agent_teams_example.py

"""
Anthropic Claude Opus 4.6 Agent Teams API 使用示例

Agent Teams 功能允许同时协调多个 AI 代理并行工作，
每个代理负责任务的不同部分并直接相互协调。

注意: 此功能目前处于 Research Preview 阶段，需要 API 访问权限
"""

import asyncio
import json
from typing import List, Dict, Any, Optional
from dataclasses import dataclass
from enum import Enum


class AgentRole(Enum):
    """代理角色定义"""
    ARCHITECT = "architect"      # 系统架构师
    DEVELOPER = "developer"      # 代码开发者
    REVIEWER = "reviewer"        # 代码审查员
    TESTER = "tester"            # 测试工程师
    DOCUMENTER = "documenter"    # 文档编写者
    OPTIMIZER = "optimizer"      # 性能优化师


@dataclass
class Agent:
    """单个代理定义"""
    id: str
    name: str
    role: AgentRole
    model: str = "claude-opus-4-6"
    system_prompt: str = ""
    tools: List[str] = None


@dataclass
class SubTask:
    """子任务定义"""
    id: str
    agent_id: str
    description: str
    dependencies: List[str] = None  # 依赖的其他任务ID
    status: str = "pending"  # pending, running, completed, failed
    result: Any = None


class AgentTeam:
    """
    Agent Team 协调器
    管理多个代理并行工作
    """

    def __init__(self, team_name: str):
        self.team_name = team_name
        self.agents: Dict[str, Agent] = {}
        self.tasks: Dict[str, SubTask] = {}
        self.message_bus: List[Dict] = []  # 代理间通信总线

    def add_agent(self, agent: Agent):
        """添加代理到团队"""
        self.agents[agent.id] = agent
        print(f"[Team] 添加代理: {agent.name} ({agent.role.value})")

    def create_task(self, agent_id: str, description: str, dependencies: List[str] = None) -> str:
        """创建子任务"""
        task_id = f"task_{len(self.tasks)}"
        task = SubTask(
            id=task_id,
            agent_id=agent_id,
            description=description,
            dependencies=dependencies or []
        )
        self.tasks[task_id] = task
        return task_id

    async def run_parallel(self, main_task: str) -> Dict[str, Any]:
        """
        并行执行所有任务

        这是 Agent Teams 的核心功能 - 多个代理同时工作
        """
        print(f"\n{'='*60}")
        print(f"🚀 Agent Team: {self.team_name}")
        print(f"📝 主任务: {main_task}")
        print(f"👥 团队成员: {len(self.agents)} 个代理")
        print(f"📋 子任务: {len(self.tasks)} 个")
        print(f"{'='*60}\n")

        # 实际使用 Anthropic API 的代码结构
        # 注意: 这是伪代码，展示 API 调用方式

        results = {}

        # 1. 分析任务依赖关系
        execution_order = self._resolve_dependencies()

        # 2. 并行执行（无依赖的任务同时运行）
        for batch in execution_order:
            batch_tasks = []
            for task_id in batch:
                task = self.tasks[task_id]
                agent = self.agents[task.agent_id]
                batch_tasks.append(self._execute_task(agent, task))

            # 并行运行
            batch_results = await asyncio.gather(*batch_tasks, return_exceptions=True)

            for task_id, result in zip(batch, batch_results):
                results[task_id] = result
                if isinstance(result, Exception):
                    print(f"❌ 任务 {task_id} 失败: {result}")
                else:
                    print(f"✅ 任务 {task_id} 完成")

        return results

    def _resolve_dependencies(self) -> List[List[str]]:
        """解析任务依赖，返回可并行执行的批次"""
        # 简化的依赖解析
        completed = set()
        pending = set(self.tasks.keys())
        batches = []

        while pending:
            batch = []
            for task_id in pending:
                task = self.tasks[task_id]
                if all(dep in completed for dep in task.dependencies):
                    batch.append(task_id)

            if not batch:
                raise ValueError("循环依赖 detected")

            batches.append(batch)
            completed.update(batch)
            pending -= set(batch)

        return batches

    async def _execute_task(self, agent: Agent, task: SubTask) -> Any:
        """执行单个任务（实际调用 Anthropic API）"""
        print(f"🤖 {agent.name} 开始工作: {task.description[:50]}...")

        # 这里会调用实际的 Anthropic API
        # client.beta.agent_teams.create(...) 或类似接口

        # 模拟执行时间
        await asyncio.sleep(1)

        result = f"[{agent.role.value}] 完成了: {task.description}"
        task.status = "completed"
        task.result = result

        return result

    def broadcast_message(self, from_agent: str, message: str):
        """代理间广播消息"""
        self.message_bus.append({
            "from": from_agent,
            "message": message,
            "timestamp": asyncio.get_event_loop().time()
        })
        print(f"📢 [{from_agent}] 广播: {message}")


class AnthropicAgentTeamsAPI:
    """
    Anthropic Agent Teams API 封装

    预期 API 接口（基于发布说明）:
    - 创建 agent team
    - 分配子任务
    - 并行执行
    - 结果协调
    """

    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.anthropic.com/v1"
        # self.client = anthropic.Anthropic(api_key=api_key)

    async def create_team(
        self,
        name: str,
        agents: List[Agent],
        coordination_mode: str = "parallel"
    ) -> AgentTeam:
        """
        创建 Agent Team

        预期 API:
        POST /v1/agent-teams
        {
            "name": "Dev Team",
            "agents": [...],
            "coordination_mode": "parallel"
        }
        """
        team = AgentTeam(name)
        for agent in agents:
            team.add_agent(agent)
        return team

    async def execute_task(
        self,
        team: AgentTeam,
        task: str,
        context: Dict[str, Any] = None
    ) -> Dict[str, Any]:
        """
        使用 Agent Team 执行任务

        预期 API:
        POST /v1/agent-teams/{team_id}/execute
        {
            "task": "Build a web app",
            "context": {...}
        }
        """
        return await team.run_parallel(task)


# ==========================================
# 使用示例
# ==========================================

async def example_software_development():
    """
    软件开发 Agent Team 示例

    模拟一个完整的开发团队并行工作
    """

    # 1. 创建 API 客户端
    api = AnthropicAgentTeamsAPI(api_key="your-api-key")

    # 2. 定义团队角色
    agents = [
        Agent(
            id="arch_1",
            name="架构师 Alice",
            role=AgentRole.ARCHITECT,
            system_prompt="你是系统架构专家，负责设计整体架构和API接口"
        ),
        Agent(
            id="dev_1",
            name="开发者 Bob",
            role=AgentRole.DEVELOPER,
            system_prompt="你是资深开发者，负责实现核心功能"
        ),
        Agent(
            id="dev_2",
            name="开发者 Carol",
            role=AgentRole.DEVELOPER,
            system_prompt="你是前端专家，负责UI实现"
        ),
        Agent(
            id="review_1",
            name="审查员 David",
            role=AgentRole.REVIEWER,
            system_prompt="你是代码审查专家，负责检查代码质量"
        ),
        Agent(
            id="test_1",
            name="测试员 Eve",
            role=AgentRole.TESTER,
            system_prompt="你是测试工程师，负责编写测试用例"
        ),
    ]

    # 3. 创建团队
    team = await api.create_team("Dify Workflow Team", agents)

    # 4. 创建并行任务
    # 架构设计（无依赖）
    arch_task = team.create_task("arch_1", "设计系统架构和数据库模型")

    # 后端开发（依赖架构）
    backend_task = team.create_task("dev_1", "实现API后端", dependencies=[arch_task])

    # 前端开发（依赖架构）
    frontend_task = team.create_task("dev_2", "实现前端界面", dependencies=[arch_task])

    # 代码审查（依赖前后端开发）
    review_task = team.create_task("review_1", "审查代码质量", dependencies=[backend_task, frontend_task])

    # 测试（依赖审查）
    test_task = team.create_task("test_1", "编写并运行测试", dependencies=[review_task])

    # 5. 并行执行
    results = await api.execute_task(
        team,
        task="开发 Dify Workflow Generator Web 界面",
        context={"tech_stack": "FastAPI + Vue.js", "timeline": "2 weeks"}
    )

    print("\n" + "="*60)
    print("📊 执行结果:")
    print("="*60)
    for task_id, result in results.items():
        print(f"\n{task_id}:")
        print(f"  结果: {result}")


async def example_workflow_optimization():
    """
    工作流优化 Agent Team 示例

    多个专业代理并行优化不同方面
    """

    api = AnthropicAgentTeamsAPI(api_key="your-api-key")

    agents = [
        Agent(id="perf_1", name="性能专家", role=AgentRole.OPTIMIZER),
        Agent(id="cost_1", name="成本专家", role=AgentRole.OPTIMIZER),
        Agent(id="sec_1", name="安全专家", role=AgentRole.REVIEWER),
        Agent(id="doc_1", name="文档专家", role=AgentRole.DOCUMENTER),
    ]

    team = await api.create_team("Optimization Team", agents)

    # 并行优化任务（无依赖）
    team.create_task("perf_1", "优化执行性能，减少延迟")
    team.create_task("cost_1", "优化API调用成本")
    team.create_task("sec_1", "安全检查，修复漏洞")
    team.create_task("doc_1", "编写技术文档")

    results = await api.execute_task(team, "全面优化现有工作流")

    return results


# ==========================================
# 实际的 API 调用代码（当 API 可用时）
# ==========================================

async def real_api_example():
    """
    使用实际 Anthropic API 的示例
    （当 Agent Teams API 正式发布后）
    """
    import anthropic

    client = anthropic.Anthropic(api_key="your-api-key")

    # 创建 agent team
    # team = client.beta.agent_teams.create(
    #     name="Development Team",
    #     agents=[
    #         {"role": "architect", "model": "claude-opus-4-6"},
    #         {"role": "developer", "model": "claude-opus-4-6"},
    #         {"role": "tester", "model": "claude-opus-4-6"},
    #     ],
    #     coordination_mode="parallel"
    # )

    # 执行任务
    # result = client.beta.agent_teams.execute(
    #     team_id=team.id,
    #     task="Build a REST API",
    #     subtasks=[
    #         {"agent": "architect", "description": "Design API"},
    #         {"agent": "developer", "description": "Implement API", "depends_on": [0]},
    #         {"agent": "tester", "description": "Test API", "depends_on": [1]},
    #     ]
    # )

    pass


if __name__ == "__main__":
    print("🚀 Anthropic Claude Opus 4.6 Agent Teams 示例")
    print("="*60)
    print("\n注意: Agent Teams 功能目前处于 Research Preview 阶段")
    print("需要申请 API 访问权限才能使用\n")

    # 运行示例
    asyncio.run(example_software_development())