chore: prepare ARTEMIS-Eapil 1.3.0 release

.env.example

@@ -11,6 +11,11 @@ ARTEMIS_STEP_SUMMARIZER_TIMEOUT_SECONDS=120
 # 失败后最多重试 5 次，由 Artemis 统一管理，SDK 不叠加重试。
 OPENAI_MAX_RETRIES=5
 OPENAI_RETRY_INTERVAL_SECONDS=10
+OPENAI_RATE_LIMIT_RETRY_SECONDS=60
+OPENAI_TOOL_FALLBACK_MODEL=gpt-6-luna
+OPENAI_TOOL_FALLBACK_REASONING_EFFORT=high
+OPENAI_TOOL_FALLBACK_MAX_RETRIES=3
+ARTEMIS_TOOL_FALLBACK_MAX_NO_PROGRESS_ATTEMPTS=3
 # default 表示不发送思考强度参数；接口不支持所选值时明确报错。
 OPENAI_REASONING_EFFORT=high
 
@@ -19,6 +24,8 @@ ARTEMIS_VIDEO_ENABLED=false
 ARTEMIS_RECORDING_ENABLED=false
 ARTEMIS_OUTPUTTER_ENABLED=true
 ARTEMIS_STEP_SUMMARIZER_ENABLED=true
+# 默认将任务截图长边压至 2048；false 保留设备原始截图。长边值可在 config/artemis.jsonc 的 agent.screenshot 中覆盖。
+ARTEMIS_SCREENSHOT_LIMIT_ENABLED=true
 # Pro 连续无工具调用或参数校验失败的最大次数；耗尽后明确失败，避免空转。
 ARTEMIS_OPERATOR_MAX_NO_PROGRESS_ATTEMPTS=3
 # Flash 连续无工具调用的最大次数；无效回复不会重复送回模型。

README.md

@@ -48,10 +48,12 @@ cd Artemis-Eapil
 3. 输入任务目标，选择 Flash 或 Pro 后执行；在工作区查看步骤、截图和结果。
 4. 通过“报告与批量”导出报告或导入 Excel 用例；任务队列支持历史搜索。
 
-预设模型为 `gpt-5.6-luna`、`mimo-v2.6-flash`，也可填写自定义名称。默认模型是 `mimo-v2.6-flash`，思考强度默认 `high`（最高可选 `max`），单次请求超时 **300 秒**，失败后最多重试 **5 次**，间隔 **10 秒**；视频分析与录屏默认关闭。模型及思考参数是否可用取决于所配置网关；不支持时会明确报错。
+预设模型为 `gpt-5.6-luna`、`gpt-6-luna`、`mimo-v2.6-flash`、`mimo-v2.6-pro`，也可填写自定义名称。默认模型是 `mimo-v2.6-flash`，思考强度默认 `high`（最高可选 `max`），单次请求超时 **300 秒**，失败后最多重试 **5 次**，间隔 **10 秒**。遇到 429 请求过多时改为每次等待 **60 秒**。连续 3 次没有有效工具调用时，本步骤改用 `gpt-6-luna`（high），兜底模型最多再尝试 3 次；下一步恢复首选模型。视频分析与录屏默认关闭。模型及思考参数是否可用取决于所配置网关；不支持时会明确报错。
 
 也可在项目 `.env` 中设置 `OPENAI_API_KEY`、`OPENAI_BASE_URL` 和 `OPENAI_MODEL`，其他参数见 [.env.example](.env.example)。新安装默认接口地址为 `https://api-n-cd.playones.com/v1`；已保存的地址不变。直接编辑环境文件后需重启相应服务。不要提交自己的密钥。
 
+“功能开关”默认将任务截图长边限制为 2048 像素；关闭后保留设备原始分辨率。可在 [config/artemis.jsonc](config/artemis.jsonc) 的 `agent.screenshot` 中覆盖 `enabled` 和 `max_long_edge`，重启后对新任务生效。
+
 报告会区分“执行完成”和“验证通过”；没有验证证据的结果标为“未验证”。任务步骤与截图可查看，设备动作回放不提供。
 
 ## 让 AI 使用

apps/admin_console/routers/automation.py

@@ -10,6 +10,7 @@ from pydantic import BaseModel, Field
 
 from apps.admin_console.services.automation_store import identifier, store
 from apps.admin_console.services import automation_service as service
+from apps.admin_console.services import batch_archive as archive_service
 from apps.admin_console.services import report_export, task_import
 
 router = APIRouter(prefix="/api/automation", tags=["automation"])
@@ -128,10 +129,13 @@ def delete_batch(batch_id: str):
     except ValueError as exc:
         raise HTTPException(400, str(exc)) from exc
     try:
+        if archive_service.status(key, require("batch", key))["status"] == "preparing":
+            raise HTTPException(409, "报告正在打包，请完成后再删除批次")
         if not service.delete_batch(key):
             raise HTTPException(404, "批次不存在")
     except ValueError as exc:
         raise HTTPException(409, str(exc)) from exc
+    archive_service.forget(key)
     return {"batch_id": key, "status": "deleted"}
 
 
@@ -144,9 +148,29 @@ def batch_action(batch_id: str, action: Literal["pause", "resume", "retry_failed
         raise HTTPException(409, str(exc)) from exc
 
 
+@router.post("/batches/{batch_id}/archive/prepare")
+def prepare_batch_archive(batch_id: str):
+    batch = require("batch", batch_id)
+    return archive_service.prepare(batch_id, batch)
+
+
+@router.get("/batches/{batch_id}/archive/status")
+def batch_archive_status(batch_id: str):
+    batch = require("batch", batch_id)
+    return archive_service.status(batch_id, batch)
+
+
 @router.get("/batches/{batch_id}/archive")
 async def batch_archive(batch_id: str):
-    require("batch", batch_id)
+    batch = require("batch", batch_id)
+    if archive_service.status(batch_id, batch)["status"] == "preparing":
+        raise HTTPException(409, "报告正在打包，请等待准备完成")
+    path = archive_service.archive_path(batch_id)
+    if (
+        not path.is_file()
+        or not batch.get("report_ready")
+        and archive_service.status(batch_id, batch)["status"] != "ready"
+    ):
         path = await asyncio.to_thread(report_export.export_batch, batch_id)
     return FileResponse(path, filename=f"批次报告-{batch_id[:8]}.zip")
 

apps/admin_console/routers/system.py

@@ -517,9 +517,12 @@ async def get_model_config_and_env():
 
 @router.get("/server-status")
 async def get_server_runtime_status():
-    """Retrieve runtime status, PID, port, and uptime of the Artemis server."""
+    """Return this server's status without scanning listening ports."""
     import os
-    from artemis.runtime.server_lifecycle import get_server_status
+    import time
+
+    from artemis.runtime.process_probe import pid_is_alive
+    from artemis.runtime.server_lifecycle import read_server_info
 
     try:
         from apps.admin_console.core.state import state
@@ -527,18 +530,34 @@ async def get_server_runtime_status():
         from admin_console.core.state import state
 
     port = getattr(state, "port", 8000)
-    status = get_server_status(port=port)
+    current_pid = os.getpid()
+    pids = {current_pid}
+    started_at = None
+    info = read_server_info()
+    if info and info.get("port") == port:
+        saved_pid = info.get("pid")
+        if isinstance(saved_pid, int) and saved_pid != current_pid and pid_is_alive(saved_pid):
+            pids.add(saved_pid)
+        if saved_pid == current_pid and isinstance(info.get("started_at"), (int, float)):
+            started_at = float(info["started_at"])
+    if started_at is None:
+        try:
+            import psutil
+
+            started_at = psutil.Process(current_pid).create_time()
+        except Exception:
+            started_at = None
     # Explicit DTO: the raw metadata file additionally holds the lifecycle
     # token, cmdline, and filesystem paths, none of which belong on the wire.
     return {
-        "running": status["running"],
+        "running": True,
-        "port": status["port"],
+        "port": port,
-        "pids": status["pids"],
+        "pids": sorted(pids),
-        "active_pid": status["active_pid"],
+        "active_pid": current_pid,
-        "uptime_seconds": status["uptime_seconds"],
+        "uptime_seconds": max(0.0, time.time() - started_at) if started_at else None,
-        "url": status["url"],
+        "url": f"http://localhost:{port}",
-        "admin_url": status["admin_url"],
+        "admin_url": f"http://localhost:{port}/admin",
-        "current_pid": os.getpid(),
+        "current_pid": current_pid,
     }
 
 

apps/admin_console/services/batch_archive.py

@@ -0,0 +1,117 @@
+"""Background preparation and observable progress for batch report downloads."""
+
+import threading
+import time
+
+from apps.admin_console.services import report_export
+
+_lock = threading.Lock()
+_states: dict[str, dict] = {}
+
+
+def archive_path(batch_id: str):
+    return report_export.EXPORT_ROOT / "batches" / batch_id / "批次报告.zip"
+
+
+def _snapshot(state: dict) -> dict:
+    elapsed = max(0, (state.get("finished_at") or time.monotonic()) - state["started_at"])
+    completed, total = state["completed"], state["total"]
+    eta = (
+        max(0, round(elapsed / completed * (total - completed)))
+        if completed and total > completed
+        else None
+    )
+    return {
+        "status": state["status"],
+        "stage": state["stage"],
+        "completed": completed,
+        "total": total,
+        "elapsed_seconds": round(elapsed),
+        "eta_seconds": eta,
+        "error": state.get("error"),
+    }
+
+
+def status(batch_id: str, batch: dict) -> dict:
+    with _lock:
+        state = _states.get(batch_id)
+        if state:
+            return _snapshot(state)
+    if batch.get("report_ready") and archive_path(batch_id).is_file():
+        return {
+            "status": "ready",
+            "stage": "报告包已就绪",
+            "completed": 0,
+            "total": 0,
+            "elapsed_seconds": 0,
+            "eta_seconds": None,
+            "error": None,
+        }
+    return {
+        "status": "idle",
+        "stage": "尚未开始打包",
+        "completed": 0,
+        "total": 0,
+        "elapsed_seconds": 0,
+        "eta_seconds": None,
+        "error": None,
+    }
+
+
+def prepare(batch_id: str, batch: dict) -> dict:
+    with _lock:
+        current = _states.get(batch_id)
+        if current and current["status"] == "preparing":
+            return _snapshot(current)
+        if batch.get("report_ready") and archive_path(batch_id).is_file():
+            _states.pop(batch_id, None)
+            return status_from_cache()
+        state = {
+            "status": "preparing",
+            "stage": "等待打包",
+            "completed": 0,
+            "total": len(report_export.store.jobs(batch_id)),
+            "started_at": time.monotonic(),
+        }
+        _states[batch_id] = state
+
+    def update(completed: int, total: int, stage: str) -> None:
+        with _lock:
+            state.update(completed=completed, total=total, stage=stage)
+
+    def run() -> None:
+        try:
+            with report_export.EXPORT_LOCK:
+                latest = report_export.store.get("batch", batch_id)
+                if not (latest and latest.get("report_ready") and archive_path(batch_id).is_file()):
+                    report_export.export_batch(batch_id, progress=update)
+            with _lock:
+                state.update(status="ready", stage="报告包已就绪", finished_at=time.monotonic())
+        except Exception as exc:
+            with _lock:
+                state.update(
+                    status="failed",
+                    stage="打包失败",
+                    error=str(exc)[:500],
+                    finished_at=time.monotonic(),
+                )
+
+    threading.Thread(target=run, name=f"batch-archive-{batch_id[:8]}", daemon=True).start()
+    return status(batch_id, batch)
+
+
+def status_from_cache() -> dict:
+    return {
+        "status": "ready",
+        "stage": "报告包已就绪",
+        "completed": 0,
+        "total": 0,
+        "elapsed_seconds": 0,
+        "eta_seconds": None,
+        "error": None,
+    }
+
+
+def forget(batch_id: str) -> None:
+    with _lock:
+        _states.pop(batch_id, None)

apps/admin_console/services/model_settings_service.py

@@ -9,6 +9,7 @@ from urllib.parse import urlsplit
 
 from pydantic import BaseModel, Field, SecretStr, field_validator
 
+from artemis.config.agent import load_agent_config
 from artemis.config.paths import get_env_file
 from artemis.config.settings import settings
 
@@ -22,6 +23,13 @@ class ModelSettingsUpdate(BaseModel):
     timeout_seconds: float = Field(default=300, gt=0, le=3600)
     max_retries: int = Field(default=5, ge=0, le=10)
     retry_interval_seconds: float = Field(default=10, ge=0, le=300)
+    rate_limit_retry_seconds: float | None = Field(default=None, ge=0, le=3600)
+    tool_fallback_model: str | None = Field(default=None, min_length=1, max_length=200)
+    tool_fallback_reasoning_effort: (
+        Literal["default", "none", "minimal", "low", "medium", "high", "xhigh", "max"] | None
+    ) = None
+    tool_fallback_max_retries: int | None = Field(default=None, ge=0, le=10)
+    tool_fallback_no_progress_attempts: int | None = Field(default=None, ge=1, le=10)
     reasoning_effort: Literal[
         "default", "none", "minimal", "low", "medium", "high", "xhigh", "max"
     ] = "high"
@@ -44,9 +52,11 @@ class ModelSettingsUpdate(BaseModel):
             raise ValueError("请输入接口根地址，例如 https://api-n-cd.playones.com/v1")
         return value
 
-    @field_validator("model")
+    @field_validator("model", "tool_fallback_model")
     @classmethod
-    def validate_model(cls, value: str) -> str:
+    def validate_model(cls, value: str | None) -> str | None:
+        if value is None:
+            return None
         value = value.strip()
         if not value or any(c in value for c in "\r\n\x00"):
             raise ValueError("模型名称不能为空或包含换行")
@@ -63,6 +73,11 @@ def get_model_settings() -> dict:
         "timeout_seconds": settings.OPENAI_TIMEOUT_SECONDS,
         "max_retries": settings.OPENAI_MAX_RETRIES,
         "retry_interval_seconds": settings.OPENAI_RETRY_INTERVAL_SECONDS,
+        "rate_limit_retry_seconds": settings.OPENAI_RATE_LIMIT_RETRY_SECONDS,
+        "tool_fallback_model": settings.OPENAI_TOOL_FALLBACK_MODEL,
+        "tool_fallback_reasoning_effort": settings.OPENAI_TOOL_FALLBACK_REASONING_EFFORT,
+        "tool_fallback_max_retries": settings.OPENAI_TOOL_FALLBACK_MAX_RETRIES,
+        "tool_fallback_no_progress_attempts": settings.ARTEMIS_TOOL_FALLBACK_MAX_NO_PROGRESS_ATTEMPTS,
         "reasoning_effort": settings.OPENAI_REASONING_EFFORT,
         "model_presets": [
             "gpt-5.6-luna",
@@ -85,6 +100,20 @@ def save_model_settings(update: ModelSettingsUpdate) -> dict:
         "OPENAI_RETRY_INTERVAL_SECONDS": str(update.retry_interval_seconds),
         "OPENAI_REASONING_EFFORT": update.reasoning_effort,
     }
+    optional_settings = {
+        "rate_limit_retry_seconds": ("OPENAI_RATE_LIMIT_RETRY_SECONDS", float),
+        "tool_fallback_model": ("OPENAI_TOOL_FALLBACK_MODEL", str),
+        "tool_fallback_reasoning_effort": ("OPENAI_TOOL_FALLBACK_REASONING_EFFORT", str),
+        "tool_fallback_max_retries": ("OPENAI_TOOL_FALLBACK_MAX_RETRIES", int),
+        "tool_fallback_no_progress_attempts": (
+            "ARTEMIS_TOOL_FALLBACK_MAX_NO_PROGRESS_ATTEMPTS",
+            int,
+        ),
+    }
+    for field_name, (env_name, expected_type) in optional_settings.items():
+        value = getattr(update, field_name)
+        if value is not None:
+            values[env_name] = str(value)
     if update.api_key and update.api_key.get_secret_value().strip():
         values["OPENAI_API_KEY"] = update.api_key.get_secret_value().strip()
     persist_environment(values)
@@ -94,6 +123,10 @@ def save_model_settings(update: ModelSettingsUpdate) -> dict:
     settings.OPENAI_MAX_RETRIES = update.max_retries
     settings.OPENAI_RETRY_INTERVAL_SECONDS = update.retry_interval_seconds
     settings.OPENAI_REASONING_EFFORT = update.reasoning_effort
+    for field_name, (env_name, expected_type) in optional_settings.items():
+        value = getattr(update, field_name)
+        if value is not None:
+            setattr(settings, env_name, expected_type(value))
     if "OPENAI_API_KEY" in values:
         settings.OPENAI_API_KEY = SecretStr(values["OPENAI_API_KEY"])
     from artemis.llm.router import ModelFactory
@@ -137,14 +170,19 @@ class CapabilitySettings(BaseModel):
     recording_enabled: bool = False
     outputter_enabled: bool = True
     step_summarizer_enabled: bool = True
+    screenshot_limit_enabled: bool = True
+    screenshot_max_long_edge: int = 2048
 
 
 def get_capability_settings() -> CapabilitySettings:
+    screenshot = load_agent_config().screenshot
     return CapabilitySettings(
         video_enabled=settings.ARTEMIS_VIDEO_ENABLED,
         recording_enabled=settings.ARTEMIS_RECORDING_ENABLED,
         outputter_enabled=settings.ARTEMIS_OUTPUTTER_ENABLED,
         step_summarizer_enabled=settings.ARTEMIS_STEP_SUMMARIZER_ENABLED,
+        screenshot_limit_enabled=screenshot.enabled,
+        screenshot_max_long_edge=screenshot.max_long_edge,
     )
 
 
@@ -154,6 +192,7 @@ def save_capability_settings(update: CapabilitySettings) -> CapabilitySettings:
         "ARTEMIS_RECORDING_ENABLED": update.recording_enabled,
         "ARTEMIS_OUTPUTTER_ENABLED": update.outputter_enabled,
         "ARTEMIS_STEP_SUMMARIZER_ENABLED": update.step_summarizer_enabled,
+        "ARTEMIS_SCREENSHOT_LIMIT_ENABLED": update.screenshot_limit_enabled,
     }
     persist_environment({key: str(value).lower() for key, value in values.items()})
     for key, value in values.items():

apps/admin_console/services/report_export.py

@@ -1,7 +1,7 @@
 """Evidence-backed Word/Excel artifacts, independent of the language model runtime."""
 
 from datetime import datetime
-from typing import cast
+from typing import Callable, cast
 import json
 from pathlib import Path
 import re
@@ -695,7 +695,7 @@ def export_session_media(session_id: str, kind: str) -> Path:
         return target
 
 
-def export_batch(batch_id: str) -> Path:
+def export_batch(batch_id: str, progress: Callable[[int, int, str], None] | None = None) -> Path:
     with EXPORT_LOCK:
         batch = store.get("batch", identifier(batch_id))
         if not batch:
@@ -704,6 +704,8 @@ def export_batch(batch_id: str) -> Path:
         directory.mkdir(parents=True, exist_ok=True)
         formats = batch["formats"]
         records = [session_report(job["session_id"]) for job in store.jobs(batch_id)]
+        if progress:
+            progress(0, len(records), "正在生成批次汇总")
         rows = [("任务编号", "任务名称", "执行状态", "验证结论", "异常原因")]
         rows += [
             (r["id"], r["name"], STATUS.get(r["status"], r["status"]), r["verdict"], r["error"])
@@ -734,9 +736,18 @@ def export_batch(batch_id: str) -> Path:
             for path in outputs:
                 archive.write(path, path.name)
             for number, report in enumerate(records, 1):
+                if progress:
+                    progress(number - 1, len(records), f"正在打包任务 {number}/{len(records)}")
+                prefix = f"任务报告/{number:04d}_{archive_case_name(report)}"
                 for path in export_session(report["id"], formats):
-                    archive.write(
+                    archive.write(path, f"{prefix}/{path.name}")
-                        path, f"任务报告/{number:04d}_{archive_case_name(report)}/{path.name}"
+                for source, name in session_screenshots(report):
-                    )
+                    archive.write(source, f"{prefix}/步骤截图/{name}")
+                for source, name in session_videos(report):
+                    archive.write(source, f"{prefix}/任务录屏/{name}")
+                if progress:
+                    progress(number, len(records), f"已打包任务 {number}/{len(records)}")
+        if progress:
+            progress(len(records), len(records), "正在完成压缩包")
         temporary.replace(target)
         return target

apps/showcase_ui/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "artemis-showcase-ui",
-  "version": "1.2.3",
+  "version": "1.3.0",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "artemis-showcase-ui",
-      "version": "1.2.3",
+      "version": "1.3.0",
       "dependencies": {
         "@angular/cdk": "^22.1.5",
         "@angular/common": "^22.1.4",

apps/showcase_ui/package.json

@@ -1,6 +1,6 @@
 {
   "name": "artemis-showcase-ui",
-  "version": "1.2.3",
+  "version": "1.3.0",
   "scripts": {
     "ng": "ng",
     "start": "ng serve --proxy-config proxy.conf.json",

apps/showcase_ui/src/app/components/automation-panel/automation-panel.component.html

@@ -71,8 +71,21 @@
       @if(b.status === 'running'){<button [disabled]="busy()" (click)="batchAction('pause')">暂停后续任务</button>}
       @if(b.status === 'draft' || b.status === 'paused'){<button [disabled]="busy()" (click)="batchAction('resume')">{{b.status === 'draft' ? '开始执行' : '继续未开始任务'}}</button>}
       @if(b.status !== 'running' && b.status !== 'deleting'){<button [disabled]="busy()" (click)="batchAction('retry_failed')">复制失败项为新批次</button>}
-       <a [href]="'/api/automation/batches/'+b.id+'/archive'">下载批次报告 ZIP</a>
+       <button type="button" [disabled]="archiveBusy()" (click)="downloadBatchArchive(b.id)">{{archiveBusy() && archiveBatchId() === b.id ? '正在准备报告包…' : '下载批次报告 ZIP'}}</button>
      </div><p class="muted">服务重启后等待手动继续。失败项不会自动重试；暂停不打断当前任务。</p>
+     @if(archiveBatchId() === b.id && archiveProgress(); as progress){
+       <div class="archive-progress" role="status" aria-live="polite" [class.archive-failed]="progress.status === 'failed'">
+         <div class="archive-progress-heading">
+           @if(progress.status === 'preparing'){<span class="archive-spinner" aria-hidden="true"></span>}
+           <strong>{{progress.stage}}</strong>
+         </div>
+         @if(progress.status === 'preparing'){
+           <div class="archive-track" role="progressbar" [attr.aria-valuenow]="progress.total ? progress.completed : null" [attr.aria-valuemin]="progress.total ? 0 : null" [attr.aria-valuemax]="progress.total || null" [attr.aria-label]="'批次报告打包进度：' + progress.stage"><span [style.width.%]="progress.total ? progress.completed / progress.total * 100 : 8"></span></div>
+           <p>已处理 {{progress.completed}} / {{progress.total}} 个任务 · 已用 {{duration(progress.elapsed_seconds)}}@if(progress.eta_seconds !== null){ · 预计还需约 {{duration(progress.eta_seconds)}}}</p>
+         } @else if(progress.status === 'failed'){<p>{{progress.error}}。可点击下载按钮重试。</p>}
+           @else if(progress.status === 'ready'){<p>打包完成，下载已开始。</p>}
+       </div>
+     }
     @if(b.report_error){<p class="error">报告生成失败：{{b.report_error}}</p>}
       @for(j of b.jobs; track j.session_id){<div class="job"><button (click)="agent.selectSession(j.session_id,true); tab.set('reports')" [title]="j.case.goal">{{j.case.case_id ? j.case.case_id + ' ' : ''}}{{j.case.name}}</button><span>{{label(j.status)}}</span>@if(j.error){<small>{{j.error}}</small>}</div>}
   </section>}

apps/showcase_ui/src/app/components/automation-panel/automation-panel.component.scss

@@ -18,6 +18,14 @@ input[type=checkbox]{width:15px;height:15px;accent-color:#2563eb;vertical-align:
 .device{background:#f8fafc;padding:12px 16px;}.device label{margin:0;}
 .record{display:flex;width:100%;text-align:left;align-items:flex-start;flex-direction:column;gap:4px;margin:8px 0 0;padding:12px;overflow-wrap:anywhere;}.record span{color:#64748b;font-size:12px;font-weight:400;}.record.selected{border-color:#93c5fd;background:#eff6ff;}.record.selected strong{color:#1d4ed8;}
 .batch-record{display:flex;gap:8px;align-items:stretch;margin-top:8px;}.batch-record .record{flex:1;min-width:0;width:auto;margin:0;}.batch-record .delete-batch{flex:0 0 40px;min-width:40px;padding:0;color:#b91c1c;}.batch-record .delete-batch:hover:not(:disabled){background:#fff1f2;border-color:#fda4af;}.batch-record .delete-batch .material-symbols-outlined{font-size:19px;}
+.archive-progress{margin:12px 0;padding:11px 13px;border:1px solid #bfdbfe;border-radius:10px;background:#f8fbff;color:#1e40af;}
+.archive-progress-heading{display:flex;align-items:center;gap:8px;}
+.archive-progress p{margin:7px 0 0;font-size:12px;color:#475569;}
+.archive-progress.archive-failed{border-color:#fecdd3;background:#fff1f2;color:#be123c;}
+.archive-track{height:7px;margin-top:10px;border-radius:999px;background:#dbeafe;overflow:hidden;}
+.archive-track span{display:block;height:100%;border-radius:inherit;background:#3b82f6;transition:width .3s ease;}
+.archive-spinner{width:13px;height:13px;border:2px solid #bfdbfe;border-top-color:#2563eb;border-radius:50%;animation:archive-spin .8s linear infinite;}
+@keyframes archive-spin{to{transform:rotate(360deg);}}
 .code{font-family:Consolas,monospace;font-size:12px;line-height:1.6;resize:vertical;background:#f8fafc;}.table-wrap{max-height:260px;overflow:auto;border:1px solid #e2e8f0;border-radius:8px;margin-bottom:14px;}table{width:100%;border-collapse:collapse;}th,td{padding:9px 10px;border-bottom:1px solid #e2e8f0;text-align:left;overflow-wrap:anywhere;}th{background:#f8fafc;color:#475569;font-weight:500;}
 .preview{white-space:pre-wrap;max-height:400px;overflow:auto;font:inherit;color:#334155;background:#f8fafc;border-radius:8px;padding:12px;border:1px solid #e2e8f0;}
 .job{display:flex;flex-wrap:wrap;gap:8px;align-items:center;padding:10px 0;border-bottom:1px solid #f1f5f9;}.job:last-child{border-bottom:0;}.job button{flex:1;justify-content:flex-start;text-align:left;min-width:150px;}.job span{font-size:12px;background:#f1f5f9;color:#475569;border-radius:5px;padding:3px 7px;}.job small{width:100%;}details{margin:14px 0;}summary{color:#475569;cursor:pointer;padding:8px 0;}

apps/showcase_ui/src/app/components/automation-panel/automation-panel.component.spec.ts

@@ -14,12 +14,13 @@ describe('AutomationPanelComponent batch deletion', () => {
     agentStatus: signal('idle'),
     sessions: signal<any[]>([])
   };
-  const http = jasmine.createSpyObj<HttpClient>('HttpClient', ['get', 'delete']);
+  const http = jasmine.createSpyObj<HttpClient>('HttpClient', ['get', 'post', 'delete']);
 
   beforeEach(async () => {
     (http.get.and as any).callFake((url: string) => of(url.endsWith('/preferences')
       ? { formats: ['docx', 'xlsx'], batch_timeout_seconds: 1800 } : []));
     http.delete.and.returnValue(of({ status: 'deleted' }));
+    http.post.and.returnValue(of({ status: 'ready', stage: '报告包已就绪', completed: 0, total: 0, elapsed_seconds: 0, eta_seconds: null, error: null }));
     http.delete.calls.reset();
     await TestBed.configureTestingModule({
       imports: [AutomationPanelComponent],
@@ -77,4 +78,57 @@ describe('AutomationPanelComponent batch deletion', () => {
     expect(root.querySelector('.report-body')?.textContent).toContain('第二行');
     expect(root.querySelector('.report-body table td')?.textContent).toBe('设置');
   });
+
+  it('prepares a batch archive before starting the browser download', async () => {
+    const fixture = TestBed.createComponent(AutomationPanelComponent);
+    const component = fixture.componentInstance;
+    component.tab.set('batches');
+    await new Promise<void>(resolve => setTimeout(resolve, 0));
+    component.batch.set({ id: 'batch-3', name: '批量测试', status: 'completed', jobs: [] });
+    const click = spyOn(HTMLAnchorElement.prototype, 'click');
+    fixture.detectChanges();
+    (fixture.nativeElement.querySelector('.actions button:last-child') as HTMLButtonElement).click();
+    await new Promise<void>(resolve => setTimeout(resolve, 0));
+    fixture.detectChanges();
+    expect(http.post).toHaveBeenCalledWith('/api/automation/batches/batch-3/archive/prepare', {});
+    expect(click).toHaveBeenCalled();
+    expect(fixture.nativeElement.querySelector('.archive-progress')?.textContent).toContain('下载已开始');
+  });
+
+  it('shows archive errors and lets the user retry', async () => {
+    http.post.and.returnValue(of({ status: 'failed', stage: '打包失败', completed: 1, total: 2, elapsed_seconds: 3, eta_seconds: null, error: '截图文件无法读取' }));
+    const fixture = TestBed.createComponent(AutomationPanelComponent);
+    const component = fixture.componentInstance;
+    component.tab.set('batches');
+    await new Promise<void>(resolve => setTimeout(resolve, 0));
+    component.batch.set({ id: 'batch-4', name: '批量测试', status: 'completed', jobs: [] });
+    fixture.detectChanges();
+    (fixture.nativeElement.querySelector('.actions button:last-child') as HTMLButtonElement).click();
+    await new Promise<void>(resolve => setTimeout(resolve, 0));
+    fixture.detectChanges();
+    expect(fixture.nativeElement.querySelector('.archive-progress')?.textContent).toContain('截图文件无法读取');
+    expect((fixture.nativeElement.querySelector('.actions button:last-child') as HTMLButtonElement).disabled).toBeFalse();
+  });
+
+  it('shows live archive progress and estimated remaining time while preparing', async () => {
+    http.post.and.returnValue(of({ status: 'preparing', stage: '正在打包任务 2/4', completed: 1, total: 4, elapsed_seconds: 12, eta_seconds: 36, error: null }));
+    (http.get.and as any).callFake((url: string) => of(url.endsWith('/status')
+      ? { status: 'ready', stage: '报告包已就绪', completed: 4, total: 4, elapsed_seconds: 13, eta_seconds: null, error: null }
+      : url.endsWith('/preferences') ? { formats: ['docx', 'xlsx'], batch_timeout_seconds: 1800 } : []));
+    const fixture = TestBed.createComponent(AutomationPanelComponent);
+    const component = fixture.componentInstance;
+    component.tab.set('batches');
+    await new Promise<void>(resolve => setTimeout(resolve, 0));
+    component.batch.set({ id: 'batch-5', name: '批量测试', status: 'completed', jobs: [] });
+    spyOn(HTMLAnchorElement.prototype, 'click');
+    fixture.detectChanges();
+    (fixture.nativeElement.querySelector('.actions button:last-child') as HTMLButtonElement).click();
+    await new Promise<void>(resolve => setTimeout(resolve, 0));
+    fixture.detectChanges();
+    const progress = fixture.nativeElement.querySelector('.archive-progress') as HTMLElement;
+    expect(progress.textContent).toContain('已处理 1 / 4 个任务');
+    expect(progress.textContent).toContain('预计还需约 36 秒');
+    expect(progress.querySelector('.archive-spinner')).not.toBeNull();
+    fixture.destroy();
+  });
 });

apps/showcase_ui/src/app/components/automation-panel/automation-panel.component.ts

@@ -7,6 +7,16 @@ import { AgentService } from '../../services/agent.service';
 import { SystemService } from '../../services/system.service';
 import { renderMarkdownToHtml } from '../../utils/markdown-parser.util';
 
+interface ArchiveProgress {
+  status: 'idle' | 'preparing' | 'ready' | 'failed';
+  stage: string;
+  completed: number;
+  total: number;
+  elapsed_seconds: number;
+  eta_seconds: number | null;
+  error: string | null;
+}
+
 @Component({
   selector: 'app-automation-panel', standalone: true, imports: [CommonModule, FormsModule],
   templateUrl: './automation-panel.component.html', styleUrl: './automation-panel.component.scss'
@@ -16,6 +26,8 @@ export class AutomationPanelComponent {
   tab = signal('reports'); error = signal(''); notice = signal(''); busy = signal(false);
   batches = signal<any[]>([]); preview = signal<any>(null);
   batch = signal<any>(null); report = signal<any>(null);
+  archiveProgress = signal<ArchiveProgress | null>(null);
+  archiveBatchId = signal(''); archiveBusy = signal(false);
   guidance = '';
   taskStatus = computed(() => {
     const sid = this.agent.currentSessionId();
@@ -40,6 +52,38 @@ export class AutomationPanelComponent {
   formats() { return [ ...(this.word ? ['docx'] : []), ...(this.excel ? ['xlsx'] : []) ]; }
   label(s: string) { return ({draft:'待开始',running:'执行中',paused:'已暂停',task_paused:'等待指导',resuming:'等待设备恢复',completed:'执行完成',failed:'失败',cancelled:'已取消',pending:'待执行',queued:'排队中',interrupted:'中断',skipped:'跳过',finalizing:'正在生成报告',deleting:'删除中，可重试清理'} as Record<string,string>)[s] || s; }
   renderReport(content: string) { return renderMarkdownToHtml(content || ''); }
+  duration(seconds: number) { const minutes = Math.floor(seconds / 60); return minutes ? `${minutes} 分 ${seconds % 60} 秒` : `${seconds} 秒`; }
+  downloadBatchArchive(id: string) { void this.prepareBatchArchive(id); }
+  private async prepareBatchArchive(id: string) {
+    if (this.archiveBusy()) return;
+    this.archiveBusy.set(true); this.archiveBatchId.set(id);
+    this.archiveProgress.set({ status: 'preparing', stage: '正在请求打包', completed: 0, total: 0, elapsed_seconds: 0, eta_seconds: null, error: null });
+    const base = `/api/automation/batches/${encodeURIComponent(id)}/archive`;
+    try {
+      let progress = await firstValueFrom(this.http.post<ArchiveProgress>(`${base}/prepare`, {}));
+      while (!this.destroyed && progress.status === 'preparing') {
+        this.archiveProgress.set(progress);
+        await new Promise<void>(resolve => setTimeout(resolve, 1000));
+        if (this.destroyed) return;
+        progress = await firstValueFrom(this.http.get<ArchiveProgress>(`${base}/status`));
+      }
+      if (this.destroyed) return;
+      this.archiveProgress.set(progress);
+      if (progress.status === 'failed') throw Error(progress.error || '批次报告打包失败');
+      if (progress.status !== 'ready') throw Error('打包任务已中断，请重试');
+      const link = document.createElement('a');
+      link.href = base;
+      link.download = `批次报告-${id.slice(0, 8)}.zip`;
+      document.body.appendChild(link); link.click(); link.remove();
+      this.notice.set('报告包已就绪，正在开始下载');
+    } catch (e: any) {
+      const detail = e.error?.detail;
+      this.archiveProgress.set({
+        ...(this.archiveProgress() || { completed: 0, total: 0, elapsed_seconds: 0, eta_seconds: null }),
+        status: 'failed', stage: '打包失败', error: typeof detail === 'string' ? detail : e.message || '下载失败'
+      });
+    } finally { this.archiveBusy.set(false); }
+  }
   async action(fn: () => Promise<void>) {
     if (this.busy()) return;
     this.busy.set(true); this.error.set(''); this.notice.set('');

apps/showcase_ui/src/app/components/floating-video-player/floating-video-player.component.html

@@ -354,6 +354,7 @@ limitations under the License.
                         <input
                           type="range"
                           class="scrubber-range"
+                          aria-label="录像进度"
                           [min]="0"
                           [max]="duration() || 100"
                           [step]="0.1"
@@ -361,6 +362,22 @@ limitations under the License.
                           (input)="onScrub($event)"
                           />
                           <div class="scrubber-progress-fill" [style.width.%]="progressPercent()"></div>
+                          @if (videoStepMarkers().length > 0) {
+                            <div class="video-markers" aria-label="录像关键步骤">
+                              @for (marker of videoStepMarkers(); track marker.time) {
+                                <button
+                                  type="button"
+                                  class="video-marker"
+                                  [class.passed]="currentTime() >= marker.time"
+                                  [class.failed]="marker.failed"
+                                  [style.left.%]="marker.percent"
+                                  [title]="videoMarkerLabel(marker)"
+                                  [attr.aria-label]="'跳转到' + videoMarkerLabel(marker)"
+                                  (click)="seekToVideoMarker(marker)"
+                                ></button>
+                              }
+                            </div>
+                          }
                         </div>
                         <!-- Bottom Controls Row -->
                         <div class="controls-row">

apps/showcase_ui/src/app/components/floating-video-player/floating-video-player.component.scss

@@ -724,6 +724,38 @@
           box-shadow: 0 0 8px rgba(2, 132, 199, 0.4);
         }
 
+        .video-markers {
+          position: absolute;
+          inset: 0;
+          z-index: 6;
+          pointer-events: none;
+
+          .video-marker {
+            position: absolute;
+            top: 50%;
+            width: 12px;
+            height: 12px;
+            padding: 0;
+            border: 2px solid #fff;
+            border-radius: 50%;
+            background: #64748b;
+            box-shadow: 0 1px 4px rgba(15, 23, 42, 0.25);
+            transform: translate(-50%, -50%);
+            cursor: pointer;
+            pointer-events: auto;
+            transition: transform 0.15s ease, background 0.15s ease;
+
+            &.passed { background: #0284c7; }
+            &.failed { background: #ef4444; }
+            &:hover, &:focus-visible {
+              z-index: 1;
+              transform: translate(-50%, -50%) scale(1.35);
+              outline: 2px solid #38bdf8;
+              outline-offset: 2px;
+            }
+          }
+        }
+
         .step-ticks-wrapper {
           position: absolute;
           left: 0;

apps/showcase_ui/src/app/components/floating-video-player/floating-video-player.component.spec.ts

@@ -0,0 +1,46 @@
+import { TestBed } from '@angular/core/testing';
+import { signal } from '@angular/core';
+import { AgentService } from '../../services/agent.service';
+import { StepReplayFrame } from '../../core/models/stream.model';
+import { FloatingVideoPlayerComponent } from './floating-video-player.component';
+
+describe('FloatingVideoPlayerComponent video markers', () => {
+  it('shows a recorded step on the video timeline and seeks when clicked', () => {
+    const start = 1_800_000_000;
+    const frame: StepReplayFrame = {
+      index: 0, stepNumber: 1, title: '步骤 1: 点击设置', imageUrl: 'step.png',
+      actionText: '点击设置', timestamp: start + 5
+    };
+    const agentService = {
+      isVideoWindowOpen: signal(true),
+      isVideoMinimized: signal(false),
+      recordingPlaybackStatus: signal('ready'),
+      playerMode: signal('video'),
+      hasCurrentSessionStepFrames: signal(true),
+      activeVideoTitle: signal('测试录像'),
+      isVideoLoading: signal(false),
+      activeVideoUrl: signal('/test.mp4'),
+      activeVideoSegments: signal([{ url: '/test.mp4', start: 0, duration: 20, width: 400, height: 800 }]),
+      currentSessionStepFrames: signal([frame]),
+      currentSession: signal({ session_id: 'test', start_time: start }),
+      currentSessionId: signal('test'),
+      videoSeekRequest: signal(null),
+      stepSeekRequest: signal(null)
+    };
+    TestBed.configureTestingModule({
+      imports: [FloatingVideoPlayerComponent],
+      providers: [{ provide: AgentService, useValue: agentService }]
+    });
+
+    const fixture = TestBed.createComponent(FloatingVideoPlayerComponent);
+    fixture.detectChanges();
+    const marker = fixture.nativeElement.querySelector('.video-marker') as HTMLButtonElement;
+    expect(marker).not.toBeNull();
+    expect(marker.getAttribute('aria-label')).toContain('步骤 1');
+    expect(marker.style.left).toBe('25%');
+
+    const seek = spyOn(fixture.componentInstance, 'seek');
+    marker.click();
+    expect(seek).toHaveBeenCalledOnceWith(5);
+  });
+});

apps/showcase_ui/src/app/components/floating-video-player/floating-video-player.component.ts

@@ -34,6 +34,7 @@ import { StepReplayFrame } from '../../core/models/stream.model';
 import { drawActionCoordinatesOnOverlay } from '../../utils/image-overlay.util';
 import { getActionIcon } from '../../utils/action-formatter.util';
 import { locateTimelineTime, sessionTimeToTimelineTime } from '../../utils/recording-timeline.util';
+import { buildVideoStepMarkers, VideoStepMarker } from '../../utils/video-step-markers.util';
 
 @Component({
   selector: 'app-floating-video-player',
@@ -83,6 +84,12 @@ export class FloatingVideoPlayerComponent implements OnDestroy {
   private stepTimer: any = null;
 
   public stepFrames = computed(() => this.agentService.currentSessionStepFrames());
+  public videoStepMarkers = computed(() => buildVideoStepMarkers(
+    this.stepFrames(),
+    this.agentService.currentSession()?.start_time,
+    this.agentService.activeVideoSegments(),
+    this.duration()
+  ));
   public totalStepFrames = computed(() => this.stepFrames().length);
   public currentStepFrame = computed<StepReplayFrame | null>(() => {
     const frames = this.stepFrames();
@@ -394,6 +401,19 @@ export class FloatingVideoPlayerComponent implements OnDestroy {
     this.seek(sessionTimeToTimelineTime(segments, sessionSeconds), autoplay);
   }
 
+  public seekToVideoMarker(marker: VideoStepMarker): void {
+    this.seek(marker.time);
+  }
+
+  public videoMarkerLabel(marker: VideoStepMarker): string {
+    const first = marker.steps[0];
+    const count = marker.steps.length;
+    const action = first.actionText || first.title;
+    return count > 1
+      ? `步骤 ${first.stepNumber} 等 ${count} 步 · ${this.formatTime(marker.time)} · ${action}`
+      : `步骤 ${first.stepNumber} · ${this.formatTime(marker.time)} · ${action}`;
+  }
+
   /** Seek on the back-to-back playlist axis used by the scrubber. */
   public seek(seconds: number, autoplay = false): void {
     const v = this.videoRef?.nativeElement;

apps/showcase_ui/src/app/components/model-settings/capability-settings.component.ts

@@ -8,6 +8,8 @@ interface Capabilities {
   recording_enabled: boolean;
   outputter_enabled: boolean;
   step_summarizer_enabled: boolean;
+  screenshot_limit_enabled: boolean;
+  screenshot_max_long_edge: number;
 }
 
 @Component({
@@ -24,6 +26,8 @@ interface Capabilities {
         <span>最终报告生成<small>根据执行记录生成任务结果报告。</small></span></label>
       <label><input type="checkbox" role="switch" [(ngModel)]="config.step_summarizer_enabled" [disabled]="busy() || loading() || !loaded()" />
         <span>步骤摘要压缩<small>Flash 模式下在后台生成摘要，减少长任务的上下文占用。</small></span></label>
+      <label><input type="checkbox" role="switch" [(ngModel)]="config.screenshot_limit_enabled" [disabled]="busy() || loading() || !loaded()" />
+        <span>限制截图分辨率<small>开启时长边最多 {{ config.screenshot_max_long_edge }} 像素；关闭后使用设备原始分辨率。JSON 配置可覆盖开关和上限。</small></span></label>
       <button type="button" (click)="save()" [disabled]="busy() || loading() || !loaded()">{{ busy() ? '正在保存…' : '保存功能设置' }}</button>
       @if (message()) { <p role="status">{{ message() }}</p> }
       @if (error()) { <p role="alert" class="error">{{ error() }}</p> }
@@ -39,7 +43,7 @@ interface Capabilities {
 export class CapabilitySettingsComponent implements OnInit {
   changed = output<Capabilities>();
   private http = inject(HttpClient);
-  config: Capabilities = { video_enabled: false, recording_enabled: false, outputter_enabled: true, step_summarizer_enabled: true };
+  config: Capabilities = { video_enabled: false, recording_enabled: false, outputter_enabled: true, step_summarizer_enabled: true, screenshot_limit_enabled: true, screenshot_max_long_edge: 2048 };
   busy = signal(false); loading = signal(true); loaded = signal(false); message = signal(''); error = signal('');
   ngOnInit(): void {
     this.http.get<Capabilities>('/api/system/capability-settings').pipe(finalize(() => this.loading.set(false))).subscribe({

apps/showcase_ui/src/app/components/model-settings/model-settings.component.spec.ts

@@ -8,6 +8,8 @@ describe('持久化模型与功能设置', () => {
   let http: HttpTestingController;
   const config = { base_url: 'https://example.test/v1', model: 'mimo-v2.6-flash',
     api_key_configured: true, timeout_seconds: 300, max_retries: 5, retry_interval_seconds: 10, reasoning_effort: 'high',
+    rate_limit_retry_seconds: 60, tool_fallback_model: 'gpt-6-luna', tool_fallback_reasoning_effort: 'high',
+    tool_fallback_max_retries: 3, tool_fallback_no_progress_attempts: 3,
     model_presets: ['gpt-5.6-luna', 'gpt-6-luna', 'mimo-v2.6-flash', 'mimo-v2.6-pro'], env_path: '/app/.env' };
   beforeEach(() => {
     TestBed.configureTestingModule({ providers: [provideHttpClient(), provideHttpClientTesting()] });
@@ -31,6 +33,9 @@ describe('持久化模型与功能设置', () => {
     expect(request.request.body.timeout_seconds).toBe(300);
     expect(request.request.body.max_retries).toBe(5);
     expect(request.request.body.retry_interval_seconds).toBe(10);
+    expect(request.request.body.rate_limit_retry_seconds).toBe(60);
+    expect(request.request.body.tool_fallback_model).toBe('gpt-6-luna');
+    expect(request.request.body.tool_fallback_no_progress_attempts).toBe(3);
     expect(request.request.body.api_key).toBeUndefined();
     request.flush({ ...config, model: 'another-model' });
     expect(component.busy()).toBeNull();
@@ -88,6 +93,29 @@ describe('持久化模型与功能设置', () => {
     expect(reopened.nativeElement.querySelector('#model-reasoning').value).toBe('max');
   });
 
+  it('高级稳定性设置可编辑并保存', async () => {
+    const fixture = TestBed.createComponent(ModelSettingsComponent);
+    fixture.detectChanges();
+    http.expectOne('/api/system/model-settings').flush(config);
+    fixture.detectChanges();
+    await fixture.whenStable();
+    const root: HTMLElement = fixture.nativeElement;
+    root.querySelector<HTMLDetailsElement>('.recovery-settings')!.open = true;
+    const fallback = root.querySelector<HTMLInputElement>('input[name="toolFallbackModel"]')!;
+    fallback.value = 'custom/fallback';
+    fallback.dispatchEvent(new Event('input'));
+    const wait = root.querySelector<HTMLInputElement>('input[name="rateLimitRetry"]')!;
+    wait.value = '90';
+    wait.dispatchEvent(new Event('input'));
+    fixture.detectChanges();
+    await fixture.whenStable();
+    root.querySelector<HTMLFormElement>('form')!.dispatchEvent(new Event('submit', { cancelable: true }));
+    const request = http.expectOne('/api/system/model-settings');
+    expect(request.request.body.tool_fallback_model).toBe('custom/fallback');
+    expect(request.request.body.rate_limit_retry_seconds).toBe(90);
+    request.flush({ ...config, tool_fallback_model: 'custom/fallback', rate_limit_retry_seconds: 90 });
+  });
+
   it('连接测试使用当前输入而不写入保存接口', () => {
     const fixture = TestBed.createComponent(ModelSettingsComponent);
     fixture.detectChanges();
@@ -110,17 +138,19 @@ describe('持久化模型与功能设置', () => {
   });
 
   it('录屏与视频分析独立保存，重新打开后保留各自状态', () => {
-    const capabilities = { video_enabled: true, recording_enabled: false, outputter_enabled: true, step_summarizer_enabled: false };
+    const capabilities = { video_enabled: true, recording_enabled: false, outputter_enabled: true, step_summarizer_enabled: false, screenshot_limit_enabled: true, screenshot_max_long_edge: 2048 };
     const fixture = TestBed.createComponent(CapabilitySettingsComponent);
     fixture.detectChanges();
     http.expectOne('/api/system/capability-settings').flush(capabilities);
     fixture.componentInstance.config.video_enabled = false;
     fixture.componentInstance.config.recording_enabled = true;
+    fixture.componentInstance.config.screenshot_limit_enabled = false;
     fixture.componentInstance.save();
     const request = http.expectOne('/api/system/capability-settings');
     expect(request.request.body.video_enabled).toBeFalse();
     expect(request.request.body.recording_enabled).toBeTrue();
-    const saved = { ...capabilities, video_enabled: false, recording_enabled: true };
+    expect(request.request.body.screenshot_limit_enabled).toBeFalse();
+    const saved = { ...capabilities, video_enabled: false, recording_enabled: true, screenshot_limit_enabled: false };
     request.flush(saved);
     fixture.destroy();
     const reopened = TestBed.createComponent(CapabilitySettingsComponent);
@@ -128,5 +158,6 @@ describe('持久化模型与功能设置', () => {
     http.expectOne('/api/system/capability-settings').flush(saved);
     expect(reopened.componentInstance.config.video_enabled).toBeFalse();
     expect(reopened.componentInstance.config.recording_enabled).toBeTrue();
+    expect(reopened.componentInstance.config.screenshot_limit_enabled).toBeFalse();
   });
 });

apps/showcase_ui/src/app/components/model-settings/model-settings.component.ts

@@ -10,6 +10,11 @@ interface ModelSettings {
   timeout_seconds: number;
   max_retries: number;
   retry_interval_seconds: number;
+  rate_limit_retry_seconds: number;
+  tool_fallback_model: string;
+  tool_fallback_reasoning_effort: string;
+  tool_fallback_max_retries: number;
+  tool_fallback_no_progress_attempts: number;
   reasoning_effort: string;
   model_presets: string[];
   env_path: string;
@@ -56,6 +61,30 @@ interface ModelSettings {
           [(ngModel)]="retries" [disabled]="busy() || loading()" /></label>
         <label>重试间隔（秒）<input name="retryInterval" type="number" min="0" max="300" required [(ngModel)]="retryInterval" [disabled]="busy() || loading()" /></label>
       </div>
+      <details class="recovery-settings">
+        <summary>稳定性与兜底模型</summary>
+        <div class="recovery-content">
+          <label>429 等待重试（秒）<input name="rateLimitRetry" type="number" min="0" max="3600" required
+            [(ngModel)]="rateLimitRetry" [disabled]="busy() || loading()" /></label>
+          <label>兜底模型名称<input name="toolFallbackModel" type="text" required autocomplete="off"
+            autocapitalize="off" spellcheck="false" [(ngModel)]="toolFallbackModel"
+            [disabled]="busy() || loading()" /></label>
+          <label>兜底思考强度<select name="toolFallbackReasoning" [(ngModel)]="toolFallbackReasoning"
+            [disabled]="busy() || loading()">
+            <option value="default">由模型默认</option><option value="none">关闭（none）</option>
+            <option value="minimal">最低（minimal）</option><option value="low">低（low）</option>
+            <option value="medium">中（medium）</option><option value="high">高（high）</option>
+            <option value="xhigh">极高（xhigh）</option><option value="max">最高（max）</option>
+          </select></label>
+          <div class="number-fields">
+            <label>兜底请求重试次数<input name="toolFallbackRetries" type="number" min="0" max="10" required
+              [(ngModel)]="toolFallbackRetries" [disabled]="busy() || loading()" /></label>
+            <label>兜底无效调用上限<input name="toolFallbackNoProgress" type="number" min="1" max="10" required
+              [(ngModel)]="toolFallbackNoProgress" [disabled]="busy() || loading()" /></label>
+          </div>
+          <small>首选模型连续 3 次未形成有效工具调用后，本步骤使用兜底模型；下一步恢复首选模型。</small>
+        </div>
+      </details>
       <div class="actions">
         <button type="submit" [disabled]="busy() || loading() || !valid()">{{ busy() === 'save' ? '正在保存…' : '保存配置' }}</button>
         <button type="button" (click)="test()" [disabled]="busy() || loading() || !valid()">
@@ -77,6 +106,10 @@ interface ModelSettings {
     input:focus, select:focus { outline: 2px solid #38bdf8; }
     .number-fields { display: grid; grid-template-columns: 1fr 1fr; gap: 16px; margin-top: 8px; }
     .number-fields input { margin-top: 8px; }
+    .recovery-settings { border: 1px solid #cbd5e1; border-radius: 10px; background: #f8fafc; padding: 10px 14px; }
+    .recovery-settings summary { cursor: pointer; font-weight: 600; color: #334155; }
+    .recovery-content { display: grid; gap: 10px; padding-top: 12px; }
+    .recovery-content label input, .recovery-content label select { margin-top: 8px; }
     .actions { display: flex; gap: 12px; margin-top: 8px; }
     button { padding: 11px 20px; border-radius: 10px; border: 1px solid #38bdf877; background: #0284c7; color: white; cursor: pointer; font: inherit; }
     button:disabled { opacity: .5; cursor: default; } .success { color: #047857; } .error { color: #be123c; }
@@ -93,6 +126,8 @@ export class ModelSettingsComponent implements OnInit {
   saved = output<void>();
   modelPresets: string[] = [];
   baseUrl = ''; model = ''; apiKey = ''; timeout = 300; retries = 5; retryInterval = 10; reasoning = 'high';
+  rateLimitRetry = 60; toolFallbackModel = 'gpt-6-luna'; toolFallbackReasoning = 'high';
+  toolFallbackRetries = 3; toolFallbackNoProgress = 3;
   busy = signal<'save' | 'test' | null>(null);
   loading = signal(true); keyConfigured = signal(false); envPath = signal('');
   message = signal(''); error = signal('');
@@ -105,16 +140,27 @@ export class ModelSettingsComponent implements OnInit {
   }
   valid(): boolean {
     return !!this.baseUrl.trim() && !!this.model.trim() && (!!this.apiKey.trim() || this.keyConfigured())
-      && this.timeout > 0 && this.timeout <= 3600 && Number.isInteger(this.retries) && this.retries >= 0 && this.retries <= 10 && this.retryInterval >= 0 && this.retryInterval <= 300;
+      && this.timeout > 0 && this.timeout <= 3600 && Number.isInteger(this.retries) && this.retries >= 0 && this.retries <= 10 && this.retryInterval >= 0 && this.retryInterval <= 300
+      && this.rateLimitRetry >= 0 && this.rateLimitRetry <= 3600 && !!this.toolFallbackModel.trim()
+      && Number.isInteger(this.toolFallbackRetries) && this.toolFallbackRetries >= 0 && this.toolFallbackRetries <= 10
+      && Number.isInteger(this.toolFallbackNoProgress) && this.toolFallbackNoProgress >= 1 && this.toolFallbackNoProgress <= 10;
   }
   private payload() {
     return { base_url: this.baseUrl.trim(), model: this.model.trim(), api_key: this.apiKey.trim() || undefined,
-      timeout_seconds: this.timeout, max_retries: this.retries, retry_interval_seconds: this.retryInterval, reasoning_effort: this.reasoning };
+      timeout_seconds: this.timeout, max_retries: this.retries, retry_interval_seconds: this.retryInterval, reasoning_effort: this.reasoning,
+      rate_limit_retry_seconds: this.rateLimitRetry, tool_fallback_model: this.toolFallbackModel.trim(),
+      tool_fallback_reasoning_effort: this.toolFallbackReasoning, tool_fallback_max_retries: this.toolFallbackRetries,
+      tool_fallback_no_progress_attempts: this.toolFallbackNoProgress };
   }
   private apply(config: ModelSettings): void {
     this.baseUrl = config.base_url; this.model = config.model; this.timeout = config.timeout_seconds;
     this.modelPresets = config.model_presets;
     this.retries = config.max_retries; this.retryInterval = config.retry_interval_seconds ?? 10; this.reasoning = config.reasoning_effort ?? 'high'; this.keyConfigured.set(config.api_key_configured); this.envPath.set(config.env_path);
+    this.rateLimitRetry = config.rate_limit_retry_seconds ?? 60;
+    this.toolFallbackModel = config.tool_fallback_model ?? 'gpt-6-luna';
+    this.toolFallbackReasoning = config.tool_fallback_reasoning_effort ?? 'high';
+    this.toolFallbackRetries = config.tool_fallback_max_retries ?? 3;
+    this.toolFallbackNoProgress = config.tool_fallback_no_progress_attempts ?? 3;
   }
   save(): void {
     if (this.busy() || !this.valid()) return;

apps/showcase_ui/src/app/utils/video-step-markers.util.spec.ts

@@ -0,0 +1,53 @@
+import { StepReplayFrame } from '../core/models/stream.model';
+import { TimelineSegment } from './recording-timeline.util';
+import { buildVideoStepMarkers } from './video-step-markers.util';
+
+describe('buildVideoStepMarkers', () => {
+  const start = 1_800_000_000;
+  const segments: TimelineSegment[] = [
+    { start: 0, duration: 10, offset_ms: 1000, duration_ms: 10000 },
+    { start: 10, duration: 10, offset_ms: 13000, duration_ms: 10000 }
+  ];
+  const frame = (stepNumber: number, seconds: number, status = 'dispatched'): StepReplayFrame => ({
+    index: stepNumber - 1,
+    stepNumber,
+    title: `步骤 ${stepNumber}`,
+    imageUrl: 'step.png',
+    timestamp: start + seconds,
+    status
+  });
+
+  it('places markers on the playable timeline across recording restart gaps', () => {
+    const markers = buildVideoStepMarkers([frame(1, 4), frame(2, 15)], start, segments, 20);
+    expect(markers.map((marker) => marker.time)).toEqual([3, 12]);
+    expect(markers.map((marker) => marker.percent)).toEqual([15, 60]);
+  });
+
+  it('snaps a step in a recording gap to the following segment', () => {
+    const markers = buildVideoStepMarkers([frame(1, 12)], start, segments, 20);
+    expect(markers[0].time).toBe(10);
+  });
+
+  it('omits steps outside the actual recording and without timestamps', () => {
+    const missing = { ...frame(4, 5), timestamp: undefined };
+    const markers = buildVideoStepMarkers(
+      [frame(1, 0), frame(2, 2), frame(3, 24), missing], start, segments, 20
+    );
+    expect(markers.map((marker) => marker.steps[0].stepNumber)).toEqual([2]);
+  });
+
+  it('groups crowded steps and keeps failure information', () => {
+    const markers = buildVideoStepMarkers(
+      [frame(1, 4), frame(2, 4.1, 'failed'), frame(3, 8)], start, segments, 20
+    );
+    expect(markers.length).toBe(2);
+    expect(markers[0].steps.map((step) => step.stepNumber)).toEqual([1, 2]);
+    expect(markers[0].failed).toBeTrue();
+  });
+
+  it('uses session seconds directly for a legacy playlist or single video', () => {
+    expect(buildVideoStepMarkers([frame(1, 6)], start, [{ start: 0, duration: 20 }], 20)[0].time).toBe(6);
+    expect(buildVideoStepMarkers([frame(1, 6)], start, [], 20)[0].time).toBe(6);
+    expect(buildVideoStepMarkers([frame(1, 6)], undefined, [], 20)).toEqual([]);
+  });
+});

apps/showcase_ui/src/app/utils/video-step-markers.util.ts

@@ -0,0 +1,52 @@
+import { StepReplayFrame } from '../core/models/stream.model';
+import { hasSessionOffsets, sessionTimeToTimelineTime, TimelineSegment } from './recording-timeline.util';
+
+export interface VideoStepMarker {
+  time: number;
+  percent: number;
+  steps: StepReplayFrame[];
+  failed: boolean;
+}
+
+/** Place recorded action steps on the video's gap-free playback timeline. */
+export function buildVideoStepMarkers(
+  frames: readonly StepReplayFrame[],
+  sessionStart: number | undefined,
+  segments: readonly TimelineSegment[],
+  duration: number
+): VideoStepMarker[] {
+  const start = Number(sessionStart);
+  if (!Number.isFinite(start) || !Number.isFinite(duration) || duration <= 0) return [];
+
+  const hasOffsets = hasSessionOffsets(segments);
+  const firstOffset = hasOffsets ? (segments[0].offset_ms as number) / 1000 : 0;
+  const lastSegment = segments[segments.length - 1];
+  const lastOffset = hasOffsets
+    ? ((lastSegment.offset_ms as number) + (lastSegment.duration_ms as number)) / 1000
+    : duration;
+
+  const positioned = frames.flatMap((frame) => {
+    const timestamp = Number(frame.timestamp);
+    if (frame.timestamp == null || !Number.isFinite(timestamp)) return [];
+    const sessionTime = timestamp - start;
+    // Do not create misleading ticks at the start/end for steps outside the recording.
+    if (sessionTime < firstOffset || sessionTime > lastOffset) return [];
+    const time = sessionTimeToTimelineTime(segments, sessionTime);
+    if (time < 0 || time > duration) return [];
+    return [{ time, frame }];
+  }).sort((a, b) => a.time - b.time);
+
+  const markers: VideoStepMarker[] = [];
+  // Nearby actions share one visible tick on a narrow player timeline.
+  const minGap = duration * 0.02;
+  for (const { time, frame } of positioned) {
+    const previous = markers[markers.length - 1];
+    if (previous && time - previous.time < minGap) {
+      previous.steps.push(frame);
+      previous.failed ||= frame.status === 'failed';
+    } else {
+      markers.push({ time, percent: (time / duration) * 100, steps: [frame], failed: frame.status === 'failed' });
+    }
+  }
+  return markers;
+}

artemis/agents/flash/runner.py

@@ -1071,6 +1071,8 @@ class FlashRunner:
         pending_notices: list[str] = []
         no_tool_attempts = 0
         no_tool_limit = settings.ARTEMIS_FLASH_MAX_NO_PROGRESS_ATTEMPTS
+        fallback_llm = None
+        using_tool_fallback = False
 
         while limit is None or turns < limit:
             control = getattr(self.ctx, "task_control", None)
@@ -1080,6 +1082,9 @@ class FlashRunner:
                     on_resume=getattr(self.ctx, "on_task_resume", None),
                 )
                 if resumed:
+                    using_tool_fallback = False
+                    no_tool_attempts = 0
+                    self._llm = llm
                     (
                         shot_path,
                         current_pre_screenshot_bytes,
@@ -1131,14 +1136,18 @@ class FlashRunner:
 
             if isinstance(control, TaskControl):
                 interrupted, response = await control.run_interruptibly(
-                    self._invoke_model(llm, current_tools, messages),
+                    self._invoke_model(
+                        fallback_llm if using_tool_fallback else llm, current_tools, messages
+                    ),
                     on_pause=getattr(self.ctx, "on_task_pause", None),
                     on_resume=getattr(self.ctx, "on_task_resume", None),
                 )
             else:
                 interrupted, response = (
                     False,
-                    await self._invoke_model(llm, current_tools, messages),
+                    await self._invoke_model(
+                        fallback_llm if using_tool_fallback else llm, current_tools, messages
+                    ),
                 )
 
             if interrupted or (
@@ -1146,6 +1155,9 @@ class FlashRunner:
             ):
                 # Do not dispatch a stale action or final answer from a prompt
                 # assembled before the instruction arrived.
+                using_tool_fallback = False
+                no_tool_attempts = 0
+                self._llm = llm
                 (
                     shot_path,
                     current_pre_screenshot_bytes,
@@ -1186,6 +1198,9 @@ class FlashRunner:
 
             tool_calls = self._resolve_tool_calls(response, raw_text)
             if isinstance(control, TaskControl) and has_pending_instruction(control.directory):
+                using_tool_fallback = False
+                no_tool_attempts = 0
+                self._llm = llm
                 (
                     shot_path,
                     current_pre_screenshot_bytes,
@@ -1207,15 +1222,27 @@ class FlashRunner:
                     final_report = {"status": "failed", "explanation": raw_text}
                     break
                 no_tool_attempts += 1
-                if no_tool_attempts >= no_tool_limit:
+                active_limit = (
+                    settings.ARTEMIS_TOOL_FALLBACK_MAX_NO_PROGRESS_ATTEMPTS
+                    if using_tool_fallback
+                    else no_tool_limit
+                )
+                if no_tool_attempts >= active_limit:
+                    if using_tool_fallback:
                         final_report = {
                             "status": "failed",
                             "explanation": (
-                            f"模型连续 {no_tool_attempts} 次未调用工具，"
+                                f"首选模型连续 {no_tool_limit} 次、兜底模型连续 "
-                            "已停止任务以避免空转和重复计费；未执行新的设备动作。"
+                                f"{no_tool_attempts} 次未调用工具，已停止任务；"
+                                "未执行新的设备动作。"
                             ),
                         }
                         break
+                    fallback_llm = get_llm(self.ctx, name="operator", use_fallback=True)
+                    using_tool_fallback = True
+                    no_tool_attempts = 0
+                    logger.warning("Flash 首选模型未调用工具，当前步骤切换到兜底模型")
+                    pending_notices.append("首选模型连续未调用工具；本步骤改用兜底模型。")
                 pending_notices.append(_NO_TOOL_CALL_NOTICE)
                 # Keep the observation and any injected user instruction, but
                 # do not feed the model its own unproductive prose on retry.
@@ -1224,6 +1251,10 @@ class FlashRunner:
                 continue
 
             no_tool_attempts = 0
+            if using_tool_fallback:
+                # A tool-producing turn completes the fallback's scope. The next
+                # observation goes back to the configured primary model.
+                self._llm = fallback_llm
             # Process tool calls
             turn = _TurnRecord()
             (
@@ -1244,6 +1275,10 @@ class FlashRunner:
                 injected=injected,
                 native_text=native_text,
             )
+            if using_tool_fallback and any(status == "success" for _, status, _ in turn.actions):
+                using_tool_fallback = False
+                self._llm = llm
+                pending_notices.append("兜底模型已完成本步骤；后续步骤恢复首选模型。")
             ledger.stage_turn(messages[turn_base:])
             previous_turn = turn
             if final_report_from_calls is not None:

artemis/agents/operator/operator.py

@@ -508,6 +508,9 @@ class OperatorNode:
         plan_gate_bounced = False
         no_progress_attempts = 0
         no_progress_limit = settings.ARTEMIS_OPERATOR_MAX_NO_PROGRESS_ATTEMPTS
+        fallback_limit = settings.ARTEMIS_TOOL_FALLBACK_MAX_NO_PROGRESS_ATTEMPTS
+        active_llm = base_llm
+        using_tool_fallback = False
 
         for iteration in range(max_iterations):
             control = getattr(self.ctx, "task_control", None)
@@ -532,7 +535,7 @@ class OperatorNode:
                     )
                 )
 
-            bound_llm = base_llm.bind_tools(tools=traced_tools)
+            bound_llm = active_llm.bind_tools(tools=traced_tools)
             interrupted, response = await self._await_or_pause(
                 invoke_llm_with_timeout_message(acomplete(bound_llm, current_messages))
             )
@@ -607,11 +610,25 @@ class OperatorNode:
 
             if not response.tool_calls:
                 no_progress_attempts += 1
-                if no_progress_attempts >= no_progress_limit:
+                if no_progress_attempts >= (
+                    fallback_limit if using_tool_fallback else no_progress_limit
+                ):
+                    if using_tool_fallback:
                         raise OperatorNoProgressError(
-                        f"模型连续 {no_progress_attempts} 次未提供可执行的工具调用，"
+                            f"首选模型连续 {no_progress_limit} 次、兜底模型连续 "
-                        "已停止任务以避免空转；未执行新的设备动作。"
+                            f"{no_progress_attempts} 次未提供可执行的工具调用；"
+                            "已停止任务以避免空转。"
                         )
+                    active_llm = get_llm(ctx=self.ctx, name="operator", use_fallback=True)
+                    using_tool_fallback = True
+                    no_progress_attempts = 0
+                    logger.warning("Operator 首选模型未调用工具，当前步骤切换到兜底模型")
+                    current_messages.append(
+                        SystemMessage(
+                            content="本步骤已切换兜底模型。请使用工具完成操作或明确报告阻碍。"
+                        )
+                    )
+                    continue
                 logger.warning("LLM returned no tool call; requesting a concrete tool call.")
                 current_messages.append(response)
                 current_messages.append(
@@ -931,11 +948,25 @@ class OperatorNode:
 
             if validation_errors:
                 no_progress_attempts += 1
-                if no_progress_attempts >= no_progress_limit:
+                if no_progress_attempts >= (
+                    fallback_limit if using_tool_fallback else no_progress_limit
+                ):
+                    if using_tool_fallback:
                         raise OperatorNoProgressError(
-                        f"模型连续 {no_progress_attempts} 次未提供可执行的工具调用，"
+                            f"首选模型连续 {no_progress_limit} 次、兜底模型连续 "
-                        "工具参数校验仍失败；已停止任务，未执行被拒绝的设备动作。"
+                            f"{no_progress_attempts} 次未提供可执行的工具调用，"
+                            "工具参数校验仍失败；已停止任务。"
                         )
+                    active_llm = get_llm(ctx=self.ctx, name="operator", use_fallback=True)
+                    using_tool_fallback = True
+                    no_progress_attempts = 0
+                    logger.warning("Operator 首选模型工具调用校验失败，当前步骤切换到兜底模型")
+                    current_messages.append(
+                        SystemMessage(
+                            content="本步骤已切换兜底模型。请修正上面的工具参数后再调用。"
+                        )
+                    )
+                    continue
                 logger.warning("Some action calls failed validation. Feeding back to LLM.")
                 continue
             no_progress_attempts = 0

artemis/config/agent.py

@@ -835,6 +835,13 @@ class ProProfileConfig(BaseModel):
     model_config = {"extra": "allow"}
 
 
+class ScreenshotConfig(BaseModel):
+    """Limit screenshots sent to agents while retaining physical device coordinates."""
+
+    enabled: bool = Field(default_factory=lambda: settings.ARTEMIS_SCREENSHOT_LIMIT_ENABLED)
+    max_long_edge: int = Field(default=2048, ge=256, le=8192)
+
+
 class AgentGlobalConfig(BaseModel):
     """Global configuration parsed from artemis.jsonc / agent_config.json."""
 
@@ -889,6 +896,10 @@ class AgentGlobalConfig(BaseModel):
         default_factory=MemoryConfig,
         description="Unified step-memory runtime and transcript scrub options.",
     )
+    screenshot: ScreenshotConfig = Field(
+        default_factory=ScreenshotConfig,
+        description="Agent screenshot size limit; device/UI coordinates remain native.",
+    )
 
     model_config = {"extra": "allow"}
 

artemis/config/settings.py

@@ -100,6 +100,11 @@ class Settings(BaseSettings):
     OPENAI_TIMEOUT_SECONDS: float = Field(default=300.0, gt=0)
     OPENAI_MAX_RETRIES: int = Field(default=5, ge=0, le=10)
     OPENAI_RETRY_INTERVAL_SECONDS: float = Field(default=10.0, ge=0, le=300)
+    OPENAI_RATE_LIMIT_RETRY_SECONDS: float = Field(default=60.0, ge=0, le=3600)
+    OPENAI_TOOL_FALLBACK_MODEL: str = Field(default="gpt-6-luna", min_length=1)
+    OPENAI_TOOL_FALLBACK_REASONING_EFFORT: str = "high"
+    OPENAI_TOOL_FALLBACK_MAX_RETRIES: int = Field(default=3, ge=0, le=10)
+    ARTEMIS_TOOL_FALLBACK_MAX_NO_PROGRESS_ATTEMPTS: int = Field(default=3, ge=1, le=10)
     OPENAI_REASONING_EFFORT: str = "high"
     ARTEMIS_CREDENTIAL_TIMEOUT_SECONDS: float = Field(default=12.0, gt=0)
     ARTEMIS_MODEL_CACHE_TTL_SECONDS: float = Field(default=10.0, ge=0)
@@ -112,6 +117,7 @@ class Settings(BaseSettings):
     ARTEMIS_RECORDING_ENABLED: bool = False
     ARTEMIS_OUTPUTTER_ENABLED: bool = True
     ARTEMIS_STEP_SUMMARIZER_ENABLED: bool = True
+    ARTEMIS_SCREENSHOT_LIMIT_ENABLED: bool = True
     # Consecutive Pro responses without a valid tool call before failing the task.
     ARTEMIS_OPERATOR_MAX_NO_PROGRESS_ATTEMPTS: int = Field(default=3, ge=1, le=20)
     # Consecutive Flash responses without any tool call before failing the task.

artemis/drivers/android/adb_driver.py

@@ -25,12 +25,14 @@ from artemis.clients.ui_automator_client import (
     UIAutomatorClient,
     _parse_hierarchy_xml_to_elements,
 )
+from artemis.config.agent import load_agent_config
 from artemis.config.paths import get_temp_dir
 from artemis.drivers.base import BaseDeviceDriver, KeyCode, ScreenData, SwipeDirection
 from artemis.toolchain import find_ffmpeg, find_scrcpy
 from artemis.utils.video import build_scrcpy_record_command
 from artemis.utils.ui_filter import filter_ui_hierarchy
 from artemis.utils.logger import get_logger
+from artemis.utils.screenshot_limit import limit_screenshot_long_edge
 
 logger = get_logger(__name__)
 
@@ -88,6 +90,7 @@ class AndroidAdbDriver(BaseDeviceDriver):
         self._device: AdbDevice | None = None
         self._recording_process: asyncio.subprocess.Process | None = None
         self._recording_output_path: Path | None = None
+        self._screenshot_config = load_agent_config().screenshot
 
     @property
     def device_id(self) -> str:
@@ -215,6 +218,13 @@ class AndroidAdbDriver(BaseDeviceDriver):
             if isinstance(screenshot_bytes, bytes) and screenshot_bytes
             else base64.b64decode(clean_b64)
         )
+        if self._screenshot_config.enabled:
+            limited_bytes = limit_screenshot_long_edge(
+                clean_bytes, self._screenshot_config.max_long_edge
+            )
+            if limited_bytes is not clean_bytes:
+                clean_bytes = limited_bytes
+                clean_b64 = base64.b64encode(clean_bytes).decode("utf-8")
 
         return ScreenData(
             screenshot_bytes=clean_bytes,

artemis/interfaces/cli/commands/init.py

@@ -117,6 +117,11 @@ def init_command() -> None:
             "OPENAI_TIMEOUT_SECONDS=300",
             "OPENAI_MAX_RETRIES=5",
             "OPENAI_RETRY_INTERVAL_SECONDS=10",
+            "OPENAI_RATE_LIMIT_RETRY_SECONDS=60",
+            "OPENAI_TOOL_FALLBACK_MODEL=gpt-6-luna",
+            "OPENAI_TOOL_FALLBACK_REASONING_EFFORT=high",
+            "OPENAI_TOOL_FALLBACK_MAX_RETRIES=3",
+            "ARTEMIS_TOOL_FALLBACK_MAX_NO_PROGRESS_ATTEMPTS=3",
             "ARTEMIS_VIDEO_ENABLED=false",
             "ARTEMIS_TRACES_DIR=./traces",
             "",

artemis/llm/reliability.py

@@ -75,7 +75,7 @@ def extract_status_code(error: BaseException) -> int | None:
         return None
 
 
-_RATE_LIMIT_MARKERS = ("rate limit", "quota", "resource_exhausted")
+_RATE_LIMIT_MARKERS = ("rate limit", "too many requests", "quota", "resource_exhausted")
 _UNAVAILABLE_MARKERS = ("unavailable", "overloaded", "high demand", "service unavailable")
 _AUTH_MARKERS = ("unauthorized", "forbidden", "api key", "credential")
 # "deadline exceeded"/"deadline_exceeded" cover google-genai SDK timeouts that
@@ -196,6 +196,10 @@ def retry_policy_for(category: FailureCategory | str) -> RetryPolicy:
         return _DEFAULT_RETRY_POLICIES[category]
     from artemis.config.settings import settings
 
+    if category is FailureCategory.RATE_LIMIT:
+        return _configured_policy(
+            settings.OPENAI_MAX_RETRIES, settings.OPENAI_RATE_LIMIT_RETRY_SECONDS
+        )
     return _configured_policy(settings.OPENAI_MAX_RETRIES, settings.OPENAI_RETRY_INTERVAL_SECONDS)
 
 

artemis/main.py

@@ -15,25 +15,36 @@
 """Backward compatibility entrypoint for artemis CLI."""
 
 import sys
+
+import typer
+
 from artemis.interfaces.cli.main import app
+from artemis.interfaces.cli.main import app as _typer_app
+
+
+def _root_tokens() -> frozenset[str]:
+    """Read command names and root flags from the registered CLI."""
+    group = typer.main.get_command(_typer_app)
+    tokens: set[str] = {"-h"}
+    tokens.update(getattr(group, "commands", {}) or {})
+    tokens.update(group.get_help_option_names(group.context_class(group)))
+    for param in group.params:
+        tokens.update(getattr(param, "opts", ()))
+        tokens.update(getattr(param, "secondary_opts", ()))
+    return frozenset(tokens)
+
+
+def normalize_argv(argv: list[str]) -> list[str]:
+    """Keep implicit ``run`` for a goal while preserving registered commands."""
+    if len(argv) > 1 and argv[1] not in _root_tokens():
+        return [argv[0], "run", *argv[1:]]
+    return list(argv)
 
 
 def cli():
     # If invoked directly as python -m artemis.main without subcommand 'run',
     # check if first argument is a goal rather than a subcommand
-    if len(sys.argv) > 1 and sys.argv[1] not in (
+    sys.argv[:] = normalize_argv(sys.argv)
-        "run",
-        "init",
-        "doctor",
-        "batch",
-        "bench",
-        "mcp",
-        "server",
-        "trace",
-        "--help",
-        "-h",
-    ):
-        sys.argv.insert(1, "run")
     app()
 
 

artemis/resources/config/artemis.jsonc

@@ -13,7 +13,14 @@
   "presets": {},
   "nodes": {
     "planner": {},
-    "operator": {},
+    "operator": {
+      "fallback": {
+        "provider": "openai",
+        "model": "${env:OPENAI_TOOL_FALLBACK_MODEL:-gpt-6-luna}",
+        "reasoning_effort": "${env:OPENAI_TOOL_FALLBACK_REASONING_EFFORT:-high}",
+        "max_retries": "${env:OPENAI_TOOL_FALLBACK_MAX_RETRIES:-3}"
+      }
+    },
     "video_analyzer": {},
     "object_detector": {},
     "explorer": {},
@@ -27,6 +34,10 @@
     "checker": {}
   },
   "agent": {
+    "screenshot": {
+      "enabled": "${env:ARTEMIS_SCREENSHOT_LIMIT_ENABLED:-true}",
+      "max_long_edge": 2048
+    },
     "flash": {
       "max_turns": 0,
       "explorer_mode": "flash",

artemis/runtime/daemon_client.py

@@ -66,7 +66,7 @@ def is_daemon_running(
 def is_artemis_daemon(
     host: str = DEFAULT_DAEMON_HOST,
     port: int = DEFAULT_DAEMON_PORT,
-    timeout: float = 0.5,
+    timeout: float = 2.0,
 ) -> bool:
     """Identity probe: is the process on ``host:port`` the Artemis daemon?
 

artemis/utils/screenshot_limit.py

@@ -0,0 +1,23 @@
+"""Prepare Android screenshots for model input without changing device coordinates."""
+
+from io import BytesIO
+
+from PIL import Image
+
+
+def limit_screenshot_long_edge(image_bytes: bytes, max_long_edge: int) -> bytes:
+    """Downscale oversized screenshots; keep smaller images byte-for-byte unchanged."""
+    try:
+        with Image.open(BytesIO(image_bytes)) as image:
+            long_edge = max(image.size)
+            if long_edge <= max_long_edge:
+                return image_bytes
+            scale = max_long_edge / long_edge
+            size = tuple(max(1, round(side * scale)) for side in image.size)
+            resized = image.convert("RGB").resize(size, Image.Resampling.LANCZOS)
+            output = BytesIO()
+            resized.save(output, format="JPEG", quality=80, optimize=True)
+            return output.getvalue()
+    except OSError:
+        # Preserve the capture path's previous behavior for invalid placeholder images.
+        return image_bytes

artemis/utils/video.py

@@ -615,9 +615,9 @@ async def render_timeline_clip(
             )
         labels.append(f"[{label}]")
     if len(labels) == 1:
-        filter_parts.append(f"{labels[0]}null[outv]")
+        filter_parts.append(f"{labels[0]}fps={fps}[outv]")
     else:
-        filter_parts.append(f"{''.join(labels)}concat=n={len(labels)}:v=1:a=0[outv]")
+        filter_parts.append(f"{''.join(labels)}concat=n={len(labels)}:v=1:a=0,fps={fps}[outv]")
 
     command.extend(
         [

config/artemis.jsonc

@@ -13,7 +13,14 @@
   "presets": {},
   "nodes": {
     "planner": {},
-    "operator": {},
+    "operator": {
+      "fallback": {
+        "provider": "openai",
+        "model": "${env:OPENAI_TOOL_FALLBACK_MODEL:-gpt-6-luna}",
+        "reasoning_effort": "${env:OPENAI_TOOL_FALLBACK_REASONING_EFFORT:-high}",
+        "max_retries": "${env:OPENAI_TOOL_FALLBACK_MAX_RETRIES:-3}"
+      }
+    },
     "video_analyzer": {},
     "object_detector": {},
     "explorer": {},
@@ -27,6 +34,10 @@
     "checker": {}
   },
   "agent": {
+    "screenshot": {
+      "enabled": "${env:ARTEMIS_SCREENSHOT_LIMIT_ENABLED:-true}",
+      "max_long_edge": 2048
+    },
     "flash": {
       "max_turns": 0,
       "explorer_mode": "flash",

docs/ai-automation.md

@@ -42,7 +42,9 @@ MCP 同步会识别已安装客户端的配置目录，为缺少 ARTEMIS 条目
 
 报告按任务或批次归档到 `artifacts/mcp_exports/`，包含本地路径及服务下载链接；删除批次时会清理对应导出副本。运行中导出是快照；执行完成不等于验证通过，报告会保留未验证、失败、跳过与中断状态。
 
-模型默认使用 `mimo-v2.6-flash`，也预设 `gpt-5.6-luna` 并支持自定义。思考强度最高支持 `max`，默认 `high`，选“由模型默认”不发送该参数；接口不支持时明确报错。单次请求 300 秒，首次失败后最多再试 5 次，每次等待 10 秒，SDK 不叠加重试。耗尽后沿用任务暂停/恢复机制，鉴权或非法参数立即失败。视频分析与录屏是独立开关，默认均关闭。每项批量用例可覆盖模式、模型、设备、录屏和超时等配置，不影响其他任务。暂停先返回 `pausing`，确认状态为 `task_paused` 才表示设备已释放；Pro 等待模型回复或只读诊断时可及时中断，恢复后重新观察设备。AI 可用 `mobile_manage_task(action="inject_instruction")` 提交指导并恢复；运行中的模型思考会中断并重新观察设备，已经开始的设备动作会安全结束。用 `guidance_history` 核对指导是否被领取、在任务流程中核对实际执行情况，或用 `resume` 单独恢复。
+模型默认使用 `mimo-v2.6-flash`，预设 `gpt-5.6-luna`、`gpt-6-luna`、`mimo-v2.6-pro`，并支持自定义。思考强度最高支持 `max`，默认 `high`，选“由模型默认”不发送该参数；接口不支持时明确报错。单次请求 300 秒，首次失败后最多再试 5 次，普通错误每次等待 10 秒，429 每次等待 60 秒，SDK 不叠加重试。首选模型连续 3 次没有有效工具调用时，当前步骤改用 `gpt-6-luna`（high），最多再试 3 次；下一步重新使用首选模型。兜底模型及次数可通过 `.env.example` 中的 `OPENAI_TOOL_FALLBACK_*` 和 `ARTEMIS_TOOL_FALLBACK_MAX_NO_PROGRESS_ATTEMPTS` 配置。请求耗尽后沿用任务暂停/恢复机制，鉴权或非法参数立即失败。视频分析与录屏是独立开关，默认均关闭。每项批量用例可覆盖模式、模型、设备、录屏和超时等配置，不影响其他任务。暂停先返回 `pausing`，确认状态为 `task_paused` 才表示设备已释放；Pro 等待模型回复或只读诊断时可及时中断，恢复后重新观察设备。AI 可用 `mobile_manage_task(action="inject_instruction")` 提交指导并恢复；运行中的模型思考会中断并重新观察设备，已经开始的设备动作会安全结束。用 `guidance_history` 核对指导是否被领取、在任务流程中核对实际执行情况，或用 `resume` 单独恢复。
+
+429 等待、兜底模型及次数也可在网页“模型配置 → 稳定性与兜底模型”或通过已授权的 `mobile_config(section="model")` 配置。批次 ZIP 还收录每项任务已保存的步骤截图和录屏。
 
 批次任务在队列中使用“用例编号 + 用例名称”显示，提示词仍是模型实际收到的内容。任务历史展示失败原因、完成时间和总耗时；导出全部资料时以用例编号命名目录。删除非活跃批次会同步清理其任务记录、步骤、截图、录屏及生成的报告，删除后不能通过旧 `request_id` 重复执行。
 

docs/automation-workspace.md

@@ -24,7 +24,7 @@
 
 服务重启后保留批次，等待用户继续未开始的用例。正在执行或暂停的任务标记中断，不自动重做；“复制失败项为新批次”保留原记录并产生新任务编号。
 
-每个任务结束后生成所选格式报告。批次结束后生成汇总及每项任务报告的 ZIP；失败、跳过和中断任务也会列入。暂停时下载的是当前快照。录屏与 AI 视频分析分别配置，录屏默认关闭；仅开启分析时，录制内容用于本次分析，结束后不保留下载文件。暂停期间不录制其他任务的设备画面。
+每个任务结束后生成所选格式报告。批次 ZIP 包含汇总、每项任务的报告和已保存的有序步骤截图；已开启录屏且文件存在时，还会按任务收录录屏片段。失败、跳过和中断任务也会列入。暂停时下载的是当前快照。录屏与 AI 视频分析分别配置，录屏默认关闭；仅开启分析时，录制内容用于本次分析，结束后不保留下载文件。暂停期间不录制其他任务的设备画面。
 
 队列中批量任务按“用例编号 + 测试用例”显示，悬停可看完整提示词；失败原因、完成时间及总耗时在历史中可查看。单任务“下载全部资料”以用例编号命名压缩包内目录。删除已结束的批次会同步删除关联任务记录、步骤、截图、录屏、报告和 MCP 导出副本；正在执行或排队的批次不能删除。
 

docs/releases/1.3.0.md

@@ -0,0 +1,15 @@
+# ARTEMIS-Eapil 1.3.0
+
+## 更新
+
+- 截图默认限制长边为 2048 像素，可在网页或任务 JSON 中关闭限制，改用设备原始分辨率。
+- 首选模型连续 3 次未完成工具调用时，当前步骤可交给默认的 `gpt-6-luna`（high）备用模型继续；下个步骤恢复首选模型。HTTP 429 默认等待 60 秒再重试。
+- 批次资料包包含各任务的报告、步骤截图和已有录屏。下载前显示打包进度、耗时和预计剩余时间；已完成的资料包直接复用。
+- 录像时间轴显示可点击的关键步骤，便于直接跳转复盘。
+- 优化服务状态探测、CLI 命令识别及视频拼接后的帧率处理。
+
+## 安装
+
+下载附件 `artemis-eapil-1.3.0-source.zip`，解压后运行 `start.bat`；国内网络使用 `start-cn.bat`。附件含可继续更新的 Git 仓库。已有安装可通过启动脚本更新；升级后重启服务、刷新网页，并重新加载 MCP。
+
+验证：Python 默认测试、Ruff、质量基线、核心类型检查、前端测试及构建通过。

mcp_server/tools/automation.py

@@ -19,6 +19,11 @@ MODEL_FIELDS = {
     "timeout_seconds",
     "max_retries",
     "retry_interval_seconds",
+    "rate_limit_retry_seconds",
+    "tool_fallback_model",
+    "tool_fallback_reasoning_effort",
+    "tool_fallback_max_retries",
+    "tool_fallback_no_progress_attempts",
     "reasoning_effort",
 }
 CAPABILITY_FIELDS = {"video_enabled", "outputter_enabled", "step_summarizer_enabled"}
@@ -122,7 +127,10 @@ async def mobile_config(
     Only set persist_defaults=true when the user requests saved/global configuration.
     model changes: model (preset or custom), base_url, reasoning_effort
     (default/none/minimal/low/medium/high/xhigh/max), timeout_seconds, max_retries,
-    retry_interval_seconds. 'default' omits reasoning; unsupported values fail visibly.
+    retry_interval_seconds, rate_limit_retry_seconds, tool_fallback_model,
+    tool_fallback_reasoning_effort, tool_fallback_max_retries,
+    tool_fallback_no_progress_attempts. 'default' omits reasoning;
+    unsupported values fail visibly.
     capabilities: video_enabled (recording + analysis), outputter_enabled,
     step_summarizer_enabled. reports: formats=['docx','xlsx'] (at least one).
     API keys are never accepted or returned: use the Web UI or .env for credentials.

pyproject.toml

@@ -14,7 +14,7 @@
 
 [project]
 name = "artemis"
-version = "1.2.3"
+version = "1.3.0"
 description = "Autonomous AI Assistant for Mobile Automation"
 
 authors = [

skills/artemis-automation/SKILL.md

@@ -19,7 +19,7 @@ description: 使用 ARTEMIS MCP 在 Android 真机或模拟器上完成自然语
 ## 结果、证据与交付
 
 - 执行状态与业务判定分开：`completed` 只说明执行结束，不等于测试通过。检查工具返回的验证摘要、预期/实际结果，以及 `mobile_inspect_trace` 的步骤/截图。证据不足标记“未验证”；前置条件不满足说明阻塞原因，不能编造通过结论。
-- 用 `mobile_export_report(session_id=...)` 获取单任务报告；`formats=["screenshots"]` 获取排序截图，`["video"]` 获取保存的录屏分段，`["all"]` 获取 Word、Excel 和所有可用资料的 ZIP。批次传 `batch_id` 获取汇总和各项任务报告的 ZIP。未指定格式时使用保存的 Word/Excel 偏好。执行中的导出只是当前快照。
+- 用 `mobile_export_report(session_id=...)` 获取单任务报告；`formats=["screenshots"]` 获取排序截图，`["video"]` 获取保存的录屏分段，`["all"]` 获取 Word、Excel 和所有可用资料的 ZIP。批次传 `batch_id` 获取汇总、各项任务报告、步骤截图及已有录屏的 ZIP。未指定格式时使用保存的 Word/Excel 偏好。执行中的导出只是当前快照。
 - 工具返回 MCP 主机上的绝对文件路径和下载 URL。向用户交付真实生成的文件链接，并说明完成、失败、未验证、跳过数量及重要限制。不要只说“报告已生成”而不交付文件。
 - 用 `mobile_search_history` 按名称、目标、编号或标签搜索全部历史；日期先按用户时区转换为 Unix 秒，`start` 含边界、`end` 不含边界。按返回的 `total/page/size` 翻页，不能把第一页当作全部历史。
 - `mobile_inspect_trace` 是只读步骤证据查看；其中历史上下文的 replay 术语不表示重新执行设备动作。
@@ -27,10 +27,10 @@ description: 使用 ARTEMIS MCP 在 Android 真机或模拟器上完成自然语
 ## 配置和恢复边界
 
 - `mobile_config` 读取非敏感设置。只有用户明确要求保存或修改默认配置时，才传 `changes` 与 `persist_defaults=true`；这些设置影响后续任务，不能为一次临时需求擅自改变以后所有任务。
-- 模型名称支持预设 `gpt-5.6-luna`、`mimo-v2.6-flash` 和自定义名称，取决于用户配置的 Responses 网关。思考强度最高支持 `max`，默认 `high`、单次请求 300 秒、失败后最多重试 5 次、固定间隔 10 秒；默认关闭视频分析与录屏。不根据模型名称猜测网关能力。
+- 模型名称支持预设 `gpt-5.6-luna`、`gpt-6-luna`、`mimo-v2.6-flash`、`mimo-v2.6-pro` 和自定义名称，取决于用户配置的 Responses 网关。思考强度最高支持 `max`，默认 `high`、单次请求 300 秒、失败后最多重试 5 次；普通错误每次等待 10 秒，429 每次等待 60 秒。首选模型连续 3 次未形成有效工具调用时，当前步骤改用 `gpt-6-luna`（high），兜底最多再尝试 3 次；下一步恢复首选模型。默认关闭视频分析与录屏。不根据模型名称猜测网关能力。
 - 强度选 `default` 表示不发送该参数，和 `none`（显式要求关闭）不同。接口拒绝强度参数时明确呈现失败，先询问用户是否改用 `default`，不能静默降级或换模型。
 - API Key 只通过网页或 `.env` 配置。不要请求用户把 key 发到聊天中，不读取/回显已有 key。保存后新任务生效；直接编辑环境文件后需重启相应服务，运行中的任务保留自己的配置。
-- 录屏与 AI 视频分析分别配置。批量任务的 `task_config` 可覆盖单项，不修改全局设置；只开视频分析时录制内容不作为可下载文件保存。暂停时停止录屏，恢复后按片段继续，不能把其他任务画面混入该任务资料。
+- 429 等待和兜底参数可在网页模型配置或经用户明确授权的 `mobile_config(section="model", persist_defaults=true)` 保存；当前任务不热切换。录屏与 AI 视频分析分别配置。批量任务的 `task_config` 可覆盖单项，不修改全局设置；只开视频分析时录制内容不作为可下载文件保存。暂停时停止录屏，恢复后按片段继续，不能把其他任务画面混入该任务资料。
 - 请求重试是模型调用恢复，不是测试用例重跑。批次默认失败继续、不自动重跑；服务重启后保留状态并暂停，用户明确要求后才继续或复制失败项。
 - 批次暂停只停止后续派发；当前任务等待指导时用 `mobile_manage_task(action="pause", trace_id=...)`，真正取消用 `stop`。用户明确要求删除批次记录时先 `mobile_batch(action="get")` 核对编号，再用 `delete`；执行中/排队中的批次不能删，删除会清除该批次的任务历史、报告和文件。服务重启后运行/暂停的任务标记中断，不自动重跑。停止可能保留已经产生的业务效果，不声称自动回滚。
 

skills/artemis-automation/references/batches.md

@@ -60,6 +60,6 @@
 
 ## 报告
 
-`mobile_export_report(batch_id=...)` 下载批次 ZIP，包含汇总和各项报告，格式由创建批次时的设置决定。单任务传 `session_id`（单任务 MCP 的 `trace_id`）以及可选 `formats=["docx"]`、`["screenshots"]`、`["video"]` 或 `["all"]`。默认格式通过 `mobile_config(section="reports")` 查看。录屏与 AI 视频分析独立，只有保存了录屏才可下载片段。
+`mobile_export_report(batch_id=...)` 下载批次 ZIP，包含汇总、各项报告和有序步骤截图；任务保存了录屏时，还包含对应录屏片段。报告格式由创建批次时的设置决定。单任务传 `session_id`（单任务 MCP 的 `trace_id`）以及可选 `formats=["docx"]`、`["screenshots"]`、`["video"]` 或 `["all"]`。默认格式通过 `mobile_config(section="reports")` 查看。录屏与 AI 视频分析独立，只有保存了录屏才可下载片段。
 
 结果文件在 MCP 主机的 `artifacts/mcp_exports/`。不要把 Office 文件内容当作文本塞入上下文；先检查工具返回的结构化结论，需要进一步审阅时再用宿主具备的文档工具打开文件。失败和未验证项也必须交付，保持原始证据。

tests/unit/admin_console/test_automation.py

@@ -3,6 +3,7 @@
 from io import BytesIO
 import json
 import sqlite3
+import threading
 import time
 from unittest.mock import AsyncMock, Mock
 from uuid import UUID
@@ -108,6 +109,63 @@ def test_invalid_archive_and_empty_template():
     assert not task_import.parse_workbook(task_import.template())["valid"]
 
 
+def test_batch_archive_preparation_reports_progress_and_reuses_ready_file(
+    client, isolated, monkeypatch
+):
+    batch = service.create_batch(payload())
+    batch_id = batch["id"]
+    started = threading.Event()
+    release = threading.Event()
+    calls = []
+
+    def slow_export(identifier, progress=None):
+        calls.append(identifier)
+        progress(0, 2, "正在打包任务 1/2")
+        started.set()
+        assert release.wait(5)
+        progress(2, 2, "正在完成压缩包")
+        path = reports.EXPORT_ROOT / "batches" / identifier / "批次报告.zip"
+        path.parent.mkdir(parents=True, exist_ok=True)
+        path.write_bytes(b"prepared archive")
+        return path
+
+    monkeypatch.setattr(reports, "export_batch", slow_export)
+    base = f"/api/automation/batches/{batch_id}/archive"
+    try:
+        response = client.post(base + "/prepare")
+        assert response.status_code == 200
+        assert response.json()["status"] == "preparing"
+        assert started.wait(2)
+        progress = client.get(base + "/status").json()
+        assert progress["total"] == 2
+        assert progress["stage"] == "正在打包任务 1/2"
+        assert client.get(base).status_code == 409
+    finally:
+        release.set()
+    for _ in range(50):
+        if client.get(base + "/status").json()["status"] == "ready":
+            break
+        time.sleep(0.02)
+    assert client.get(base + "/status").json()["status"] == "ready"
+    assert client.get(base).content == b"prepared archive"
+    assert calls == [batch_id]
+
+
+def test_completed_batch_reuses_existing_archive(client, isolated, monkeypatch):
+    batch = service.create_batch(payload())
+    batch_id = batch["id"]
+    isolated.put("batch", batch_id, {**batch, "status": "completed", "report_ready": True})
+    path = reports.EXPORT_ROOT / "batches" / batch_id / "批次报告.zip"
+    path.parent.mkdir(parents=True)
+    path.write_bytes(b"existing archive")
+    exporter = Mock(side_effect=AssertionError("archive must not be rebuilt"))
+    monkeypatch.setattr(reports, "export_batch", exporter)
+    base = f"/api/automation/batches/{batch_id}/archive"
+    assert client.post(base + "/prepare").json()["status"] == "ready"
+    assert client.get(base).content == b"existing archive"
+    exporter.assert_not_called()
+
+
 def test_preview_does_not_enqueue(client, isolated):
     response = client.post(
         "/api/automation/import/preview",
@@ -318,6 +376,38 @@ async def test_fail_fast_includes_skipped_reports(isolated, monkeypatch):
         assert sum(n.endswith(".docx") for n in z.namelist()) == 3
 
 
+def test_batch_archive_contains_each_task_media_when_present(isolated, monkeypatch, tmp_path):
+    batch = service.create_batch(payload())
+    first, second = isolated.jobs(batch["id"])
+    image1 = tmp_path / "first.png"
+    image2 = tmp_path / "second.png"
+    video = tmp_path / "recording.mp4"
+    image1.write_bytes(b"first screenshot")
+    image2.write_bytes(b"second screenshot")
+    video.write_bytes(b"saved recording")
+    images = {first["session_id"]: image1, second["session_id"]: image2}
+    monkeypatch.setattr(
+        reports,
+        "session_screenshots",
+        lambda report: [(images[report["id"]], "0001_步骤0001_操作前.png")],
+    )
+    monkeypatch.setattr(
+        reports,
+        "session_videos",
+        lambda report: (
+            [(video, "0001_recording.mp4")] if report["id"] == first["session_id"] else []
+        ),
+    )
+
+    archive = reports.export_batch(batch["id"])
+    with zipfile.ZipFile(archive) as z:
+        names = z.namelist()
+        assert len([name for name in names if "/步骤截图/" in name]) == 2
+        assert len([name for name in names if "/任务录屏/" in name]) == 1
+        assert all(name.startswith("任务报告/") for name in names if "步骤截图" in name)
+        assert z.read(next(name for name in names if "/任务录屏/" in name)) == b"saved recording"
+
+
 @pytest.mark.asyncio
 async def test_report_failure_does_not_strand_job(isolated, monkeypatch):
     job = isolated.jobs(service.create_batch(payload())["id"])[0]

tests/unit/admin_console/test_model_settings.py

@@ -23,10 +23,16 @@ def isolated_settings(monkeypatch, tmp_path):
         "OPENAI_TIMEOUT_SECONDS",
         "OPENAI_MAX_RETRIES",
         "OPENAI_RETRY_INTERVAL_SECONDS",
+        "OPENAI_RATE_LIMIT_RETRY_SECONDS",
+        "OPENAI_TOOL_FALLBACK_MODEL",
+        "OPENAI_TOOL_FALLBACK_REASONING_EFFORT",
+        "OPENAI_TOOL_FALLBACK_MAX_RETRIES",
+        "ARTEMIS_TOOL_FALLBACK_MAX_NO_PROGRESS_ATTEMPTS",
         "OPENAI_REASONING_EFFORT",
         "ARTEMIS_VIDEO_ENABLED",
         "ARTEMIS_OUTPUTTER_ENABLED",
         "ARTEMIS_STEP_SUMMARIZER_ENABLED",
+        "ARTEMIS_SCREENSHOT_LIMIT_ENABLED",
     ]
     for key in keys:
         monkeypatch.setattr(settings, key, getattr(settings, key))
@@ -42,6 +48,11 @@ def test_save_reload_and_blank_key_preserves_secret(isolated_settings):
         api_key="new-test-secret",
         timeout_seconds=75,
         max_retries=2,
+        rate_limit_retry_seconds=90,
+        tool_fallback_model="custom/fallback",
+        tool_fallback_reasoning_effort="max",
+        tool_fallback_max_retries=2,
+        tool_fallback_no_progress_attempts=4,
     )
     result = service.save_model_settings(update)
     assert result["api_key_configured"]
@@ -49,12 +60,16 @@ def test_save_reload_and_blank_key_preserves_secret(isolated_settings):
     saved = dotenv_values(isolated_settings)
     assert saved["OPENAI_API_KEY"] == "new-test-secret"
     assert saved["OPENAI_MODEL"] == "my-model"
+    assert saved["OPENAI_RATE_LIMIT_RETRY_SECONDS"] == "90.0"
+    assert saved["OPENAI_TOOL_FALLBACK_MODEL"] == "custom/fallback"
+    assert saved["ARTEMIS_TOOL_FALLBACK_MAX_NO_PROGRESS_ATTEMPTS"] == "4"
     assert saved["UNRELATED"] == "keep"
     assert "# user settings" in isolated_settings.read_text(encoding="utf-8")
     reloaded = Settings(_env_file=isolated_settings)
     assert reloaded.OPENAI_MODEL == "my-model"
     assert parse_llm_config().operator.model == "my-model"
     assert parse_llm_config().operator.timeout == 75
+    assert parse_llm_config().operator.fallback.model == "custom/fallback"
     service.save_model_settings(update.model_copy(update={"api_key": SecretStr("")}))
     assert dotenv_values(isolated_settings)["OPENAI_API_KEY"] == "new-test-secret"
 
@@ -93,21 +108,38 @@ def test_rejects_invalid_base_url(url):
 def test_capabilities_persist_and_apply_to_new_tasks(isolated_settings):
     result = service.save_capability_settings(
         service.CapabilitySettings(
-            video_enabled=False, outputter_enabled=False, step_summarizer_enabled=False
+            video_enabled=False,
+            outputter_enabled=False,
+            step_summarizer_enabled=False,
+            screenshot_limit_enabled=False,
         )
     )
     assert result.video_enabled is False
     saved = dotenv_values(isolated_settings)
     assert saved["ARTEMIS_VIDEO_ENABLED"] == "false"
+    assert saved["ARTEMIS_SCREENSHOT_LIMIT_ENABLED"] == "false"
     config = load_agent_config()
     assert config.video_analyzer.enabled is False
     assert config.pro.video_analyzer.enabled is False
     assert config.outputter.enabled is False
     assert config.flash.step_summarizer.enabled is False
+    assert config.screenshot.enabled is False
     service.save_capability_settings(service.CapabilitySettings(video_enabled=True))
     assert load_agent_config().video_analyzer.enabled is True
 
 
+def test_screenshot_json_can_override_environment(monkeypatch, tmp_path):
+    config_path = tmp_path / "artemis.jsonc"
+    config_path.write_text(
+        '{"agent": {"screenshot": {"enabled": true, "max_long_edge": 1024}}}',
+        encoding="utf-8",
+    )
+    monkeypatch.setenv("ARTEMIS_SCREENSHOT_LIMIT_ENABLED", "false")
+    screenshot = load_agent_config(config_path).screenshot
+    assert screenshot.enabled is True
+    assert screenshot.max_long_edge == 1024
+
+
 def test_failed_write_keeps_running_settings(monkeypatch, isolated_settings):
     previous = settings.OPENAI_MODEL
     monkeypatch.setattr(

tests/unit/agents/test_flash_runner_ledger.py

@@ -384,7 +384,7 @@ def _make_runner(mock_context, responses, max_turns=None):
     pending = list(responses)
 
     async def _invoke(llm, tools, messages):
-        runner.calls.append({"tools": list(tools), "messages": list(messages)})
+        runner.calls.append({"llm": llm, "tools": list(tools), "messages": list(messages)})
         return pending.pop(0)
 
     runner._invoke_model = AsyncMock(side_effect=_invoke)
@@ -647,9 +647,11 @@ async def test_flash_stops_repeated_no_tool_responses(mock_context, monkeypatch,
     from artemis.config.settings import settings
 
     monkeypatch.setattr(settings, "ARTEMIS_FLASH_MAX_NO_PROGRESS_ATTEMPTS", attempts)
-    responses = [AIMessage(content="I will click now.", tool_calls=[]) for _ in range(attempts)]
+    monkeypatch.setattr(settings, "ARTEMIS_TOOL_FALLBACK_MAX_NO_PROGRESS_ATTEMPTS", 3)
+    responses = [AIMessage(content="I will click now.", tool_calls=[]) for _ in range(attempts + 3)]
     with (
         patch("artemis.controllers.unified_controller.get_driver"),
+        patch("artemis.agents.flash.runner.get_llm", return_value=Mock()) as get_fallback,
         patch(
             "artemis.agents.flash.runner.capture_screenshot_and_parse_ui",
             AsyncMock(return_value=_INITIAL_OBSERVATION),
@@ -659,11 +661,55 @@ async def test_flash_stops_repeated_no_tool_responses(mock_context, monkeypatch,
         result = await runner.run(State(initial_goal="Open Settings"))
 
     assert result["status"] == "failed"
-    assert f"连续 {attempts} 次未调用工具" in result["explanation"]
+    assert f"连续 {attempts} 次" in result["explanation"]
-    assert runner._invoke_model.await_count == attempts
+    assert "兜底模型连续 3 次" in result["explanation"]
+    assert runner._invoke_model.await_count == attempts + 3
+    get_fallback.assert_called_once_with(mock_context, name="operator", use_fallback=True)
     runner.executor.execute.assert_not_awaited()
 
 
+@pytest.mark.asyncio
+async def test_flash_uses_fallback_for_one_tool_turn_then_primary(mock_context):
+    no_call = AIMessage(content="I will click now.")
+    observe = AIMessage(
+        content="Check the target.",
+        tool_calls=[
+            {
+                "name": "ask_explorer",
+                "args": {"question": "Find Settings"},
+                "id": "fallback-observe",
+            }
+        ],
+    )
+    click = AIMessage(
+        content="Tap Settings.",
+        tool_calls=[{"name": "click", "args": {"target": 1}, "id": "fallback-click"}],
+    )
+    fallback = Mock()
+    responses = [no_call, no_call, no_call, observe, click, _report()]
+    with (
+        patch("artemis.controllers.unified_controller.get_driver"),
+        patch("artemis.agents.flash.runner.get_llm", return_value=fallback),
+        patch(
+            "artemis.agents.flash.runner.capture_screenshot_and_parse_ui",
+            AsyncMock(return_value=_INITIAL_OBSERVATION),
+        ),
+    ):
+        runner = _make_runner(mock_context, responses, max_turns=0)
+        result = await runner.run(State(initial_goal="Open Settings"))
+
+    primary = runner.calls[0]["llm"]
+    assert [call["llm"] for call in runner.calls] == [
+        primary,
+        primary,
+        primary,
+        fallback,
+        fallback,
+        primary,
+    ]
+    assert result["status"] == "completed"
+
+
 # ---------------------------------------------------------------------------
 # _TurnRecord.result: the turn's execution result in the validator-report shape
 # ---------------------------------------------------------------------------

tests/unit/agents/test_operator_recovery.py

@@ -107,23 +107,56 @@ async def test_invalid_target_can_be_corrected_before_limit(operator, monkeypatc
 @pytest.mark.parametrize("limit", [1, 3])
 async def test_empty_responses_fail_at_configured_limit(operator, monkeypatch, limit):
     monkeypatch.setattr(settings, "ARTEMIS_OPERATOR_MAX_NO_PROGRESS_ATTEMPTS", limit)
+    monkeypatch.setattr(settings, "ARTEMIS_TOOL_FALLBACK_MAX_NO_PROGRESS_ATTEMPTS", 3)
+    fallback = Mock()
+    get_fallback = Mock(return_value=fallback)
+    monkeypatch.setattr("artemis.agents.operator.operator.get_llm", get_fallback)
     invoke = AsyncMock(return_value=AIMessage(content="I will click now."))
     monkeypatch.setattr("artemis.agents.operator.operator.acomplete", invoke)
     node, state = operator
     with pytest.raises(OperatorNoProgressError, match="未提供可执行的工具调用"):
         await node._invoke_llm_loop(Mock(), [], [], [], state)
-    assert invoke.await_count == limit
+    assert invoke.await_count == limit + 3
+    get_fallback.assert_called_once_with(ctx=node.ctx, name="operator", use_fallback=True)
+    assert invoke.call_args_list[-1].args[0] is fallback.bind_tools.return_value
+
+
+@pytest.mark.asyncio
+async def test_operator_fallback_is_scoped_to_current_step(operator, monkeypatch):
+    node, state = operator
+    primary = Mock()
+    fallback = Mock()
+    get_fallback = Mock(return_value=fallback)
+    monkeypatch.setattr("artemis.agents.operator.operator.get_llm", get_fallback)
+    invoke = AsyncMock(side_effect=[AIMessage(content="no tool")] * 3 + [click(), click()])
+    monkeypatch.setattr("artemis.agents.operator.operator.acomplete", invoke)
+
+    first = await node._invoke_llm_loop(primary, [], [], [], state)
+    second = await node._invoke_llm_loop(primary, [], [], [], state)
+
+    assert first[0] and second[0]
+    assert get_fallback.call_count == 1
+    assert [call.args[0] for call in invoke.call_args_list] == [
+        primary.bind_tools.return_value,
+        primary.bind_tools.return_value,
+        primary.bind_tools.return_value,
+        fallback.bind_tools.return_value,
+        primary.bind_tools.return_value,
+    ]
 
 
 @pytest.mark.asyncio
 async def test_invalid_calls_and_prose_share_bounded_recovery(operator, monkeypatch):
-    invoke = AsyncMock(side_effect=[click("missing"), AIMessage(content="Retry."), click("14")])
+    invoke = AsyncMock(
+        side_effect=[click("missing"), AIMessage(content="Retry."), click("14")] + [click("14")] * 3
+    )
+    monkeypatch.setattr("artemis.agents.operator.operator.get_llm", Mock(return_value=Mock()))
     monkeypatch.setattr("artemis.agents.operator.operator.acomplete", invoke)
     node, state = operator
     messages = []
     with pytest.raises(OperatorNoProgressError, match="校验仍失败"):
         await node._invoke_llm_loop(Mock(), messages, [], [], state)
-    assert invoke.await_count == 3
+    assert invoke.await_count == 6
     assert any(isinstance(m, ToolMessage) and m.status == "error" for m in messages)
     assert not any(isinstance(m, ToolMessage) and m.content == "Action Recorded" for m in messages)
 

tests/unit/mcp/test_automation_tools.py

@@ -32,6 +32,14 @@ async def test_config_requires_explicit_persistence_and_excludes_secrets(monkeyp
         "reasoning_effort": "high",
     }
 
+    await tools.mobile_config(
+        changes={"rate_limit_retry_seconds": 90, "tool_fallback_model": "gpt-6-luna"},
+        persist_defaults=True,
+    )
+    payload = request.call_args.kwargs["json"]
+    assert payload["rate_limit_retry_seconds"] == 90
+    assert payload["tool_fallback_model"] == "gpt-6-luna"
+
 
 @pytest.mark.asyncio
 async def test_preview_is_read_only_and_create_is_draft(monkeypatch, tmp_path):

tests/unit/runtime/test_server_process_lifecycle.py

@@ -225,6 +225,20 @@ async def test_api_server_status():
         assert "current_pid" in data
 
 
+@pytest.mark.asyncio
+async def test_api_server_status_does_not_scan_ports():
+    with patch(
+        "artemis.runtime.server_lifecycle.get_server_status",
+        side_effect=AssertionError("port scan"),
+    ):
+        async with AsyncClient(
+            transport=ASGITransport(app=app), base_url="http://localhost"
+        ) as client:
+            response = await client.get("/api/system/server-status")
+    assert response.status_code == 200
+    assert response.json()["running"] is True
+
+
 @pytest.mark.asyncio
 async def test_api_restart_endpoint():
     """Verify POST /api/system/restart accepts request and schedules restart."""

tests/unit/test_drivers.py

@@ -15,14 +15,18 @@
 """Unit tests for Unified Base Device Driver and implementations."""
 
 import base64
+from io import BytesIO
 from types import SimpleNamespace
 from unittest.mock import AsyncMock, MagicMock
 
 import pytest
+from PIL import Image
 
 from artemis.drivers.base import KeyCode, ScreenData, SwipeDirection
 from artemis.drivers.mock.mock_driver import MockDeviceDriver
 from artemis.drivers.android.adb_driver import AndroidAdbDriver
+from artemis.config.agent import ScreenshotConfig
+from artemis.utils.screenshot_limit import limit_screenshot_long_edge
 
 
 _ONE_PIXEL_PNG = base64.b64encode(
@@ -32,6 +36,13 @@ _ONE_PIXEL_PNG = base64.b64encode(
 ).decode("ascii")
 
 
+def test_screenshot_limit_keeps_small_and_invalid_images_unchanged():
+    small_image = base64.b64decode(_ONE_PIXEL_PNG)
+    assert limit_screenshot_long_edge(small_image, 2048) is small_image
+    invalid_image = b"not an image"
+    assert limit_screenshot_long_edge(invalid_image, 2048) is invalid_image
+
+
 @pytest.mark.asyncio
 async def test_mock_driver_actions():
     """Verify MockDeviceDriver records and performs all core actions."""
@@ -141,6 +152,36 @@ async def test_android_driver_preserves_elements_from_combined_screen_data():
     mock_ui_client.get_hierarchy.assert_not_called()
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize("enabled", [True, False])
+async def test_android_screenshot_limit_preserves_native_coordinates(enabled):
+    image = Image.new("RGB", (1200, 2600), "white")
+    buffer = BytesIO()
+    image.save(buffer, format="JPEG")
+    original = buffer.getvalue()
+    elements = [{"text": "目标", "bounds": "[100,200][300,400]"}]
+    ui_client = MagicMock()
+    ui_client.get_screen_data.return_value = SimpleNamespace(
+        base64=base64.b64encode(original).decode("ascii"),
+        hierarchy_xml="<hierarchy />",
+        elements=elements,
+        width=1200,
+        height=2600,
+    )
+    driver = AndroidAdbDriver("emulator-5554", MagicMock(), ui_adb_client=ui_client)
+    driver._screenshot_config = ScreenshotConfig(enabled=enabled, max_long_edge=2048)
+
+    screen = await driver.get_screen_data(skip_settling=True)
+
+    with Image.open(BytesIO(screen.screenshot_bytes)) as result:
+        assert result.size == ((945, 2048) if enabled else (1200, 2600))
+    assert screen.width == 1200 and screen.height == 2600
+    assert screen.ui_elements == elements
+    assert screen.screenshot_base64 == base64.b64encode(screen.screenshot_bytes).decode("ascii")
+    if not enabled:
+        assert screen.screenshot_bytes == original
+
+
 @pytest.mark.asyncio
 async def test_android_driver_parses_xml_hierarchy_fallback():
     """XML-only clients remain supported without discarding their hierarchy."""

tests/unit/test_llm_gateway.py

@@ -301,6 +301,32 @@ async def test_configured_retry_budget_is_shared_by_identical_fallback(monkeypat
     assert llm_service._FAILED_ENDPOINTS.get() is None
 
 
+@pytest.mark.asyncio
+async def test_429_waits_one_minute_then_retries(monkeypatch):
+    from unittest.mock import AsyncMock
+    from artemis.llm.reliability import retry_policy_for
+
+    class RateLimited(Exception):
+        status_code = 429
+
+    calls = 0
+
+    async def call_once():
+        nonlocal calls
+        calls += 1
+        if calls == 1:
+            raise RateLimited("Too Many Requests")
+        return "recovered"
+
+    sleep = AsyncMock()
+    monkeypatch.setattr(llm_service, "retry_policy_for", retry_policy_for)
+    monkeypatch.setattr(llm_service.asyncio, "sleep", sleep)
+    monkeypatch.setattr(llm_service.settings, "OPENAI_RATE_LIMIT_RETRY_SECONDS", 60)
+    assert await llm_service._run_with_recovery(call_once, max_retries=1) == "recovered"
+    assert calls == 2
+    sleep.assert_awaited_once_with(60)
+
+
 @pytest.mark.asyncio
 async def test_request_deadline_cancels_attempt_and_respects_zero_retries(monkeypatch):
     import asyncio

tests/unit/test_llm_reliability.py

@@ -49,6 +49,15 @@ def test_non_retryable_categories_get_single_attempt():
     assert retry_policy_for(FailureCategory.RATE_LIMIT).max_attempts > 1
 
 
+def test_too_many_requests_uses_one_minute_retry_delay(monkeypatch):
+    from artemis.config.settings import settings
+
+    monkeypatch.setattr(settings, "OPENAI_RATE_LIMIT_RETRY_SECONDS", 60)
+    failure = classify_failure(RuntimeError("Too Many Requests"))
+    assert failure.category is FailureCategory.RATE_LIMIT
+    assert retry_policy_for(failure.category).delay_for(1) == 60
+
+
 def test_breaker_only_trips_on_transient_categories():
     breaker = CircuitBreaker(threshold=2, cooldown_seconds=10.0)
     auth = classify_failure(RuntimeError("401 unauthorized"))

tests/unit/test_main_entrypoint.py

@@ -0,0 +1,12 @@
+"""The compatibility entry point follows the commands registered by Typer."""
+
+from artemis.main import normalize_argv
+
+
+def test_registered_commands_and_flags_are_not_treated_as_goals():
+    for token in ("ui", "status", "helper", "restart", "stop", "--help", "--version", "-v"):
+        assert normalize_argv(["artemis", token]) == ["artemis", token]
+
+
+def test_bare_goal_still_uses_run_command():
+    assert normalize_argv(["artemis", "打开设置"]) == ["artemis", "run", "打开设置"]

tests/unit/test_unified_controller_video.py

@@ -207,6 +207,30 @@ async def test_analyzer_clip_stays_continuous_across_orientation_segments(tmp_pa
     assert len(frames) >= 20
 
 
+@pytest.mark.asyncio
+async def test_analyzer_clip_pins_requested_fps_across_recording_gap(tmp_path):
+    ffmpeg = get_ffmpeg_path()
+    first = tmp_path / "first.mkv"
+    second = tmp_path / "second.mkv"
+    output = tmp_path / "clip.mp4"
+    _make_solid_clip(ffmpeg, "red", "108x242", 1.0, first)
+    _make_solid_clip(ffmpeg, "blue", "242x108", 1.0, second)
+    assert await render_timeline_clip(
+        [{"path": first, "start": 0.0, "end": 1.0}, {"path": second, "start": 1.5, "end": 2.5}],
+        0.0,
+        2.5,
+        output,
+        canvas_width=180,
+        canvas_height=320,
+        fps=7,
+    )
+    capture = cv2.VideoCapture(str(output))
+    try:
+        assert capture.get(cv2.CAP_PROP_FPS) == pytest.approx(7.0, abs=0.1)
+    finally:
+        capture.release()
+
+
 def test_timeline_pieces_fill_restart_gaps(tmp_path):
     """Holes between segments (and a window starting in one) become black gaps."""
     first = tmp_path / "recording.mkv"

uv.lock

@@ -206,7 +206,7 @@ wheels = [
 
 [[package]]
 name = "artemis"
-version = "1.2.3"
+version = "1.3.0"
 source = { editable = "." }
 dependencies = [
     { name = "adbutils" },