Tokens counter

interfaces/tui/agent.py

@@ -56,7 +56,7 @@ WELCOME_ART = """
 ╚══════════════════════════════════════════╝
 """
 
-def log(app, role, text, model_info=None):
+def log(app, role, text, model_info=None, usage=None):
     entry = {
         "role": role,
         "text": text,
@@ -65,6 +65,9 @@ def log(app, role, text, model_info=None):
     if model_info:
         # Display pair: (provider, model)
         entry["model_info"] = model_info
+    if usage:
+        # Token pemakaian: prompt/completion/total dari provider
+        entry["usage"] = usage
     app.log.append(entry)
     return entry
 
@@ -76,20 +79,23 @@ def _pair(model_info):
     return (model_info.get("provider"), model_info.get("model"))
 
 
-def _tag_ai_logs(app, prev_entries, pair):
+def _tag_ai_logs(app, prev_entries, pair, usage=None):
-    """Tempelkan info model yang benar-benar dipakai ke entri log AI yang baru
+    """Tempelkan info model (dan token usage) yang benar-benar dipakai ke entri
-    dibuat pada langkah ini.
+    log AI yang baru dibuat pada langkah ini.
 
     Berbasis identitas entri, bukan index: entri "Thinking..." di-pop setelah
     llm.chat() kembali sehingga index entri AI ikut bergeser. `prev_entries`
     menahan referensi entri lama agar id-nya tidak dipakai ulang oleh Python.
     """
-    if not pair:
+    if not pair and not usage:
         return
     seen = {id(e) for e in prev_entries}
     for entry in app.log:
         if entry.get("role") == "ai" and id(entry) not in seen:
-            entry["model_info"] = pair
+            if pair:
+                entry["model_info"] = pair
+            if usage:
+                entry["usage"] = usage
 
 
 class CursesObserver(agent_loop.AgentObserver):
@@ -109,10 +115,13 @@ class CursesObserver(agent_loop.AgentObserver):
     def __init__(self, app):
         self.app = app
         self.pair = None
+        self.usage = None          # token pemakaian langkah LLM terakhir
         self._buf = []
         self._placeholder = None
         self._stamp = None
         self._stamp_step = None
+        self._tool_total = 0       # jumlah tool call yang dijadwalkan langkah ini
+        self._tool_seen = 0        # tool call yang sudah di-log langkah ini
 
     # ------------------------------------------------------------- lifecycle ---
     def on_run_start(self):
@@ -126,6 +135,9 @@ class CursesObserver(agent_loop.AgentObserver):
         self._stamp_step = ntro.start()
         self._buf = []
         self._placeholder = None
+        self.usage = None
+        self._tool_total = 0
+        self._tool_seen = 0
         log(self.app, "system", f"  step {step + 1} \u2014 Thinking...")
         # Ditahan setelah "Thinking..." masuk, supaya referensi entri lama ikut
         # terjaga dan id()-nya tidak dipakai ulang saat entri itu di-pop.
@@ -135,8 +147,11 @@ class CursesObserver(agent_loop.AgentObserver):
     def on_step_end(self, step):
         ntro.end(self._stamp_step)
 
-    def on_response(self, model_info, warning):
+    def on_response(self, model_info, warning, usage=None):
         self.pair = _pair(model_info)
+        # Token pemakaian langkah LLM ini (untuk label usage di entri ai).
+        # Total satu turn sudah diakumulasi engine di AgentResult.usage.
+        self.usage = usage
         self._pop_thinking()
         if warning:
             log(self.app, "system", f"  {warning}")
@@ -155,15 +170,22 @@ class CursesObserver(agent_loop.AgentObserver):
         # Narasi assistant sebelum tools; placeholder streaming dibuang karena
         # konten yang sama akan di-log utuh di sini.
         self._drop_placeholder()
+        # Baris token satu panggilan LLM dititipkan ke tool_call TERAKHIR dari
+        # langkah ini (lihat on_tool_call), supaya urutan render tetap benar.
+        self._tool_total = len(names)
+        self._tool_seen = 0
         if content and content.strip():
-            log(self.app, "ai", content, model_info=self.pair)
+            log(self.app, "ai", content, model_info=self.pair, usage=self.usage)
             _tag_ai_logs(self.app, self._log_prev, self.pair)
 
     def on_tool_call(self, name, arguments):
-        log(self.app, "tool_call", json.dumps({
+        entry = log(self.app, "tool_call", json.dumps({
             "name": name,
             "arguments": arguments,
         }), model_info=self.pair)
+        self._tool_seen += 1
+        if self.usage and self._tool_seen >= self._tool_total:
+            entry["usage"] = self.usage
         self._follow()
 
     # ---------------------------------------------------------------- final ---
@@ -176,15 +198,25 @@ class CursesObserver(agent_loop.AgentObserver):
             return
 
         if result.is_multimodal:
-            log(self.app, "ai", "[Image generated]", model_info=self.pair)
+            entry = log(self.app, "ai", "[Image generated]",
+                        model_info=self.pair, usage=result.usage)
         elif content is None:
             # Respons kosong: tidak ada yang ditampilkan selain pemisah.
-            pass
+            entry = None
         elif self._placeholder is None and content:
             # Tidak ada chunk streaming sama sekali (provider tidak mengirim
             # delta) — pastikan teks tetap terlihat.
-            log(self.app, "ai", content, model_info=self.pair)
+            entry = log(self.app, "ai", content,
-        # placeholder streaming tidak perlu diisi ulang: isinya sudah teks final
+                        model_info=self.pair, usage=result.usage)
+        else:
+            # placeholder streaming tidak perlu diisi ulang: isinya sudah teks
+            # final. Gunakan sebagai target tempelan usage.
+            entry = self._placeholder
+
+        # Total token satu turn (akumulasi semua langkah LLM) ditaruh di
+        # entri respons final, dirender setelah teks pesan.
+        if entry is not None and result.usage:
+            entry["usage"] = result.usage
 
         _tag_ai_logs(self.app, self._log_prev, self.pair)
         log(self.app, "sep", "")

interfaces/tui/app.py

@@ -214,16 +214,21 @@ class HendrikTUI:
                 pair = (mi.get("provider"), mi.get("model")) if mi else None
                 # Log tool calls jika ada
                 if msg.get("tool_calls"):
-                    for tc in msg["tool_calls"]:
+                    tcl = msg["tool_calls"]
+                    for j, tc in enumerate(tcl):
+                        # Baris token langkah ini dititipkan di tool_call terakhir
+                        # (konvensi sama dengan CursesObserver.on_tool_call).
+                        last = (j == len(tcl) - 1)
                         log(self, "tool_call", json.dumps({
                             "name": tc["function"]["name"],
                             "arguments": tc["function"]["arguments"],
-                        }), model_info=pair)
+                        }), model_info=pair,
+                            usage=msg.get("usage") if last else None)
                 # Log AI content jika ada dan ini adalah response final
                 next_role = session.messages[i + 1]["role"] if i + 1 < len(session.messages) else None
                 if next_role in ("user", None):
                     if msg.get("content"):
-                        log(self, "ai", msg["content"])
+                        log(self, "ai", msg["content"], usage=msg.get("usage"))
                         if pair:
                             self.log[-1]["model_info"] = pair
             elif msg["role"] == "tool":

interfaces/tui/render.py

@@ -25,6 +25,7 @@ C_INPUT_BORDER = theme.C_INPUT_BORDER
 C_STATUS_INFO = theme.C_STATUS_INFO
 C_WELCOME = theme.C_WELCOME
 C_TOOL_CALL = theme.C_TOOL_CALL
+C_USAGE = theme.C_USAGE
 
 
 def draw(app, stdscr):
@@ -68,6 +69,35 @@ def _model_info_line(item):
     return f" {m_name} " if m_name else ""
 
 
+def _fmt_tokens(n):
+    """1234 -> '1.2k', 999 -> '999'."""
+    if n >= 1000:
+        return f"{n / 1000:.1f}k".replace(".0k", "k")
+    return str(n)
+
+
+def _usage_line(item, label="tokens"):
+    """Baris token usage dari entri log; "" jika tidak ada.
+
+    Contoh: ' tokens  in 48.2k → out 260 · total 48.5k · ctx 48.2k '.
+    `ctx` = ukuran context window saat itu (prompt tokens panggilan terakhir;
+    pada riwayat lama tanpa data itu, jatuh ke total in).
+    `label` membedakan cakupan angka: 'tokens' untuk total satu turn (blok ai
+    final), 'step tokens' untuk satu panggilan LLM (blok tool_call)."""
+    usage = item.get("usage")
+    if not isinstance(usage, dict):
+        return ""
+    try:
+        pt = int(usage.get("prompt_tokens") or 0)
+        ct = int(usage.get("completion_tokens") or 0)
+        tt = int(usage.get("total_tokens") or (pt + ct))
+        ctx = int(usage.get("context_tokens") or pt or tt)
+    except (TypeError, ValueError):
+        return ""
+    return (f" {label}  in {_fmt_tokens(pt)} → out {_fmt_tokens(ct)}"
+            f" · total {_fmt_tokens(tt)} · ctx {_fmt_tokens(ctx)} ")
+
+
 def draw_chat(app, stdscr):
     # Area chat — dari baris 2 sampai baris (h - 11).
     # Bisa di-scroll dengan Page Up / Page Down.
@@ -159,6 +189,10 @@ def draw_chat(app, stdscr):
             label = f" {app.character_name} ({item['time']}) "
             _add_row([(C_AI, label)])
             _wrap_render(text, indent=1, color=C_INPUT, bold=False)
+            # Baris token usage setelah teks pesan respons
+            usage_line = _usage_line(item)
+            if usage_line:
+                _add_row([(C_USAGE, usage_line, False)])
         elif role == "system":
             lines = text.split("\n")
             _add_row([(C_SYSTEM, lines[0])])
@@ -208,6 +242,11 @@ def draw_chat(app, stdscr):
                     (C_TOOL_CALL, "unknown"),
                     (C_AI, f" ({item['time']}) "),
                 ])
+            # Baris token panggilan LLM yang menghasilkan tool call ini,
+            # ditempel di tool_call terakhir langkah (lihat agent.CursesObserver).
+            usage_line = _usage_line(item, label="step tokens")
+            if usage_line:
+                _add_row([(C_USAGE, usage_line, False)])
             # Blank line di bawah
             _add_blank()
         elif role == "error":

interfaces/tui/theme.py

@@ -21,6 +21,7 @@ C_HINT_DISABLED = 13 # hint disabled (abu-abu)
 C_WELCOME = 14       # welcome art: light blue
 C_TOOL_CALL = 15     # tool call: kuning terang
 C_TOOL_RESULT = 16   # tool result: magenta muda
+C_USAGE = 22         # token usage: abu-abu
 
 # -- Menu bar colors (id 17+) --
 C_MENU = 17          # background menubar (sama dengan header)
@@ -49,6 +50,7 @@ def init_colors():
     curses.init_pair(C_WELCOME, curses.COLOR_BLUE + 8, -1)  # light blue
     curses.init_pair(C_TOOL_CALL, curses.COLOR_YELLOW + 8, -1)  # bright yellow
     curses.init_pair(C_TOOL_RESULT, curses.COLOR_MAGENTA + 8, -1)  # bright magenta
+    curses.init_pair(C_USAGE, 8, -1)  # abu-abu di atas bg default
 
     # Menu bar
     curses.init_pair(C_MENU, curses.COLOR_BLACK, curses.COLOR_BLUE)

lib/agent_loop.py

@@ -67,18 +67,20 @@ class SessionStore:
     def append(self, message):
         if not self.keep_model_info:
             message.pop('model_info', None)
+            message.pop('usage', None)   # metadata display, bukan payload LLM
         self.messages.append(message)
         if self._on_append:
             self._on_append(message)
 
 class AgentResult:
     # Hasil satu kali jalan loop.
-    def __init__(self, content=None, session_ended=False, model_info=None, warning=None, is_multimodal=False):
+    def __init__(self, content=None, session_ended=False, model_info=None, warning=None, is_multimodal=False, usage=None):
         self.content = content              # str | None
         self.session_ended = session_ended  # tool end_session pernah dipanggil
         self.model_info = model_info or {}  # model yang benar-benar menjawab
         self.warning = warning              # mis. fallback chain model
         self.is_multimodal = is_multimodal
+        self.usage = usage                  # token turn ini: prompt/completion/total (dict | None)
 
 
 class AgentObserver:
@@ -100,8 +102,11 @@ class AgentObserver:
     def on_step_start(self, step):
         pass
 
-    def on_response(self, model_info, warning):
+    def on_response(self, model_info, warning, usage=None):
-        """llm.chat() sudah kembali, sebelum engine memproses response."""
+        """llm.chat() sudah kembali, sebelum engine memproses response.
+
+        `usage` : token pemakaian satu permintaan (prompt/completion/total),
+                  None jika provider tidak mengirimnya."""
         pass
 
     def on_step_end(self, step):
@@ -182,6 +187,25 @@ def run_agent_loop(store, llm_client, TOOLS, TOOL_HANDLERS, max_iterations,
     warning = None
     stream_cb = observer.on_stream_chunk if observer.wants_streaming else None
 
+    # Akumulator token satu turn (semua langkah LLM dijumlahkan — tiap
+    # panggilan mengirim ulang konteks sehingga dihitung ulang oleh provider).
+    any_usage = False
+    turn_usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
+
+    def _accumulate(u):
+        nonlocal any_usage
+        if not isinstance(u, dict):
+            return
+        any_usage = True
+        for k in ("prompt_tokens", "completion_tokens", "total_tokens"):
+            v = u.get(k)
+            if isinstance(v, (int, float)):
+                turn_usage[k] += int(v)
+        # Ukuran context window saat itu = prompt tokens panggilan terakhir.
+        pt = u.get("prompt_tokens")
+        if isinstance(pt, (int, float)):
+            turn_usage["context_tokens"] = int(pt)
+
     observer.on_run_start()
 
     for step in range(max_iterations):
@@ -199,7 +223,9 @@ def run_agent_loop(store, llm_client, TOOLS, TOOL_HANDLERS, max_iterations,
         # auto-switch / rotasi key / fallback tercatat dengan benar.
         model_info = getattr(response, "model_info", None) or {}
         warning = getattr(response, "warning", None)
-        observer.on_response(model_info, warning)
+        usage = getattr(response, "usage", None)
+        _accumulate(usage)
+        observer.on_response(model_info, warning, usage)
 
         if response.tool_calls:
             amsg = {
@@ -209,6 +235,11 @@ def run_agent_loop(store, llm_client, TOOLS, TOOL_HANDLERS, max_iterations,
             }
             if model_info:
                 amsg['model_info'] = model_info
+            if isinstance(usage, dict) and usage:
+                # Usage per langkah (bukan akumulasi turn) — dipakai interface
+                # untuk menampilkan token di blok tool call yang dihasilkannya.
+                # Hanya metadata display: dibuang sebelum jadi payload LLM.
+                amsg['usage'] = dict(usage)
             store.append(amsg)
 
             observer.on_tool_calls(response.content or "",
@@ -243,6 +274,10 @@ def run_agent_loop(store, llm_client, TOOLS, TOOL_HANDLERS, max_iterations,
             msg = {'role': 'assistant', 'content': response.content}
             if model_info:
                 msg['model_info'] = model_info
+            if any_usage:
+                # Total token turn ini — ikut dipersist supaya tampil saat
+                # session dibuka kembali.
+                msg['usage'] = dict(turn_usage)
             store.append(msg)
 
             if is_multimodal:
@@ -258,6 +293,7 @@ def run_agent_loop(store, llm_client, TOOLS, TOOL_HANDLERS, max_iterations,
                 model_info=model_info,
                 warning=warning,
                 is_multimodal=is_multimodal,
+                usage=dict(turn_usage) if any_usage else None,
             )
             observer.on_final(final)
             observer.on_run_end()

services/llm_client.py

@@ -35,6 +35,8 @@ class LLMClient:
             self.content    = gadget.strip_thinking(raw_content) if isinstance(raw_content, str) else raw_content # Auto-strip <thinking> dari content
             self.tool_calls = msg.get('tool_calls', None) # Ambil tool calls
             self.warning    = None
+            # Pemakaian token dari provider (prompt/completion/total) jika ada.
+            self.usage      = None
             # Model yang benar-benar dipakai menghasilkan pesan ini. Bisa berbeda
             # dengan model awal permintaan karena auto-switch/fallback.
             self.model_info = model_info or {}
@@ -232,23 +234,36 @@ class LLMClient:
     # ------------------------------------------------------- single attempt ---
 
     def _chat_once(self, base_url, model, api_key, messages, tools=None,
-                   on_stream_chunk=None, disable_reasoning=False, tool_reminder=None):
+                   on_stream_chunk=None, disable_reasoning=False, tool_reminder=None,
+                   include_usage=True):
         url = f"{base_url.rstrip('/')}/chat/completions"
+        # Sanitasi: buang metadata display (model_info/usage) dari pesan sebelum
+        # jadi payload — beberapa provider memvalidasi field pesan dengan ketat.
+        msgs_clean = [
+            {k: v for k, v in m.items() if k not in ("model_info", "usage")}
+            if isinstance(m, dict) else m
+            for m in (messages or [])
+        ]
         payload = {
             "model": model,
-            "messages": messages,
+            "messages": msgs_clean,
             "stream": True  # Enable streaming
         }
+        if include_usage:
+            # Minta provider menyertakan pemakaian token (input/output) pada
+            # chunk terakhir stream. Provider yang tidak mendukungnya akan
+            # di-retry tanpa parameter ini (lihat penanganan HTTP 400).
+            payload["stream_options"] = {"include_usage": True}
         if tools:
             payload["tools"] = tools
             payload["tool_choice"] = "auto"
             if tool_reminder:
                 # Inject tool reminder sebagai system message sebelum user message terakhir
-                for i in range(len(messages) - 1, -1, -1):
+                for i in range(len(msgs_clean) - 1, -1, -1):
-                    if messages[i].get("role") == "user":
+                    if msgs_clean[i].get("role") == "user":
-                        payload["messages"] = messages[:i] + [
+                        payload["messages"] = msgs_clean[:i] + [
                             {"role": "system", "content": tool_reminder}
-                        ] + messages[i:]
+                        ] + msgs_clean[i:]
                         break
 
         # Hanya kirim parameter reasoning jika diminta eksplisit
@@ -268,6 +283,7 @@ class LLMClient:
         reasoning_content = ""
         full_multimodal_content = []
         stream_started = False
+        usage = None  # prompt/completion/total tokens dari chunk terakhir
 
         try:
             self.cancel_requested = False
@@ -292,9 +308,15 @@ class LLMClient:
                     except json.JSONDecodeError:
                         continue
 
+                    # Pemakaian token datang di chunk penutup (choices kosong
+                    # pada banyak provider) — ambil selama masih None / terbaru.
+                    if chunk.get('usage'):
+                        usage = chunk['usage']
+
                     # Parse delta dari chunk
-                    delta = chunk.get('choices', [{}])[0].get('delta', {})
+                    choices = chunk.get('choices') or []
-                    finish_reason = chunk.get('choices', [{}])[0].get('finish_reason', None)
+                    delta = choices[0].get('delta', {}) if choices else {}
+                    finish_reason = choices[0].get('finish_reason', None) if choices else None
 
                     # Stream reasoning content jika ada
                     if 'reasoning_content' in delta:
@@ -399,6 +421,16 @@ class LLMClient:
                         return result
                 except Exception:
                     pass
+            if include_usage and e.code in (400, 422) and "stream_options" in (body_text or "").lower():
+                # Provider menolak parameter stream_options — ulangi permintaan
+                # tanpanya; token usage tidak tersedia tapi respons tetap jalan.
+                return self._chat_once(
+                    base_url, model, api_key, messages, tools=tools,
+                    on_stream_chunk=on_stream_chunk,
+                    disable_reasoning=disable_reasoning,
+                    tool_reminder=tool_reminder,
+                    include_usage=False,
+                )
             detail = f" - {body_text[:500]}" if body_text else ""
             if e.code in (401, 403):
                 raise _LLMFail("auth", f"HTTP {e.code} {e.reason}{detail}")
@@ -448,4 +480,6 @@ class LLMClient:
         message.pop('reasoning_content', None)
         message.pop('reasoning', None)
 
-        return self.Message(message)
+        result = self.Message(message)
+        result.usage = usage
+        return result