增强LLM运行分析与按维度统计视图

admin/dashboard/blueprints/system.py

@@ -395,6 +395,46 @@ def _extract_ai_runtime_snapshot() -> dict:
     return snapshot
 
 
+def _build_llm_runtime_analytics_payload() -> dict:
+    """构建 LLM 最近窗口分析载荷。
+
+    为什么单独抽这个函数：
+    1. 首页 AI 卡片只看摘要，而 `system_llm` 页面需要更细粒度的分组表；
+    2. 两边都依赖同一套运行时快照，避免把 scene/backend/provider/model 聚合逻辑散在多个接口里；
+    3. 第一阶段先做“最近窗口分析”，让管理员快速识别慢场景、失败模型和异常后端。
+    """
+    runtime_breakdown = UnifiedLLMClient.get_runtime_breakdown() or {}
+    overview_snapshot = _extract_ai_runtime_snapshot()
+    catalog_summary = _extract_llm_catalog_summary()
+
+    return {
+        "overview": {
+            "window_size": _safe_int(runtime_breakdown.get("window_size")),
+            "total_calls": _safe_int(runtime_breakdown.get("total_calls")),
+            "success_calls": _safe_int(runtime_breakdown.get("success_calls")),
+            "failed_calls": _safe_int(runtime_breakdown.get("failed_calls")),
+            "success_rate": _safe_float(runtime_breakdown.get("success_rate")),
+            "avg_latency_ms": _safe_float(runtime_breakdown.get("avg_latency_ms")),
+            "last_error": str(runtime_breakdown.get("last_error") or "").strip(),
+            "status": str(overview_snapshot.get("status") or "warning").strip(),
+            "summary": str(overview_snapshot.get("summary") or "").strip(),
+            "last_call": dict(runtime_breakdown.get("last_call") or {}),
+            "provider_count": _safe_int(catalog_summary.get("provider_count")),
+            "scene_count": _safe_int(catalog_summary.get("scene_count")),
+            "target_count": _safe_int(catalog_summary.get("target_count")),
+            "default_scene": str(catalog_summary.get("default_scene") or "").strip(),
+            "default_backend": str(catalog_summary.get("default_backend") or "").strip(),
+            "has_routing": bool(catalog_summary.get("has_routing")),
+        },
+        # 这里保留原始最近窗口明细，方便后续如果要做“最近 10 次调用”列表时直接复用。
+        "recent_rows": runtime_breakdown.get("rows", []) or [],
+        "by_scene": runtime_breakdown.get("by_scene", []) or [],
+        "by_backend": runtime_breakdown.get("by_backend", []) or [],
+        "by_provider": runtime_breakdown.get("by_provider", []) or [],
+        "by_model": runtime_breakdown.get("by_model", []) or [],
+    }
+
+
 def _extract_scheduler_runtime_snapshot() -> dict:
     """聚合 async_job 运行态，生成首页任务调度摘要。
 
@@ -1115,6 +1155,26 @@ def get_system_llm_config():
         return jsonify({"success": False, "message": str(e)}), 500
 
 
+@system_bp.route('/api/system/llm_runtime_analytics', methods=['GET'])
+@login_required
+def get_system_llm_runtime_analytics():
+    """返回 LLM 最近窗口分析结果。
+
+    这里不主动发起探活请求，也不做持久化成本结算，只消费统一客户端已经记录的最近窗口埋点：
+    1. 避免后台刷新页面反过来给 AI 服务制造额外压力；
+    2. 先把“按场景/后端/模型看成功率与耗时”做扎实；
+    3. 为后续真正的 token 成本中心预留接口形态。
+    """
+    try:
+        return jsonify({
+            "success": True,
+            "data": _build_llm_runtime_analytics_payload(),
+        })
+    except Exception as e:
+        logger.error(f"读取 LLM 运行分析失败: {e}")
+        return jsonify({"success": False, "message": str(e)}), 500
+
+
 @system_bp.route('/api/system/llm_config', methods=['POST'])
 @login_required
 def update_system_llm_config():

admin/dashboard/templates/system_llm.html

@@ -8,14 +8,181 @@
         <div class="page-hero-copy">
             <div class="page-eyebrow">LLM Catalog</div>
             <h1>LLM目录配置</h1>
-            <p>按 Provider 模板、Dify 应用、Scene 绑定三层维护，减少重复配置和切换成本。</p>
+            <p>按 Provider 模板、Dify 应用、Scene 绑定三层维护，并结合最近窗口运行分析判断哪条 AI 路由更慢、更容易失败。</p>
         </div>
         <div class="page-hero-actions">
-            <el-button size="mini" plain @click="loadLlmConfig">刷新</el-button>
+            <el-button size="mini" plain :loading="runtimeAnalyticsLoading" @click="reloadPageData">刷新</el-button>
             <el-button size="mini" type="success" @click="saveLlmConfig">保存配置</el-button>
         </div>
     </div>
 
+    <el-card class="workspace-card" shadow="hover">
+        <div slot="header" class="workspace-header">
+            <div>
+                <h3>AI运行分析</h3>
+                <p>基于统一 LLM 客户端最近窗口埋点做被动观测，不额外发起探活请求。</p>
+            </div>
+            <div class="config-meta">
+                <span>窗口容量：{% raw %}{{ runtimeAnalytics.overview.window_size || 0 }}{% endraw %}</span>
+                <span>默认场景：{% raw %}{{ runtimeAnalytics.overview.default_scene || '-' }}{% endraw %}</span>
+                <span>默认目标：{% raw %}{{ runtimeAnalytics.overview.default_backend || '-' }}{% endraw %}</span>
+            </div>
+        </div>
+
+        <div class="runtime-summary-grid">
+            <div class="runtime-summary-card">
+                <div class="summary-label">最近调用</div>
+                <div class="summary-value">{% raw %}{{ runtimeAnalytics.overview.total_calls || 0 }}{% endraw %}</div>
+                <div class="summary-hint">成功 {% raw %}{{ runtimeAnalytics.overview.success_calls || 0 }}{% endraw %} / 失败 {% raw %}{{ runtimeAnalytics.overview.failed_calls || 0 }}{% endraw %}</div>
+            </div>
+            <div class="runtime-summary-card">
+                <div class="summary-label">成功率</div>
+                <div class="summary-value">{% raw %}{{ formatPercent(runtimeAnalytics.overview.success_rate) }}{% endraw %}</div>
+                <div class="summary-hint">按最近窗口实时汇总</div>
+            </div>
+            <div class="runtime-summary-card">
+                <div class="summary-label">平均耗时</div>
+                <div class="summary-value">{% raw %}{{ formatLatency(runtimeAnalytics.overview.avg_latency_ms) }}{% endraw %}</div>
+                <div class="summary-hint">用于快速识别慢场景</div>
+            </div>
+            <div class="runtime-summary-card">
+                <div class="summary-label">路由规模</div>
+                <div class="summary-value">{% raw %}{{ runtimeAnalytics.overview.scene_count || 0 }}{% endraw %}</div>
+                <div class="summary-hint">场景数 / 目标数 {% raw %}{{ runtimeAnalytics.overview.target_count || 0 }}{% endraw %}</div>
+            </div>
+        </div>
+
+        <div class="runtime-overview-panel">
+            <div class="runtime-status-row">
+                <el-tag size="mini" :type="statusTagType(runtimeAnalytics.overview.status)">
+                    {% raw %}{{ statusText(runtimeAnalytics.overview.status) }}{% endraw %}
+                </el-tag>
+                <span class="runtime-overview-text">{% raw %}{{ runtimeAnalytics.overview.summary || '最近窗口内暂无统一 LLM 调用记录' }}{% endraw %}</span>
+            </div>
+            <div class="runtime-overview-meta">
+                <span>Provider 模板：{% raw %}{{ runtimeAnalytics.overview.provider_count || 0 }}{% endraw %}</span>
+                <span>最近场景：{% raw %}{{ runtimeAnalytics.overview.last_call.scene || '-' }}{% endraw %}</span>
+                <span>最近后端：{% raw %}{{ runtimeAnalytics.overview.last_call.backend || '-' }}{% endraw %}</span>
+                <span>最近模型：{% raw %}{{ runtimeAnalytics.overview.last_call.model || '-' }}{% endraw %}</span>
+                <span>最近时间：{% raw %}{{ runtimeAnalytics.overview.last_call.timestamp || '-' }}{% endraw %}</span>
+            </div>
+            <div class="runtime-error-box" v-if="runtimeAnalytics.overview.last_error">
+                <strong>最近错误：</strong>
+                <span>{% raw %}{{ runtimeAnalytics.overview.last_error }}{% endraw %}</span>
+            </div>
+        </div>
+
+        <div class="runtime-table-grid">
+            <el-card class="analytics-card" shadow="never">
+                <div slot="header" class="runtime-table-header">
+                    <div>
+                        <h4>按场景统计</h4>
+                        <p>定位哪个业务场景最常调用、最容易失败。</p>
+                    </div>
+                </div>
+                <el-table v-if="runtimeAnalytics.by_scene.length" :data="runtimeAnalytics.by_scene" size="mini" style="width: 100%">
+                    <el-table-column prop="key" label="Scene" min-width="150" show-overflow-tooltip></el-table-column>
+                    <el-table-column prop="total_calls" label="调用数" width="80"></el-table-column>
+                    <el-table-column label="成功率" width="100">
+                        <template slot-scope="scope">
+                            {% raw %}{{ formatPercent(scope.row.success_rate) }}{% endraw %}
+                        </template>
+                    </el-table-column>
+                    <el-table-column label="平均耗时" width="110">
+                        <template slot-scope="scope">
+                            {% raw %}{{ formatLatency(scope.row.avg_latency_ms) }}{% endraw %}
+                        </template>
+                    </el-table-column>
+                    <el-table-column prop="failed_calls" label="失败数" width="80"></el-table-column>
+                    <el-table-column prop="last_call_at" label="最近调用" min-width="150"></el-table-column>
+                    <el-table-column prop="last_error" label="最近错误" min-width="220" show-overflow-tooltip></el-table-column>
+                </el-table>
+                <el-empty v-else description="最近窗口内暂无场景调用数据"></el-empty>
+            </el-card>
+
+            <el-card class="analytics-card" shadow="never">
+                <div slot="header" class="runtime-table-header">
+                    <div>
+                        <h4>按后端统计</h4>
+                        <p>观察 backend 层是否存在集中失败或慢请求。</p>
+                    </div>
+                </div>
+                <el-table v-if="runtimeAnalytics.by_backend.length" :data="runtimeAnalytics.by_backend" size="mini" style="width: 100%">
+                    <el-table-column prop="key" label="Backend" min-width="150" show-overflow-tooltip></el-table-column>
+                    <el-table-column prop="total_calls" label="调用数" width="80"></el-table-column>
+                    <el-table-column label="成功率" width="100">
+                        <template slot-scope="scope">
+                            {% raw %}{{ formatPercent(scope.row.success_rate) }}{% endraw %}
+                        </template>
+                    </el-table-column>
+                    <el-table-column label="平均耗时" width="110">
+                        <template slot-scope="scope">
+                            {% raw %}{{ formatLatency(scope.row.avg_latency_ms) }}{% endraw %}
+                        </template>
+                    </el-table-column>
+                    <el-table-column prop="failed_calls" label="失败数" width="80"></el-table-column>
+                    <el-table-column prop="last_call_at" label="最近调用" min-width="150"></el-table-column>
+                    <el-table-column prop="last_error" label="最近错误" min-width="220" show-overflow-tooltip></el-table-column>
+                </el-table>
+                <el-empty v-else description="最近窗口内暂无后端调用数据"></el-empty>
+            </el-card>
+
+            <el-card class="analytics-card" shadow="never">
+                <div slot="header" class="runtime-table-header">
+                    <div>
+                        <h4>按 Provider 统计</h4>
+                        <p>区分 Dify 与 OpenAI Compatible 等不同接入形态的表现。</p>
+                    </div>
+                </div>
+                <el-table v-if="runtimeAnalytics.by_provider.length" :data="runtimeAnalytics.by_provider" size="mini" style="width: 100%">
+                    <el-table-column prop="key" label="Provider" min-width="150" show-overflow-tooltip></el-table-column>
+                    <el-table-column prop="total_calls" label="调用数" width="80"></el-table-column>
+                    <el-table-column label="成功率" width="100">
+                        <template slot-scope="scope">
+                            {% raw %}{{ formatPercent(scope.row.success_rate) }}{% endraw %}
+                        </template>
+                    </el-table-column>
+                    <el-table-column label="平均耗时" width="110">
+                        <template slot-scope="scope">
+                            {% raw %}{{ formatLatency(scope.row.avg_latency_ms) }}{% endraw %}
+                        </template>
+                    </el-table-column>
+                    <el-table-column prop="failed_calls" label="失败数" width="80"></el-table-column>
+                    <el-table-column prop="last_call_at" label="最近调用" min-width="150"></el-table-column>
+                    <el-table-column prop="last_error" label="最近错误" min-width="220" show-overflow-tooltip></el-table-column>
+                </el-table>
+                <el-empty v-else description="最近窗口内暂无 Provider 调用数据"></el-empty>
+            </el-card>
+
+            <el-card class="analytics-card" shadow="never">
+                <div slot="header" class="runtime-table-header">
+                    <div>
+                        <h4>按模型统计</h4>
+                        <p>帮助判断是否需要按场景切换模型或做降级策略。</p>
+                    </div>
+                </div>
+                <el-table v-if="runtimeAnalytics.by_model.length" :data="runtimeAnalytics.by_model" size="mini" style="width: 100%">
+                    <el-table-column prop="key" label="Model" min-width="150" show-overflow-tooltip></el-table-column>
+                    <el-table-column prop="total_calls" label="调用数" width="80"></el-table-column>
+                    <el-table-column label="成功率" width="100">
+                        <template slot-scope="scope">
+                            {% raw %}{{ formatPercent(scope.row.success_rate) }}{% endraw %}
+                        </template>
+                    </el-table-column>
+                    <el-table-column label="平均耗时" width="110">
+                        <template slot-scope="scope">
+                            {% raw %}{{ formatLatency(scope.row.avg_latency_ms) }}{% endraw %}
+                        </template>
+                    </el-table-column>
+                    <el-table-column prop="failed_calls" label="失败数" width="80"></el-table-column>
+                    <el-table-column prop="last_call_at" label="最近调用" min-width="150"></el-table-column>
+                    <el-table-column prop="last_error" label="最近错误" min-width="220" show-overflow-tooltip></el-table-column>
+                </el-table>
+                <el-empty v-else description="最近窗口内暂无模型调用数据"></el-empty>
+            </el-card>
+        </div>
+    </el-card>
+
     <el-card class="workspace-card" shadow="hover">
         <div slot="header" class="workspace-header">
             <div>
@@ -215,6 +382,30 @@
                 currentView: '17',
                 configPath: '',
                 topologyRows: [],
+                runtimeAnalyticsLoading: false,
+                runtimeAnalytics: {
+                    overview: {
+                        window_size: 0,
+                        total_calls: 0,
+                        success_calls: 0,
+                        failed_calls: 0,
+                        success_rate: 0,
+                        avg_latency_ms: 0,
+                        last_error: '',
+                        status: 'warning',
+                        summary: '',
+                        last_call: {},
+                        provider_count: 0,
+                        scene_count: 0,
+                        target_count: 0,
+                        default_scene: '',
+                        default_backend: ''
+                    },
+                    by_scene: [],
+                    by_backend: [],
+                    by_provider: [],
+                    by_model: []
+                },
                 catalog: {
                     default_scene: '',
                     providers: [],
@@ -240,12 +431,45 @@
         },
         mounted() {
             this.currentView = '17';
-            this.loadLlmConfig();
+            this.reloadPageData();
         },
         methods: {
             newUid() {
                 return `${Date.now()}_${Math.random().toString(36).slice(2, 8)}`;
             },
+            // 统一刷新配置与运行分析，避免管理员点一次“刷新”只能看到半套信息。
+            async reloadPageData() {
+                await Promise.all([
+                    this.loadLlmConfig(),
+                    this.loadRuntimeAnalytics()
+                ]);
+            },
+            statusTagType(status) {
+                if (status === 'healthy') {
+                    return 'success';
+                }
+                if (status === 'danger') {
+                    return 'danger';
+                }
+                return 'warning';
+            },
+            statusText(status) {
+                if (status === 'healthy') {
+                    return '运行正常';
+                }
+                if (status === 'danger') {
+                    return '需要立即处理';
+                }
+                return '需要关注';
+            },
+            formatPercent(value) {
+                const numeric = Number(value || 0);
+                return `${numeric.toFixed(2)}%`;
+            },
+            formatLatency(value) {
+                const numeric = Number(value || 0);
+                return `${numeric.toFixed(2)} ms`;
+            },
             // Provider 模板：只放公共字段，避免 Dify 每个应用重复填写。
             newProvider() {
                 return {
@@ -396,6 +620,46 @@
                 }
                 return this.difyAppNameOptions;
             },
+            async loadRuntimeAnalytics() {
+                this.runtimeAnalyticsLoading = true;
+                try {
+                    const response = await axios.get('/api/system/llm_runtime_analytics');
+                    if (!response.data.success) {
+                        this.$message.error(response.data.message || '读取 AI 运行分析失败');
+                        return;
+                    }
+                    const data = response.data.data || {};
+                    const overview = data.overview || {};
+                    // 这里做前端兜底结构归一化，避免后端未来新增字段时影响当前页面渲染。
+                    this.runtimeAnalytics = {
+                        overview: {
+                            window_size: overview.window_size || 0,
+                            total_calls: overview.total_calls || 0,
+                            success_calls: overview.success_calls || 0,
+                            failed_calls: overview.failed_calls || 0,
+                            success_rate: overview.success_rate || 0,
+                            avg_latency_ms: overview.avg_latency_ms || 0,
+                            last_error: overview.last_error || '',
+                            status: overview.status || 'warning',
+                            summary: overview.summary || '',
+                            last_call: overview.last_call || {},
+                            provider_count: overview.provider_count || 0,
+                            scene_count: overview.scene_count || 0,
+                            target_count: overview.target_count || 0,
+                            default_scene: overview.default_scene || '',
+                            default_backend: overview.default_backend || ''
+                        },
+                        by_scene: data.by_scene || [],
+                        by_backend: data.by_backend || [],
+                        by_provider: data.by_provider || [],
+                        by_model: data.by_model || []
+                    };
+                } catch (error) {
+                    this.$message.error(error.response?.data?.message || '读取 AI 运行分析失败');
+                } finally {
+                    this.runtimeAnalyticsLoading = false;
+                }
+            },
             async loadLlmConfig() {
                 try {
                     const response = await axios.get('/api/system/llm_config');
@@ -474,7 +738,7 @@
                     const response = await axios.post('/api/system/llm_config', payload);
                     if (response.data.success) {
                         this.$message.success(response.data.message || '保存成功');
-                        this.loadLlmConfig();
+                        this.reloadPageData();
                     } else {
                         this.$message.error(response.data.message || '保存失败');
                     }
@@ -513,6 +777,86 @@
         gap: 8px;
         flex-wrap: wrap;
     }
+    .runtime-summary-grid {
+        display: grid;
+        grid-template-columns: repeat(4, minmax(180px, 1fr));
+        gap: 14px;
+        margin-bottom: 16px;
+    }
+    .runtime-summary-card {
+        padding: 16px 18px;
+        border-radius: 16px;
+        border: 1px solid rgba(148,163,184,0.18);
+        background: linear-gradient(180deg, rgba(255,255,255,0.96), rgba(241,245,249,0.88));
+    }
+    .summary-label {
+        font-size: 12px;
+        color: #64748b;
+        margin-bottom: 8px;
+    }
+    .summary-value {
+        font-size: 28px;
+        line-height: 1;
+        font-weight: 700;
+        color: #0f172a;
+        margin-bottom: 8px;
+    }
+    .summary-hint {
+        font-size: 12px;
+        color: #475569;
+    }
+    .runtime-overview-panel {
+        padding: 16px 18px;
+        border-radius: 16px;
+        background: rgba(15, 23, 42, 0.03);
+        border: 1px solid rgba(148,163,184,0.14);
+        margin-bottom: 18px;
+    }
+    .runtime-status-row {
+        display: flex;
+        align-items: center;
+        gap: 10px;
+        flex-wrap: wrap;
+        margin-bottom: 10px;
+    }
+    .runtime-overview-text {
+        color: #0f172a;
+        font-size: 14px;
+    }
+    .runtime-overview-meta {
+        display: flex;
+        gap: 12px 18px;
+        flex-wrap: wrap;
+        color: #64748b;
+        font-size: 12px;
+    }
+    .runtime-error-box {
+        margin-top: 12px;
+        padding: 10px 12px;
+        border-radius: 10px;
+        background: rgba(239, 68, 68, 0.08);
+        color: #991b1b;
+        font-size: 12px;
+        line-height: 1.6;
+    }
+    .runtime-table-grid {
+        display: grid;
+        grid-template-columns: repeat(2, minmax(0, 1fr));
+        gap: 16px;
+    }
+    .analytics-card {
+        border: 1px solid rgba(148,163,184,0.16);
+        border-radius: 16px;
+    }
+    .runtime-table-header h4 {
+        font-size: 16px;
+        margin-bottom: 4px;
+        color: #0f172a;
+    }
+    .runtime-table-header p {
+        color: #64748b;
+        font-size: 12px;
+    }
     .section-list { display: flex; flex-direction: column; gap: 12px; }
     .entry-card { border: 1px solid rgba(148,163,184,0.16); border-radius: 14px; }
     .entry-header { display: flex; align-items: center; justify-content: space-between; gap: 12px; }
@@ -541,6 +885,8 @@
     @media (max-width: 960px) {
         .page-hero { flex-direction: column; align-items: flex-start; }
         .workspace-header { flex-direction: column; align-items: flex-start; }
+        .runtime-summary-grid { grid-template-columns: 1fr; }
+        .runtime-table-grid { grid-template-columns: 1fr; }
         .entry-grid { grid-template-columns: 1fr; }
         .scene-row { grid-template-columns: 1fr; }
     }

docs/工程优化与Feature清单.md

@@ -555,6 +555,12 @@
 
 - 让 AI 能力更可控、更可衡量
 
+当前进展：
+
+- 第一阶段已完成：后台 `LLM目录配置` 页面已补充“AI运行分析”区块，可查看最近窗口内统一 LLM 调用的成功率、平均耗时、失败次数与最近错误
+- 第一阶段已完成：已支持按 `scene / backend / provider / model` 四个维度聚合最近窗口调用数据，便于快速识别慢场景、异常后端与高失败模型
+- 当前仍以“最近窗口运行分析”为主，暂未引入持久化 token 成本结算；后续可在确认治理需求后继续扩展预算、告警与降级策略
+
 建议内容：
 
 - 统计各插件 token 消耗

utils/ai/unified_llm.py

@@ -109,6 +109,107 @@ class UnifiedLLMClient:
             "last_error": last_error,
         }
 
+    @staticmethod
+    def _normalize_runtime_dimension_value(value: Any, fallback_label: str = "(未标记)") -> str:
+        """把分组维度统一格式化，避免后台表格里出现空白 key。
+
+        这里保留一个显式的“未标记”占位，有两个目的：
+        1. 便于管理员快速发现是哪个插件/场景没有正确传 scene、backend、model；
+        2. 比直接丢弃空值更安全，避免分析数据被“悄悄吃掉”。
+        """
+        text = str(value or "").strip()
+        return text or fallback_label
+
+    @classmethod
+    def _build_runtime_breakdown_rows(
+        cls,
+        rows: List[Dict[str, Any]],
+        dimension: str,
+        fallback_label: str = "(未标记)",
+    ) -> List[Dict[str, Any]]:
+        """按指定维度聚合最近窗口调用记录。
+
+        设计说明：
+        1. 这里只聚合最近窗口内存数据，不引入新表，也不做持久化成本结算；
+        2. 第一阶段目标是先让管理员看见“哪类调用更慢、更容易失败”；
+        3. 等后续确认成本治理真的需要时，再把 token/金额沉淀到持久化表里。
+        """
+        grouped_rows: Dict[str, Dict[str, Any]] = {}
+
+        for row in rows:
+            group_key = cls._normalize_runtime_dimension_value(row.get(dimension), fallback_label)
+            metric_row = grouped_rows.setdefault(
+                group_key,
+                {
+                    "key": group_key,
+                    "dimension": dimension,
+                    "total_calls": 0,
+                    "success_calls": 0,
+                    "failed_calls": 0,
+                    "latency_sum_ms": 0.0,
+                    "avg_latency_ms": 0.0,
+                    "success_rate": 0.0,
+                    "last_call_at": "",
+                    "last_trace_id": "",
+                    "last_error": "",
+                },
+            )
+
+            metric_row["total_calls"] += 1
+            if bool(row.get("success")):
+                metric_row["success_calls"] += 1
+            else:
+                metric_row["failed_calls"] += 1
+
+            metric_row["latency_sum_ms"] += float(row.get("latency_ms") or 0.0)
+
+            # deque 本身按时间顺序追加，因此后遍历到的同组记录就是更“新”的一次调用。
+            # 这里直接覆盖最近调用信息，成本低，也足够支撑后台最近窗口分析表。
+            metric_row["last_call_at"] = str(row.get("timestamp") or "").strip()
+            metric_row["last_trace_id"] = str(row.get("trace_id") or "").strip()
+            if not bool(row.get("success")) and row.get("error"):
+                metric_row["last_error"] = str(row.get("error") or "").strip()
+
+        result_rows: List[Dict[str, Any]] = []
+        for item in grouped_rows.values():
+            total_calls = int(item.get("total_calls") or 0)
+            success_calls = int(item.get("success_calls") or 0)
+            item["avg_latency_ms"] = round((item.get("latency_sum_ms", 0.0) / total_calls), 2) if total_calls else 0.0
+            item["success_rate"] = round((success_calls / total_calls) * 100, 2) if total_calls else 0.0
+            item.pop("latency_sum_ms", None)
+            result_rows.append(item)
+
+        return sorted(
+            result_rows,
+            key=lambda item: (
+                -int(item.get("total_calls") or 0),
+                -int(item.get("failed_calls") or 0),
+                str(item.get("key") or ""),
+            ),
+        )
+
+    @classmethod
+    def get_runtime_breakdown(cls) -> Dict[str, Any]:
+        """返回最近窗口 LLM 调用的多维度聚合分析结果。
+
+        返回结构专门给后台“AI 成本与策略中心”第一阶段使用：
+        1. 先围绕 scene / backend / provider / model 做聚合；
+        2. 重点回答成功率、平均耗时、失败次数、最近错误；
+        3. 暂不承诺长期留存，只服务于最近窗口的运行分析。
+        """
+        with cls._runtime_lock:
+            rows = list(cls._runtime_metrics)
+
+        snapshot = cls.get_runtime_snapshot()
+        return {
+            **snapshot,
+            "rows": rows,
+            "by_scene": cls._build_runtime_breakdown_rows(rows, "scene"),
+            "by_backend": cls._build_runtime_breakdown_rows(rows, "backend"),
+            "by_provider": cls._build_runtime_breakdown_rows(rows, "provider"),
+            "by_model": cls._build_runtime_breakdown_rows(rows, "model"),
+        }
+
     def is_available(self) -> bool:
         if not self.enabled:
             return False