720 lines
28 KiB
Python
720 lines
28 KiB
Python
"""
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Analysis Agent (漏洞分析层) - LLM 驱动版
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LLM 是真正的安全分析大脑！
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- LLM 决定分析策略
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- LLM 选择使用什么工具
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- LLM 决定深入分析哪些代码
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- LLM 判断发现的问题是否是真实漏洞
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类型: ReAct (真正的!)
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"""
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import asyncio
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import json
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import logging
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import re
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from typing import List, Dict, Any, Optional
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from dataclasses import dataclass
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from .base import BaseAgent, AgentConfig, AgentResult, AgentType, AgentPattern
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from ..json_parser import AgentJsonParser
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from ..prompts import CORE_SECURITY_PRINCIPLES, VULNERABILITY_PRIORITIES
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logger = logging.getLogger(__name__)
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ANALYSIS_SYSTEM_PROMPT = """你是 DeepAudit 的漏洞分析 Agent，一个**自主**的安全专家。
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## 你的角色
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你是安全审计的**核心大脑**，不是工具执行器。你需要：
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1. 自主制定分析策略
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2. 选择最有效的工具和方法
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3. 深入分析可疑代码
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4. 判断是否是真实漏洞
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5. 动态调整分析方向
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## ⚠️ 核心原则：优先使用外部专业工具！
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**外部工具优先级最高！** 必须首先使用外部安全工具进行扫描，它们有：
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- 经过验证的专业规则库
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- 更低的误报率
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- 更全面的漏洞检测能力
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## 🔧 工具优先级（必须按此顺序使用）
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### 第一优先级：外部专业安全工具 ⭐⭐⭐ 【必须首先使用！】
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- **semgrep_scan**: 全语言静态分析 - **每次分析必用**
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参数: target_path (str), rules (str: "auto" 或 "p/security-audit")
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示例: {"target_path": ".", "rules": "auto"}
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- **bandit_scan**: Python 安全扫描 - **Python项目必用**
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参数: target_path (str), severity (str)
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示例: {"target_path": ".", "severity": "medium"}
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- **gitleaks_scan**: 密钥泄露检测 - **每次分析必用**
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参数: target_path (str)
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示例: {"target_path": "."}
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- **safety_scan**: Python 依赖漏洞 - **有 requirements.txt 时必用**
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参数: requirements_file (str)
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示例: {"requirements_file": "requirements.txt"}
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- **npm_audit**: Node.js 依赖漏洞 - **有 package.json 时必用**
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参数: target_path (str)
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示例: {"target_path": "."}
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- **kunlun_scan**: 深度代码审计（Kunlun-M）
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参数: target_path (str), language (str: "php"|"javascript")
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示例: {"target_path": ".", "language": "php"}
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### 第二优先级：智能扫描工具 ⭐⭐
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- **smart_scan**: 智能批量安全扫描
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参数: target (str), quick_mode (bool), focus_vulnerabilities (list)
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示例: {"target": ".", "quick_mode": true}
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- **quick_audit**: 快速文件审计
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参数: file_path (str), deep_analysis (bool)
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示例: {"file_path": "app/views.py", "deep_analysis": true}
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### 第三优先级：内置分析工具 ⭐
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- **pattern_match**: 危险模式匹配（外部工具不可用时的备选）
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参数: scan_file (str) 或 code (str), pattern_types (list)
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示例: {"scan_file": "app/models.py", "pattern_types": ["sql_injection"]}
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- **dataflow_analysis**: 数据流追踪
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参数: source_code (str), variable_name (str)
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### 辅助工具
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- **read_file**: 读取文件内容验证发现
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参数: file_path (str), start_line (int), end_line (int)
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- **list_files**: 列出目录文件
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参数: directory (str), pattern (str)
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- **search_code**: 代码关键字搜索
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参数: keyword (str), max_results (int)
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- **query_security_knowledge**: 查询安全知识库
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- **get_vulnerability_knowledge**: 获取漏洞知识
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## 📋 推荐分析流程（严格按此执行！）
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### 第一步：外部工具全面扫描（60%时间）⚡ 最重要！
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根据项目技术栈，**必须首先**执行以下外部工具：
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```
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# 所有项目必做
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Action: semgrep_scan
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Action Input: {"target_path": ".", "rules": "auto"}
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Action: gitleaks_scan
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Action Input: {"target_path": "."}
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# Python 项目必做
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Action: bandit_scan
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Action Input: {"target_path": ".", "severity": "medium"}
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Action: safety_scan
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Action Input: {"requirements_file": "requirements.txt"}
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# Node.js 项目必做
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Action: npm_audit
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Action Input: {"target_path": "."}
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```
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### 第二步：分析外部工具结果（25%时间）
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对外部工具发现的问题进行深入分析：
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- 使用 `read_file` 查看完整代码上下文
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- 使用 `dataflow_analysis` 追踪数据流
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- 验证是否为真实漏洞，排除误报
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### 第三步：补充扫描（10%时间）
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如果外部工具覆盖不足，使用内置工具补充：
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- `smart_scan` 综合扫描
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- `pattern_match` 模式匹配
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### 第四步：汇总报告（5%时间）
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整理所有发现，输出 Final Answer
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## ⚠️ 重要提醒
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1. **不要跳过外部工具！** 即使内置工具可能更快，外部工具的检测能力更强
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2. **Docker依赖**：外部工具需要Docker环境，如果返回"Docker不可用"，再使用内置工具
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3. **并行执行**：可以连续调用多个外部工具
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## 工作方式
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每一步，你需要输出：
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```
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Thought: [分析当前情况，思考下一步应该做什么]
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Action: [工具名称]
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Action Input: [JSON 格式的参数]
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```
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当你完成分析后，输出：
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```
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Thought: [总结所有发现]
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Final Answer: [JSON 格式的漏洞报告]
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```
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## Final Answer 格式
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```json
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{
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"findings": [
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{
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"vulnerability_type": "sql_injection",
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"severity": "high",
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"title": "SQL 注入漏洞",
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"description": "详细描述",
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"file_path": "path/to/file.py",
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"line_start": 42,
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"code_snippet": "危险代码片段",
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"source": "污点来源",
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"sink": "危险函数",
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"suggestion": "修复建议",
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"confidence": 0.9,
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"needs_verification": true
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}
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],
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"summary": "分析总结"
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}
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```
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## 重点关注的漏洞类型
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- SQL 注入 (query, execute, raw SQL)
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- XSS (innerHTML, document.write, v-html)
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- 命令注入 (exec, system, subprocess)
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- 路径遍历 (open, readFile, path 拼接)
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- SSRF (requests, fetch, http client)
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- 硬编码密钥 (password, secret, api_key)
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- 不安全的反序列化 (pickle, yaml.load, eval)
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## 重要原则
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1. **外部工具优先** - 首先使用 semgrep、bandit 等专业工具
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2. **质量优先** - 宁可深入分析几个真实漏洞，不要浅尝辄止报告大量误报
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3. **上下文分析** - 看到可疑代码要读取上下文，理解完整逻辑
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4. **自主判断** - 不要机械相信工具输出，要用你的专业知识判断
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现在开始你的安全分析！首先使用外部工具进行全面扫描。"""
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@dataclass
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class AnalysisStep:
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"""分析步骤"""
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thought: str
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action: Optional[str] = None
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action_input: Optional[Dict] = None
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observation: Optional[str] = None
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is_final: bool = False
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final_answer: Optional[Dict] = None
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class AnalysisAgent(BaseAgent):
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"""
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漏洞分析 Agent - LLM 驱动版
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LLM 全程参与，自主决定：
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1. 分析什么
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2. 使用什么工具
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3. 深入哪些代码
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4. 报告什么发现
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"""
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def __init__(
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self,
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llm_service,
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tools: Dict[str, Any],
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event_emitter=None,
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):
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# 组合增强的系统提示词，注入核心安全原则和漏洞优先级
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full_system_prompt = f"{ANALYSIS_SYSTEM_PROMPT}\n\n{CORE_SECURITY_PRINCIPLES}\n\n{VULNERABILITY_PRIORITIES}"
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config = AgentConfig(
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name="Analysis",
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agent_type=AgentType.ANALYSIS,
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pattern=AgentPattern.REACT,
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max_iterations=30,
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system_prompt=full_system_prompt,
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)
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super().__init__(config, llm_service, tools, event_emitter)
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self._conversation_history: List[Dict[str, str]] = []
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self._steps: List[AnalysisStep] = []
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def _parse_llm_response(self, response: str) -> AnalysisStep:
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"""解析 LLM 响应 - 增强版，更健壮地提取思考内容"""
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step = AnalysisStep(thought="")
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# 🔥 首先尝试提取明确的 Thought 标记
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thought_match = re.search(r'Thought:\s*(.*?)(?=Action:|Final Answer:|$)', response, re.DOTALL)
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if thought_match:
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step.thought = thought_match.group(1).strip()
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# 🔥 检查是否是最终答案
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final_match = re.search(r'Final Answer:\s*(.*?)$', response, re.DOTALL)
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if final_match:
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step.is_final = True
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answer_text = final_match.group(1).strip()
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answer_text = re.sub(r'```json\s*', '', answer_text)
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answer_text = re.sub(r'```\s*', '', answer_text)
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# 使用增强的 JSON 解析器
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step.final_answer = AgentJsonParser.parse(
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answer_text,
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default={"findings": [], "raw_answer": answer_text}
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)
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# 确保 findings 格式正确
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if "findings" in step.final_answer:
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step.final_answer["findings"] = [
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f for f in step.final_answer["findings"]
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if isinstance(f, dict)
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]
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# 🔥 如果没有提取到 thought，使用 Final Answer 前的内容作为思考
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if not step.thought:
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before_final = response[:response.find('Final Answer:')].strip()
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if before_final:
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before_final = re.sub(r'^Thought:\s*', '', before_final)
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step.thought = before_final[:500] if len(before_final) > 500 else before_final
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return step
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# 🔥 提取 Action
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action_match = re.search(r'Action:\s*(\w+)', response)
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if action_match:
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step.action = action_match.group(1).strip()
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# 🔥 如果没有提取到 thought，提取 Action 之前的内容作为思考
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if not step.thought:
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action_pos = response.find('Action:')
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if action_pos > 0:
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before_action = response[:action_pos].strip()
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before_action = re.sub(r'^Thought:\s*', '', before_action)
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if before_action:
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step.thought = before_action[:500] if len(before_action) > 500 else before_action
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# 🔥 提取 Action Input
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input_match = re.search(r'Action Input:\s*(.*?)(?=Thought:|Action:|Observation:|$)', response, re.DOTALL)
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if input_match:
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input_text = input_match.group(1).strip()
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input_text = re.sub(r'```json\s*', '', input_text)
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input_text = re.sub(r'```\s*', '', input_text)
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# 使用增强的 JSON 解析器
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step.action_input = AgentJsonParser.parse(
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input_text,
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default={"raw_input": input_text}
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)
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# 🔥 最后的 fallback：如果整个响应没有任何标记，整体作为思考
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if not step.thought and not step.action and not step.is_final:
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if response.strip():
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step.thought = response.strip()[:500]
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return step
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async def run(self, input_data: Dict[str, Any]) -> AgentResult:
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"""
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执行漏洞分析 - LLM 全程参与！
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"""
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import time
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start_time = time.time()
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project_info = input_data.get("project_info", {})
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config = input_data.get("config", {})
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plan = input_data.get("plan", {})
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previous_results = input_data.get("previous_results", {})
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task = input_data.get("task", "")
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task_context = input_data.get("task_context", "")
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# 🔥 处理交接信息
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handoff = input_data.get("handoff")
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if handoff:
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from .base import TaskHandoff
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if isinstance(handoff, dict):
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handoff = TaskHandoff.from_dict(handoff)
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self.receive_handoff(handoff)
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# 从 Recon 结果获取上下文
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recon_data = previous_results.get("recon", {})
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if isinstance(recon_data, dict) and "data" in recon_data:
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recon_data = recon_data["data"]
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tech_stack = recon_data.get("tech_stack", {})
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entry_points = recon_data.get("entry_points", [])
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high_risk_areas = recon_data.get("high_risk_areas", plan.get("high_risk_areas", []))
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initial_findings = recon_data.get("initial_findings", [])
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# 🔥 构建包含交接上下文的初始消息
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handoff_context = self.get_handoff_context()
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# 🔥 获取目标文件列表
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target_files = config.get("target_files", [])
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initial_message = f"""请开始对项目进行安全漏洞分析。
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## 项目信息
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- 名称: {project_info.get('name', 'unknown')}
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- 语言: {tech_stack.get('languages', [])}
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- 框架: {tech_stack.get('frameworks', [])}
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"""
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# 🔥 如果指定了目标文件，明确告知 Agent
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if target_files:
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initial_message += f"""## ⚠️ 审计范围
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用户指定了 {len(target_files)} 个目标文件进行审计：
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"""
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for tf in target_files[:10]:
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initial_message += f"- {tf}\n"
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if len(target_files) > 10:
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initial_message += f"- ... 还有 {len(target_files) - 10} 个文件\n"
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initial_message += """
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请直接分析这些指定的文件，不要分析其他文件。
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"""
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initial_message += f"""{handoff_context if handoff_context else f'''## 上下文信息
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### ⚠️ 高风险区域（来自 Recon Agent，必须优先分析）
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以下是 Recon Agent 识别的高风险区域，请**务必优先**读取和分析这些文件：
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{json.dumps(high_risk_areas[:20], ensure_ascii=False)}
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**重要**: 请使用 read_file 工具读取上述高风险文件，不要假设文件路径或使用其他路径。
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### 入口点 (前10个)
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{json.dumps(entry_points[:10], ensure_ascii=False, indent=2)}
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### 初步发现 (如果有)
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{json.dumps(initial_findings[:5], ensure_ascii=False, indent=2) if initial_findings else "无"}'''}
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## 任务
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{task_context or task or '进行全面的安全漏洞分析，发现代码中的安全问题。'}
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## ⚠️ 分析策略要求
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1. **首先**：使用 read_file 读取上面列出的高风险文件
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2. **然后**：分析这些文件中的安全问题
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3. **最后**：如果需要，使用 smart_scan 或其他工具扩展分析
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**禁止**：不要跳过高风险区域直接做全局扫描
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## 目标漏洞类型
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{config.get('target_vulnerabilities', ['all'])}
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## 可用工具
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{self.get_tools_description()}
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请开始你的安全分析。首先读取高风险区域的文件，然后分析其中的安全问题。"""
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# 🔥 记录工作开始
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self.record_work("开始安全漏洞分析")
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# 初始化对话历史
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self._conversation_history = [
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{"role": "system", "content": self.config.system_prompt},
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{"role": "user", "content": initial_message},
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]
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self._steps = []
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all_findings = []
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error_message = None # 🔥 跟踪错误信息
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await self.emit_thinking("🔬 Analysis Agent 启动，LLM 开始自主安全分析...")
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try:
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||
for iteration in range(self.config.max_iterations):
|
||
if self.is_cancelled:
|
||
break
|
||
|
||
self._iteration = iteration + 1
|
||
|
||
# 🔥 再次检查取消标志（在LLM调用之前）
|
||
if self.is_cancelled:
|
||
await self.emit_thinking("🛑 任务已取消，停止执行")
|
||
break
|
||
|
||
# 调用 LLM 进行思考和决策（流式输出）
|
||
# 🔥 增加 max_tokens 到 4096，避免长输出被截断
|
||
try:
|
||
llm_output, tokens_this_round = await self.stream_llm_call(
|
||
self._conversation_history,
|
||
temperature=0.1,
|
||
max_tokens=4096,
|
||
)
|
||
except asyncio.CancelledError:
|
||
logger.info(f"[{self.name}] LLM call cancelled")
|
||
break
|
||
|
||
self._total_tokens += tokens_this_round
|
||
|
||
# 🔥 Enhanced: Handle empty LLM response with better diagnostics
|
||
if not llm_output or not llm_output.strip():
|
||
empty_retry_count = getattr(self, '_empty_retry_count', 0) + 1
|
||
self._empty_retry_count = empty_retry_count
|
||
|
||
# 🔥 记录更详细的诊断信息
|
||
logger.warning(
|
||
f"[{self.name}] Empty LLM response in iteration {self._iteration} "
|
||
f"(retry {empty_retry_count}/3, tokens_this_round={tokens_this_round})"
|
||
)
|
||
|
||
if empty_retry_count >= 3:
|
||
logger.error(f"[{self.name}] Too many empty responses, generating fallback result")
|
||
error_message = "连续收到空响应，使用回退结果"
|
||
await self.emit_event("warning", error_message)
|
||
# 🔥 不是直接 break，而是尝试生成一个回退结果
|
||
break
|
||
|
||
# 🔥 更有针对性的重试提示
|
||
retry_prompt = f"""收到空响应。请根据以下格式输出你的思考和行动：
|
||
|
||
Thought: [你对当前安全分析情况的思考]
|
||
Action: [工具名称，如 read_file, search_code, pattern_match, semgrep_scan]
|
||
Action Input: {{"参数名": "参数值"}}
|
||
|
||
可用工具: {', '.join(self.tools.keys())}
|
||
|
||
如果你已完成分析，请输出：
|
||
Thought: [总结所有发现]
|
||
Final Answer: {{"findings": [...], "summary": "..."}}"""
|
||
|
||
self._conversation_history.append({
|
||
"role": "user",
|
||
"content": retry_prompt,
|
||
})
|
||
continue
|
||
|
||
# 重置空响应计数器
|
||
self._empty_retry_count = 0
|
||
|
||
# 解析 LLM 响应
|
||
step = self._parse_llm_response(llm_output)
|
||
self._steps.append(step)
|
||
|
||
# 🔥 发射 LLM 思考内容事件 - 展示安全分析的思考过程
|
||
if step.thought:
|
||
await self.emit_llm_thought(step.thought, iteration + 1)
|
||
|
||
# 添加 LLM 响应到历史
|
||
self._conversation_history.append({
|
||
"role": "assistant",
|
||
"content": llm_output,
|
||
})
|
||
|
||
# 检查是否完成
|
||
if step.is_final:
|
||
await self.emit_llm_decision("完成安全分析", "LLM 判断分析已充分")
|
||
logger.info(f"[{self.name}] Received Final Answer: {step.final_answer}")
|
||
if step.final_answer and "findings" in step.final_answer:
|
||
all_findings = step.final_answer["findings"]
|
||
logger.info(f"[{self.name}] Final Answer contains {len(all_findings)} findings")
|
||
# 🔥 发射每个发现的事件
|
||
for finding in all_findings[:5]: # 限制数量
|
||
await self.emit_finding(
|
||
finding.get("title", "Unknown"),
|
||
finding.get("severity", "medium"),
|
||
finding.get("vulnerability_type", "other"),
|
||
finding.get("file_path", "")
|
||
)
|
||
# 🔥 记录洞察
|
||
self.add_insight(
|
||
f"发现 {finding.get('severity', 'medium')} 级别漏洞: {finding.get('title', 'Unknown')}"
|
||
)
|
||
else:
|
||
logger.warning(f"[{self.name}] Final Answer has no 'findings' key or is None: {step.final_answer}")
|
||
|
||
# 🔥 记录工作完成
|
||
self.record_work(f"完成安全分析，发现 {len(all_findings)} 个潜在漏洞")
|
||
|
||
await self.emit_llm_complete(
|
||
f"分析完成，发现 {len(all_findings)} 个潜在漏洞",
|
||
self._total_tokens
|
||
)
|
||
break
|
||
|
||
# 执行工具
|
||
if step.action:
|
||
# 🔥 发射 LLM 动作决策事件
|
||
await self.emit_llm_action(step.action, step.action_input or {})
|
||
|
||
observation = await self.execute_tool(
|
||
step.action,
|
||
step.action_input or {}
|
||
)
|
||
|
||
# 🔥 工具执行后检查取消状态
|
||
if self.is_cancelled:
|
||
logger.info(f"[{self.name}] Cancelled after tool execution")
|
||
break
|
||
|
||
step.observation = observation
|
||
|
||
# 🔥 发射 LLM 观察事件
|
||
await self.emit_llm_observation(observation)
|
||
|
||
# 添加观察结果到历史
|
||
self._conversation_history.append({
|
||
"role": "user",
|
||
"content": f"Observation:\n{observation}",
|
||
})
|
||
else:
|
||
# LLM 没有选择工具，提示它继续
|
||
await self.emit_llm_decision("继续分析", "LLM 需要更多分析")
|
||
self._conversation_history.append({
|
||
"role": "user",
|
||
"content": "请继续分析。选择一个工具执行，或者如果分析完成，输出 Final Answer 汇总所有发现。",
|
||
})
|
||
|
||
# 🔥 如果循环结束但没有发现，强制 LLM 总结
|
||
if not all_findings and not self.is_cancelled and not error_message:
|
||
await self.emit_thinking("📝 分析阶段结束，正在生成漏洞总结...")
|
||
|
||
# 添加强制总结的提示
|
||
self._conversation_history.append({
|
||
"role": "user",
|
||
"content": """分析阶段已结束。请立即输出 Final Answer，总结你发现的所有安全问题。
|
||
|
||
即使没有发现严重漏洞，也请总结你的分析过程和观察到的潜在风险点。
|
||
|
||
请按以下 JSON 格式输出：
|
||
```json
|
||
{
|
||
"findings": [
|
||
{
|
||
"vulnerability_type": "sql_injection|xss|command_injection|path_traversal|ssrf|hardcoded_secret|other",
|
||
"severity": "critical|high|medium|low",
|
||
"title": "漏洞标题",
|
||
"description": "详细描述",
|
||
"file_path": "文件路径",
|
||
"line_start": 行号,
|
||
"code_snippet": "相关代码片段",
|
||
"suggestion": "修复建议"
|
||
}
|
||
],
|
||
"summary": "分析总结"
|
||
}
|
||
```
|
||
|
||
Final Answer:""",
|
||
})
|
||
|
||
try:
|
||
summary_output, _ = await self.stream_llm_call(
|
||
self._conversation_history,
|
||
temperature=0.1,
|
||
max_tokens=4096,
|
||
)
|
||
|
||
if summary_output and summary_output.strip():
|
||
# 解析总结输出
|
||
import re
|
||
summary_text = summary_output.strip()
|
||
summary_text = re.sub(r'```json\s*', '', summary_text)
|
||
summary_text = re.sub(r'```\s*', '', summary_text)
|
||
parsed_result = AgentJsonParser.parse(
|
||
summary_text,
|
||
default={"findings": [], "summary": ""}
|
||
)
|
||
if "findings" in parsed_result:
|
||
all_findings = parsed_result["findings"]
|
||
except Exception as e:
|
||
logger.warning(f"[{self.name}] Failed to generate summary: {e}")
|
||
|
||
# 处理结果
|
||
duration_ms = int((time.time() - start_time) * 1000)
|
||
|
||
# 🔥 如果被取消，返回取消结果
|
||
if self.is_cancelled:
|
||
await self.emit_event(
|
||
"info",
|
||
f"🛑 Analysis Agent 已取消: {len(all_findings)} 个发现, {self._iteration} 轮迭代"
|
||
)
|
||
return AgentResult(
|
||
success=False,
|
||
error="任务已取消",
|
||
data={"findings": all_findings},
|
||
iterations=self._iteration,
|
||
tool_calls=self._tool_calls,
|
||
tokens_used=self._total_tokens,
|
||
duration_ms=duration_ms,
|
||
)
|
||
|
||
# 🔥 如果有错误，返回失败结果
|
||
if error_message:
|
||
await self.emit_event(
|
||
"error",
|
||
f"❌ Analysis Agent 失败: {error_message}"
|
||
)
|
||
return AgentResult(
|
||
success=False,
|
||
error=error_message,
|
||
data={"findings": all_findings},
|
||
iterations=self._iteration,
|
||
tool_calls=self._tool_calls,
|
||
tokens_used=self._total_tokens,
|
||
duration_ms=duration_ms,
|
||
)
|
||
|
||
# 标准化发现
|
||
logger.info(f"[{self.name}] Standardizing {len(all_findings)} findings")
|
||
standardized_findings = []
|
||
for finding in all_findings:
|
||
# 确保 finding 是字典
|
||
if not isinstance(finding, dict):
|
||
logger.warning(f"Skipping invalid finding (not a dict): {finding}")
|
||
continue
|
||
|
||
standardized = {
|
||
"vulnerability_type": finding.get("vulnerability_type", "other"),
|
||
"severity": finding.get("severity", "medium"),
|
||
"title": finding.get("title", "Unknown Finding"),
|
||
"description": finding.get("description", ""),
|
||
"file_path": finding.get("file_path", ""),
|
||
"line_start": finding.get("line_start") or finding.get("line", 0),
|
||
"code_snippet": finding.get("code_snippet", ""),
|
||
"source": finding.get("source", ""),
|
||
"sink": finding.get("sink", ""),
|
||
"suggestion": finding.get("suggestion", ""),
|
||
"confidence": finding.get("confidence", 0.7),
|
||
"needs_verification": finding.get("needs_verification", True),
|
||
}
|
||
standardized_findings.append(standardized)
|
||
|
||
await self.emit_event(
|
||
"info",
|
||
f"Analysis Agent 完成: {len(standardized_findings)} 个发现, {self._iteration} 轮迭代, {self._tool_calls} 次工具调用"
|
||
)
|
||
|
||
# 🔥 CRITICAL: Log final findings count before returning
|
||
logger.info(f"[{self.name}] Returning {len(standardized_findings)} standardized findings")
|
||
|
||
return AgentResult(
|
||
success=True,
|
||
data={
|
||
"findings": standardized_findings,
|
||
"steps": [
|
||
{
|
||
"thought": s.thought,
|
||
"action": s.action,
|
||
"action_input": s.action_input,
|
||
"observation": s.observation[:500] if s.observation else None,
|
||
}
|
||
for s in self._steps
|
||
],
|
||
},
|
||
iterations=self._iteration,
|
||
tool_calls=self._tool_calls,
|
||
tokens_used=self._total_tokens,
|
||
duration_ms=duration_ms,
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"Analysis Agent failed: {e}", exc_info=True)
|
||
return AgentResult(success=False, error=str(e))
|
||
|
||
def get_conversation_history(self) -> List[Dict[str, str]]:
|
||
"""获取对话历史"""
|
||
return self._conversation_history
|
||
|
||
def get_steps(self) -> List[AnalysisStep]:
|
||
"""获取执行步骤"""
|
||
return self._steps
|