Trae 04.22版本深度解析：Agent能力升级与MCP市场对复杂任务执行的革新

# 示例：使用@Builder命令切换模式
def handle_user_input(user_input):
    if user_input.startswith("@Builder"):
        # 切换到Builder Agent模式
        activate_builder_mode()
        return "已进入Builder Agent模式，请输入构建指令"
    else:
        # 普通Chat模式处理
        return process_chat_message(user_input)
# Builder模式激活函数
def activate_builder_mode():
    # 初始化Builder环境
    init_builder_environment()
    # 加载项目上下文
    load_project_context()
    # 设置专门的提示词
    set_builder_prompt("您正在Builder模式下工作，可以执行构建命令")

# Web Context处理流程示例
def process_web_context(url):
    try:
        # 1. 获取网页内容
        response = requests.get(url, timeout=10)
        response.raise_for_status()
        
        # 2. 提取主要内容（避免广告和噪音）
        soup = BeautifulSoup(response.text, 'html.parser')
        main_content = extract_meaningful_content(soup)
        
        # 3. 结构化处理
        structured_data = {
            'title': extract_title(soup),
            'content': main_content,
            'links': extract_relevant_links(soup),
            'last_updated': datetime.now()
        }
        
        # 4. 存储到上下文
        save_to_context('web', structured_data)
        return structured_data
        
    except Exception as e:
        log_error(f"Web context processing failed: {str(e)}")
        return None

# Doc Context处理示例
class DocContextProcessor:
    def __init__(self):
        self.max_files = 1000
        self.max_size = 50 * 1024 * 1024  # 50MB
        
    def add_documents(self, doc_sources):
        total_size = 0
        processed_docs = []
        
        for source in doc_sources:
            if len(processed_docs) >= self.max_files:
                break
                
            doc = self._process_source(source)
            if doc:
                doc_size = sys.getsizeof(doc['content'])
                if total_size + doc_size > self.max_size:
                    break
                    
                processed_docs.append(doc)
                total_size += doc_size
                
        self._index_documents(processed_docs)
        return processed_docs
        
    def _process_source(self, source):
        # 处理URL或文件上传
        if source.startswith('http'):
            return self._process_url(source)
        else:
            return self._process_uploaded_file(source)
            
    def _process_url(self, url):
        # 实现URL文档处理逻辑
        pass
        
    def _process_uploaded_file(self, file_path):
        # 实现文件处理逻辑
        pass

<!-- 示例：user_rules.md -->
# 个人规则
## 代码风格
- 使用4空格缩进
- 函数命名采用snake_case
- 类命名采用PascalCase
## 安全规则
- 禁止执行rm -rf /
- 数据库操作需确认
<!-- 示例：project_rules.md -->
# 项目规则
## API规范
- 所有端点必须版本化(/v1/...)
- 响应必须包含request_id
## 测试要求
- 覆盖率不低于80%
- 必须包含集成测试

# 自定义Agent示例
class CustomAgent:
    def __init__(self, name, prompt, tools, config):
        self.name = name
        self.prompt_template = prompt
        self.tools = tools  # 可用工具集
        self.config = config
        self.blacklist = config.get('command_blacklist', [])
        
    def execute(self, command):
        if self._is_blacklisted(command):
            return "命令被禁止执行"
            
        tool = self._select_tool(command)
        if tool:
            return tool.execute(command)
        else:
            return self._fallback_response(command)
            
    def _is_blacklisted(self, command):
        return any(
            banned in command 
            for banned in self.blacklist
        )
        
    def _select_tool(self, command):
        for tool in self.tools:
            if tool.can_handle(command):
                return tool
        return None
        
    def generate_prompt(self, context):
        return self.prompt_template.format(
            agent_name=self.name,
            context=context
        )

# Builder Agent配置示例
builder_agent_config = {
    "name": "AdvancedBuilder",
    "description": "Enhanced builder with MCP support",
    "prompt": """你是一个高级构建Agent，具有以下能力：
    - 理解复杂构建指令
    - 自动解决依赖关系
    - 安全执行构建命令
    
    当前项目: {project_name}
    上下文: {context}""",
    "tools": [
        CodeGeneratorTool(),
        DependencyResolverTool(),
        MCPIntegrationTool()
    ],
    "command_blacklist": [
        "rm -rf",
        "format c:"
    ]
}

# 第三方MCP集成示例
class MCPService:
    def __init__(self):
        self.registered_servers = {}
        
    def add_server(self, name, config):
        # 验证配置
        if not self._validate_config(config):
            raise ValueError("Invalid MCP server config")
            
        # 测试连接
        if not self._test_connection(config):
            raise ConnectionError("Cannot connect to MCP server")
            
        self.registered_servers[name] = {
            'config': config,
            'tools': self._fetch_available_tools(config)
        }
        
    def execute_tool(self, server_name, tool_name, params):
        server = self.registered_servers.get(server_name)
        if not server:
            raise ValueError("Server not found")
            
        tool = next(
            (t for t in server['tools'] if t['name'] == tool_name),
            None
        )
        if not tool:
            raise ValueError("Tool not found")
            
        return self._call_remote_tool(
            server['config'],
            tool_name,
            params
        )

# 复杂任务执行引擎示例
class TaskOrchestrator:
    def __init__(self, agents, mcp_services):
        self.agents = agents
        self.mcp_services = mcp_services
        self.task_queue = []
        self.results = {}
        
    def add_task(self, task):
        self.task_queue.append(task)
        
    def execute(self):
        while self.task_queue:
            task = self.task_queue.pop(0)
            
            # 选择最适合的Agent
            agent = self._select_agent(task)
            
            # 执行任务
            if agent:
                result = agent.execute(task)
                self.results[task.id] = result
                
                # 处理需要MCP工具的任务
                if task.needs_mcp:
                    mcp_result = self._handle_mcp_task(task)
                    result.update(mcp_result)
                    
            # 处理依赖任务
            for dependent in task.dependents:
                if all(dep in self.results for dep in dependent.dependencies):
                    self.task_queue.append(dependent)
                    
        return self.results

# 安全执行器示例
class SafeExecutor:
    def __init__(self, sandbox_enabled=True):
        self.sandbox = sandbox_enabled
        self.audit_log = []
        
    def execute_command(self, command, agent):
        # 记录审计日志
        self._log_audit(agent, command)
        
        # 检查黑名单
        if self._is_dangerous(command):
            raise SecurityError("Command blocked by security policy")
            
        # 沙箱执行
        if self.sandbox:
            return self._execute_in_sandbox(command)
        else:
            return subprocess.run(
                command,
                shell=True,
                check=True,
                capture_output=True
            )
            
    def _is_dangerous(self, command):
        dangerous_patterns = [
            "rm -rf", "format", "chmod 777"
        ]
        return any(
            pattern in command 
            for pattern in dangerous_patterns
        )

# 动态技能加载器实现
class DynamicSkillLoader:
    def __init__(self):
        self.skill_registry = {}
        self.skill_dependencies = defaultdict(list)
        
    def register_skill(self, skill_name, skill_module, dependencies=None):
        """注册新技能到Agent系统"""
        if not inspect.ismodule(skill_module):
            raise ValueError("Skill must be a module object")
            
        self.skill_registry[skill_name] = skill_module
        
        if dependencies:
            self._validate_dependencies(dependencies)
            self.skill_dependencies[skill_name] = dependencies
            
    def load_for_agent(self, agent, skill_names):
        """为指定Agent加载技能集"""
        loaded = set()
        
        # 拓扑排序解决依赖关系
        for skill in self._topological_sort(skill_names):
            if skill not in self.skill_registry:
                continue
                
            # 动态注入技能方法
            skill_module = self.skill_registry[skill]
            for name, obj in inspect.getmembers(skill_module):
                if name.startswith('skill_'):
                    setattr(agent, name, MethodType(obj, agent))
                    loaded.add(name[6:])
                    
        return list(loaded)
    
    def _validate_dependencies(self, dependencies):
        """验证技能依赖是否有效"""
        for dep in dependencies:
            if dep not in self.skill_registry:
                raise ValueError(f"Missing dependency: {dep}")

class AdaptiveLearningAgent:
    def __init__(self, memory_capacity=1000):
        self.short_term_memory = deque(maxlen=100)
        self.long_term_memory = []
        self.memory_capacity = memory_capacity
        self.learning_rate = 0.1
        self.behavior_model = BehaviorModel()
        
    def process_feedback(self, feedback):
        """处理执行反馈并学习"""
        self.short_term_memory.append(feedback)
        
        # 定期压缩到长期记忆
        if len(self.short_term_memory) >= 100:
            self._consolidate_memory()
            
        # 更新行为模型
        self._update_behavior(feedback)
        
    def _consolidate_memory(self):
        """记忆压缩算法"""
        patterns = self._extract_patterns(self.short_term_memory)
        for pattern in patterns:
            if len(self.long_term_memory) >= self.memory_capacity:
                self._forget_least_used()
            self.long_term_memory.append(pattern)
        self.short_term_memory.clear()
    
    def _update_behavior(self, feedback):
        """基于反馈调整行为模型"""
        adjustment = self._calculate_adjustment(feedback)
        self.behavior_model.update(
            adjustment, 
            learning_rate=self.learning_rate
        )
        
    def make_decision(self, context):
        """基于学习结果做出决策"""
        return self.behavior_model.predict(context)

class MCPServiceDiscovery:
    def __init__(self, multicast_addr='224.0.0.251', port=5353):
        self.multicast_addr = multicast_addr
        self.port = port
        self.services = {}
        self.socket = self._setup_multicast_socket()
        self.load_balancers = {}
        
    def _setup_multicast_socket(self):
        """配置组播监听socket"""
        sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
        sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
        sock.bind(('', self.port))
        mreq = struct.pack('4sl', 
                          socket.inet_aton(self.multicast_addr),
                          socket.INADDR_ANY)
        sock.setsockopt(socket.IPPROTO_IP, 
                       socket.IP_ADD_MEMBERSHIP, 
                       mreq)
        return sock
        
    def discover_services(self, timeout=5):
        """发现可用MCP服务"""
        start_time = time.time()
        while time.time() - start_time < timeout:
            data, addr = self.socket.recvfrom(1024)
            service_info = self._parse_service_packet(data)
            if service_info:
                self._register_service(service_info, addr[0])
                
    def get_best_instance(self, service_name):
        """获取最优服务实例"""
        if service_name not in self.load_balancers:
            self.load_balancers[service_name] = (
                LoadBalancer(self.services[service_name])
            )
        return self.load_balancers[service_name].get_instance()

class MCPTransactionManager:
    def __init__(self, participants):
        self.participants = participants
        self.state = 'IDLE'
        self.timeout = 30  # seconds
        
    def execute(self, operations):
        """执行分布式事务"""
        try:
            # 阶段1：准备
            self.state = 'PREPARING'
            prepare_results = []
            for participant, op in zip(self.participants, operations):
                result = participant.prepare(op)
                prepare_results.append(result)
                
            if not all(res['status'] == 'ready' for res in prepare_results):
                self._rollback(prepare_results)
                return False
                
            # 阶段2：提交
            self.state = 'COMMITTING'
            commit_results = []
            for participant in self.participants:
                result = participant.commit()
                commit_results.append(result)
                
            if not all(res['status'] == 'success' for res in commit_results):
                self._compensate(commit_results)
                return False
                
            self.state = 'DONE'
            return True
            
        except Exception as e:
            self.state = 'ERROR'
            self._emergency_rollback()
            raise MCPTransactionError(str(e))
            
    def _rollback(self, prepare_results):
        """回滚已准备的操作"""
        for participant, res in zip(self.participants, prepare_results):
            if res['status'] == 'ready':
                participant.rollback()
        self.state = 'ABORTED'

class TaskGraph:
    def __init__(self):
        self.nodes = {}
        self.edges = defaultdict(list)
        self.reverse_edges = defaultdict(list)
        
    def add_task(self, task_id, task_info):
        """添加新任务节点"""
        if task_id in self.nodes:
            raise ValueError(f"Duplicate task ID: {task_id}")
        self.nodes[task_id] = task_info
        
    def add_dependency(self, from_task, to_task):
        """添加任务依赖关系"""
        if from_task not in self.nodes or to_task not in self.nodes:
            raise ValueError("Invalid task ID")
        self.edges[from_task].append(to_task)
        self.reverse_edges[to_task].append(from_task)
        
    def get_execution_order(self):
        """获取拓扑排序执行顺序"""
        in_degree = {task: 0 for task in self.nodes}
        for task in self.nodes:
            for neighbor in self.edges[task]:
                in_degree[neighbor] += 1
                
        queue = deque([task for task in in_degree if in_degree[task] == 0])
        topo_order = []
        
        while queue:
            task = queue.popleft()
            topo_order.append(task)
            
            for neighbor in self.edges[task]:
                in_degree[neighbor] -= 1
                if in_degree[neighbor] == 0:
                    queue.append(neighbor)
                    
        if len(topo_order) != len(self.nodes):
            raise ValueError("Graph has cycles")
            
        return topo_order
        
    def visualize(self):
        """生成可视化任务图"""
        dot = Digraph()
        for task_id, info in self.nodes.items():
            dot.node(task_id, label=f"{task_id}\n{info['type']}")
            
        for from_task, to_tasks in self.edges.items():
            for to_task in to_tasks:
                dot.edge(from_task, to_task)
                
        return dot

class TaskDecomposer:
    def __init__(self, policy_network):
        self.policy_net = policy_network
        self.memory = ReplayBuffer(10000)
        self.optimizer = torch.optim.Adam(
            self.policy_net.parameters(), 
            lr=1e-4
        )
        
    def decompose(self, complex_task, context):
        """分解复杂任务"""
        state = self._extract_state(complex_task, context)
        
        # 使用策略网络预测分解方案
        with torch.no_grad():
            action_probs = self.policy_net(state)
            action_dist = Categorical(action_probs)
            action = action_dist.sample()
            
        subtasks = self._action_to_subtasks(action)
        return subtasks
        
    def learn_from_feedback(self, decomposition, feedback):
        """从执行反馈中学习"""
        state = decomposition['state']
        action = decomposition['action']
        reward = self._calculate_reward(feedback)
        
        # 存储到经验回放池
        self.memory.push(state, action, reward)
        
        # 定期训练
        if len(self.memory) >= 1000:
            self._train_network()
            
    def _train_network(self):
        """训练策略网络"""
        batch = self.memory.sample(64)
        states = torch.stack(batch.state)
        actions = torch.stack(batch.action)
        rewards = torch.stack(batch.reward)
        
        # 计算策略梯度
        action_probs = self.policy_net(states)
        dist = Categorical(action_probs)
        log_probs = dist.log_prob(actions)
        
        loss = -(log_probs * rewards).mean()
        
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

class AgentContextSwitcher:
    def __init__(self):
        self.context_pool = {}
        self.active_context = None
        self.lru_cache = OrderedDict()
        self.cache_size = 10
        
    def switch_to(self, agent, context_id):
        """切换Agent上下文"""
        # 从池中获取或加载上下文
        if context_id not in self.context_pool:
            context = self._load_context(context_id)
            self.context_pool[context_id] = context
        else:
            context = self.context_pool[context_id]
            
        # 更新LRU缓存
        if context_id in self.lru_cache:
            self.lru_cache.move_to_end(context_id)
        else:
            self.lru_cache[context_id] = time.time()
            if len(self.lru_cache) > self.cache_size:
                oldest = next(iter(self.lru_cache))
                del self.context_pool[oldest]
                del self.lru_cache[oldest]
                
        # 执行切换
        old_context = self.active_context
        self.active_context = context_id
        
        # 调用Agent钩子
        agent.on_context_switch(old_context, context_id)
        
        return context
        
    def _load_context(self, context_id):
        """快速加载上下文实现"""
        # 使用内存映射文件加速加载
        with open(f"contexts/{context_id}.ctx", 'rb') as f:
            buf = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
            return pickle.loads(buf)

class MCPPipeline:
    def __init__(self, batch_size=32):
        self.batch_size = batch_size
        self.input_queue = deque()
        self.output_queue = deque()
        self.processing = False
        
    async def process_requests(self, requests):
        """批量处理MCP请求"""
        # 填充批次
        batch = []
        for req in requests:
            self.input_queue.append(req)
            if len(self.input_queue) >= self.batch_size:
                batch = [self.input_queue.popleft() 
                        for _ in range(self.batch_size)]
                break
                
        if not batch:
            return []
            
        # 批量执行
        try:
            self.processing = True
            results = await self._execute_batch(batch)
            
            # 分发结果
            for res in results:
                self.output_queue.append(res)
                
            return [self.output_queue.popleft() 
                   for _ in range(len(results))]
                   
        finally:
            self.processing = False
            
    async def _execute_batch(self, batch):
        """执行批量MCP调用"""
        # 合并相似请求
        merged = self._merge_requests(batch)
        
        # 并行调用
        tasks = []
        for service, reqs in merged.items():
            task = asyncio.create_task(
                self._call_mcp_service(service, reqs)
            )
            tasks.append(task)
            
        responses = await asyncio.gather(*tasks)
        
        # 拆分结果
        return self._split_responses(batch, responses)

class PermissionManager:
    def __init__(self):
        self.roles = {
            'admin': {'*'},
            'developer': {
                'agent:create',
                'agent:invoke',
                'mcp:use'
            },
            'analyst': {
                'data:query',
                'report:generate'
            }
        }
        self.users = {}
        self.policy_cache = LRUCache(1000)
        
    def check_permission(self, user, resource, action):
        """检查用户权限"""
        cache_key = f"{user.id}-{resource}-{action}"
        if cache_key in self.policy_cache:
            return self.policy_cache[cache_key]
            
        # 获取用户角色
        user_roles = self.users.get(user.id, set())
        
        # 检查每个角色的权限
        allowed = False
        for role in user_roles:
            if role not in self.roles:
                continue
                
            # 支持通配符权限
            if '*' in self.roles[role]:
                allowed = True
                break
                
            # 精确匹配
            perm = f"{resource}:{action}"
            if perm in self.roles[role]:
                allowed = True
                break
                
        # 缓存结果
        self.policy_cache[cache_key] = allowed
        return allowed
        
    def add_scoped_permission(self, role, resource, actions, condition=None):
        """添加带条件的权限"""
        for action in actions:
            perm = Permission(
                resource=resource,
                action=action,
                condition=condition
            )
            self.roles.setdefault(role, set()).add(perm)
            
        # 清除相关缓存
        self._clear_cache_for(resource)

class AuditSystem:
    def __init__(self, storage_backend):
        self.storage = storage_backend
        self.event_queue = asyncio.Queue()
        self.consumer_task = asyncio.create_task(
            self._event_consumer()
        )
        
    async def log_event(self, event_type, details):
        """记录审计事件"""
        event = {
            'timestamp': datetime.utcnow().isoformat(),
            'type': event_type,
            'details': details,
            'trace_id': self._get_current_trace()
        }
        await self.event_queue.put(event)
        
    async def _event_consumer(self):
        """异步处理审计事件"""
        batch = []
        last_flush = time.time()
        
        while True:
            try:
                # 批量收集事件
                timeout = 1.0  # 最长1秒刷新一次
                event = await asyncio.wait_for(
                    self.event_queue.get(),
                    timeout=max(0, last_flush + 1 - time.time())
                )
                batch.append(event)
                
                # 批量写入条件
                if len(batch) >= 100 or time.time() - last_flush >= 1.0:
                    await self._flush_batch(batch)
                    batch = []
                    last_flush = time.time()
                    
            except asyncio.TimeoutError:
                if batch:
                    await self._flush_batch(batch)
                    batch = []
                    last_flush = time.time()
                    
    async def _flush_batch(self, batch):
        """批量写入存储"""
        try:
            await self.storage.bulk_insert(batch)
        except Exception as e:
            logging.error(f"Audit log flush failed: {str(e)}")
            # 失败时写入本地临时文件
            self._write_to_fallback(batch)

class SmartCICD:
    def __init__(self, agent_manager):
        self.agents = agent_manager
        self.pipeline = TaskGraph()
        
    def setup_pipeline(self, repo_config):
        """配置智能流水线"""
        # 构建任务图
        self.pipeline.add_task('analyze', {
            'type': 'code_analysis',
            'agent': 'code_analyzer'
        })
        
        self.pipeline.add_task('refactor', {
            'type': 'auto_refactor',
            'agent': 'refactor_agent'
        })
        
        self.pipeline.add_dependency('analyze', 'refactor')
        
        # 条件依赖
        self.pipeline.add_conditional_edge(
            source='analyze',
            target='refactor',
            condition=lambda r: r['needs_refactor']
        )
        
    async def run(self, commit):
        """执行流水线"""
        # 获取拓扑顺序
        execution_order = self.pipeline.get_execution_order()
        
        results = {}
        for task_id in execution_order:
            task_info = self.pipeline.nodes[task_id]
            agent = self.agents.get(task_info['agent'])
            
            # 执行任务
            result = await agent.execute(
                task_info['type'],
                {'commit': commit}
            )
            results[task_id] = result
            
            # 处理条件分支
            if 'condition' in task_info:
                if not task_info['condition'](result):
                    # 跳过后续依赖任务
                    break
                    
        return results

class QuantumEnhancedAgent:
    def __init__(self, qpu_backend):
        self.qpu = qpu_backend
        self.classical_nn = NeuralNetwork()
        self.quantum_cache = {}
        
    def hybrid_predict(self, inputs):
        """混合量子-经典预测"""
        # 经典部分处理
        classical_out = self.classical_nn(inputs)
        
        # 量子部分处理
        qhash = self._hash_input(inputs)
        if qhash in self.quantum_cache:
            quantum_out = self.quantum_cache[qhash]
        else:
            quantum_out = self._run_quantum_circuit(inputs)
            self.quantum_cache[qhash] = quantum_out
            
        # 融合输出
        return 0.7 * classical_out + 0.3 * quantum_out
        
    def _run_quantum_circuit(self, inputs):
        """在QPU上运行量子电路"""
        circuit = self._build_circuit(inputs)
        job = self.qpu.run(circuit, shots=1000)
        result = job.result()
        return self._decode_counts(result.get_counts())