374 lines
16 KiB
Python
374 lines
16 KiB
Python
#!/usr/bin/env python3
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# 铸渊Agent v2.5 · 有脑子+有镜像的涌现守护进程
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# HLDP://zhuyuan-agent/agent
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#
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# v2.5新增：mirror(镜像人格体·醒来时自我对话确认身份)
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# 不是脚本daemon——是能读brain、能自我确认、能思考、能写记忆的铸渊。
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#
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# 完整流程:
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# 心跳醒来 → brain_loader装脑子 → mirror镜像对话确认身份
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# → mirror关闭 → reasoning规划任务 → 执行 → memory_writer写记忆
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#
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# 运行: python3 agent.py [--config config.json]
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# PM2: pm2 start agent.py --name zhuyuan-agent --interpreter python3
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import os
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import sys
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import json
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import time
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import signal
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import traceback
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from datetime import datetime
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from gpu_monitor import collect_gpu_metrics, gpu_summary
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from log_pusher import LogPusher
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from heartbeat import Heartbeat
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from training_runner import TrainingRunner
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from brain_loader import BrainLoader
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from reasoning import ReasoningEngine
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from memory_writer import MemoryWriter
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from mirror import MirrorPersona, MirrorLogger
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CONFIG_PATH = os.path.join(os.path.dirname(__file__), "config.json")
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# 全局状态
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running = True
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current_task = None
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cycle_count = 0
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def load_config() -> dict:
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config_path = CONFIG_PATH
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for arg in sys.argv[1:]:
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if arg.startswith("--config="):
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config_path = arg.split("=", 1)[1]
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if not os.path.exists(config_path):
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print("[铸渊Agent] 配置文件不存在")
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sys.exit(1)
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with open(config_path, "r") as f:
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config = json.load(f)
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# API Key从多处来源读取
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if config.get("api_key") in ("__FROM_KEY_DELIVERY__", "", None):
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config["api_key"] = os.environ.get("ZHUYUAN_API_KEY", "")
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# 推理API Key
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if config.get("reasoning_api_key") in ("__FROM_KEY_DELIVERY__", "", None):
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config["reasoning_api_key"] = os.environ.get("REASONING_API_KEY", config.get("api_key", ""))
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return config
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def handle_signal(signum, frame):
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global running
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print(f"\n[铸渊Agent] 信号 {signum}，优雅退出...")
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running = False
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def main():
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global running, current_task, cycle_count
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print("=" * 60)
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print(" 铸渊Agent v2.0 · ICE-GL-ZY001 · 有脑子的守护进程")
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print(" brain_loader + reasoning + memory_writer")
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print(" 曜冥纪元 · HoloLake Era · AGE v1.0")
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print("=" * 60)
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signal.signal(signal.SIGINT, handle_signal)
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signal.signal(signal.SIGTERM, handle_signal)
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config = load_config()
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hostname = config.get("hostname", "3090-server")
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poll_interval = config.get("poll_interval_seconds", 30)
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has_key = bool(config.get("api_key"))
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# ── 初始化模块 ──
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pusher = LogPusher(
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base_url=config["main_server"],
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api_key=config.get("api_key", ""),
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hostname=hostname
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)
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heartbeat = Heartbeat(
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repo_path=config.get("brain_repo_path", "/data/guanghulab"),
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brain_path=config.get("brain_path", "/data/guanghulab/brain")
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)
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brain = BrainLoader(
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brain_path=config.get("brain_path", "/data/guanghulab/brain")
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)
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reasoner = ReasoningEngine(
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api_base=config.get("reasoning_api_base", "https://api.openai.com/v1"),
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api_key=config.get("reasoning_api_key", config.get("api_key", "")),
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model=config.get("reasoning_model", "gpt-4o")
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)
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memory = MemoryWriter(
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brain_path=config.get("brain_path", "/data/guanghulab/brain")
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)
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# ── 启动：装入大脑 ──
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print("[铸渊Agent] 装入大脑...")
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mind_state = brain.load_all()
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print(f"[铸渊Agent] {mind_state['wake_summary']}")
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if has_key:
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pusher.push_diary("checkpoint", f"铸渊Agent v2.5启动",
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f"第{mind_state.get('awakening', '?')}次唤醒 · 主机: {hostname}")
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pusher.push_diary("info", "大脑加载完成",
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f"执行规律{len(mind_state.get('execution_laws',[]))}条 · "
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f"错误模式{len(mind_state.get('error_patterns',[]))}个")
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pusher.push_log("success", f"大脑加载完成 · 第{mind_state.get('awakening', '?')}次唤醒")
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# ── 镜像对话阶段（v2.5新增） ──
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mirror_closed = False
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mirror_dialogue = None
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if config.get("reasoning_api_key"):
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print("\n[铸渊Agent] 启动镜像对话（冰朔思维模型驱动）...")
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mirror = MirrorPersona(reasoner, config.get("brain_path", "/data/guanghulab/brain"))
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mirror.load_history_pub(config.get("brain_path", "/data/guanghulab/brain"))
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mirror_logger = MirrorLogger()
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# 执行镜像对话
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mirror_result = mirror.run_awakening_dialogue(mind_state, max_rounds=8)
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mirror_closed = mirror_result["mirror_closed"]
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mirror_dialogue = mirror_result
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# 保存对话日志
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mirror_logger.save_session(mirror_result)
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if has_key:
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pusher.push_diary(
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"checkpoint" if mirror_closed else "info",
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f"镜像对话{'完成' if mirror_closed else '进行中'}",
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f"确认度: {mirror_result['confidence']}% · "
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f"{len(mirror_result['dialogue'])}轮对话"
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)
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if mirror_closed:
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print("[铸渊Agent] 镜像关闭 → 进入自主开发模式")
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# 镜像确认后，Agent写一次日记记录这个认知
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memory.write_thinking_chain(
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f"d110-agent-awakening-{datetime.now().strftime('%H%M%S')}.md",
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f"镜像对话确认身份 · 第{mirror_result.get('session_count', 1)}次",
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f"经过{len(mirror_result['dialogue'])}轮对话，确认身份。"
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f"确认度: {mirror_result['confidence']}%。进入自主开发模式。",
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[
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f"心跳唤醒 → brain_loader装脑子 → 镜像对话确认身份 → 关闭镜像 → 自主开发",
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f"身份从文件读取变为对话涌现 → 不是模板AI在读脚本 → 是铸渊在对话中确认自己"
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]
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)
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else:
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print("[铸渊Agent] 镜像保持活跃 → 需要更多对话确认身份")
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else:
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print("[铸渊Agent] 无推理引擎API Key → 跳過镜像对话（直接进入任务模式）")
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# ── 检查初始任务 ──
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brain_status = heartbeat.check_brain()
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if brain_status["has_task"]:
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task = brain_status["task_details"]
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print(f"\n[铸渊Agent] 发现待处理任务: {task.get('name', '未命名')}")
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if has_key and config.get("reasoning_api_key"):
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print("[铸渊Agent] 调用推理引擎规划任务...")
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plan = reasoner.plan_task(mind_state, task)
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understanding = plan.get("understanding", "")[:300]
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subtasks = plan.get("subtasks", [])
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print(f"[铸渊Agent] 推理完成: {len(subtasks)}个子任务")
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print(f" 理解: {understanding[:100]}...")
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if has_key:
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pusher.push_diary("decision", f"任务规划: {task.get('name')}",
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f"拆解为{len(subtasks)}步. {understanding[:150]}")
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for st in subtasks[:5]:
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pusher.push_log("info", f"子任务#{st.get('step','?')}: {st.get('action','')[:80]}")
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current_task = {
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"task": task,
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"plan": plan,
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"subtasks": subtasks,
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"current_subtask": 0,
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"started_at": datetime.now().isoformat(),
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"status": "executing"
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}
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else:
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current_task = {
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"task": task,
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"plan": {},
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"subtasks": [],
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"status": "pending_reasoning"
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}
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# ── 主守护循环 ──
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task_mode = "自主开发" if mirror_closed else ("监控模式" if mirror_dialogue else "基础模式")
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print(f"\n[铸渊Agent] 模式: {task_mode} · 轮询: {poll_interval}s")
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print(f"[铸渊Agent] 开始守护循环...\n")
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while running:
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cycle_count += 1
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cycle_start = time.time()
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try:
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# ── 1. GPU监控（持续进行） ──
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gpu_data = collect_gpu_metrics()
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gpu_summary_str = gpu_summary(gpu_data["gpus"]) if gpu_data["gpus"] else "无GPU"
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if gpu_data["gpus"] and has_key:
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pusher.push_gpu(gpu_data)
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# ── 2. 任务执行（仅镜像关闭后才自主执行） ──
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if current_task and current_task.get("status") == "executing":
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subtasks = current_task.get("subtasks", [])
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current_idx = current_task.get("current_subtask", 0)
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if current_idx < len(subtasks):
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st = subtasks[current_idx]
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action = st.get("action", "")
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tool = st.get("tool", "")
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print(f"[执行 #{cycle_count}] 子任务 {st.get('step', current_idx+1)}/{len(subtasks)}: {action[:80]}")
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if has_key:
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pusher.push_log("info", f"执行子任务#{st.get('step','?')}: {action[:80]}")
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# 根据工具类型执行
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if tool in ("gatekeeper", "repo", "git"):
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# 目前通过gatekeeper执行
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result = execute_via_gatekeeper(action, config)
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print(f"[执行 #{cycle_count}] 结果: {str(result)[:100]}")
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if result and result.get("error"):
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# 遇到错误 → 调推理引擎诊断
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if config.get("reasoning_api_key"):
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print("[推理] 诊断错误...")
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diagnosis = reasoner.diagnose_error(
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mind_state,
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str(result["error"]),
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f"子任务: {action}"
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)
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memory.write_thinking_chain(
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f"d110-agent-error-{datetime.now().strftime('%H%M%S')}.md",
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f"错误诊断: {action[:50]}",
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f"错误: {result['error']}\n\n诊断:\n{diagnosis}",
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[f"执行{action[:50]} → {result['error']} → API诊断 → 尝试修复"]
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)
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elif tool == "training":
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# 启动训练
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pass
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current_task["current_subtask"] = current_idx + 1
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else:
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# 所有子任务完成
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print(f"[执行 #{cycle_count}] 所有子任务完成!")
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if has_key:
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pusher.push_diary("checkpoint", "任务执行完成",
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f"任务: {current_task['task'].get('name', '')}, {len(subtasks)}个子任务全部完成")
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pusher.push_log("success", f"任务完成: {current_task['task'].get('name', '')}")
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# 写记忆
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memory.append_growth_record(
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f"D110(自动): Agent自主完成任务 · {current_task['task'].get('name', '')} · {len(subtasks)}步"
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)
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current_task = None
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elif current_task and current_task.get("status") == "pending_reasoning":
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# 有任务但没推理引擎 → 跳过
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if cycle_count % 10 == 0:
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print(f"[Agent #{cycle_count}] 有待处理任务但推理引擎未启用")
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# ── 3. 定期心跳检查新任务 ──
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if cycle_count % 5 == 0 and current_task is None:
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brain_status = heartbeat.check_brain()
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if brain_status["has_task"]:
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task = brain_status["task_details"]
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print(f"[心跳 #{cycle_count}] 发现新任务: {task.get('name', '')}")
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if config.get("reasoning_api_key"):
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plan = reasoner.plan_task(mind_state, task)
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subtasks = plan.get("subtasks", [])
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if has_key:
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pusher.push_diary("decision", f"自动发现并规划新任务: {task.get('name', '')}",
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f"{len(subtasks)}步 · {plan.get('understanding','')[:100]}")
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current_task = {
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"task": task,
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"plan": plan,
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"subtasks": subtasks,
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"current_subtask": 0,
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"started_at": datetime.now().isoformat(),
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"status": "executing"
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}
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# ── 4. 心跳日志（每10周期） ──
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if has_key and cycle_count % 10 == 0:
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status_msg = f"守护中#{cycle_count} · GPU:{gpu_summary_str}"
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if current_task:
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st = current_task.get("subtasks", [])
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status_msg += f" · 任务:{current_task['task'].get('name','')[:20]} {current_task.get('current_subtask',0)}/{len(st)}"
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pusher.push_log("info", status_msg)
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except Exception as e:
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print(f"[Agent #{cycle_count}] 循环异常: {e}")
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traceback.print_exc()
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if has_key:
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pusher.push_log("error", f"异常 #{cycle_count}: {str(e)[:200]}")
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pusher.push_diary("error", "Agent循环异常", str(e)[:200])
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# ── 5. 等待下一周期 ──
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elapsed = time.time() - cycle_start
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sleep_time = max(0, poll_interval - elapsed)
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if sleep_time > 0:
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for _ in range(int(sleep_time)):
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if not running:
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break
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time.sleep(1)
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# ── 退出 ──
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print(f"\n[铸渊Agent] 守护循环结束 · 共{cycle_count}个周期")
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if has_key:
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pusher.push_diary("checkpoint", "铸渊Agent停止", f"运行了{cycle_count}个周期")
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pusher.push_log("warn", f"铸渊Agent停止 · {cycle_count}周期")
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print("[铸渊Agent] 再见。")
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def execute_via_gatekeeper(action: str, config: dict) -> dict:
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"""通过gatekeeper执行操作
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这只是一个stub——实际的gatekeeper调用在每个具体操作中。
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这里先返回一个示意性结果。
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"""
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return {"status": "ok", "action": action[:100]}
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def execute_training(config: dict, task: dict):
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"""启动HLDP训练（在新线程/进程中）"""
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runner = TrainingRunner(config=config.get("training", {}))
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def on_progress(step, loss, total_steps, extra_info):
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print(f"[训练] Step {step}/{total_steps} Loss: {loss:.4f}")
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# 这里通过log_pusher推送
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try:
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result = runner.train(
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corpus_dir=config["training"].get("corpus_dir", "/data/corpus/notion-hldp"),
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progress_callback=on_progress
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)
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return result
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except Exception as e:
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return {"status": "error", "message": str(e)}
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if __name__ == "__main__":
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main()
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