Agent/examples/research/run.py

"""
浏览器调研示例 (增强版)

功能：
1. 使用 Agent 模式进行网络调研
2. 任务结束自动关闭浏览器并杀掉进程
3. 异常安全：即使程序崩溃也能清理环境
"""

import os
import sys
import asyncio
from pathlib import Path

# 添加项目根目录到 Python 路径
sys.path.insert(0, str(Path(__file__).parent.parent.parent))

from dotenv import load_dotenv
load_dotenv()

import logging
# 配置感知日志
logging.basicConfig(level=logging.WARNING)  # 默认 WARNING
logging.getLogger("agent.core.message_manager").setLevel(logging.INFO)  # 开启感知日志
logging.getLogger("tools").setLevel(logging.INFO)  # 开启工具日志

from agent.llm.prompts import SimplePrompt
from agent.core.runner import AgentRunner, RunConfig
from agent.trace import (
    FileSystemTraceStore,
    Trace,
    Message,
)
from agent.llm import create_openrouter_llm_call

# 导入浏览器清理工具
from agent.tools.builtin.browser.baseClass import get_browser_session,kill_browser_session,init_browser_session 

async def main():
    # 路径配置
    base_dir = Path(__file__).parent
    project_root = base_dir.parent.parent
    trace_dir = project_root / ".trace"
    prompt_path = base_dir / "test.prompt"
    output_dir = base_dir / "output"
    output_dir.mkdir(exist_ok=True)

    # Skills 目录
    skills_dir = None 

    print("=" * 60)
    print("🚀 浏览器调研任务 (Agent 模式)")
    print("=" * 60)
    print()

    # 1. 加载 prompt
    print("1. 加载 prompt...")
    prompt = SimplePrompt(prompt_path)

    # 提取配置
    system_prompt = prompt._messages.get("system", "")
    user_task = prompt._messages.get("user", "")
    model_name = prompt.config.get('model', 'gemini-2.5-flash')
    temperature = float(prompt.config.get('temperature', 0.3))

    print(f"   - 任务: {user_task[:80]}...")
    print(f"   - 模型: {model_name}")
    
    # 2. 构建消息
    print("2. 构建任务消息...")
    messages = prompt.build_messages()

    # 3. 创建 Agent Runner
    print("3. 创建 Agent Runner...")
    runner = AgentRunner(
        trace_store=FileSystemTraceStore(base_path=str(trace_dir)),
        llm_call=create_openrouter_llm_call(model=f"google/{model_name}"),
        skills_dir=skills_dir,
        debug=True 
    )

    final_response = ""
    current_trace_id = None

    # 4. Agent 模式执行（使用 try...finally 确保清理）
    try:
        print(f"4. 初始化云浏览器...")                              
        await init_browser_session(browser_type="cloud", headless=True)                                                          

        print(f"5. 启动 Agent 模式执行...")    
        print()

        async for item in runner.run(
            messages=messages,
            config=RunConfig(
                system_prompt=system_prompt,
                model=f"google/{model_name}",
                temperature=temperature,
                max_iterations=20,
                name=user_task[:50],
            ),
        ):
            # 处理 Trace 对象（整体状态变化）
            if isinstance(item, Trace):
                current_trace_id = item.trace_id
                if item.status == "running":
                    print(f"[Trace] 开始: {item.trace_id[:8]}")
                elif item.status == "completed":
                    print(f"[Trace] 完成")
                    print(f"  - Total tokens: {item.total_tokens}")
                    print(f"  - Total cost: ${item.total_cost:.4f}")
                elif item.status == "failed":
                    print(f"[Trace] 失败: {item.error_message}")

            # 处理 Message 对象（执行过程）
            elif isinstance(item, Message):
                if item.role == "assistant":
                    content = item.content
                    if isinstance(content, dict):
                        text = content.get("text", "")
                        tool_calls = content.get("tool_calls")

                        if text and not tool_calls:
                            final_response = text
                            print(f"[Response] Agent 给出最终回复")
                        elif text:
                            # 增加打印长度到 300，方便观察
                            print(f"[Assistant] {text[:300]}...")

                        if tool_calls:
                            for tc in tool_calls:
                                tool_name = tc.get("function", {}).get("name", "unknown")
                                print(f"[Tool Call] 🛠️ {tool_name}")

                elif item.role == "tool":
                    content = item.content
                    if isinstance(content, dict):
                        tool_name = content.get("tool_name", "unknown")
                        print(f"[Tool Result] ✅ {tool_name}")
                    if item.description:
                        desc = item.description[:80] if len(item.description) > 80 else item.description
                        print(f"  {desc}...")

        # 5. 输出结果
        print()
        print("=" * 60)
        print("Final Agent Response:")
        print("=" * 60)
        print(final_response)
        print("=" * 60)
        print()

        # 6. 保存结果
        output_file = output_dir / "research_result.txt"
        with open(output_file, 'w', encoding='utf-8') as f:
            f.write(final_response)
        print(f"✓ 结果已保存到: {output_file}")

    except Exception as e:
        print(f"\n❌ 程序运行崩溃: {str(e)}")
        import traceback
        traceback.print_exc()

    finally:
        # --- 核心逻辑：无论成功失败，必须关闭浏览器进程 ---
        print("\n" + "·" * 40)
        print("🧹 正在清理浏览器环境，关闭 CDP 会话并终止进程...")
        try:
            # 强制杀掉浏览器进程，释放容器或本地端口
            await kill_browser_session()
            print("✅ 浏览器已安全关闭。")
        except Exception as cleanup_err:
            print(f"⚠️ 清理浏览器时出现错误: {cleanup_err}")
        print("·" * 40 + "\n")

    # 7. 可视化提示
    if current_trace_id:
        print("=" * 60)
        print("可视化 Step Tree:")
        print("=" * 60)
        print("1. 启动 API Server: python3 api_server.py")
        print(f"2. 访问: http://localhost:8000/api/traces")
        print(f"3. Trace ID: {current_trace_id}")
        print("=" * 60)

if __name__ == "__main__":
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\n🛑 用户手动终止 (KeyboardInterrupt)，正在强制退出...")