Merge branch 'ji'

frontend-web/src/components/workspace/messages/message-token-usage.tsx

@@ -88,7 +88,13 @@ export function MessageTokenUsage({
   // otherwise wait until usage arrives so the bar doesn't render on its own.
   const usage = getUsageMetadata(message);
   const showUsage = enabled && !isLoading && Boolean(usage);
-  const showSpeed = speedEnabled && resolvedMetric !== undefined && resolvedMetric.durationMs > 0;
+  // The speed pill is shown once per turn — only on the turn-final AI message
+  // — so a turn with several LLM calls no longer renders a pill per call.
+  const showSpeed =
+    speedEnabled &&
+    resolvedMetric !== undefined &&
+    resolvedMetric.durationMs > 0 &&
+    resolvedMetric.isTurnFinal;
 
   if (!showUsage && !showSpeed) {
     return null;

frontend-web/src/components/workspace/messages/use-message-metrics.ts

@@ -36,6 +36,13 @@ export interface MessageMetric {
    * only to add a "~" prefix when the value is approximate.
    */
   fromUsage: boolean;
+  /**
+   * True iff this is the turn-final AI message — the last AI message before
+   * the next human message. An agentic turn makes several LLM calls, each its
+   * own AI message; the speed pill is shown only on the turn-final one so a
+   * turn renders a single pill instead of one per call.
+   */
+  isTurnFinal: boolean;
 }
 
 type Sample = { at: number; chars: number };
@@ -161,6 +168,25 @@ export function useMessageMetrics(
     return () => window.clearInterval(interval);
   }, [isLoading]);
 
+  // Identify the turn-final AI message of each turn — the last AI message
+  // (with a recorded metric) before the next human message. Only these show
+  // the speed pill, collapsing a multi-LLM-call turn to a single pill.
+  const turnFinalIds = new Set<string>();
+  let pendingTurnFinal: string | null = null;
+  for (let i = 0; i < messages.length; i++) {
+    const message = messages[i];
+    if (!message) continue;
+    if (message.type === "human") {
+      if (pendingTurnFinal) turnFinalIds.add(pendingTurnFinal);
+      pendingTurnFinal = null;
+      continue;
+    }
+    if (isAiMessage(message) && timingRef.current.has(getMessageId(message, i))) {
+      pendingTurnFinal = getMessageId(message, i);
+    }
+  }
+  if (pendingTurnFinal) turnFinalIds.add(pendingTurnFinal);
+
   // Build the snapshot map. We compute on every render so consumers see the
   // freshest duration; the underlying timing storage is the source of truth.
   const out = new Map<string, MessageMetric>();
@@ -212,6 +238,7 @@ export function useMessageMetrics(
       durationMs,
       isActive,
       fromUsage,
+      isTurnFinal: turnFinalIds.has(id),
     });
   }
 

frontend-web/src/pages/AdminUserActivityPage.tsx

@@ -71,67 +71,38 @@ function MessageRow({ message }: { message: AdminMessage }) {
   );
 }
 
-/** Render one memory bucket: context summaries + facts. */
+/** Render one Memory V2 bucket: a flat list of USER.md / MEMORY.md entries. */
 function MemoryBucketView({ bucket }: { bucket: AdminMemoryBucket }) {
-  const data = bucket.data as {
-    user?: Record<string, { summary?: string }>;
-    history?: Record<string, { summary?: string }>;
-    facts?: Array<{ content?: string; category?: string; confidence?: number }>;
-  };
-  const sections: Array<[string, string]> = [];
-  const labels: Record<string, string> = {
-    workContext: "工作背景",
-    personalContext: "个人背景",
-    topOfMind: "近期关注",
-    recentMonths: "近几个月",
-    earlierContext: "更早记录",
-    longTermBackground: "长期背景",
-  };
-  for (const group of [data.user, data.history]) {
-    for (const [key, value] of Object.entries(group ?? {})) {
-      const summary = value?.summary?.trim();
-      if (summary) sections.push([labels[key] ?? key, summary]);
-    }
-  }
-  const facts = (data.facts ?? []).filter((f) => f.content?.trim());
+  const raw = (bucket.data as { entries?: unknown }).entries;
+  const entries = Array.isArray(raw)
+    ? raw.filter(
+        (e): e is string => typeof e === "string" && e.trim().length > 0,
+      )
+    : [];
 
   return (
     <div className="rounded-md border p-4">
       <div className="mb-3 flex items-center gap-2">
         <Badge variant={bucket.name === "global" ? "default" : "secondary"}>
-          {bucket.name === "global" ? "全局记忆" : `Agent: ${bucket.name}`}
+          {bucket.name === "global" ? "用户画像" : `Agent: ${bucket.name}`}
         </Badge>
+        {entries.length > 0 && (
+          <span className="text-muted-foreground text-xs">
+            {entries.length} 条
+          </span>
+        )}
       </div>
-      {sections.length === 0 && facts.length === 0 && (
+      {entries.length === 0 ? (
         <p className="text-muted-foreground text-sm">该记忆库暂无内容。</p>
-      )}
-      {sections.length > 0 && (
-        <div className="flex flex-col gap-2">
-          {sections.map(([label, summary]) => (
-            <div key={label}>
-              <span className="text-muted-foreground text-xs">{label}</span>
-              <p className="text-sm">{summary}</p>
-            </div>
+      ) : (
+        <ul className="flex flex-col gap-1.5">
+          {entries.map((entry, i) => (
+            <li key={i} className="flex items-start gap-2 text-sm">
+              <span className="text-muted-foreground">·</span>
+              <span className="flex-1 whitespace-pre-wrap">{entry}</span>
+            </li>
           ))}
-        </div>
-      )}
-      {facts.length > 0 && (
-        <div className="mt-3">
-          <span className="text-muted-foreground text-xs">事实记录（{facts.length}）</span>
-          <ul className="mt-1 flex flex-col gap-1">
-            {facts.map((fact, i) => (
-              <li key={i} className="flex items-start gap-2 text-sm">
-                <span className="text-muted-foreground">·</span>
-                <span className="flex-1">{fact.content}</span>
-                {fact.category && (
-                  <Badge variant="outline" className="shrink-0 text-xs">
-                    {fact.category}
-                  </Badge>
-                )}
-              </li>
-            ))}
-          </ul>
-        </div>
+        </ul>
       )}
     </div>
   );

frontend-web/vite.config.ts

@@ -1,13 +1,13 @@
-import path from "node:path";
-import { fileURLToPath } from "node:url";
-
-import react from "@vitejs/plugin-react";
-import { defineConfig } from "vite";
+import path from "node:path";
+import { fileURLToPath } from "node:url";
+
+import react from "@vitejs/plugin-react";
+import { defineConfig } from "vite";
 
 const rootDir = fileURLToPath(new URL(".", import.meta.url));
 
-export default defineConfig({
-  plugins: [react()],
+export default defineConfig({
+  plugins: [react()],
   resolve: {
     alias: [
       {
@@ -32,6 +32,7 @@ export default defineConfig({
       },
     ],
   },
+  "base":"./",
   server: {
     port: 5173,
   },

offline-backend-20260512/backend/CLAUDE.md

@@ -173,7 +173,7 @@ Lead-agent middlewares are assembled in strict append order across `packages/har
 16. **SubagentLimitMiddleware** - Truncates excess `task` tool calls from model response to enforce `MAX_CONCURRENT_SUBAGENTS` limit (optional, if `subagent_enabled`)
 17. **LoopDetectionMiddleware** - Detects repeated tool-call loops; hard-stop responses clear both structured `tool_calls` and raw provider tool-call metadata before forcing a final text answer
 18. **ClarificationMiddleware** - Intercepts `ask_clarification` tool calls, interrupts via `Command(goto=END)` (must be last)
-8a. **ToolMetricsMiddleware** - Appended immediately after `ToolErrorHandlingMiddleware` in `_build_runtime_middlewares`, so it sits *deepest* in the `wrap_tool_call` chain. Records one `tool_call_metrics` row per tool/skill/MCP invocation — timing duration and bucketing failures into a coarse `error_category` (rate_limited / ip_blocked / auth_error / timeout / network_error / not_found / empty_result / tool_error). It catches both raised exceptions (before #8 converts them) and *self-handled* errors returned as error `ToolMessage`s (search tools that swallow HTTP 429/403). It also captures the targeted Agent Skill into `tool_call_metrics.skill_name`: the lead agent invokes a skill by `read_file`-ing its `/mnt/skills/<category>/<name>/SKILL.md` path (per the `<skill_system>` prompt block — there is no dedicated "use skill" tool), so `skill_name` is recovered by scanning every tool call's string arguments for the `skills/{public,custom}/{name}/` path shape (`_SKILL_PATH_RE`), plus the explicit `name` argument of `skill_view`/`skill_manage`/`skill_archive`. `skill_view`-specific success detection handles its swallowed failures (`Skill 'x' not found.` returned as a plain string rather than raised). Regression coverage: `tests/test_tool_metrics_middleware.py`. Writes are best-effort fire-and-forget. Surfaced by the admin `GET /api/admin/tool-metrics` router (which has a `kind=skill` filter and a per-skill `/summary` breakdown).
+8a. **ToolMetricsMiddleware** - Appended immediately after `ToolErrorHandlingMiddleware` in `_build_runtime_middlewares`, so it sits *deepest* in the `wrap_tool_call` chain. Records one `tool_call_metrics` row per tool/skill/MCP invocation — timing duration and bucketing failures into a coarse `error_category` (rate_limited / ip_blocked / auth_error / timeout / network_error / not_found / empty_result / tool_error). It catches both raised exceptions (before #8 converts them) and *self-handled* errors returned as error `ToolMessage`s (search tools that swallow HTTP 429/403). It also captures the targeted Agent Skill into `tool_call_metrics.skill_name`: the lead agent invokes a skill by `read_file`-ing its `/mnt/skills/<category>/<name>/SKILL.md` path (per the `<skill_system>` prompt block — there is no dedicated "use skill" tool), so `skill_name` is recovered by scanning every tool call's string arguments for the `skills/{public,custom}/{name}/` path shape (`_SKILL_PATH_RE`), plus the explicit `name` argument of `skill_view`/`skill_manage`/`skill_archive`. `skill_view`-specific success detection handles its swallowed failures (`Skill 'x' not found.` returned as a plain string rather than raised). Regression coverage: `tests/test_tool_metrics_middleware.py`. Writes are best-effort fire-and-forget. **Per-run skill merging**: `SqlToolMetricsStore.record` upserts skill rows by `(run_id, skill_name)` instead of inserting one row per call — a skill touched by several tool calls / retries inside one run collapses to a single row. Any success in the run wins (a skill that failed then finally succeeded is recorded as one `success`); a skill that only ever failed yields one `error` row whose `error_message` accumulates every attempt's message; durations are summed. Non-skill tool calls, and skill calls with no `run_id`, keep one row each. The read→merge→write is guarded by a per-event-loop `asyncio.Lock`. Surfaced by the admin `GET /api/admin/tool-metrics` router (which has a `kind=skill` filter and a per-skill `/summary` breakdown).
 9. **PromptPrefixMiddleware** - Injects the admin-configured prompt prefix into the model request only. The prefix is tagged onto the first human message's `additional_kwargs['prompt_prefix']` by the Gateway (`app.gateway.services`) and prepended to content here at call time, so the persisted/displayed message stays prefix-free
 10. **SummarizationMiddleware** - Context reduction when approaching token limits (optional, if enabled)
 11. **TodoListMiddleware** - Task tracking with `write_todos` tool (optional, if plan_mode)

offline-backend-20260512/backend/app/gateway/routers/admin_users.py

@@ -23,7 +23,7 @@ from app.gateway.deps import (
     get_current_user_from_request,
     get_thread_store,
 )
-from deerflow.agents.memory.updater import get_memory_data
+from deerflow.agents.memory import BuiltinFileProvider
 from deerflow.config.agents_config import AGENT_NAME_PATTERN
 from deerflow.config.paths import get_paths
 from deerflow.runtime import serialize_channel_values
@@ -136,24 +136,40 @@ class AdminMemoryResponse(BaseModel):
     buckets: list[AdminMemoryBucket] = Field(default_factory=list)
 
 
+def _read_memory_bucket(user_id: str, agent_id: str | None) -> dict:
+    """Read one Memory V2 file into a JSON-able ``{"entries": [...]}`` dict.
+
+    ``agent_id is None`` -> the user's ``USER.md`` (profile, cross-agent);
+    otherwise that agent's ``MEMORY.md`` (per-agent working notes).
+    """
+    provider = BuiltinFileProvider()
+    provider.initialize(user_id=user_id, agent_id=agent_id)
+    target = "user" if agent_id is None else "memory"
+    return {"entries": provider.read(target)}
+
+
 @router.get("/{user_id}/memory", response_model=AdminMemoryResponse)
 async def get_user_memory(user_id: str, request: Request) -> AdminMemoryResponse:
-    """Return every memory bucket (global + per-agent) for one user."""
+    """Return every memory bucket for one user.
+
+    Memory V2 layout: the ``global`` bucket is ``USER.md`` (user profile);
+    each per-agent bucket is that agent's ``MEMORY.md`` working notes.
+    """
     await _require_admin(request)
 
     buckets: list[AdminMemoryBucket] = [
-        AdminMemoryBucket(name="global", data=get_memory_data(None, user_id=user_id))
+        AdminMemoryBucket(name="global", data=_read_memory_bucket(user_id, None))
     ]
 
     agents_root = get_paths().user_dir(user_id) / "agents"
     if agents_root.is_dir():
         for entry in sorted(agents_root.iterdir(), key=lambda p: p.name.lower()):
-            if not entry.is_dir() or not (entry / "memory.json").exists():
+            if not entry.is_dir() or not (entry / "MEMORY.md").exists():
                 continue
             if not AGENT_NAME_PATTERN.match(entry.name):
                 continue
             try:
-                data = get_memory_data(entry.name, user_id=user_id)
+                data = _read_memory_bucket(user_id, entry.name)
             except Exception:
                 logger.warning("Failed to read memory bucket %s for user", entry.name)
                 continue

offline-backend-20260512/backend/packages/harness/deerflow/persistence/tool_metrics/__init__.py

@@ -5,6 +5,14 @@ writes one row recording duration and outcome. Failures are bucketed into a
 coarse ``error_category`` (rate_limited / ip_blocked / auth_error / timeout /
 network_error / not_found / empty_result / tool_error) so administrators can
 spot retrieval outages, blocked IPs, and quota problems at a glance.
+
+**Skill-call merging**: a skill is often touched by several tool calls inside
+one run (read ``SKILL.md``, run a script, retry after a failure). Rather than
+one row per call, ``SqlToolMetricsStore.record`` upserts a single row per
+``(run_id, skill_name)``: any success in the run wins (a skill that failed then
+finally succeeded is recorded as one success), and a skill that only ever
+failed yields one ``error`` row whose ``error_message`` accumulates every
+attempt's message. Non-skill tool calls keep one row each.
 """
 
 from deerflow.persistence.tool_metrics.base import ToolMetricRecord, ToolMetricsStore

offline-backend-20260512/backend/packages/harness/deerflow/persistence/tool_metrics/sql.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import asyncio
 import logging
 from collections.abc import AsyncIterator
 from datetime import UTC, datetime
@@ -15,11 +16,99 @@ from deerflow.persistence.tool_metrics.model import ToolCallMetricRow
 
 logger = logging.getLogger(__name__)
 
+# Separator joining every failed attempt's message inside one merged skill row.
+_ERROR_SEP = "\n--- \n"
+# Hard cap on the accumulated ``error_message`` of a merged row.
+_MERGED_ERROR_LIMIT = 2000
+
+# A single skill invocation often spans several tool calls (read SKILL.md, run
+# a script, ...), and the model may retry a skill within the same run. Those
+# fire-and-forget writes can interleave on the event loop, so the read → merge →
+# write of a skill row is guarded by a lock. ``asyncio.Lock`` binds to the loop
+# it is used on; the lock is therefore (re)created whenever the running loop
+# changes (the Gateway keeps one persistent loop, while the ``asyncio.run``
+# fallback path spins up a throwaway loop per call — already serialized).
+_merge_lock: asyncio.Lock | None = None
+_merge_lock_loop: object = None
+
+
+def _skill_merge_lock() -> asyncio.Lock:
+    global _merge_lock, _merge_lock_loop
+    loop = asyncio.get_running_loop()
+    if _merge_lock is None or _merge_lock_loop is not loop:
+        _merge_lock = asyncio.Lock()
+        _merge_lock_loop = loop
+    return _merge_lock
+
 
 def _normalize_created_at(value: datetime) -> datetime:
     return value if value.tzinfo else value.replace(tzinfo=UTC)
 
 
+def _merge_error_messages(existing: str | None, new: str | None) -> str | None:
+    """Append a failed attempt's message to the accumulated error text.
+
+    All failures are kept ("都统计下来"), separated and capped at
+    :data:`_MERGED_ERROR_LIMIT` characters.
+    """
+    new = (new or "").strip()
+    if not new:
+        return existing
+    existing = (existing or "").strip()
+    if not existing:
+        return new
+    if new in existing:
+        # Identical retry failure — no point storing the same line twice.
+        return existing
+    combined = existing + _ERROR_SEP + new
+    if len(combined) > _MERGED_ERROR_LIMIT:
+        combined = combined[: _MERGED_ERROR_LIMIT - 3] + "..."
+    return combined
+
+
+def _merge_skill_row(existing: ToolCallMetricRow, metric: ToolMetricRecord) -> None:
+    """Fold another same-run, same-skill call into an existing metric row.
+
+    Outcome rules:
+    - any success in the run -> the row becomes a single ``success`` and earlier
+      failures are dropped ("先报错、最后成功 -> 记一次成功");
+    - all calls failed -> a single ``error`` row whose ``error_message``
+      accumulates every attempt's message ("一直报错 -> 记一次，报错信息都统计下来").
+    """
+    existing.duration_ms = (existing.duration_ms or 0) + (metric.duration_ms or 0)
+    if metric.status == "success":
+        if existing.status != "success":
+            existing.status = "success"
+            existing.error_category = None
+            existing.error_message = None
+        return
+    # metric.status == "error"
+    if existing.status == "success":
+        # The skill already succeeded once this run — keep that outcome.
+        return
+    existing.status = "error"
+    if metric.error_category:
+        existing.error_category = metric.error_category
+    existing.error_message = _merge_error_messages(existing.error_message, metric.error_message)
+
+
+def _row_from_metric(metric: ToolMetricRecord) -> ToolCallMetricRow:
+    return ToolCallMetricRow(
+        id=metric.id,
+        created_at=_normalize_created_at(metric.created_at),
+        user_id=metric.user_id,
+        thread_id=metric.thread_id,
+        run_id=metric.run_id,
+        agent_name=metric.agent_name,
+        tool_name=metric.tool_name,
+        skill_name=metric.skill_name,
+        duration_ms=metric.duration_ms,
+        status=metric.status,
+        error_category=metric.error_category,
+        error_message=metric.error_message,
+    )
+
+
 class SqlToolMetricsStore(ToolMetricsStore):
     """SQLite/Postgres/MySQL-backed implementation; writes are fire-and-forget."""
 
@@ -27,28 +116,48 @@ class SqlToolMetricsStore(ToolMetricsStore):
         self._sf = session_factory
 
     async def record(self, metric: ToolMetricRecord) -> None:
-        row = ToolCallMetricRow(
-            id=metric.id,
-            created_at=_normalize_created_at(metric.created_at),
-            user_id=metric.user_id,
-            thread_id=metric.thread_id,
-            run_id=metric.run_id,
-            agent_name=metric.agent_name,
-            tool_name=metric.tool_name,
-            skill_name=metric.skill_name,
-            duration_ms=metric.duration_ms,
-            status=metric.status,
-            error_category=metric.error_category,
-            error_message=metric.error_message,
-        )
+        # Skill calls are merged per run: the model may touch one skill across
+        # several tool calls / retries, and the admin wants a single row per
+        # (run, skill) instead of one per call. Non-skill tool calls, and skill
+        # calls with no run_id, fall back to a plain insert.
+        if metric.skill_name and metric.run_id:
+            await self._record_skill_merged(metric)
+            return
         try:
             async with self._sf() as session:
-                session.add(row)
+                session.add(_row_from_metric(metric))
                 await session.commit()
         except Exception:
             # Metrics are best-effort; never surface a write failure to the runtime.
             logger.warning("Failed to persist tool_call_metrics row %s", metric.id, exc_info=True)
 
+    async def _record_skill_merged(self, metric: ToolMetricRecord) -> None:
+        """Upsert a per-(run, skill) row, folding repeated calls into one."""
+        try:
+            async with _skill_merge_lock():
+                async with self._sf() as session:
+                    existing = await session.scalar(
+                        select(ToolCallMetricRow)
+                        .where(
+                            ToolCallMetricRow.run_id == metric.run_id,
+                            ToolCallMetricRow.skill_name == metric.skill_name,
+                        )
+                        .order_by(ToolCallMetricRow.created_at)
+                        .limit(1)
+                    )
+                    if existing is None:
+                        session.add(_row_from_metric(metric))
+                    else:
+                        _merge_skill_row(existing, metric)
+                    await session.commit()
+        except Exception:
+            logger.warning(
+                "Failed to merge tool_call_metrics for skill %s (run %s)",
+                metric.skill_name,
+                metric.run_id,
+                exc_info=True,
+            )
+
     def _apply_filters(self, stmt, query: ToolMetricsQuery):
         if query.user_id:
             stmt = stmt.where(ToolCallMetricRow.user_id == query.user_id)