项目中是否有其他文件引用了InsightEngine.llms或LLMClient, 以了解这个模块的使用范围。
16:19:21
Completed: search_code
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
6ms
16:19:23
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 看来base.py文件可能不在审计范围内, 或者它可能是一个不存在的文件。让我尝试搜索"InsightEngine"来了解这个模块在整个项目中的使用情况, 以及是否有其他相关的LLM或大语言模型相关的文件。
16:19:25
Completed: search_code
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
8ms
16:19:48
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 看起来搜索范围仅限于用户指定的单个文件。根据用户的要求, 审计范围限定为1个目标文件: InsightEngine/llms/__init__.py。这意味着我只能分析这个文件, 而不能访问项目中的其他文件。
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
2025-12-12 16:30:57 +08:00
让我总结一下从这个文件中收集到的信息:
1. 这是一个名为LLM的模块, 为Insight Engine提供统一的OpenAI兼容客户端
2. 它从.base模块导入了LLMClient类
3. 它通过__all__列表暴露了LLMClient类
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
2025-12-12 16:30:57 +08:00
由于base.py文件不在审计范围内, 我无法获取LLMClient的具体实现。但根据文件描述, 这个模块似乎是一个大语言模型(LLM)客户端, 可能与OpenAI API兼容。
16:19:58
Completed: reflect
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
0ms
2025-12-12 16:30:57 +08:00
16:19:59
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 我已经完成了对指定文件的分析。由于审计范围被限定为单个文件,我无法获取更多关于项目结构、技术栈、入口点等信息。我现在可以总结收集到的信息并输出最终结果。
Final Answer:
```json
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
{
2025-12-12 16:30:57 +08:00
"project_structure": {
"directories": [],
"config_files": [],
"total_files": 1
},
"tech_stack": {
"languages": ["Python"],
"frameworks": [],
"databases": []
},
"entry_points": [
{"type": "module_init", "file": "InsightEngine/llms/__init__.py", "line": 6}
],
"high_risk_areas": [],
"dependencies": {
"internal": ["base.LLMClient"],
"external": []
},
"initial_findings": []
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
}
2025-12-12 16:30:57 +08:00
```
16:20:05
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
✅ recon Agent 完成
Orchestrator
2025-12-12 16:30:57 +08:00
16:20:07
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Orchestrator
2025-12-12 16:30:57 +08:00
Thought: Recon Agent的结果显示只分析了1个文件, 这与项目目录结构中显示的多个目录和文件不符。这可能是因为Recon Agent没有正确扫描整个项目结构。我需要重新调度Recon Agent, 或者直接转向Analysis Agent进行更深入的分析。考虑到项目结构复杂, 包含多个引擎模块, 我应该尝试让Analysis Agent直接对整个项目进行安全审计, 重点关注用户指定的漏洞类型: SQL注入、XSS、命令注入、路径遍历和SSRF。
16:20:15
📤 调度 analysis Agent: 对整个项目进行安全审计, 重点关注SQL注入、XSS、命令注入、路径遍历和SSRF漏洞...
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Orchestrator
2025-12-12 16:30:57 +08:00
16:20:18
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Analysis
2025-12-12 16:30:57 +08:00
Thought: 我需要分析指定的文件 InsightEngine/llms/__init__.py。首先, 我应该读取这个文件的内容, 了解其结构和功能, 然后根据代码内容选择合适的分析工具来检测潜在的安全漏洞。
16:20:21
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Completed: read_file
Analysis
1ms
2025-12-12 16:30:57 +08:00
16:20:23
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Analysis
2025-12-12 16:30:57 +08:00
Thought: 这个文件是一个简单的Python模块初始化文件, 它只导入了`LLMClient`类并定义了`__all__`列表。文件本身非常短,没有明显的安全漏洞。但是,为了全面分析,我需要查看被导入的`base.py`文件,因为实际的实现逻辑可能在那里。我应该继续读取`base.py`文件来进行更深入的安全分析。
16:20:25
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Completed: read_file
Analysis
0ms
2025-12-12 16:30:57 +08:00
16:20:43
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Analysis
2025-12-12 16:30:57 +08:00
Thought: 我只能分析用户指定的文件,即 InsightEngine/llms/__init__.py。这个文件本身非常简单, 只包含导入语句和模块定义, 没有明显的安全漏洞。由于文件内容非常有限, 我可以使用 pattern_match 工具来进一步确认是否存在任何潜在的安全模式,尽管从目前来看,这个文件几乎不可能包含安全漏洞。
16:20:49
Completed: pattern_match
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Analysis
5ms
2025-12-12 16:30:57 +08:00
16:21:41
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Analysis
2025-12-12 16:30:57 +08:00
Thought: 我已经完成了对 InsightEngine/llms/__init__.py 文件的分析。这个文件是一个简单的Python模块初始化文件, 只包含导入语句和模块定义, 没有包含任何可能存在安全漏洞的代码。pattern_match 工具也没有检测到任何已知的危险模式。由于这个文件非常简单且只包含导入语句,它本身不会构成安全风险。我应该总结分析结果。
16:21:48
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Completed: reflect
Analysis
2025-12-12 16:30:57 +08:00
0ms
16:21:50
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Analysis
2025-12-12 16:30:57 +08:00
Thought: 我已经完成了对 InsightEngine/llms/__init__.py 文件的全面安全分析。这个文件非常简单, 只包含导入语句和模块定义, 没有包含任何可能存在安全漏洞的代码。pattern_match 工具也没有检测到任何已知的危险模式。由于这个文件本身不包含任何业务逻辑或敏感操作,它不会构成安全风险。现在我可以提供最终的分析结果。
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
2025-12-12 16:30:57 +08:00
Final Answer: {
"findings": [],
"summary": "已完成对 InsightEngine/llms/__init__.py 文件的安全分析。该文件是一个简单的Python模块初始化文件, 只包含导入语句和模块定义, 没有包含任何可能存在安全漏洞的代码。pattern_match 工具也没有检测到任何已知的危险模式。由于这个文件本身不包含任何业务逻辑或敏感操作,它不会构成安全风险。"
}
16:22:00
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Analysis
2025-12-12 16:30:57 +08:00
```json
{
"findings": [],
"summary": "已完成对 InsightEngine/llms/__init__.py 文件的安全分析。该文件是一个简单的Python模块初始化文件, 只包含导入语句和模块定义, 没有包含任何可能存在安全漏洞的代码。pattern_match 工具也没有检测到任何已知的危险模式。由于这个文件本身不包含任何业务逻辑或敏感操作,它不会构成安全风险。"
}
```
16:22:04
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
✅ analysis Agent 完成
Orchestrator
2025-12-12 16:30:57 +08:00
16:22:05
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Orchestrator
2025-12-12 16:30:57 +08:00
Thought: Analysis Agent也没有发现任何漏洞, 这很奇怪, 因为项目结构复杂且包含多个模块。可能是因为Agent没有正确访问到所有文件。让我尝试直接查看一些关键文件, 特别是入口点文件如app.py和report_engine_only.py, 这些通常是用户交互的主要入口点, 可能包含安全漏洞。
16:22:11
📤 调度 recon Agent: 重新分析项目,重点关注入口点文件和主要模块...
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Orchestrator
2025-12-12 16:30:57 +08:00
16:22:18
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 我需要收集项目信息, 但审计范围限定为1个目标文件: InsightEngine/llms/__init__.py。用户还提到之前的分析可能没有正确访问到所有文件, 需要关注app.py、report_engine_only.py等入口点文件。我应该先读取指定的目标文件, 然后探索项目结构以获取更全面的信息。
16:22:22
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Completed: read_file
Recon
2025-12-12 16:30:57 +08:00
1ms
16:22:23
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 我已经读取了指定的目标文件 InsightEngine/llms/__init__.py, 这是一个简单的模块初始化文件, 它从 .base 导入了 LLMClient 类。现在我需要探索项目结构,了解整个项目的组织方式,特别是用户提到的 app.py、report_engine_only.py 等入口点文件。我将列出根目录的内容来获取项目结构。
16:22:28
Completed: list_files
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
18ms
16:23:05
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 系统显示审计范围限定为1个指定文件, 但我注意到用户在任务上下文中特别提到了需要关注app.py、report_engine_only.py等入口点文件。我应该尝试搜索这些关键文件, 看看它们是否存在于项目中。我将先搜索app.py文件。
16:23:08
Completed: search_code
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
17ms
16:23:11
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 没有找到app.py文件。让我尝试搜索report_engine_only.py文件, 看看它是否存在。
16:23:12
Completed: search_code
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
9ms
16:23:43
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 看来系统中确实只有指定的1个文件。让我尝试搜索其他可能的入口点文件, 比如main.py或index.py, 看看是否有其他入口点。
16:23:46
Completed: search_code
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
19ms
16:23:47
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 系统中似乎只有指定的1个文件。让我尝试搜索Python文件中常见的入口点模式, 比如"if __name__ == '__main__'",看看是否有其他入口点代码。
16:23:50
Completed: search_code
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
9ms
16:23:54
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 系统中确实只有指定的1个文件。让我尝试搜索其他可能的关键词, 比如"Flask"、"Django"等框架相关的关键词,看看是否能推断出技术栈。
16:23:57
Completed: search_code
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
9ms
16:23:58
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 让我尝试搜索其他常见的技术栈关键词,比如"import"语句,看看能收集到什么信息。
16:24:00
Completed: search_code
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
9ms
16:24:01
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 我找到了一个import语句, 它从.base模块导入了LLMClient。让我尝试搜索其他可能的技术栈关键词, 比如"from"语句,看看是否有其他导入。
16:24:03
Completed: search_code
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
8ms
16:24:19
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Recon
2025-12-12 16:30:57 +08:00
Thought: 我已经收集了足够的信息。系统中只有指定的1个文件: InsightEngine/llms/__init__.py。这是一个简单的模块初始化文件, 它从.base模块导入了LLMClient类。由于系统中只有这一个文件, 我无法收集到更多的项目结构、技术栈、入口点等信息。现在我将整理收集到的信息并输出结果。
Final Answer:
```json
{
"project_structure": {
"directories": ["InsightEngine/llms"],
"config_files": [],
"total_files": 1
},
"tech_stack": {
"languages": ["Python"],
"frameworks": [],
"databases": []
},
"entry_points": [
{"type": "module_init", "file": "InsightEngine/llms/__init__.py", "line": 6}
],
"high_risk_areas": [],
"dependencies": {},
"initial_findings": []
}
```
16:24:52
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
✅ recon Agent 完成
Orchestrator
2025-12-12 16:30:57 +08:00
16:24:54
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Orchestrator
2025-12-12 16:30:57 +08:00
Thought: Recon Agent仍然只找到了1个文件, 这与项目目录结构严重不符。看起来Agent可能无法正确访问项目中的所有文件。考虑到这种情况, 我应该尝试直接查看一些关键文件的内容, 特别是app.py和report_engine_only.py, 这些是项目的主要入口点。我将尝试使用Verification Agent来直接检查这些文件。
16:24:58
📤 调度 verification Agent: 直接检查项目入口点文件app.py和report_engine_only.py的安全问题...
feat(agent): implement comprehensive agent architecture with knowledge base and persistence layer
- Add database migrations for agent checkpoints and tree node tracking
- Implement core agent execution framework with executor, state management, and message handling
- Create knowledge base system with framework-specific modules (Django, FastAPI, Flask, Express, React, Supabase)
- Add vulnerability knowledge modules covering authentication, cryptography, injection, XSS, XXE, SSRF, path traversal, deserialization, and race conditions
- Introduce new agent tools: thinking tool, reporting tool, and agent-specific utilities
- Implement LLM memory compression and prompt caching for improved performance
- Add agent registry and persistence layer for checkpoint management
- Refactor agent implementations (analysis, recon, verification, orchestrator) with enhanced capabilities
- Remove legacy agent implementations (analysis_v2, react_agent)
- Update API endpoints for agent task creation and project management
- Add frontend components for agent task creation and enhanced audit UI
- Consolidate agent service architecture with improved separation of concerns
- This refactoring provides a scalable foundation for multi-agent collaboration with knowledge-driven decision making and state persistence
2025-12-12 15:27:12 +08:00
Orchestrator
2025-12-12 16:30:57 +08:00
16:24:58
✅ verification Agent 完成
Orchestrator
16:24:58
Orchestrator
Thought: 所有Agent似乎都无法正确访问项目中的文件, 这可能是由于项目访问权限或路径问题导致的。根据用户提供的信息, 我们知道项目包含多个目录和文件, 但所有Agent都只返回了很少的信息。在这种情况下, 我应该基于用户提供的信息和目录结构进行推理分析, 并尝试总结可能的安全风险点。
16:25:11
Orchestrator
Thought: 虽然所有Agent都无法直接访问项目文件进行详细分析, 但我已经基于项目目录结构进行了推理分析。由于系统限制无法获取更多详细信息, 我应该结束审计并总结我的发现和建议。
后端日志:
lintsinghua@lintsinghuadeMacBook-Pro backend % uv run uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
INFO: Will watch for changes in these directories: ['/Users/lintsinghua/XCodeReviewer/backend']
INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
INFO: Started reloader process [94873] using WatchFiles
INFO: Started server process [94875]
INFO: Waiting for application startup.
INFO:app.main:DeepAudit 后端服务启动中...
INFO:app.db.init_db:开始初始化数据库...
INFO:app.db.init_db:演示账户已存在: demo@example.com
INFO:app.db.init_db:演示数据已存在,跳过创建
INFO:app.services.init_templates:开始初始化系统模板和规则...
INFO:app.services.init_templates:✓ 系统模板和规则初始化完成
INFO:app.db.init_db:数据库初始化完成
INFO:app.main:✓ 数据库初始化完成
INFO:app.main:==================================================
INFO:app.main:DeepAudit 后端服务已启动
INFO:app.main:API 文档: http://localhost:8000/docs
INFO:app.main:==================================================
INFO:app.main:演示账户: demo@example.com / demo123
INFO:app.main:==================================================
INFO: Application startup complete.
[Branch] 项目: d, 类型: github, URL: https://github.com/666ghj/BettaFish
[Branch] GitHub Token: 未配置, GitLab Token: 未配置
[Branch] 警告: GitHub Token 未配置,可能会遇到 API 限制
INFO:httpx:HTTP Request: GET https://api.github.com/repos/666ghj/BettaFish/branches?per_page=100 "HTTP/1.1 200 OK"
[Branch] 成功获取 7 个分支
INFO:httpx:HTTP Request: GET https://api.github.com/repos/666ghj/BettaFish/git/trees/main?recursive=1 "HTTP/1.1 200 OK"
INFO:app.api.v1.endpoints.agent_tasks:Created agent task 8717c2de-fedc-4d39-bb19-6b186da9e610 for project d
INFO:app.api.v1.endpoints.agent_tasks:✅ Cloned repository https://github.com/666ghj/BettaFish (branch: main) to /tmp/deepaudit/8717c2de-fedc-4d39-bb19-6b186da9e610
INFO:app.api.v1.endpoints.agent_tasks:🚀 Task 8717c2de-fedc-4d39-bb19-6b186da9e610 started with Dynamic Agent Tree architecture
WARNING:app.services.agent.agents.recon:[Recon] Empty LLM response in iteration 1
WARNING:app.services.agent.agents.analysis:[Analysis] Empty LLM response in iteration 4
ERROR:app.api.v1.endpoints.agent_tasks:Task 8717c2de-fedc-4d39-bb19-6b186da9e610 failed: property 'progress_percentage' of 'AgentTask' object has no setter
Traceback (most recent call last):
File "/Users/lintsinghua/XCodeReviewer/backend/app/api/v1/endpoints/agent_tasks.py", line 392, in _execute_agent_task
task.progress_percentage = 100.0
^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: property 'progress_percentage' of 'AgentTask' object has no setter
WARNING: WatchFiles detected changes in 'app/services/llm/adapters/litellm_adapter.py'. Reloading...