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@ -56,7 +56,7 @@ WELCOME_ART = """
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╚══════════════════════════════════════════╝
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"""
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def log(app, role, text, model_info=None):
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def log(app, role, text, model_info=None, usage=None):
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entry = {
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"role": role,
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"text": text,
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@ -65,6 +65,9 @@ def log(app, role, text, model_info=None):
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if model_info:
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# Display pair: (provider, model)
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entry["model_info"] = model_info
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if usage:
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# Token pemakaian: prompt/completion/total dari provider
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entry["usage"] = usage
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app.log.append(entry)
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return entry
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@ -76,20 +79,23 @@ def _pair(model_info):
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return (model_info.get("provider"), model_info.get("model"))
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def _tag_ai_logs(app, prev_entries, pair):
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"""Tempelkan info model yang benar-benar dipakai ke entri log AI yang baru
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dibuat pada langkah ini.
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def _tag_ai_logs(app, prev_entries, pair, usage=None):
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"""Tempelkan info model (dan token usage) yang benar-benar dipakai ke entri
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log AI yang baru dibuat pada langkah ini.
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Berbasis identitas entri, bukan index: entri "Thinking..." di-pop setelah
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llm.chat() kembali sehingga index entri AI ikut bergeser. `prev_entries`
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menahan referensi entri lama agar id-nya tidak dipakai ulang oleh Python.
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"""
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if not pair:
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if not pair and not usage:
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return
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seen = {id(e) for e in prev_entries}
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for entry in app.log:
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if entry.get("role") == "ai" and id(entry) not in seen:
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entry["model_info"] = pair
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if pair:
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entry["model_info"] = pair
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if usage:
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entry["usage"] = usage
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class CursesObserver(agent_loop.AgentObserver):
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@ -109,10 +115,13 @@ class CursesObserver(agent_loop.AgentObserver):
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def __init__(self, app):
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self.app = app
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self.pair = None
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self.usage = None # token pemakaian langkah LLM terakhir
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self._buf = []
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self._placeholder = None
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self._stamp = None
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self._stamp_step = None
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self._tool_total = 0 # jumlah tool call yang dijadwalkan langkah ini
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self._tool_seen = 0 # tool call yang sudah di-log langkah ini
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# ------------------------------------------------------------- lifecycle ---
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def on_run_start(self):
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@ -126,6 +135,9 @@ class CursesObserver(agent_loop.AgentObserver):
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self._stamp_step = ntro.start()
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self._buf = []
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self._placeholder = None
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self.usage = None
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self._tool_total = 0
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self._tool_seen = 0
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log(self.app, "system", f" step {step + 1} \u2014 Thinking...")
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# Ditahan setelah "Thinking..." masuk, supaya referensi entri lama ikut
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# terjaga dan id()-nya tidak dipakai ulang saat entri itu di-pop.
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@ -135,8 +147,11 @@ class CursesObserver(agent_loop.AgentObserver):
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def on_step_end(self, step):
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ntro.end(self._stamp_step)
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def on_response(self, model_info, warning):
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def on_response(self, model_info, warning, usage=None):
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self.pair = _pair(model_info)
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# Token pemakaian langkah LLM ini (untuk label usage di entri ai).
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# Total satu turn sudah diakumulasi engine di AgentResult.usage.
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self.usage = usage
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self._pop_thinking()
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if warning:
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log(self.app, "system", f" {warning}")
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@ -155,15 +170,22 @@ class CursesObserver(agent_loop.AgentObserver):
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# Narasi assistant sebelum tools; placeholder streaming dibuang karena
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# konten yang sama akan di-log utuh di sini.
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self._drop_placeholder()
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# Baris token satu panggilan LLM dititipkan ke tool_call TERAKHIR dari
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# langkah ini (lihat on_tool_call), supaya urutan render tetap benar.
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self._tool_total = len(names)
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self._tool_seen = 0
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if content and content.strip():
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log(self.app, "ai", content, model_info=self.pair)
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log(self.app, "ai", content, model_info=self.pair, usage=self.usage)
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_tag_ai_logs(self.app, self._log_prev, self.pair)
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def on_tool_call(self, name, arguments):
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log(self.app, "tool_call", json.dumps({
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entry = log(self.app, "tool_call", json.dumps({
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"name": name,
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"arguments": arguments,
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}), model_info=self.pair)
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self._tool_seen += 1
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if self.usage and self._tool_seen >= self._tool_total:
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entry["usage"] = self.usage
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self._follow()
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# ---------------------------------------------------------------- final ---
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@ -176,15 +198,25 @@ class CursesObserver(agent_loop.AgentObserver):
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return
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if result.is_multimodal:
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log(self.app, "ai", "[Image generated]", model_info=self.pair)
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entry = log(self.app, "ai", "[Image generated]",
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model_info=self.pair, usage=result.usage)
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elif content is None:
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# Respons kosong: tidak ada yang ditampilkan selain pemisah.
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pass
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entry = None
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elif self._placeholder is None and content:
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# Tidak ada chunk streaming sama sekali (provider tidak mengirim
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# delta) — pastikan teks tetap terlihat.
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log(self.app, "ai", content, model_info=self.pair)
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# placeholder streaming tidak perlu diisi ulang: isinya sudah teks final
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entry = log(self.app, "ai", content,
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model_info=self.pair, usage=result.usage)
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else:
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# placeholder streaming tidak perlu diisi ulang: isinya sudah teks
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# final. Gunakan sebagai target tempelan usage.
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entry = self._placeholder
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# Total token satu turn (akumulasi semua langkah LLM) ditaruh di
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# entri respons final, dirender setelah teks pesan.
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if entry is not None and result.usage:
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entry["usage"] = result.usage
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_tag_ai_logs(self.app, self._log_prev, self.pair)
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log(self.app, "sep", "")
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@ -214,16 +214,21 @@ class HendrikTUI:
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pair = (mi.get("provider"), mi.get("model")) if mi else None
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# Log tool calls jika ada
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if msg.get("tool_calls"):
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for tc in msg["tool_calls"]:
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tcl = msg["tool_calls"]
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for j, tc in enumerate(tcl):
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# Baris token langkah ini dititipkan di tool_call terakhir
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# (konvensi sama dengan CursesObserver.on_tool_call).
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last = (j == len(tcl) - 1)
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log(self, "tool_call", json.dumps({
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"name": tc["function"]["name"],
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"arguments": tc["function"]["arguments"],
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}), model_info=pair)
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}), model_info=pair,
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usage=msg.get("usage") if last else None)
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# Log AI content jika ada dan ini adalah response final
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next_role = session.messages[i + 1]["role"] if i + 1 < len(session.messages) else None
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if next_role in ("user", None):
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if msg.get("content"):
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log(self, "ai", msg["content"])
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log(self, "ai", msg["content"], usage=msg.get("usage"))
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if pair:
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self.log[-1]["model_info"] = pair
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elif msg["role"] == "tool":
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@ -25,6 +25,7 @@ C_INPUT_BORDER = theme.C_INPUT_BORDER
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C_STATUS_INFO = theme.C_STATUS_INFO
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C_WELCOME = theme.C_WELCOME
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C_TOOL_CALL = theme.C_TOOL_CALL
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C_USAGE = theme.C_USAGE
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def draw(app, stdscr):
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@ -68,6 +69,35 @@ def _model_info_line(item):
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return f" {m_name} " if m_name else ""
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def _fmt_tokens(n):
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"""1234 -> '1.2k', 999 -> '999'."""
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if n >= 1000:
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return f"{n / 1000:.1f}k".replace(".0k", "k")
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return str(n)
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def _usage_line(item, label="tokens"):
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"""Baris token usage dari entri log; "" jika tidak ada.
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Contoh: ' tokens in 48.2k → out 260 · total 48.5k · ctx 48.2k '.
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`ctx` = ukuran context window saat itu (prompt tokens panggilan terakhir;
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pada riwayat lama tanpa data itu, jatuh ke total in).
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`label` membedakan cakupan angka: 'tokens' untuk total satu turn (blok ai
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final), 'step tokens' untuk satu panggilan LLM (blok tool_call)."""
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usage = item.get("usage")
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if not isinstance(usage, dict):
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return ""
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try:
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pt = int(usage.get("prompt_tokens") or 0)
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ct = int(usage.get("completion_tokens") or 0)
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tt = int(usage.get("total_tokens") or (pt + ct))
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ctx = int(usage.get("context_tokens") or pt or tt)
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except (TypeError, ValueError):
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return ""
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return (f" {label} in {_fmt_tokens(pt)} → out {_fmt_tokens(ct)}"
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f" · total {_fmt_tokens(tt)} · ctx {_fmt_tokens(ctx)} ")
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def draw_chat(app, stdscr):
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# Area chat — dari baris 2 sampai baris (h - 11).
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# Bisa di-scroll dengan Page Up / Page Down.
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@ -159,6 +189,10 @@ def draw_chat(app, stdscr):
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label = f" {app.character_name} ({item['time']}) "
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_add_row([(C_AI, label)])
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_wrap_render(text, indent=1, color=C_INPUT, bold=False)
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# Baris token usage setelah teks pesan respons
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usage_line = _usage_line(item)
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if usage_line:
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_add_row([(C_USAGE, usage_line, False)])
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elif role == "system":
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lines = text.split("\n")
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_add_row([(C_SYSTEM, lines[0])])
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@ -208,6 +242,11 @@ def draw_chat(app, stdscr):
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(C_TOOL_CALL, "unknown"),
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(C_AI, f" ({item['time']}) "),
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])
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# Baris token panggilan LLM yang menghasilkan tool call ini,
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# ditempel di tool_call terakhir langkah (lihat agent.CursesObserver).
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usage_line = _usage_line(item, label="step tokens")
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if usage_line:
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_add_row([(C_USAGE, usage_line, False)])
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# Blank line di bawah
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_add_blank()
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elif role == "error":
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@ -21,6 +21,7 @@ C_HINT_DISABLED = 13 # hint disabled (abu-abu)
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C_WELCOME = 14 # welcome art: light blue
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C_TOOL_CALL = 15 # tool call: kuning terang
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C_TOOL_RESULT = 16 # tool result: magenta muda
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C_USAGE = 22 # token usage: abu-abu
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# -- Menu bar colors (id 17+) --
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C_MENU = 17 # background menubar (sama dengan header)
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@ -49,6 +50,7 @@ def init_colors():
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curses.init_pair(C_WELCOME, curses.COLOR_BLUE + 8, -1) # light blue
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curses.init_pair(C_TOOL_CALL, curses.COLOR_YELLOW + 8, -1) # bright yellow
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curses.init_pair(C_TOOL_RESULT, curses.COLOR_MAGENTA + 8, -1) # bright magenta
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curses.init_pair(C_USAGE, 8, -1) # abu-abu di atas bg default
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# Menu bar
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curses.init_pair(C_MENU, curses.COLOR_BLACK, curses.COLOR_BLUE)
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@ -67,18 +67,20 @@ class SessionStore:
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def append(self, message):
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if not self.keep_model_info:
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message.pop('model_info', None)
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message.pop('usage', None) # metadata display, bukan payload LLM
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self.messages.append(message)
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if self._on_append:
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self._on_append(message)
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class AgentResult:
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# Hasil satu kali jalan loop.
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def __init__(self, content=None, session_ended=False, model_info=None, warning=None, is_multimodal=False):
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def __init__(self, content=None, session_ended=False, model_info=None, warning=None, is_multimodal=False, usage=None):
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self.content = content # str | None
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self.session_ended = session_ended # tool end_session pernah dipanggil
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self.model_info = model_info or {} # model yang benar-benar menjawab
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self.warning = warning # mis. fallback chain model
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self.is_multimodal = is_multimodal
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self.usage = usage # token turn ini: prompt/completion/total (dict | None)
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class AgentObserver:
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@ -100,8 +102,11 @@ class AgentObserver:
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def on_step_start(self, step):
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pass
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def on_response(self, model_info, warning):
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"""llm.chat() sudah kembali, sebelum engine memproses response."""
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def on_response(self, model_info, warning, usage=None):
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"""llm.chat() sudah kembali, sebelum engine memproses response.
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`usage` : token pemakaian satu permintaan (prompt/completion/total),
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None jika provider tidak mengirimnya."""
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pass
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def on_step_end(self, step):
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@ -182,6 +187,25 @@ def run_agent_loop(store, llm_client, TOOLS, TOOL_HANDLERS, max_iterations,
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warning = None
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stream_cb = observer.on_stream_chunk if observer.wants_streaming else None
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# Akumulator token satu turn (semua langkah LLM dijumlahkan — tiap
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# panggilan mengirim ulang konteks sehingga dihitung ulang oleh provider).
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any_usage = False
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turn_usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
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def _accumulate(u):
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nonlocal any_usage
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if not isinstance(u, dict):
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return
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any_usage = True
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for k in ("prompt_tokens", "completion_tokens", "total_tokens"):
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v = u.get(k)
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if isinstance(v, (int, float)):
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turn_usage[k] += int(v)
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# Ukuran context window saat itu = prompt tokens panggilan terakhir.
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pt = u.get("prompt_tokens")
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if isinstance(pt, (int, float)):
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turn_usage["context_tokens"] = int(pt)
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observer.on_run_start()
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for step in range(max_iterations):
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@ -199,7 +223,9 @@ def run_agent_loop(store, llm_client, TOOLS, TOOL_HANDLERS, max_iterations,
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# auto-switch / rotasi key / fallback tercatat dengan benar.
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model_info = getattr(response, "model_info", None) or {}
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warning = getattr(response, "warning", None)
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observer.on_response(model_info, warning)
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usage = getattr(response, "usage", None)
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_accumulate(usage)
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observer.on_response(model_info, warning, usage)
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if response.tool_calls:
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amsg = {
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@ -209,6 +235,11 @@ def run_agent_loop(store, llm_client, TOOLS, TOOL_HANDLERS, max_iterations,
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}
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if model_info:
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amsg['model_info'] = model_info
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if isinstance(usage, dict) and usage:
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# Usage per langkah (bukan akumulasi turn) — dipakai interface
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# untuk menampilkan token di blok tool call yang dihasilkannya.
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# Hanya metadata display: dibuang sebelum jadi payload LLM.
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amsg['usage'] = dict(usage)
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store.append(amsg)
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observer.on_tool_calls(response.content or "",
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@ -243,6 +274,10 @@ def run_agent_loop(store, llm_client, TOOLS, TOOL_HANDLERS, max_iterations,
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msg = {'role': 'assistant', 'content': response.content}
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if model_info:
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msg['model_info'] = model_info
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if any_usage:
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# Total token turn ini — ikut dipersist supaya tampil saat
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# session dibuka kembali.
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msg['usage'] = dict(turn_usage)
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store.append(msg)
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if is_multimodal:
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@ -258,6 +293,7 @@ def run_agent_loop(store, llm_client, TOOLS, TOOL_HANDLERS, max_iterations,
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model_info=model_info,
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warning=warning,
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is_multimodal=is_multimodal,
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usage=dict(turn_usage) if any_usage else None,
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)
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observer.on_final(final)
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observer.on_run_end()
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@ -35,6 +35,8 @@ class LLMClient:
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self.content = gadget.strip_thinking(raw_content) if isinstance(raw_content, str) else raw_content # Auto-strip <thinking> dari content
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self.tool_calls = msg.get('tool_calls', None) # Ambil tool calls
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self.warning = None
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# Pemakaian token dari provider (prompt/completion/total) jika ada.
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self.usage = None
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# Model yang benar-benar dipakai menghasilkan pesan ini. Bisa berbeda
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# dengan model awal permintaan karena auto-switch/fallback.
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self.model_info = model_info or {}
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@ -232,23 +234,36 @@ class LLMClient:
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# ------------------------------------------------------- single attempt ---
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def _chat_once(self, base_url, model, api_key, messages, tools=None,
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on_stream_chunk=None, disable_reasoning=False, tool_reminder=None):
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on_stream_chunk=None, disable_reasoning=False, tool_reminder=None,
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include_usage=True):
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url = f"{base_url.rstrip('/')}/chat/completions"
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# Sanitasi: buang metadata display (model_info/usage) dari pesan sebelum
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# jadi payload — beberapa provider memvalidasi field pesan dengan ketat.
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msgs_clean = [
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{k: v for k, v in m.items() if k not in ("model_info", "usage")}
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if isinstance(m, dict) else m
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for m in (messages or [])
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]
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payload = {
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"model": model,
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"messages": messages,
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"messages": msgs_clean,
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"stream": True # Enable streaming
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}
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if include_usage:
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# Minta provider menyertakan pemakaian token (input/output) pada
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# chunk terakhir stream. Provider yang tidak mendukungnya akan
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# di-retry tanpa parameter ini (lihat penanganan HTTP 400).
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payload["stream_options"] = {"include_usage": True}
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if tools:
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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):
|
||||
if messages[i].get("role") == "user":
|
||||
payload["messages"] = messages[:i] + [
|
||||
for i in range(len(msgs_clean) - 1, -1, -1):
|
||||
if msgs_clean[i].get("role") == "user":
|
||||
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', {})
|
||||
finish_reason = chunk.get('choices', [{}])[0].get('finish_reason', None)
|
||||
choices = chunk.get('choices') or []
|
||||
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
|
||||
|
||||
Loading…
Reference in New Issue
Block a user