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+#!/usr/bin/env python3
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+"""
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+run_cyber.py — run_procedure_dsl.py 的 Cyber Agent 移植版 (POC)。
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+
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+与 run_procedure_dsl.py 的唯一区别是**执行引擎**:
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+ - run_procedure_dsl.py: Claude Agent SDK (ClaudeSDKClient) → 走 ~/.claude OAuth Max,
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+ 只能跑 Anthropic 协议端点。
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+ - run_cyber.py: 本仓库自研 AgentRunner (agent/core/runner.py) → 多 Provider。
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+ 本 POC 默认走 OpenRouter (create_openrouter_llm_call), 一个 provider 通打
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+ GPT / Gemini / Qwen / DeepSeek / Claude 全家, 换模型只改 --model 字符串。
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+
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+复用 run_procedure_dsl.py 的:
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+ - 起手 prompt 全文 (_build_initial_blocks 的 text 块) —— 三阶段指令一字不改。
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+ - 图片抽取 (_images_from_source) + 客户端下载缓存 (_url_to_cached_path)。
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+图片块从 Anthropic base64 格式转成 OpenRouter 要的 OpenAI `image_url` data-URL 格式。
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+
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+子 Agent (phase-2a-normalizer / phase-2b-matcher) 定义见同目录 presets_cyber.json,
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+对位原脚本的 AgentDefinition。Phase 2 并行靠 RunConfig.parallel_tool_execution=True:
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+主 Agent 在一轮里同时发两个 `agent(agent_type=..., task=...)` 调用即并行 (各自保留写权限);
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+不走 explore 模式 (那会把子 Agent 降级为只读, 无法写 patch)。
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+
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+用法 (与 run_procedure_dsl.py 对齐):
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+ python run_cyber.py input/case-2-raw.json --out-dir case-2-cyber
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+ python run_cyber.py input/case-2-raw.json --out-dir case-2-cyber \
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+ --model openai/gpt-4o
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+ # 中断后续跑 (从 outputs/<out-dir>/.trace_id 读 trace 接着跑):
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+ python run_cyber.py input/case-2-raw.json --out-dir case-2-cyber --resume
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+
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+⚠️ POC 已知缺口 (非 Claude 模型上需逐步调):
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+ - 起手 prompt 与 spec/ 里写的是 Claude 工具名 (Read/Write/Bash)。Cyber 实际工具是
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+ read_file/write_file/edit_file/bash_command。下方 _CYBER_RUNTIME_NOTE 给了映射, 但
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+ spec 文档内 `详见 Read(...)` 这类示例仍是 Claude 名 —— 弱模型可能被带偏, 需观察 trace。
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+ - edit_file 在非 Claude 模型上的 exact-match 命中率不如 Claude, workflow.json 反复 Edit
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+ 可能卡壳。先拿单 case smoke test, 别直接上量。
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+"""
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+import argparse
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+import asyncio
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+import base64
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+import importlib.util
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+import logging
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+import os
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+import sys
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+import time
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+from datetime import datetime
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+from pathlib import Path
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+from typing import Any, Dict, List
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+
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+# run_cyber.py → procedure-dsl/
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+DSL_ROOT = Path(__file__).resolve().parent
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+
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+
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+def _find_repo_root(start: Path) -> Path:
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+ """向上找含 pyproject.toml 的目录 (cyber-agent 仓库根), 用于 sys.path 兜底。"""
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+ for p in [start, *start.parents]:
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+ if (p / "pyproject.toml").exists():
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+ return p
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+ return start
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+
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+
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+REPO_ROOT = _find_repo_root(DSL_ROOT)
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+for _p in (str(REPO_ROOT), str(DSL_ROOT)):
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+ if _p not in sys.path:
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+ sys.path.insert(0, _p)
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+
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+
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+def _load_sibling_module(name: str, path: Path):
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+ """按文件路径 import 同目录脚本 (run_procedure_dsl.py 不是包, 用 spec 加载)。"""
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+ spec = importlib.util.spec_from_file_location(name, path)
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+ mod = importlib.util.module_from_spec(spec)
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+ spec.loader.exec_module(mod)
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+ return mod
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+
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+
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+# 复用 run_procedure_dsl.py 的纯函数 (它 main() 有 __main__ 守卫, import 无副作用)。
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+_rpd = _load_sibling_module("run_procedure_dsl", DSL_ROOT / "run_procedure_dsl.py")
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+_build_initial_blocks = _rpd._build_initial_blocks
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+_images_from_source = _rpd._images_from_source
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+_url_to_cached_path = _rpd._url_to_cached_path
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+_derive_case_id = _rpd._derive_case_id
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+_resolve_out_dir = _rpd._resolve_out_dir
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+_MEDIA_TYPE = _rpd._MEDIA_TYPE
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+
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+
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+# 追加给主 Agent 的 Cyber 运行时说明 (原 prompt 是 Claude 工具名, 这里给映射 + 子 agent 分发约定)。
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+_CYBER_RUNTIME_NOTE = """
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+
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+## ⚙️ Cyber Agent 运行时差异 (本次执行引擎不是 Claude Code, 工具名不同!)
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+
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+上文出现的 Claude 风格工具名, 在本运行时对应如下 (调用时用**右边**的名字):
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+- `Read(file_path=X)` → `read_file(path=X)`
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+- `Write(file_path=X, content=Y)` → `write_file(path=X, content=Y)`
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+- `Edit(file_path=X, ...)` → `edit_file(...)`
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+- `Bash(command=C)` → `bash_command(command=C)`
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+- `Glob` → `glob_files`，`Grep` → `grep_content`
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+- 读图用 `read_images`（若需要主动看本地图）。
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+
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+## 🔱 Phase 2 子 Agent 分发 (对应原 `Task`/`Agent` 工具)
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+
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+进入 Phase 2 (归一化与分类匹配) 时, **不要**自己手调 taxonomy-lookup.py 逐条决策。
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+先用 prepare-subtask.py 生成 _scratch/task_2a.json 和 task_2b.json, 然后**在同一轮里**
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+发出两个子 Agent 调用 (它们会并行执行, 各自写 patch 文件):
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+
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+ agent(agent_type="phase-2a-normalizer", task="处理 <case_dir>/_scratch/task_2a.json, 产出 patch_2a.json")
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+ agent(agent_type="phase-2b-matcher", task="处理 <case_dir>/_scratch/task_2b.json, 产出 patch_2b.json")
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+
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+两个子 Agent 都返回后, 用 `bash_command` 跑 wf-patch.py 把 patch_2a.json / patch_2b.json
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+回填进 workflow.json, 再继续 Phase 3。
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+"""
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+
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+
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+def _to_openai_content(text: str, images: List[str]) -> List[Dict[str, Any]]:
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+ """把 (text, 图URL列表) 拼成 OpenAI 格式的 content blocks (OpenRouter / 各家通吃)。
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+
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+ - 文本块: {"type": "text", "text": ...}
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+ - 图片块: {"type": "image_url", "image_url": {"url": "data:<mime>;base64,<...>"}}
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+ URL 先经 run_procedure_dsl._url_to_cached_path 客户端下载缓存 (绕图床 robots.txt)。
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+ 单张图失败不阻塞整批。
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+ """
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+ blocks: List[Dict[str, Any]] = [{"type": "text", "text": text}]
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+ n_ok, n_fail = 0, 0
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+ for ref in images:
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+ try:
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+ if ref.startswith(("http://", "https://")):
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+ local = _url_to_cached_path(ref)
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+ else:
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+ local = Path(ref).expanduser().resolve()
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+ if not local.exists():
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+ raise FileNotFoundError(ref)
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+ mime = _MEDIA_TYPE.get(local.suffix.lower(), "image/png")
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+ data = base64.standard_b64encode(local.read_bytes()).decode()
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+ blocks.append({
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+ "type": "image_url",
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+ "image_url": {"url": f"data:{mime};base64,{data}"},
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+ })
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+ n_ok += 1
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+ except Exception as e:
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+ n_fail += 1
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+ print(f"[image] skip {ref[:80]}... ({type(e).__name__}: {e})", flush=True)
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+ if images:
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+ print(f"[image] {n_ok}/{len(images)} 成功 base64 化, {n_fail} 失败已跳过", flush=True)
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+ return blocks
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+
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+
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+def _trace_append(trace_path: Path, chunk: str) -> None:
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+ with trace_path.open("a", encoding="utf-8") as f:
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+ f.write(chunk)
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+
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+
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+async def run(args: argparse.Namespace) -> int:
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+ from agent.core.runner import AgentRunner, RunConfig
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+ from agent.core.presets import load_presets_from_json
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+ from agent.trace import FileSystemTraceStore, Trace, Message
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+ from agent.llm import create_openrouter_llm_call
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+
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+ workdir = DSL_ROOT
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+ source_path = Path(args.source).expanduser().resolve()
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+ if not source_path.exists():
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+ print(f"❌ source not found: {source_path}", file=sys.stderr)
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+ return 1
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+
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+ out_dir = _resolve_out_dir(args.out_dir, workdir)
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+ out_dir.mkdir(parents=True, exist_ok=True)
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+ (out_dir / "_scratch").mkdir(exist_ok=True)
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+ trace_id_file = out_dir / ".trace_id"
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+ trace_path = out_dir / "_trace_cyber.md"
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+
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+ # 注册子 Agent presets (phase-2a-normalizer / phase-2b-matcher / main)。
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+ presets_json = DSL_ROOT / "presets_cyber.json"
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+ if presets_json.exists():
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+ load_presets_from_json(str(presets_json))
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+ else:
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+ print(f"⚠️ 缺少 {presets_json}, 子 Agent 分发会失败", file=sys.stderr)
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+
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+ # source 路径给 Agent (workdir 相对优先)。
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+ try:
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+ source_for_agent = source_path.relative_to(workdir).as_posix()
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+ except ValueError:
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+ source_for_agent = str(source_path)
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+
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+ # resume: 读已存 trace_id, 只发增量 "接着做" 消息。
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+ resume_tid = None
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+ if args.resume:
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+ if not trace_id_file.exists():
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+ print(f"❌ --resume 但无 {trace_id_file}; 先正常跑一次", file=sys.stderr)
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+ return 1
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+ resume_tid = trace_id_file.read_text(encoding="utf-8").strip() or None
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+
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+ images = _images_from_source(source_path) + (args.extra_image or [])
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+
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+ if resume_tid:
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+ msgs = [{"role": "user", "content": (
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+ f"上次中断了, 接续做 case-{args.case_id} 的提取流程。\n"
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+ f"先用 bash_command `ls` 看 {out_dir.as_posix()}/ 当前已落盘哪些产物, "
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+ f"再 read_file 这些**当前磁盘版本** (understanding.md / workflow.json) 接着跑, "
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+ f"不要凭记忆。Phase 2 仍按子 Agent 分发约定 (agent(agent_type=...))。"
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+ )}]
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+ else:
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+ # 复用原脚本的起手 prompt 全文 (取 text 块), 再补 Cyber 运行时说明。
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+ anth_blocks = _build_initial_blocks(
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+ source_for_agent, args.case_id, args.out_dir, images, workdir, "spec"
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+ )
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+ base_text = anth_blocks[0]["text"] + _CYBER_RUNTIME_NOTE
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+ msgs = [{"role": "user", "content": _to_openai_content(base_text, images)}]
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+
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+ cfg = RunConfig(
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+ model=args.model,
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+ temperature=0.3,
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+ max_iterations=args.max_turns,
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+ agent_type="main",
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+ name=f"procedure-dsl case-{args.case_id} (cyber)",
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+ tool_groups=["core"],
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+ parallel_tool_execution=True, # Phase 2 同轮发两个 agent() 即并行
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+ enable_prompt_caching=False, # 非 Claude 模型无效, 关掉省得干扰
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+ goal_compression="on_overflow",
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+ trace_id=resume_tid,
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+ )
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+
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+ print(f"[setup] engine = Cyber AgentRunner (OpenRouter)")
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+ print(f"[setup] model = {args.model}")
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+ print(f"[setup] source = {source_path}")
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+ print(f"[setup] case_id = {args.case_id}")
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+ print(f"[setup] out_dir = {out_dir}")
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+ print(f"[setup] images = {len(images)}")
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+ print(f"[setup] max_iter = {args.max_turns}")
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+ print(f"[setup] resume = {resume_tid[:8] + '...' if resume_tid else 'no'}")
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+ print(flush=True)
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+
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+ now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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+ _trace_append(trace_path, f"\n\n---\n\n## ▶ {'Resume' if resume_tid else 'Fresh'} @ {now}\n"
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+ f"- model: `{args.model}` · case: `{args.case_id}` · images: `{len(images)}`\n")
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+
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+ runner = AgentRunner(
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+ llm_call=create_openrouter_llm_call(model=args.model),
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+ trace_store=FileSystemTraceStore(base_path=str(out_dir / ".trace")),
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+ )
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+
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+ turn = 0
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+ t0 = time.time()
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+ status = "unknown"
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+ try:
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+ async for item in runner.run(messages=msgs, config=cfg):
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+ if isinstance(item, Trace):
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+ status = item.status
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+ if item.trace_id:
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+ trace_id_file.write_text(item.trace_id, encoding="utf-8")
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+ print(f"[trace] {item.trace_id} status={item.status}", flush=True)
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|
+ elif isinstance(item, Message):
|
|
|
|
|
+ role = getattr(item, "role", "?")
|
|
|
|
|
+ content = getattr(item, "content", "") or ""
|
|
|
|
|
+ tool_calls = getattr(item, "tool_calls", None)
|
|
|
|
|
+ if role == "assistant":
|
|
|
|
|
+ turn += 1
|
|
|
|
|
+ if content:
|
|
|
|
|
+ print(f"\n[turn {turn} · text]\n{content}\n", flush=True)
|
|
|
|
|
+ _trace_append(trace_path, f"\n### Turn {turn}\n> {content[:2000]}\n")
|
|
|
|
|
+ for tc in (tool_calls or []):
|
|
|
|
|
+ fn = (tc.get("function") or {}) if isinstance(tc, dict) else {}
|
|
|
|
|
+ nm = fn.get("name", tc.get("name", "?") if isinstance(tc, dict) else "?")
|
|
|
|
|
+ ar = str(fn.get("arguments", ""))[:200]
|
|
|
|
|
+ print(f"[turn {turn} · tool] {nm}({ar})", flush=True)
|
|
|
|
|
+ _trace_append(trace_path, f"- `{nm}` — `{ar}`\n")
|
|
|
|
|
+ elif role == "tool":
|
|
|
|
|
+ preview = str(content)[:300]
|
|
|
|
|
+ print(f" ↳ [tool result] {preview}", flush=True)
|
|
|
|
|
+ except KeyboardInterrupt:
|
|
|
|
|
+ print(f"\n⚠️ 中断. {out_dir}/ 产物已保留. 续跑: --resume", file=sys.stderr)
|
|
|
|
|
+ return 130
|
|
|
|
|
+ except Exception as e:
|
|
|
|
|
+ logging.exception("cyber run failed")
|
|
|
|
|
+ print(f"❌ {type(e).__name__}: {e}", file=sys.stderr)
|
|
|
|
|
+ return 1
|
|
|
|
|
+
|
|
|
|
|
+ elapsed = time.time() - t0
|
|
|
|
|
+ print(f"\n[done] status={status} turns={turn} wall={elapsed:.1f}s", flush=True)
|
|
|
|
|
+ _trace_append(trace_path, f"\n### ◀ done · status={status} · turns={turn} · {elapsed:.1f}s\n")
|
|
|
|
|
+ return 0 if status in ("completed", "unknown") else 2
|
|
|
|
|
+
|
|
|
|
|
+
|
|
|
|
|
+def _parse_args() -> argparse.Namespace:
|
|
|
|
|
+ p = argparse.ArgumentParser(
|
|
|
|
|
+ description="跑 procedure-dsl 提取流程 (Cyber AgentRunner + OpenRouter)",
|
|
|
|
|
+ formatter_class=argparse.RawDescriptionHelpFormatter,
|
|
|
|
|
+ epilog=__doc__,
|
|
|
|
|
+ )
|
|
|
|
|
+ p.add_argument("source", help="原始 post 文件 (input/case-N-raw.json)")
|
|
|
|
|
+ p.add_argument("--out-dir", required=True,
|
|
|
|
|
+ help="输出目录名, 落在 outputs/ 下. case_id 自动从 basename 推。")
|
|
|
|
|
+ p.add_argument("--extra-image", action="append", default=[],
|
|
|
|
|
+ help="额外配图 (本地路径 or URL), 可多次。")
|
|
|
|
|
+ p.add_argument("--model", default="openai/gpt-4o",
|
|
|
|
|
+ help="OpenRouter 模型名 (default: openai/gpt-4o). "
|
|
|
|
|
+ "如 google/gemini-2.5-pro / qwen/qwen-max / anthropic/claude-sonnet-4.5")
|
|
|
|
|
+ p.add_argument("--max-turns", type=int, default=300, help="最大迭代轮数 (default: 300)")
|
|
|
|
|
+ p.add_argument("--resume", action="store_true",
|
|
|
|
|
+ help="从 outputs/<out-dir>/.trace_id 读 trace 续跑")
|
|
|
|
|
+ return p.parse_args()
|
|
|
|
|
+
|
|
|
|
|
+
|
|
|
|
|
+def main() -> None:
|
|
|
|
|
+ for stream in (sys.stdout, sys.stderr):
|
|
|
|
|
+ if hasattr(stream, "reconfigure"):
|
|
|
|
|
+ stream.reconfigure(encoding="utf-8", errors="replace")
|
|
|
|
|
+ logging.basicConfig(level=logging.WARNING)
|
|
|
|
|
+ args = _parse_args()
|
|
|
|
|
+ args.case_id = _derive_case_id(args.out_dir)
|
|
|
|
|
+ sys.exit(asyncio.run(run(args)))
|
|
|
|
|
+
|
|
|
|
|
+
|
|
|
|
|
+if __name__ == "__main__":
|
|
|
|
|
+ main()
|
