feat(metrics): lokaler LLM-Verbrauch + Claude-Ersparnis in Diagnostic

metrics.jsonl-Eintraege tragen jetzt 'source' (claude|local|fast-path).
- log_local_call(): echte usage-Tokens vom Adapter (prompt/completion),
  sonst chars/4-Schaetzung. Geloggt pro Tool-Runde im lokalen Fast-Lane.
- log_fast_path(): reiner Skill, 0 Prompt-Tokens — gesparter Claude-Call.
- aggregate() liefert zusaetzlich by_source (calls/tokens_in/tokens_out).
  Alt-Eintraege ohne source zaehlen als claude (rueckwaerts-kompatibel).

Diagnostic Gehirn-Tab: neue Card "Lokales LLM & Claude-Ersparnis" — pro
Fenster (1h/5h/24h/30d) gesparte Claude-Calls (local + fast-path) und
lokale Token-Last (eigene HW, kein Quota) + Info-Block.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

aria-brain/metrics.py

@@ -52,25 +52,55 @@ def _messages_tokens(messages: list) -> int:
     return total
 
 
-def log_call(model: str, messages_in: list, reply_text: str = "") -> None:
-    """Eine Call-Metric anhaengen. Robust gegen Fehler (silent fail)."""
+def _append(model: str, tokens_in: int, tokens_out: int, source: str) -> None:
+    """Ein Metric-Entry auf Disk anhaengen. Robust (silent fail)."""
     try:
-        tokens_in = _messages_tokens(messages_in)
-        tokens_out = _estimate_tokens(reply_text)
         line = json.dumps({
             "ts": int(time.time() * 1000),
             "model": model,
-            "in": tokens_in,
-            "out": tokens_out,
+            "in": int(tokens_in),
+            "out": int(tokens_out),
+            "source": source,          # "claude" | "local" | "fast-path"
         })
         METRICS_FILE.parent.mkdir(parents=True, exist_ok=True)
         with METRICS_FILE.open("a", encoding="utf-8") as f:
             f.write(line + "\n")
-        # Sanftes Rotate ohne hohe IO-Kosten — nur alle 1000 Calls checken
         if (tokens_in + tokens_out) % 1000 < 4:
             _maybe_rotate()
     except Exception as exc:
-        logger.warning("metrics.log_call: %s", exc)
+        logger.warning("metrics._append: %s", exc)
+
+
+def log_call(model: str, messages_in: list, reply_text: str = "",
+             source: str = "claude") -> None:
+    """Claude-Call-Metric anhaengen (Tokens per chars/4-Schaetzung)."""
+    _append(model, _messages_tokens(messages_in), _estimate_tokens(reply_text), source)
+
+
+def log_local_call(model: str, messages_in: list, reply_text: str = "",
+                   usage: dict | None = None) -> None:
+    """Lokaler-LLM-Call-Metric. Nutzt echte usage-Tokens (prompt/completion)
+    wenn der Adapter sie liefert, sonst chars/4-Schaetzung wie bei Claude.
+    Quelle = 'local' — damit die Ersparnis-Rechnung local von claude trennt."""
+    tokens_in = tokens_out = None
+    if isinstance(usage, dict):
+        pt = usage.get("prompt_tokens")
+        ct = usage.get("completion_tokens")
+        if isinstance(pt, (int, float)):
+            tokens_in = int(pt)
+        if isinstance(ct, (int, float)):
+            tokens_out = int(ct)
+    if tokens_in is None:
+        tokens_in = _messages_tokens(messages_in)
+    if tokens_out is None:
+        tokens_out = _estimate_tokens(reply_text)
+    _append(model or "local", tokens_in, tokens_out, "local")
+
+
+def log_fast_path(reply_text: str = "") -> None:
+    """Fast-Path (reiner Skill, KEIN LLM) — spart einen ganzen Claude-Call zum
+    Nulltarif. tokens_in=0 (kein Prompt ans LLM), out = winzige Quittung."""
+    _append("fast-path", 0, _estimate_tokens(reply_text), "fast-path")
 
 
 def _maybe_rotate() -> None:
@@ -95,6 +125,11 @@ def aggregate(window_seconds: int) -> dict:
     tokens_in = 0
     tokens_out = 0
     by_model: dict[str, int] = {}
+    # Aufschluesselung nach Quelle (claude / local / fast-path) fuer die
+    # Ersparnis-Anzeige im Diagnostic.
+    def _src_bucket() -> dict:
+        return {"calls": 0, "tokens_in": 0, "tokens_out": 0}
+    by_source: dict[str, dict] = {}
     if METRICS_FILE.exists():
         try:
             for raw in METRICS_FILE.read_text(encoding="utf-8").splitlines():
@@ -107,11 +142,19 @@ def aggregate(window_seconds: int) -> dict:
                     continue
                 if obj.get("ts", 0) < cutoff_ms:
                     continue
+                ti = int(obj.get("in") or 0)
+                to = int(obj.get("out") or 0)
                 calls += 1
-                tokens_in += int(obj.get("in") or 0)
-                tokens_out += int(obj.get("out") or 0)
+                tokens_in += ti
+                tokens_out += to
                 m = obj.get("model", "?")
                 by_model[m] = by_model.get(m, 0) + 1
+                # Alt-Eintraege ohne 'source' zaehlen als claude (Rueckwaerts-Kompat).
+                src = obj.get("source") or "claude"
+                b = by_source.setdefault(src, _src_bucket())
+                b["calls"] += 1
+                b["tokens_in"] += ti
+                b["tokens_out"] += to
         except Exception as exc:
             logger.warning("metrics aggregate: %s", exc)
     return {
@@ -120,6 +163,7 @@ def aggregate(window_seconds: int) -> dict:
         "tokens_in": tokens_in,
         "tokens_out": tokens_out,
         "by_model": by_model,
+        "by_source": by_source,
     }