feat(router): improve direct_chat fast-path and add 103 token env sync

Route explicit text-only Q&A away from Agent before page-generation rules,
and add a script to enable Router + tighter memory budgets on 103 without
changing the global deepseek-v4-pro model.

Co-authored-by: Cursor <cursoragent@cursor.com>

chat-intent-router-rules.mjs

@@ -103,6 +103,25 @@ export const DIRECT_CHAT_FAQ_RULES = [
       /^.{0,16}(?:有什么建议|给点建议|怎么看)[？?]?$/u,
     ],
   },
+  {
+    id: 'text_only_declined_page',
+    reason: '明确要求纯文字、不生成页面/链接',
+    patterns: [
+      /(?:不要|别|无需|不需要|禁止).{0,12}(?:生成|做|制作|创建|发布).{0,8}(?:页面|网页|HTML|html|H5|h5|链接)/u,
+      /(?:纯文字|只要文字|文字回答|不要页面|不用页面|别做页面)/u,
+    ],
+  },
+  {
+    id: 'brief_intro',
+    reason: '短篇幅介绍/说明（纯文字）',
+    patterns: [
+      /(?:用|以).{0,6}(?:一|两|二|三|四|五|几|\d+).{0,4}(?:句话|句).{0,32}(?:介绍|说明|描述|讲讲|推荐)/u,
+      /^(?:简单|简要|大概|概括)(?:介绍|说明|描述|讲讲).{0,48}[？?]?$/u,
+      /^简要介绍.{0,48}(?:区别|差异|不同|对比|比较)[？?]?$/u,
+      /^(?:推荐|介绍)(?:一个|一下).{0,32}(?:理由|原因)[，,]?一句话/u,
+      /(?:推荐|介绍).{0,32}一句话.{0,16}(?:说明|讲讲).{0,12}(?:理由|原因)/u,
+    ],
+  },
 ];
 
 const MAX_FAQ_TEXT_LENGTH = 200;
@@ -111,10 +130,26 @@ const MAX_FAQ_TEXT_LENGTH = 200;
  * @param {string} text
  * @returns {{ id: string, reason: string } | null}
  */
+const EXPLICIT_TEXT_ONLY_PAGE_DECLINE = /(?:不要|别|无需|不需要|禁止).{0,12}(?:生成|做|制作|创建|发布).{0,8}(?:页面|网页|HTML|html|H5|h5|链接)/u;
+
+export function isExplicitTextOnlyRequest(text) {
+  const normalized = String(text ?? '').trim();
+  if (!normalized) return false;
+  return EXPLICIT_TEXT_ONLY_PAGE_DECLINE.test(normalized)
+    || /(?:纯文字|只要文字|文字回答|不要页面|不用页面|别做页面)/u.test(normalized);
+}
+
 export function matchDirectChatFaqRule(text) {
   const normalized = String(text ?? '').trim();
   if (!normalized || normalized.length > MAX_FAQ_TEXT_LENGTH) return null;
-  if (FAQ_EXCLUSION_PATTERNS.some((pattern) => pattern.test(normalized))) return null;
+  const declinedPage = EXPLICIT_TEXT_ONLY_PAGE_DECLINE.test(normalized)
+    || /(?:纯文字|只要文字|文字回答|不要页面|不用页面|别做页面)/u.test(normalized);
+  if (
+    !declinedPage
+    && FAQ_EXCLUSION_PATTERNS.some((pattern) => pattern.test(normalized))
+  ) {
+    return null;
+  }
   for (const rule of DIRECT_CHAT_FAQ_RULES) {
     if (rule.patterns.some((pattern) => pattern.test(normalized))) {
       return { id: rule.id, reason: rule.reason };

chat-intent-router.mjs

@@ -15,11 +15,11 @@ import {
   resolveMemoryInterventionMode,
 } from './memory-intervention.mjs';
 import { filterMemoriesByQuery } from './memory-legacy-fallback.mjs';
-import { matchDirectChatFaqRule } from './chat-intent-router-rules.mjs';
+import { matchDirectChatFaqRule, isExplicitTextOnlyRequest } from './chat-intent-router-rules.mjs';
 import { isGoalRunIntent } from './goal-run-intent.mjs';
 import { pgvectorMemoryBackendInternals } from './memory-v2-pgvector.mjs';
 
-export { matchDirectChatFaqRule, DIRECT_CHAT_FAQ_RULES, FAQ_EXCLUSION_PATTERNS } from './chat-intent-router-rules.mjs';
+export { matchDirectChatFaqRule, DIRECT_CHAT_FAQ_RULES, FAQ_EXCLUSION_PATTERNS, isExplicitTextOnlyRequest } from './chat-intent-router-rules.mjs';
 
 export const CHAT_INTENT_ROUTE = {
   DIRECT_CHAT: 'direct_chat',
@@ -1083,6 +1083,14 @@ export function classifyWithRules({
       reason: '用户在询问个人记忆或历史对话',
     }, { source: 'rule' }), decisionContext);
   }
+  if (includeIntentPatterns && normalized && isExplicitTextOnlyRequest(normalized)) {
+    const faqMatch = matchDirectChatFaqRule(normalized);
+    return finalizeRouterClassification(normalizeClassification({
+      route: CHAT_INTENT_ROUTE.DIRECT_CHAT,
+      confidence: 0.96,
+      reason: faqMatch?.reason ?? '用户明确要求纯文字、不生成页面',
+    }, { source: 'rule' }), decisionContext);
+  }
   if (hasPriorAgentConversation(sessionId, sessionMessageCount)) {
     const reason = isAgentSessionContinueText(normalized)
       ? 'Agent 会话确认/续聊'
@@ -1093,6 +1101,16 @@ export function classifyWithRules({
       reason,
     }, { source: 'rule' }), decisionContext);
   }
+  if (includeIntentPatterns && normalized) {
+    const faqMatch = matchDirectChatFaqRule(normalized);
+    if (faqMatch) {
+      return finalizeRouterClassification(normalizeClassification({
+        route: CHAT_INTENT_ROUTE.DIRECT_CHAT,
+        confidence: 0.94,
+        reason: faqMatch.reason,
+      }, { source: 'rule' }), decisionContext);
+    }
+  }
   if (includeIntentPatterns && normalized && isPageDataDevIntent(normalized)) {
     return finalizeRouterClassification(normalizeClassification({
       route: CHAT_INTENT_ROUTE.AGENT,
@@ -1142,16 +1160,6 @@ export function classifyWithRules({
   if (includeIntentPatterns && normalized && isRealtimeInfoQuestion(normalized)) {
     return finalizeRouterClassification(buildRealtimeInfoClassification(), decisionContext);
   }
-  if (includeIntentPatterns && normalized) {
-    const faqMatch = matchDirectChatFaqRule(normalized);
-    if (faqMatch) {
-      return finalizeRouterClassification(normalizeClassification({
-        route: CHAT_INTENT_ROUTE.DIRECT_CHAT,
-        confidence: 0.94,
-        reason: faqMatch.reason,
-      }, { source: 'rule' }), decisionContext);
-    }
-  }
   if (
     includeIntentPatterns &&
     normalized &&

chat-intent-router.test.mjs

@@ -109,6 +109,25 @@ test('classifyWithRules routes greetings to direct chat', () => {
   assert.equal(result.source, 'rule');
 });
 
+test('classifyWithRules routes explicit text-only Q&A to direct chat', () => {
+  for (const text of [
+    '用三句话介绍苏州园林，不要生成页面',
+    '简要介绍一下 Rust 和 Go 的区别',
+    '推荐一个最适合第一次去的，一句话说明理由',
+  ]) {
+    const result = classifyWithRules({
+      text,
+      userMessage: {
+        role: 'user',
+        content: [{ type: 'text', text }],
+        metadata: { displayText: text },
+      },
+    });
+    assert.equal(result.route, CHAT_INTENT_ROUTE.DIRECT_CHAT, text);
+    assert.equal(result.source, 'rule', text);
+  }
+});
+
 test('classifyWithRules routes page generation to agent orchestration', () => {
   const result = classifyWithRules({
     text: '帮我做一个秋夜诗的 H5 页面',

scenarios/token-benchmark-chat.json

@@ -0,0 +1,39 @@
+{
+  "id": "token-benchmark-chat",
+  "name": "Token 基准：Direct Chat 三轮",
+  "description": "简单对话，不走做页面 Agent，对比 pro vs flash",
+  "account": {
+    "username": "john2",
+    "password": "888888"
+  },
+  "steps": [
+    {
+      "action": "chat",
+      "label": "打招呼",
+      "message": "hi",
+      "expect": {
+        "assistantMinChars": 1,
+        "timeoutMs": 120000
+      }
+    },
+    {
+      "action": "chat",
+      "label": "苏州简介",
+      "message": "用三句话介绍苏州园林，不要生成页面",
+      "expect": {
+        "assistantMinChars": 20,
+        "timeoutMs": 120000,
+        "replyKeywords": ["苏州"]
+      }
+    },
+    {
+      "action": "chat",
+      "label": "追问",
+      "message": "推荐一个最适合第一次去的，一句话说明理由",
+      "expect": {
+        "assistantMinChars": 10,
+        "timeoutMs": 120000
+      }
+    }
+  ]
+}

scenarios/token-benchmark-page.json

@@ -0,0 +1,26 @@
+{
+  "id": "token-benchmark-page",
+  "name": "Token 基准：独立苏州页面",
+  "description": "每次使用唯一文件名，避免复用已有 HTML 污染上下文",
+  "account": {
+    "username": "john2",
+    "password": "888888"
+  },
+  "steps": [
+    {
+      "action": "chat",
+      "label": "生成独立苏州页面",
+      "message": "请帮我做一个全新的苏州一日游攻略页面，保存为 public/token-bench-{{PROFILE}}-{{RUN_ID}}.html，不要修改或复用已有页面，做完直接给我链接。",
+      "expect": {
+        "assistantMinChars": 20,
+        "timeoutMs": 600000,
+        "replyKeywords": ["苏州"],
+        "page": {
+          "keywords": ["苏州"],
+          "requirePublicLink": true,
+          "requireHttp200": true
+        }
+      }
+    }
+  ]
+}

scripts/benchmark-token-optimization.mjs

@@ -0,0 +1,542 @@
+#!/usr/bin/env node
+/**
+ * Local A/B benchmark: DeepSeek token usage vs scenario quality.
+ *
+ * Usage:
+ *   node scripts/benchmark-token-optimization.mjs
+ *   node scripts/benchmark-token-optimization.mjs --scenario john2-suzhou-page
+ *   node scripts/benchmark-token-optimization.mjs --phase baseline
+ *   node scripts/benchmark-token-optimization.mjs --phase optimized
+ *   node scripts/benchmark-token-optimization.mjs --restore
+ *
+ * Does NOT touch production. Restores model + memory admin config on exit.
+ */
+import fs from 'node:fs';
+import path from 'node:path';
+import { fileURLToPath } from 'node:url';
+import { createDbPool } from '../db.mjs';
+import { createLlmProviderService } from '../llm-providers.mjs';
+import { createMemoryV2AdminConfigService } from '../memory-v2-admin-config.mjs';
+import { loadH5Environment } from './load-env.mjs';
+import {
+  createReporter,
+  createAgentRun,
+  extractAssistantTexts,
+  extractPublicLinks,
+  getSession,
+  loadScenario,
+  loginViaApi,
+  resolvePortalBase,
+  snapshotPublicHtml,
+  verifyPageAccess,
+  waitForAssistantGrowth,
+  waitForRunTerminal,
+} from './scenario-test-lib.mjs';
+
+const scriptDir = path.dirname(fileURLToPath(import.meta.url));
+const root = path.join(scriptDir, '..');
+const statePath = path.join(root, '.token-benchmark-state.json');
+
+loadH5Environment(scriptDir);
+
+const PROFILES = {
+  baseline: {
+    label: 'baseline (deepseek-v4-pro + 默认记忆预算)',
+    model: 'deepseek-v4-pro',
+    memoryPatch: null,
+  },
+  optimized: {
+    label: 'optimized (deepseek-v4-flash + 收紧记忆/Router)',
+    model: 'deepseek-v4-flash',
+    memoryPatch: {
+      runtimeControl: {
+        agentResolveLimit: '3',
+      },
+      retriever: {
+        limit: '4',
+        tokenBudget: '900',
+      },
+      persona: {
+        maxTokens: '300',
+      },
+      chatIntentRouter: {
+        enabled: true,
+        shadowMode: false,
+        model: 'deepseek-chat',
+        memoryResolveLimit: '2',
+      },
+    },
+  },
+};
+
+function parseArgs(argv) {
+  let scenarioId = 'token-benchmark-page';
+  let phase = 'both';
+  let port = Number(process.env.H5_PORT ?? 8081);
+  for (let i = 2; i < argv.length; i += 1) {
+    const arg = argv[i];
+    if (arg === '--scenario' && argv[i + 1]) scenarioId = argv[++i];
+    else if (arg === '--phase' && argv[i + 1]) phase = argv[++i];
+    else if (arg === '--port' && argv[i + 1]) port = Number(argv[++i]);
+    else if (arg === '--restore') phase = 'restore';
+    else if (arg === '-h' || arg === '--help') {
+      console.log(`Usage:
+  node scripts/benchmark-token-optimization.mjs [--scenario <id>] [--phase baseline|optimized|both|restore]`);
+      process.exit(0);
+    } else {
+      throw new Error(`Unknown argument: ${arg}`);
+    }
+  }
+  if (!['baseline', 'optimized', 'both', 'restore'].includes(phase)) {
+    throw new Error(`Invalid --phase: ${phase}`);
+  }
+  return { scenarioId, phase, port };
+}
+
+async function ensurePortalReady(baseUrl) {
+  const response = await fetch(`${baseUrl}/auth/status`);
+  if (!response.ok) {
+    throw new Error(`Portal 未就绪: ${baseUrl}/auth/status -> ${response.status}`);
+  }
+}
+
+async function readCurrentModel(pool) {
+  const [rows] = await pool.query(
+    `SELECT default_model FROM h5_llm_provider_keys WHERE is_selected = 1 AND status = 'active' LIMIT 1`,
+  );
+  return rows[0]?.default_model ?? null;
+}
+
+async function saveState(pool, memoryConfigService) {
+  const model = await readCurrentModel(pool);
+  const memoryAdmin = await memoryConfigService.getAdminConfig().catch(() => null);
+  const payload = {
+    savedAt: Date.now(),
+    model,
+    memoryConfig: memoryAdmin?.config ?? null,
+  };
+  fs.writeFileSync(statePath, `${JSON.stringify(payload, null, 2)}\n`, 'utf8');
+  return payload;
+}
+
+function loadSavedState() {
+  if (!fs.existsSync(statePath)) return null;
+  return JSON.parse(fs.readFileSync(statePath, 'utf8'));
+}
+
+async function applyProfile(pool, llmProviderService, memoryConfigService, profileKey) {
+  const profile = PROFILES[profileKey];
+  if (!profile) throw new Error(`Unknown profile: ${profileKey}`);
+
+  const modelResult = await llmProviderService.setGlobalModel(profile.model);
+  if (!modelResult?.ok) {
+    throw new Error(`切换模型失败 (${profile.model}): ${modelResult?.message ?? 'unknown'}`);
+  }
+
+  if (profile.memoryPatch) {
+    await memoryConfigService.updateAdminConfig(profile.memoryPatch, {
+      updatedBy: 'token-benchmark',
+    });
+  }
+
+  const currentModel = await readCurrentModel(pool);
+  console.log(`\n==> 已应用 ${profile.label}`);
+  console.log(`    全局模型: ${currentModel}`);
+  if (profile.memoryPatch) {
+    console.log('    记忆/Router 预算: 已写入 h5_memory_v2_admin_config');
+  }
+}
+
+async function restoreSavedState(pool, llmProviderService, memoryConfigService) {
+  const saved = loadSavedState();
+  if (!saved) {
+    console.log('无 .token-benchmark-state.json，跳过恢复');
+    return false;
+  }
+
+  if (saved.model) {
+    const result = await llmProviderService.setGlobalModel(saved.model);
+    if (!result?.ok) {
+      console.warn(`恢复模型失败: ${result?.message ?? 'unknown'}`);
+    } else {
+      console.log(`已恢复全局模型: ${saved.model}`);
+    }
+  }
+
+  if (saved.memoryConfig) {
+    await memoryConfigService.updateAdminConfig(saved.memoryConfig, {
+      updatedBy: 'token-benchmark-restore',
+    });
+    console.log('已恢复 memory v2 admin config');
+  }
+
+  fs.unlinkSync(statePath);
+  return true;
+}
+
+async function collectUsageMetrics(pool, requestIds) {
+  if (!requestIds.length) {
+    return { inputTokens: 0, outputTokens: 0, costCents: 0, records: [] };
+  }
+  const placeholders = requestIds.map(() => '?').join(', ');
+  const [rows] = await pool.query(
+    `SELECT request_id, input_tokens, output_tokens, cost_cents, billing_source, created_at
+     FROM h5_usage_records
+     WHERE request_id IN (${placeholders})
+     ORDER BY created_at ASC`,
+    requestIds,
+  );
+  let inputTokens = 0;
+  let outputTokens = 0;
+  let costCents = 0;
+  for (const row of rows) {
+    inputTokens += Number(row.input_tokens ?? 0);
+    outputTokens += Number(row.output_tokens ?? 0);
+    costCents += Number(row.cost_cents ?? 0);
+  }
+  return { inputTokens, outputTokens, costCents, records: rows };
+}
+
+async function collectRunEventTokens(pool, runIds) {
+  if (!runIds.length) return { inputTokens: 0, outputTokens: 0, events: [] };
+  const placeholders = runIds.map(() => '?').join(', ');
+  const [rows] = await pool.query(
+    `SELECT run_id, data_json, created_at
+     FROM h5_agent_run_events
+     WHERE run_id IN (${placeholders}) AND event_type = 'session_finished'
+     ORDER BY created_at ASC`,
+    runIds,
+  );
+  let inputTokens = 0;
+  let outputTokens = 0;
+  const events = [];
+  for (const row of rows) {
+    const data = typeof row.data_json === 'string'
+      ? JSON.parse(row.data_json)
+      : row.data_json;
+    const tokenState = data?.tokenState ?? data?.token_state ?? null;
+    if (!tokenState) continue;
+    const input = Number(
+      tokenState.accumulatedInputTokens
+      ?? tokenState.accumulated_input_tokens
+      ?? tokenState.inputTokens
+      ?? tokenState.input_tokens
+      ?? 0,
+    );
+    const output = Number(
+      tokenState.accumulatedOutputTokens
+      ?? tokenState.accumulated_output_tokens
+      ?? tokenState.outputTokens
+      ?? tokenState.output_tokens
+      ?? 0,
+    );
+    inputTokens += input;
+    outputTokens += output;
+    events.push({ runId: row.run_id, input, output, tokenState });
+  }
+  return { inputTokens, outputTokens, events };
+}
+
+async function runScenarioBenchmark(scenario, port, { profileKey = 'run', runId = Date.now() } = {}) {
+  const reporter = createReporter();
+  const baseUrl = resolvePortalBase(port);
+  const account = {
+    username: scenario.account?.username ?? 'john2',
+    password: process.env.JOHN_PASSWORD
+      ?? process.env.H5_ACCESS_PASSWORD
+      ?? scenario.account?.password
+      ?? '888888',
+  };
+
+  await ensurePortalReady(baseUrl);
+
+  const auth = await loginViaApi(baseUrl, account, reporter);
+  const publishKey = auth.user?.id ?? auth.user?.publishSlug ?? account.username;
+
+  let sessionId = null;
+  let assistantCount = 0;
+  let assistantCombinedLength = 0;
+  const tracked = {
+    requestIds: [],
+    runIds: [],
+    sessionIds: [],
+    replies: [],
+    pageLinks: [],
+    elapsedMs: 0,
+    profileKey,
+    runId,
+  };
+  const started = Date.now();
+
+  for (const step of scenario.steps ?? []) {
+    if (step.action !== 'chat') continue;
+
+    const message = String(step.message ?? '')
+      .replaceAll('{{PROFILE}}', profileKey)
+      .replaceAll('{{RUN_ID}}', String(runId));
+
+    let htmlBefore = [];
+    if (step.expect?.page) {
+      htmlBefore = await snapshotPublicHtml(publishKey);
+    }
+
+    const run = await createAgentRun(baseUrl, auth.cookie, {
+      message,
+      sessionId: null,
+      selectedChatSkill: step.selectedChatSkill ?? null,
+    });
+    tracked.requestIds.push(run.requestId);
+    tracked.runIds.push(run.runId);
+
+    const terminal = await waitForRunTerminal(
+      baseUrl,
+      auth.cookie,
+      run.runId,
+      step.expect?.timeoutMs ?? 600_000,
+    );
+    sessionId = terminal.sessionId ?? terminal.agent_session_id ?? run.sessionId ?? sessionId;
+    if (sessionId) tracked.sessionIds.push(sessionId);
+
+    if (terminal.status === 'failed') {
+      reporter.fail('run 终态', terminal.error ?? 'failed');
+      continue;
+    }
+    reporter.pass('run 终态', terminal.status);
+
+    const reply = await waitForAssistantGrowth(baseUrl, auth.cookie, sessionId, {
+      previousCount: assistantCount,
+      previousCombinedLength: assistantCombinedLength,
+      minChars: step.expect?.assistantMinChars ?? 1,
+      timeoutMs: step.expect?.timeoutMs ?? 120_000,
+    });
+
+    if (!reply) {
+      reporter.fail('assistant 回复', '超时未收到新回复');
+      continue;
+    }
+
+    assistantCount = reply.count;
+    assistantCombinedLength = reply.combined.length;
+    tracked.replies.push({
+      label: step.label ?? step.message,
+      chars: reply.combined.length,
+      elapsedMs: reply.elapsedMs,
+      text: reply.combined,
+    });
+    reporter.pass('assistant 回复', `${reply.combined.length} 字 / ${reply.elapsedMs}ms`);
+
+    const keywords = step.expect?.replyKeywords ?? [];
+    if (keywords.length) {
+      const hit = keywords.filter((word) => reply.combined.includes(word));
+      if (hit.length === 0) reporter.fail('回复关键词', `未命中: ${keywords.join(', ')}`);
+      else reporter.pass('回复关键词', hit.join(', '));
+    }
+
+    tracked.pageLinks.push(...extractPublicLinks(reply.combined, baseUrl));
+
+    if (step.expect?.page) {
+      await verifyPageAccess({
+        baseUrl,
+        cookie: auth.cookie,
+        publishKey,
+        replyText: reply.combined,
+        htmlBefore,
+        expect: step.expect.page,
+        uploadedAssetIds: [],
+        reporter,
+      });
+    }
+  }
+
+  tracked.elapsedMs = Date.now() - started;
+  return {
+    reporter,
+    tracked,
+    passed: reporter.issues.length === 0,
+    issueCount: reporter.issues.length,
+  };
+}
+
+function pctDelta(before, after) {
+  if (!before) return after ? 100 : 0;
+  return Math.round(((after - before) / before) * 1000) / 10;
+}
+
+function printComparison(baseline, optimized) {
+  console.log('\n========================================');
+  console.log('Token 优化 A/B 对比（本地）');
+  console.log('========================================\n');
+
+  const rows = [
+    ['指标', 'Baseline', 'Optimized', '变化'],
+    [
+      '输入 Token (usage)',
+      String(baseline.usage.inputTokens),
+      String(optimized.usage.inputTokens),
+      `${pctDelta(baseline.usage.inputTokens, optimized.usage.inputTokens)}%`,
+    ],
+    [
+      '输出 Token (usage)',
+      String(baseline.usage.outputTokens),
+      String(optimized.usage.outputTokens),
+      `${pctDelta(baseline.usage.outputTokens, optimized.usage.outputTokens)}%`,
+    ],
+    [
+      '总 Token (usage)',
+      String(baseline.usage.inputTokens + baseline.usage.outputTokens),
+      String(optimized.usage.inputTokens + optimized.usage.outputTokens),
+      `${pctDelta(
+        baseline.usage.inputTokens + baseline.usage.outputTokens,
+        optimized.usage.inputTokens + optimized.usage.outputTokens,
+      )}%`,
+    ],
+    [
+      '扣费 (分)',
+      String(baseline.usage.costCents),
+      String(optimized.usage.costCents),
+      `${pctDelta(baseline.usage.costCents, optimized.usage.costCents)}%`,
+    ],
+    [
+      '场景耗时 (s)',
+      String(Math.round(baseline.tracked.elapsedMs / 1000)),
+      String(Math.round(optimized.tracked.elapsedMs / 1000)),
+      `${pctDelta(baseline.tracked.elapsedMs, optimized.tracked.elapsedMs)}%`,
+    ],
+    [
+      '回复字数',
+      String(baseline.tracked.replies.at(-1)?.chars ?? 0),
+      String(optimized.tracked.replies.at(-1)?.chars ?? 0),
+      `${pctDelta(
+        baseline.tracked.replies.at(-1)?.chars ?? 0,
+        optimized.tracked.replies.at(-1)?.chars ?? 0,
+      )}%`,
+    ],
+    [
+      '质量检查',
+      baseline.passed ? '通过' : `失败 ${baseline.issueCount} 项`,
+      optimized.passed ? '通过' : `失败 ${optimized.issueCount} 项`,
+      optimized.passed === baseline.passed ? '持平' : (optimized.passed ? '改善' : '下降'),
+    ],
+  ];
+
+  const widths = [0, 1, 2, 3].map((col) => Math.max(...rows.map((row) => row[col].length)));
+  for (const row of rows) {
+    console.log(row.map((cell, i) => cell.padEnd(widths[i] + 2)).join(''));
+  }
+
+  console.log('\n--- Baseline 页面链接 ---');
+  for (const link of baseline.tracked.pageLinks) console.log(link);
+  console.log('\n--- Optimized 页面链接 ---');
+  for (const link of optimized.tracked.pageLinks) console.log(link);
+
+  if (baseline.passed && optimized.passed) {
+    const totalBefore = baseline.usage.inputTokens + baseline.usage.outputTokens;
+    const totalAfter = optimized.usage.inputTokens + optimized.usage.outputTokens;
+    const saved = totalBefore - totalAfter;
+    console.log(`\n结论: 质量检查均通过。Token 总量 ${totalBefore} → ${totalAfter}（${saved >= 0 ? '节省' : '增加'} ${Math.abs(saved)}）`);
+    if (saved > 0 && optimized.passed) {
+      console.log('效果未打折，可继续观察更多场景。');
+    } else if (saved <= 0) {
+      console.log('Token 未下降，需检查模型/Router/记忆配置是否生效。');
+    }
+  } else {
+    console.log('\n结论: 存在质量回归，不建议直接上生产。');
+    if (!optimized.passed) {
+      console.log('Optimized 失败项:');
+      for (const issue of optimized.reporter.issues) {
+        console.log(`  - ${issue.label}: ${issue.detail}`);
+      }
+    }
+  }
+}
+
+async function runPhase(pool, llmProviderService, memoryConfigService, profileKey, scenario, port) {
+  await applyProfile(pool, llmProviderService, memoryConfigService, profileKey);
+  const runId = `${profileKey}-${Date.now()}`;
+  console.log(`\n>>> 开始 ${profileKey} 场景跑分 (runId=${runId})...`);
+  const result = await runScenarioBenchmark(scenario, port, { profileKey, runId });
+  const usage = await collectUsageMetrics(pool, result.tracked.requestIds);
+  const eventTokens = await collectRunEventTokens(pool, result.tracked.runIds);
+  console.log(`\n--- ${profileKey} token 汇总 ---`);
+  console.log(`usage records: in=${usage.inputTokens} out=${usage.outputTokens} cost=${usage.costCents}分`);
+  console.log(`run events:    in=${eventTokens.inputTokens} out=${eventTokens.outputTokens}`);
+  return { ...result, usage, eventTokens, profileKey };
+}
+
+async function main() {
+  const { scenarioId, phase, port } = parseArgs(process.argv);
+  const pool = createDbPool(process.env);
+  const llmProviderService = createLlmProviderService(pool, {
+    apiTarget: process.env.TKMIND_API_TARGET ?? 'https://127.0.0.1:18006',
+    apiSecret: process.env.TKMIND_SERVER__SECRET_KEY ?? 'local-dev-secret',
+  });
+  const memoryConfigService = createMemoryV2AdminConfigService(pool);
+
+  try {
+    if (phase === 'restore') {
+      await restoreSavedState(pool, llmProviderService, memoryConfigService);
+      return;
+    }
+
+    const scenario = await loadScenario(scenarioId);
+    console.log(`场景: ${scenario.name ?? scenario.id}`);
+    console.log(`Portal: ${resolvePortalBase(port)}`);
+
+    if (phase === 'both') {
+      await saveState(pool, memoryConfigService);
+    }
+
+    let baselineResult = null;
+    let optimizedResult = null;
+
+    if (phase === 'both' || phase === 'baseline') {
+      baselineResult = await runPhase(
+        pool,
+        llmProviderService,
+        memoryConfigService,
+        'baseline',
+        scenario,
+        port,
+      );
+    }
+
+    if (phase === 'both' || phase === 'optimized') {
+      optimizedResult = await runPhase(
+        pool,
+        llmProviderService,
+        memoryConfigService,
+        'optimized',
+        scenario,
+        port,
+      );
+    }
+
+    if (baselineResult && optimizedResult) {
+      printComparison(baselineResult, optimizedResult);
+    }
+
+    if (phase === 'both') {
+      await restoreSavedState(pool, llmProviderService, memoryConfigService);
+      console.log('\n已恢复 benchmark 前的模型与 memory 配置。');
+    }
+  } finally {
+    await pool.end?.();
+  }
+}
+
+main().catch(async (error) => {
+  console.error(error instanceof Error ? error.stack ?? error.message : error);
+  try {
+    const pool = createDbPool(process.env);
+    const llmProviderService = createLlmProviderService(pool, {
+      apiTarget: process.env.TKMIND_API_TARGET ?? 'https://127.0.0.1:18006',
+      apiSecret: process.env.TKMIND_SERVER__SECRET_KEY ?? 'local-dev-secret',
+    });
+    const memoryConfigService = createMemoryV2AdminConfigService(pool);
+    await restoreSavedState(pool, llmProviderService, memoryConfigService);
+    await pool.end?.();
+  } catch {
+    // best effort restore
+  }
+  process.exit(1);
+});

scripts/sync-token-optimization-env-103.sh

@@ -0,0 +1,76 @@
+#!/usr/bin/env bash
+# Sync token-optimization Router / memory env to 103 Portal (.env only).
+# Does NOT change global LLM model (keep deepseek-v4-pro for Agent quality).
+#
+# Usage:
+#   bash scripts/sync-token-optimization-env-103.sh          # dry-run
+#   bash scripts/sync-token-optimization-env-103.sh --apply  # write + restart portal
+set -euo pipefail
+
+HOST="${MEMIND_103_HOST:-john@58.38.22.103}"
+REMOTE_ENV="/Users/john/Project/Memind/.env"
+APPLY=0
+if [[ "${1:-}" == "--apply" ]]; then
+  APPLY=1
+fi
+
+PATCH_LINES=(
+  "MEMIND_CHAT_LLM_ROUTER_ENABLED=1"
+  "MEMIND_CHAT_LLM_ROUTER_SHADOW=0"
+  "MEMIND_CHAT_ROUTER_CANARY_USER_IDS="
+  "MEMIND_CHAT_ROUTER_MODEL=deepseek-chat"
+  "MEMIND_CHAT_ROUTER_MEMORY_LIMIT=2"
+  "MEMIND_CHAT_ROUTER_TIMEOUT_MS=2500"
+  "MEMIND_CHAT_ROUTER_MIN_CONFIDENCE=0.65"
+  "MEMORY_AGENT_RESOLVE_LIMIT=3"
+  "MEMORY_RETRIEVER_LIMIT=4"
+  "MEMORY_RETRIEVER_TOKEN_BUDGET=900"
+  "MEMORY_PERSONA_MAX_TOKENS=300"
+)
+
+echo "==> Target: ${HOST}:${REMOTE_ENV}"
+echo "==> Patch:"
+printf '  %s\n' "${PATCH_LINES[@]}"
+echo
+
+if [[ "${APPLY}" -ne 1 ]]; then
+  echo "Dry-run only. Re-run with --apply to write and restart Portal."
+  exit 0
+fi
+
+PATCH_B64=$(printf '%s\n' "${PATCH_LINES[@]}" | base64 | tr -d '\n')
+
+ssh "${HOST}" "bash -s" <<REMOTE
+set -euo pipefail
+ENV_FILE="${REMOTE_ENV}"
+BACKUP="\${ENV_FILE}.bak-token-opt-\$(date +%Y%m%d-%H%M%S)"
+cp "\${ENV_FILE}" "\${BACKUP}"
+echo "Backed up to \${BACKUP}"
+
+upsert() {
+  local key="\$1" val="\$2"
+  if grep -q "^\${key}=" "\${ENV_FILE}"; then
+    sed -i '' "s|^\${key}=.*|\${key}=\${val}|" "\${ENV_FILE}"
+  else
+    printf '\n%s=%s\n' "\${key}" "\${val}" >> "\${ENV_FILE}"
+  fi
+}
+
+while IFS='=' read -r key val; do
+  [[ -z "\${key}" ]] && continue
+  upsert "\${key}" "\${val}"
+done <<'ENVPATCH'
+$(printf '%s\n' "${PATCH_LINES[@]}")
+ENVPATCH
+
+echo "==> Updated keys:"
+grep -E '^(MEMIND_CHAT_|MEMORY_AGENT_RESOLVE|MEMORY_RETRIEVER|MEMORY_PERSONA_MAX)' "\${ENV_FILE}" || true
+
+GUI="\$(/usr/bin/stat -f %u /dev/console)"
+launchctl kickstart -k "gui/\${GUI}/cn.tkmind.memind-portal"
+sleep 3
+curl -sf http://127.0.0.1:8081/api/status >/dev/null
+echo "Portal restarted and /api/status OK"
+REMOTE
+
+echo "Done."