feat: add tool-call fallback and resilient batch exports

.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
 

README.md

@@ -48,7 +48,7 @@ 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。
 

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/model_settings_service.py

@@ -23,6 +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"
@@ -45,9 +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")
@@ -64,6 +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",
@@ -86,6 +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)
@@ -95,6 +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

apps/admin_console/services/report_export.py

@@ -734,9 +734,12 @@ def export_batch(batch_id: str) -> Path:
             for path in outputs:
                 archive.write(path, path.name)
             for number, report in enumerate(records, 1):
+                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}")
         temporary.replace(target)
         return target

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();

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;

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 = (
-                    final_report = {
+                    settings.ARTEMIS_TOOL_FALLBACK_MAX_NO_PROGRESS_ATTEMPTS
-                        "status": "failed",
+                    if using_tool_fallback
-                        "explanation": (
+                    else no_tool_limit
-                            f"模型连续 {no_tool_attempts} 次未调用工具，"
+                )
-                            "已停止任务以避免空转和重复计费；未执行新的设备动作。"
+                if no_tool_attempts >= active_limit:
-                        ),
+                    if using_tool_fallback:
-                    }
+                        final_report = {
-                    break
+                            "status": "failed",
+                            "explanation": (
+                                f"首选模型连续 {no_tool_limit} 次、兜底模型连续 "
+                                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 >= (
-                    raise OperatorNoProgressError(
+                    fallback_limit if using_tool_fallback else no_progress_limit
-                        f"模型连续 {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；未执行新的设备动作。"
+                    if using_tool_fallback:
+                        raise OperatorNoProgressError(
+                            f"首选模型连续 {no_progress_limit} 次、兜底模型连续 "
+                            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 >= (
-                    raise OperatorNoProgressError(
+                    fallback_limit if using_tool_fallback else no_progress_limit
-                        f"模型连续 {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，未执行被拒绝的设备动作。"
+                    if using_tool_fallback:
+                        raise OperatorNoProgressError(
+                            f"首选模型连续 {no_progress_limit} 次、兜底模型连续 "
+                            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/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)

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": {},

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/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": {},

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。
 

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.

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

@@ -318,6 +318,38 @@ 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

@@ -23,6 +23,11 @@ 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",
@@ -43,6 +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"]
@@ -50,12 +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"
 

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_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"