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

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feat(agent): implement Agent audit module with LangGraph integration - Introduce new Agent audit functionality for autonomous code security analysis and vulnerability verification. - Add API endpoints for managing Agent tasks and configurations. - Implement UI components for Agent mode selection and embedding model configuration. - Enhance the overall architecture with a focus on RAG (Retrieval-Augmented Generation) for improved code semantic search. - Create a sandbox environment for secure execution of vulnerability tests. - Update documentation to include details on the new Agent audit features and usage instructions.
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"""
DeepAudit Agent 服务模块
refactor(agent): 移除LangGraph工作流并迁移到动态Agent树架构 重构Agent服务架构，从基于LangGraph的状态图迁移到动态Agent树结构。主要变更包括： - 删除graph模块及相关测试 - 更新agent/__init__.py导入和文档 - 在projects端点添加对新AgentTask模型的统计支持 - 简化工作流描述为START→Orchestrator→[Recon/Analysis/Verification]→Report→END 新架构使用OrchestratorAgent作为编排层，动态调度子Agent完成任务，提高灵活性和可扩展性。
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基于动态 Agent 树架构的 AI 代码安全审计
feat(agent): implement Agent audit module with LangGraph integration - Introduce new Agent audit functionality for autonomous code security analysis and vulnerability verification. - Add API endpoints for managing Agent tasks and configurations. - Implement UI components for Agent mode selection and embedding model configuration. - Enhance the overall architecture with a focus on RAG (Retrieval-Augmented Generation) for improved code semantic search. - Create a sandbox environment for secure execution of vulnerability tests. - Update documentation to include details on the new Agent audit features and usage instructions.
2025-12-11 19:09:10 +08:00
refactor(agent): 移除LangGraph工作流并迁移到动态Agent树架构 重构Agent服务架构，从基于LangGraph的状态图迁移到动态Agent树结构。主要变更包括： - 删除graph模块及相关测试 - 更新agent/__init__.py导入和文档 - 在projects端点添加对新AgentTask模型的统计支持 - 简化工作流描述为START→Orchestrator→[Recon/Analysis/Verification]→Report→END 新架构使用OrchestratorAgent作为编排层，动态调度子Agent完成任务，提高灵活性和可扩展性。
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架构:
- OrchestratorAgent 作为编排层，动态调度子 Agent
- ReconAgent 负责侦察和文件分析
- AnalysisAgent 负责漏洞分析
- VerificationAgent 负责验证发现
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
工作流:
refactor(agent): 移除LangGraph工作流并迁移到动态Agent树架构 重构Agent服务架构，从基于LangGraph的状态图迁移到动态Agent树结构。主要变更包括： - 删除graph模块及相关测试 - 更新agent/__init__.py导入和文档 - 在projects端点添加对新AgentTask模型的统计支持 - 简化工作流描述为START→Orchestrator→[Recon/Analysis/Verification]→Report→END 新架构使用OrchestratorAgent作为编排层，动态调度子Agent完成任务，提高灵活性和可扩展性。
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START → Orchestrator → [Recon/Analysis/Verification] → Report → END
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
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支持动态创建子Agent进行专业化分析
feat(agent): implement Agent audit module with LangGraph integration - Introduce new Agent audit functionality for autonomous code security analysis and vulnerability verification. - Add API endpoints for managing Agent tasks and configurations. - Implement UI components for Agent mode selection and embedding model configuration. - Enhance the overall architecture with a focus on RAG (Retrieval-Augmented Generation) for improved code semantic search. - Create a sandbox environment for secure execution of vulnerability tests. - Update documentation to include details on the new Agent audit features and usage instructions.
2025-12-11 19:09:10 +08:00
"""
# 事件管理
from .event_manager import EventManager, AgentEventEmitter
# Agent 类
from .agents import (
BaseAgent, AgentConfig, AgentResult,
OrchestratorAgent, ReconAgent, AnalysisAgent, VerificationAgent,
)
refactor(agent): 移除LangGraph工作流并迁移到动态Agent树架构 重构Agent服务架构，从基于LangGraph的状态图迁移到动态Agent树结构。主要变更包括： - 删除graph模块及相关测试 - 更新agent/__init__.py导入和文档 - 在projects端点添加对新AgentTask模型的统计支持 - 简化工作流描述为START→Orchestrator→[Recon/Analysis/Verification]→Report→END 新架构使用OrchestratorAgent作为编排层，动态调度子Agent完成任务，提高灵活性和可扩展性。
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# 核心模块（状态管理、注册表、消息）
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
from .core import (
AgentState, AgentStatus,
AgentRegistry, agent_registry,
AgentMessage, MessageType, MessagePriority, MessageBus,
)
refactor(agent): 移除LangGraph工作流并迁移到动态Agent树架构 重构Agent服务架构，从基于LangGraph的状态图迁移到动态Agent树结构。主要变更包括： - 删除graph模块及相关测试 - 更新agent/__init__.py导入和文档 - 在projects端点添加对新AgentTask模型的统计支持 - 简化工作流描述为START→Orchestrator→[Recon/Analysis/Verification]→Report→END 新架构使用OrchestratorAgent作为编排层，动态调度子Agent完成任务，提高灵活性和可扩展性。
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# 知识模块系统（基于RAG）
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
from .knowledge import (
KnowledgeLoader, knowledge_loader,
get_available_modules, get_module_content,
SecurityKnowledgeRAG, security_knowledge_rag,
SecurityKnowledgeQueryTool, GetVulnerabilityKnowledgeTool,
)
refactor(agent): 移除LangGraph工作流并迁移到动态Agent树架构 重构Agent服务架构，从基于LangGraph的状态图迁移到动态Agent树结构。主要变更包括： - 删除graph模块及相关测试 - 更新agent/__init__.py导入和文档 - 在projects端点添加对新AgentTask模型的统计支持 - 简化工作流描述为START→Orchestrator→[Recon/Analysis/Verification]→Report→END 新架构使用OrchestratorAgent作为编排层，动态调度子Agent完成任务，提高灵活性和可扩展性。
2025-12-25 17:58:14 +08:00
# 协作工具
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
from .tools import (
ThinkTool, ReflectTool,
CreateVulnerabilityReportTool,
feat: 新增安全工具集成和漏洞知识库扩展 - 添加 Bandit 和 Safety 安全工具到依赖项 - 新增 CSRF、业务逻辑和开放重定向漏洞知识文档 - 实现安全工具一键安装脚本和文档 - 改进模式匹配工具支持直接文件扫描 - 增强遥测模块和 Agent 审计功能 - 修复验证节点中 findings 合并逻辑 - 优化前端 Agent 审计界面和状态展示
2025-12-13 12:35:03 +08:00
FinishScanTool,
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
CreateSubAgentTool, SendMessageTool, ViewAgentGraphTool,
WaitForMessageTool, AgentFinishTool,
)
refactor(agent): 移除LangGraph工作流并迁移到动态Agent树架构 重构Agent服务架构，从基于LangGraph的状态图迁移到动态Agent树结构。主要变更包括： - 删除graph模块及相关测试 - 更新agent/__init__.py导入和文档 - 在projects端点添加对新AgentTask模型的统计支持 - 简化工作流描述为START→Orchestrator→[Recon/Analysis/Verification]→Report→END 新架构使用OrchestratorAgent作为编排层，动态调度子Agent完成任务，提高灵活性和可扩展性。
2025-12-25 17:58:14 +08:00
# 遥测模块
feat: 新增安全工具集成和漏洞知识库扩展 - 添加 Bandit 和 Safety 安全工具到依赖项 - 新增 CSRF、业务逻辑和开放重定向漏洞知识文档 - 实现安全工具一键安装脚本和文档 - 改进模式匹配工具支持直接文件扫描 - 增强遥测模块和 Agent 审计功能 - 修复验证节点中 findings 合并逻辑 - 优化前端 Agent 审计界面和状态展示
2025-12-13 12:35:03 +08:00
from .telemetry import Tracer, get_global_tracer, set_global_tracer
feat(agent): implement Agent audit module with LangGraph integration - Introduce new Agent audit functionality for autonomous code security analysis and vulnerability verification. - Add API endpoints for managing Agent tasks and configurations. - Implement UI components for Agent mode selection and embedding model configuration. - Enhance the overall architecture with a focus on RAG (Retrieval-Augmented Generation) for improved code semantic search. - Create a sandbox environment for secure execution of vulnerability tests. - Update documentation to include details on the new Agent audit features and usage instructions.
2025-12-11 19:09:10 +08:00
__all__ = [
# 事件管理
"EventManager",
"AgentEventEmitter",
refactor(agent): 移除LangGraph工作流并迁移到动态Agent树架构 重构Agent服务架构，从基于LangGraph的状态图迁移到动态Agent树结构。主要变更包括： - 删除graph模块及相关测试 - 更新agent/__init__.py导入和文档 - 在projects端点添加对新AgentTask模型的统计支持 - 简化工作流描述为START→Orchestrator→[Recon/Analysis/Verification]→Report→END 新架构使用OrchestratorAgent作为编排层，动态调度子Agent完成任务，提高灵活性和可扩展性。
2025-12-25 17:58:14 +08:00
feat(agent): implement Agent audit module with LangGraph integration - Introduce new Agent audit functionality for autonomous code security analysis and vulnerability verification. - Add API endpoints for managing Agent tasks and configurations. - Implement UI components for Agent mode selection and embedding model configuration. - Enhance the overall architecture with a focus on RAG (Retrieval-Augmented Generation) for improved code semantic search. - Create a sandbox environment for secure execution of vulnerability tests. - Update documentation to include details on the new Agent audit features and usage instructions.
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# Agent 类
"BaseAgent",
"AgentConfig",
"AgentResult",
"OrchestratorAgent",
"ReconAgent",
"AnalysisAgent",
"VerificationAgent",
refactor(agent): 移除LangGraph工作流并迁移到动态Agent树架构 重构Agent服务架构，从基于LangGraph的状态图迁移到动态Agent树结构。主要变更包括： - 删除graph模块及相关测试 - 更新agent/__init__.py导入和文档 - 在projects端点添加对新AgentTask模型的统计支持 - 简化工作流描述为START→Orchestrator→[Recon/Analysis/Verification]→Report→END 新架构使用OrchestratorAgent作为编排层，动态调度子Agent完成任务，提高灵活性和可扩展性。
2025-12-25 17:58:14 +08:00
# 核心模块
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
"AgentState",
"AgentStatus",
"AgentRegistry",
"agent_registry",
"AgentMessage",
"MessageType",
"MessagePriority",
"MessageBus",
refactor(agent): 移除LangGraph工作流并迁移到动态Agent树架构 重构Agent服务架构，从基于LangGraph的状态图迁移到动态Agent树结构。主要变更包括： - 删除graph模块及相关测试 - 更新agent/__init__.py导入和文档 - 在projects端点添加对新AgentTask模型的统计支持 - 简化工作流描述为START→Orchestrator→[Recon/Analysis/Verification]→Report→END 新架构使用OrchestratorAgent作为编排层，动态调度子Agent完成任务，提高灵活性和可扩展性。
2025-12-25 17:58:14 +08:00
# 知识模块（基于RAG）
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
"KnowledgeLoader",
"knowledge_loader",
"get_available_modules",
"get_module_content",
"SecurityKnowledgeRAG",
"security_knowledge_rag",
"SecurityKnowledgeQueryTool",
"GetVulnerabilityKnowledgeTool",
refactor(agent): 移除LangGraph工作流并迁移到动态Agent树架构 重构Agent服务架构，从基于LangGraph的状态图迁移到动态Agent树结构。主要变更包括： - 删除graph模块及相关测试 - 更新agent/__init__.py导入和文档 - 在projects端点添加对新AgentTask模型的统计支持 - 简化工作流描述为START→Orchestrator→[Recon/Analysis/Verification]→Report→END 新架构使用OrchestratorAgent作为编排层，动态调度子Agent完成任务，提高灵活性和可扩展性。
2025-12-25 17:58:14 +08:00
# 协作工具
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
"ThinkTool",
"ReflectTool",
"CreateVulnerabilityReportTool",
feat: 新增安全工具集成和漏洞知识库扩展 - 添加 Bandit 和 Safety 安全工具到依赖项 - 新增 CSRF、业务逻辑和开放重定向漏洞知识文档 - 实现安全工具一键安装脚本和文档 - 改进模式匹配工具支持直接文件扫描 - 增强遥测模块和 Agent 审计功能 - 修复验证节点中 findings 合并逻辑 - 优化前端 Agent 审计界面和状态展示
2025-12-13 12:35:03 +08:00
"FinishScanTool",
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
"CreateSubAgentTool",
"SendMessageTool",
"ViewAgentGraphTool",
"WaitForMessageTool",
"AgentFinishTool",
refactor(agent): 移除LangGraph工作流并迁移到动态Agent树架构 重构Agent服务架构，从基于LangGraph的状态图迁移到动态Agent树结构。主要变更包括： - 删除graph模块及相关测试 - 更新agent/__init__.py导入和文档 - 在projects端点添加对新AgentTask模型的统计支持 - 简化工作流描述为START→Orchestrator→[Recon/Analysis/Verification]→Report→END 新架构使用OrchestratorAgent作为编排层，动态调度子Agent完成任务，提高灵活性和可扩展性。
2025-12-25 17:58:14 +08:00
# 遥测模块
feat: 新增安全工具集成和漏洞知识库扩展 - 添加 Bandit 和 Safety 安全工具到依赖项 - 新增 CSRF、业务逻辑和开放重定向漏洞知识文档 - 实现安全工具一键安装脚本和文档 - 改进模式匹配工具支持直接文件扫描 - 增强遥测模块和 Agent 审计功能 - 修复验证节点中 findings 合并逻辑 - 优化前端 Agent 审计界面和状态展示
2025-12-13 12:35:03 +08:00
"Tracer",
"get_global_tracer",
"set_global_tracer",
feat(agent): implement Agent audit module with LangGraph integration - Introduce new Agent audit functionality for autonomous code security analysis and vulnerability verification. - Add API endpoints for managing Agent tasks and configurations. - Implement UI components for Agent mode selection and embedding model configuration. - Enhance the overall architecture with a focus on RAG (Retrieval-Augmented Generation) for improved code semantic search. - Create a sandbox environment for secure execution of vulnerability tests. - Update documentation to include details on the new Agent audit features and usage instructions.
2025-12-11 19:09:10 +08:00
]