🤖 基于 OpenClaw 的 AI Bugfix Agent

class CollectorAgent(BaseAgent):
    """问题收集智能体 - 负责接收、分类、优先级排序 Bug 反馈"""
    
    def __init__(self, config):
        super().__init__(
            name="CollectorAgent",
            skills=[
                ReceiveBugSkill(),
                ClassifyBugSkill(),
                PrioritizeBugSkill(),
                CreateTicketSkill()
            ]
        )
    
    async def perceive(self, context):
        """感知：从多渠道收集 Bug 信息"""
        bug_reports = []
        
        # 从各渠道收集
        for channel in ["web", "slack", "email", "monitor"]:
            reports = await self.skills.receive_bug.execute(channel)
            bug_reports.extend(reports)
        
        return bug_reports
    
    async def execute(self, bug_reports):
        """执行：分类、优先级排序、创建工单"""
        tickets = []
        
        for report in bug_reports:
            # 分类
            category = await self.skills.classify.execute(report)
            
            # 优先级评估
            priority = await self.skills.prioritize.execute(
                report, category
            )
            
            # 创建工单
            ticket = await self.skills.create_ticket.execute(
                report, category, priority
            )
            
            tickets.append(ticket)
        
        return tickets
    
    async def reflect(self, tickets):
        """反思：评估收集效果，优化分类规则"""
        for ticket in tickets:
            if ticket.category_confidence < 0.7:
                # 低置信度，需要优化分类模型
                await self.optimize_classifier(ticket)
            
            # 记录指标
            await self.metrics.record("bug_collected", 1)

class LocatorAgent(BaseAgent):
    """问题定位智能体 - 负责 Git 定位、根因分析、责任归属"""
    
    def __init__(self, config):
        super().__init__(
            name="LocatorAgent",
            skills=[
                GitBlameSkill(),
                AnalyzeLogSkill(),
                RootCauseSkill(),
                ImpactAnalysisSkill(),
                CodeOwnershipSkill()
            ]
        )
    
    async def perceive(self, ticket):
        """感知：收集错误信息、日志、堆栈"""
        error_info = {
            "stack_trace": ticket.stack_trace,
            "error_logs": await self.search_logs(ticket),
            "recent_changes": await self.get_recent_commits(ticket),
            "affected_files": ticket.affected_files
        }
        return error_info
    
    async def execute(self, error_info):
        """执行：Git 定位、根因分析、责任归属"""
        # Git Blame 分析代码归属
        ownership = await self.skills.code_ownership.execute(
            error_info.affected_files
        )
        
        # 根因分析
        root_cause = await self.skills.root_cause.execute(
            error_info.stack_trace,
            error_info.error_logs
        )
        
        # 影响范围分析
        impact = await self.skills.impact_analysis.execute(
            root_cause.affected_module
        )
        
        return LocalizationResult(
            root_cause=root_cause,
            ownership=ownership,
            impact=impact,
            confidence=root_cause.confidence
        )
    
    async def reflect(self, result):
        """反思：评估定位准确性，优化分析模型"""
        if result.confidence < 0.8:
            # 低置信度，标记需要人工介入
            await self.flag_for_human_review(result)
        
        # 记录定位指标
        await self.metrics.record("location_accuracy", result.confidence)

class FixerAgent(BaseAgent):
    """代码修复智能体 - 负责分析代码、生成修复方案、提交代码"""
    
    def __init__(self, config):
        super().__init__(
            name="FixerAgent",
            skills=[
                CodeAnalyzeSkill(),
                FixGenerateSkill(),
                BranchCreateSkill(),
                CommitCodeSkill(),
                CreatePRSkill()
            ],
            llm_provider="claude-code"
        )
    
    async def perceive(self, localization):
        """感知：获取定位结果、相关代码"""
        code_context = await self.get_code_context(
            localization.root_cause.file_path,
            localization.root_cause.line_number
        )
        
        return {
            "root_cause": localization.root_cause,
            "code_context": code_context,
            "similar_bugs": await self.search_similar_bugs()
        }
    
    async def execute(self, context):
        """执行：生成修复方案、创建分支、提交代码"""
        # 使用 Claude Code 分析并生成修复方案
        fix_plan = await self.skills.fix_generate.execute(
            context.root_cause,
            context.code_context,
            context.similar_bugs
        )
        
        # 生成多个候选方案
        candidates = await self.generate_multiple_fixes(fix_plan)
        
        # 选择最优方案
        best_fix = await self.select_best_fix(candidates)
        
        # 创建修复分支
        branch = await self.skills.branch_create.execute(
            ticket_id=context.root_cause.ticket_id
        )
        
        # 提交修复代码
        commit = await self.skills.commit_code.execute(
            branch=branch,
            fix=best_fix,
            message=f"fix: {context.root_cause.description}"
        )
        
        return FixResult(
            fix=best_fix,
            branch=branch,
            commit=commit,
            candidates=candidates
        )
    
    async def reflect(self, result):
        """反思：评估修复质量，优化生成模型"""
        # 记录修复方案质量
        await self.metrics.record("fix_quality", result.fix.quality_score)
        
        # 如果质量低，收集反馈用于模型优化
        if result.fix.quality_score < 0.7:
            await self.collect_feedback_for_training(result)

class BaseSkill(ABC):
    """Skill 基类 - 所有 Skills 的抽象"""
    
    def __init__(self, name, description):
        self.name = name
        self.description = description
        self.metrics = SkillMetrics()
    
    @abstractmethod
    async def execute(self, **kwargs):
        """执行 Skill"""
        pass
    
    async def validate(self, input_data):
        """验证输入"""
        pass
    
    async def log(self, action, result):
        """记录审计日志"""
        await AuditLogger.log(
            skill=self.name,
            action=action,
            result=result,
            timestamp=datetime.utcnow()
        )


class SkillChain:
    """Skill 链 - 组合多个 Skills 形成复杂能力"""
    
    def __init__(self, skills):
        self.skills = skills
        self.execution_graph = ExecutionGraph()
    
    async def execute(self, input_data):
        """按依赖图执行 Skills"""
        results = {}
        
        for skill in self.execution_graph.topological_sort():
            # 获取依赖的输入
            skill_input = self.get_dependencies(skill, results)
            
            # 执行 Skill
            result = await skill.execute(**skill_input)
            
            # 存储结果供后续 Skill 使用
            results[skill.name] = result
        
        return results

# 定义完整的 Bug 修复 Skill 链
bugfix_chain = SkillChain([
    # 阶段 1: 问题收集
    ReceiveBugSkill(),
    ClassifyBugSkill(),
    PrioritizeBugSkill(),
    CreateTicketSkill(),
    
    # 阶段 2: 问题定位
    GitBlameSkill(),
    AnalyzeLogSkill(),
    RootCauseSkill(),
    ImpactAnalysisSkill(),
    
    # 阶段 3: 代码修复
    CodeAnalyzeSkill(),
    FixGenerateSkill(),
    BranchCreateSkill(),
    CommitCodeSkill(),
    
    # 阶段 4: 测试验证
    RunUnitTestSkill(),
    RunIntegrationSkill(),
    RegressionTestSkill(),
    CoverageCheckSkill(),
    
    # 阶段 5: 部署发布
    CodeReviewSkill(),
    CanaryDeploySkill(),
    MonitorSkill(),
    RollbackSkill()
])

# 执行 Skill 链
result = await bugfix_chain.execute(initial_bug_report)

class SandboxExecutor:
    """沙箱执行器 - 在隔离环境中执行代码"""
    
    def __init__(self, config):
        self.docker_client = docker.from_env()
        self.config = config
        self.sandboxes = {}
    
    async def create_sandbox(self, sandbox_id, requirements):
        """创建沙箱环境"""
        container = self.docker_client.containers.run(
            image="python:3.11-slim",
            name=f"bugfix-sandbox-{sandbox_id}",
            command="sleep infinity",
            detach=True,
            # 资源限制
            cpu_quota=200000,  # 2 Core
            mem_limit="4g",
            # 网络隔离
            network="none",
            # 文件系统隔离
            volumes={
                "/tmp/sandbox/{sandbox_id}": {
                    "bind": "/workspace",
                    "mode": "rw"
                }
            },
            # 安全配置
            security_opt=["no-new-privileges:true"],
            cap_drop=["ALL"],
            read_only=True,
            tmpfs={"/tmp": "rw,noexec,nosuid,size=1g"}
        )
        
        self.sandboxes[sandbox_id] = container
        return container
    
    async def execute_code(self, sandbox_id, code, timeout=1800):
        """在沙箱中执行代码"""
        container = self.sandboxes[sandbox_id]
        
        # 写入代码
        await self.write_to_container(container, "/workspace/main.py", code)
        
        # 执行代码 (带超时)
        result = container.exec_run(
            cmd="python /workspace/main.py",
            demux=True,
            workdir="/workspace"
        )
        
        # 收集输出
        output = {
            "stdout": result.output[0].decode(),
            "stderr": result.output[1].decode(),
            "exit_code": result.exit_code
        }
        
        return output
    
    async def cleanup_sandbox(self, sandbox_id):
        """清理沙箱"""
        container = self.sandboxes.get(sandbox_id)
        if container:
            container.stop()
            container.remove()
            del self.sandboxes[sandbox_id]

class AuditLog:
    """审计日志数据结构"""
    
    def __init__(self):
        self.trace_id = uuid.uuid4()  # 全链路追踪 ID
        self.timestamp = datetime.utcnow()
        self.agent_name = None
        self.skill_name = None
        self.action = None
        self.input_data = None
        self.output_data = None
        self.execution_time = None
        self.status = None
        self.error_message = None
        self.operator = None  # AI Agent 或 人类
        self.decision_reason = None  # 决策依据
    
    def to_dict(self):
        return {
            "trace_id": str(self.trace_id),
            "timestamp": self.timestamp.isoformat(),
            "agent": self.agent_name,
            "skill": self.skill_name,
            "action": self.action,
            "input": self.input_data,
            "output": self.output_data,
            "execution_time_ms": self.execution_time,
            "status": self.status,
            "error": self.error_message,
            "operator": self.operator,
            "decision_reason": self.decision_reason
        }


class AuditLogger:
    """审计日志记录器"""
    
    @staticmethod
    async def log(**kwargs):
        """记录审计日志"""
        log_entry = AuditLog(**kwargs)
        
        # 写入数据库
        await db.audit_logs.insert_one(log_entry.to_dict())
        
        # 写入 Elasticsearch (用于检索)
        await es.index(
            index="audit-logs",
            body=log_entry.to_dict()
        )
    
    @staticmethod
    async def query_by_trace_id(trace_id):
        """按追踪 ID 查询完整链路"""
        logs = await db.audit_logs.find(
            {"trace_id": trace_id}
        ).sort("timestamp", 1)
        
        return [log async for log in logs]
    
    @staticmethod
    async def generate_audit_report(ticket_id):
        """生成审计报告"""
        logs = await db.audit_logs.find(
            {"ticket_id": ticket_id}
        )
        
        report = {
            "ticket_id": ticket_id,
            "total_actions": await logs.count(),
            "timeline": [log async for log in logs],
            "agents_involved": await logs.distinct("agent"),
            "total_execution_time": await calculate_total_time(logs),
            "success_rate": await calculate_success_rate(logs)
        }
        
        return report