Refactor agent loop

interfaces/tui/agent.py

@@ -1,9 +1,8 @@
 import json
 import threading
 from datetime import datetime
-import config
+
 from lib import ntro, agent_loop
-from tools.vision import ImagePayload
 
 
 def _add_msg(app, role, content, **kwargs):
@@ -15,6 +14,19 @@ def _add_msg(app, role, content, **kwargs):
             app.current_session.doc_id, role, content, **kwargs
         )
 
+
+def _persist_msg(app, message):
+    """Persist satu pesan yang baru ditulis engine ke store TUI.
+
+    Dipasang sebagai SessionStore.on_append: engine menulis ke app.messages,
+    kita hanya menyalinnya ke session DB (TinyDB)."""
+    if not app.current_session:
+        return
+    extra = {k: v for k, v in message.items() if k not in ("role", "content")}
+    app.session_mgr.add_message(
+        app.current_session.doc_id, message["role"], message["content"], **extra
+    )
+
 WELCOME_ART = """
 ,--.  ,--.,------.,--.  ,--.,------.  ,------. ,--.,--. ,--. 
 |  '--'  ||  .---'|  ,'.|  ||  .-.  \\ |  .--. '|  ||  .'   / 
@@ -54,6 +66,7 @@ def log(app, role, text, model_info=None):
         # Display pair: (provider, model)
         entry["model_info"] = model_info
     app.log.append(entry)
+    return entry
 
 
 def _pair(model_info):
@@ -79,6 +92,145 @@ def _tag_ai_logs(app, prev_entries, pair):
             entry["model_info"] = pair
 
 
+class CursesObserver(agent_loop.AgentObserver):
+    """Terjemahkan hook engine ke state tampilan TUI (app.log, scroll, ntro).
+
+    Semua yang tadinya hardcoded di dalam _agent_loop pindah ke sini, sehingga
+    lib.agent_loop tidak lagi mengenal curses. Interface lain cukup membuat
+    observer sendiri tanpa menyentuh loop.
+    """
+
+    wants_streaming = True
+
+    # Konten final dengan awalan ini datang dari LLMClient sebagai kegagalan,
+    # bukan jawaban normal — tampilkan sebagai error, bukan balasan AI.
+    _ERROR_PREFIXES = ("Error:", "HTTP Error:")
+
+    def __init__(self, app):
+        self.app = app
+        self.pair = None
+        self._buf = []
+        self._placeholder = None
+        self._stamp = None
+        self._stamp_step = None
+
+    # ------------------------------------------------------------- lifecycle ---
+    def on_run_start(self):
+        self._stamp = ntro.start()
+
+    def on_run_end(self):
+        ntro.end(self._stamp)
+        self.app.agent_done.set()
+
+    def on_step_start(self, step):
+        self._stamp_step = ntro.start()
+        self._buf = []
+        self._placeholder = None
+        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.
+        self._log_prev = list(self.app.log)
+        self._follow()
+
+    def on_step_end(self, step):
+        ntro.end(self._stamp_step)
+
+    def on_response(self, model_info, warning):
+        self.pair = _pair(model_info)
+        self._pop_thinking()
+        if warning:
+            log(self.app, "system", f"  {warning}")
+
+    # --------------------------------------------------------------- stream ---
+    def on_stream_chunk(self, chunk):
+        if self._placeholder is None:
+            # Placeholder dibuat di chunk pertama; isinya diperbarui real-time.
+            self._placeholder = log(self.app, "ai", "")
+        self._buf.append(chunk)
+        self._placeholder["text"] = "".join(self._buf)
+        self._follow()
+
+    # ----------------------------------------------------------------- tools ---
+    def on_tool_calls(self, content, names):
+        # Narasi assistant sebelum tools; placeholder streaming dibuang karena
+        # konten yang sama akan di-log utuh di sini.
+        self._drop_placeholder()
+        if content and content.strip():
+            log(self.app, "ai", content, model_info=self.pair)
+            _tag_ai_logs(self.app, self._log_prev, self.pair)
+
+    def on_tool_call(self, name, arguments):
+        log(self.app, "tool_call", json.dumps({
+            "name": name,
+            "arguments": arguments,
+        }), model_info=self.pair)
+        self._follow()
+
+    # ---------------------------------------------------------------- final ---
+    def on_final(self, result):
+        content = result.content
+        if isinstance(content, str) and content.startswith(self._ERROR_PREFIXES):
+            self._drop_placeholder()
+            log(self.app, "error", content)
+            log(self.app, "sep", "")
+            return
+
+        if result.is_multimodal:
+            log(self.app, "ai", "[Image generated]", model_info=self.pair)
+        elif content is None:
+            # Respons kosong: tidak ada yang ditampilkan selain pemisah.
+            pass
+        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)
+        # placeholder streaming tidak perlu diisi ulang: isinya sudah teks final
+
+        _tag_ai_logs(self.app, self._log_prev, self.pair)
+        log(self.app, "sep", "")
+
+    def on_max_iterations(self, max_iterations=None):
+        log(self.app, "error", "Max iterations reached without final answer.")
+
+    # ----------------------------------------------------------------- utils ---
+    def _follow(self):
+        if self.app.scroll_follow:
+            self.app.scroll = 999999
+
+    def _pop_thinking(self):
+        for i in range(len(self.app.log) - 1, -1, -1):
+            if self.app.log[i].get("role") == "system" and "Thinking" in self.app.log[i].get("text", ""):
+                self.app.log.pop(i)
+                break
+
+    def _drop_placeholder(self):
+        if self._placeholder is None:
+            return
+        entry, self._placeholder = self._placeholder, None
+        for i in range(len(self.app.log) - 1, -1, -1):
+            if self.app.log[i] is entry:
+                self.app.log.pop(i)
+                break
+
+
+def _run_agent(app):
+    """Entry point thread agent: satu-satunya tempat TUI memanggil engine."""
+    store = agent_loop.SessionStore(
+        app.messages,
+        on_append=lambda msg: _persist_msg(app, msg),
+        keep_model_info=True,
+    )
+    agent_loop.run_agent_loop(
+        store,
+        app.llm,
+        app.TOOLS,
+        app.TOOL_HANDLERS,
+        app.agent_max_iterations,
+        observer=CursesObserver(app),
+        tool_reminder=None,
+    )
+
+
 def submit(app, stdscr):
     query = "\n".join(app.input_buffer).strip()
     if not query:
@@ -107,147 +259,8 @@ def submit(app, stdscr):
 
     app.agent_done.clear()
     app.agent_thread = threading.Thread(
-        target=_agent_loop,
+        target=_run_agent,
         args=(app,),
         daemon=True,
     )
     app.agent_thread.start()
-
-
-def _agent_loop(app):
-    stamp = ntro.start()
-
-    for step in range(app.agent_max_iterations):
-        stamp_step = ntro.start()
-        log(app, "system", f"  step {step + 1} \u2014 Thinking...")
-        if app.scroll_follow:
-            app.scroll = 999999
-
-        # Streaming response
-        stream_buffer = []
-        placeholder_marker = None
-
-        def on_stream_chunk(chunk):
-            nonlocal placeholder_marker
-            # Buat placeholder di chunk pertama saja
-            if placeholder_marker is None:
-                placeholder_marker = f"_stream_placeholder_{step}"
-                log(app, "ai", placeholder_marker)
-            stream_buffer.append(chunk)
-            current_text = ''.join(stream_buffer)
-            # Update placeholder secara real-time
-            for i in range(len(app.log) - 1, -1, -1):
-                # Cari placeholder berdasarkan content aslinya (atau apa yang sudah diupdate)
-                # Karena placeholder text berubah seiring streaming, kita harus teliti
-                if app.log[i].get('role') == 'ai' and (app.log[i].get('text') == placeholder_marker or (i > 0 and app.log[i-1].get('role') == 'system' and 'Thinking' in app.log[i-1].get('text', ''))):
-                    app.log[i]['text'] = current_text
-                    break
-            if app.scroll_follow:
-                app.scroll = 999999
-
-        skill = config.AGENT_SKILLS
-        tool_reminder = None
-
-        log_prev = list(app.log)
-        response = app.llm.chat(app.messages, tools=app.TOOLS, on_stream_chunk=on_stream_chunk, tool_reminder=tool_reminder)
-
-        # Model final: diambil dari response, bukan dari state llm saat ini,
-        # supaya auto-switch/rotasi key/fallback ikut tercatat dengan benar.
-        used = getattr(response, "model_info", None) or {}
-        pair = _pair(used)
-        akwargs = {"model_info": used} if used else {}
-
-        # Hapus "Thinking..." log
-        for i in range(len(app.log) - 1, -1, -1):
-            if app.log[i].get('role') == 'system' and 'Thinking' in app.log[i].get('text', ''):
-                app.log.pop(i)
-                break
-
-        if response.warning:
-            log(app, "system", f"  {response.warning}")
-
-        # Cek apakah response adalah error dari LLM client
-        content = response.content
-        is_error = isinstance(content, str) and (content.startswith("Error:") or content.startswith("HTTP Error:"))
-
-        if is_error:
-            # Hapus "Thinking..." log dan placeholder
-            for i in range(len(app.log) - 1, -1, -1):
-                if app.log[i].get('role') == 'system' and 'Thinking' in app.log[i].get('text', ''):
-                    app.log.pop(i)
-                    break
-            if placeholder_marker is not None:
-                for i in range(len(app.log) - 1, -1, -1):
-                    if app.log[i].get('role') == 'ai':
-                        text = app.log[i].get('text', '')
-                        if placeholder_marker in text or text == "" or text == "...":
-                            app.log.pop(i)
-                            break
-            log(app, "error", content)
-            _add_msg(app, "assistant", content)
-            log(app, "sep", "")
-            ntro.end(stamp)
-            app.agent_done.set()
-            return
-
-        # Cek apakah ada tool_calls
-        has_tool_calls = bool(response.tool_calls)
-
-        if has_tool_calls:
-            # Hapus placeholder jika ada tool_calls (cari semua AI log yang mungkin placeholder)
-            if placeholder_marker is not None:
-                for i in range(len(app.log) - 1, -1, -1):
-                    # Jika ini adalah log AI yang muncul tepat setelah "Thinking..." atau contains placeholder marker
-                    if app.log[i].get('role') == 'ai':
-                        text = app.log[i].get('text', '')
-                        # Hapus jika text-nya adalah placeholder atau jika itu adalah entry AI yang baru saja kita buat untuk streaming
-                        if placeholder_marker in text or text == "" or text == "...":
-                            app.log.pop(i)
-            
-            _add_msg(app, "assistant", response.content, tool_calls=response.tool_calls, **akwargs)
-
-            # Log content AI dulu (penjelasan sebelum tools)
-            if response.content and response.content.strip():
-                log(app, "ai", response.content, model_info=pair)
-                _tag_ai_logs(app, log_prev, pair)
-
-            # Log dan execute tool_calls
-            for tc in response.tool_calls:
-                tname = tc["function"]["name"]
-                targs = tc["function"]["arguments"]
-                log(app, "tool_call", json.dumps({
-                    "name": tname,
-                    "arguments": targs,
-                }), model_info=pair)
-                if app.scroll_follow:
-                    app.scroll = 999999
-                result = agent_loop.execute_tool(tc, app.TOOL_HANDLERS)
-                if isinstance(result, ImagePayload):
-                    _add_msg(app, "tool", [
-                        {"type": "text", "text": f"[Image loaded: {result.path}]"},
-                        {"type": "image_url", "image_url": {"url": f"data:{result.mime};base64,{result.data}"}}
-                    ], tool_call_id=tc["id"])
-                else:
-                    _add_msg(app, "tool", str(result), tool_call_id=tc["id"])
-        else:
-            has_content = bool(response.content) or (isinstance(response.content, list) and len(response.content) > 0)
-            if has_content:
-                if isinstance(response.content, list):
-                    log(app, "ai", "[Image generated]", model_info=pair)
-                _add_msg(app, "assistant", response.content, **akwargs)
-                _tag_ai_logs(app, log_prev, pair)
-            log(app, "sep", "")
-            ntro.end(stamp)
-            app.agent_done.set()
-            return
-        ntro.end(stamp_step)
-
-    log(app, "error", "Max iterations reached without final answer.")
-    _add_msg(app, "assistant", "Max iterations reached without final answer.")
-    ntro.end(stamp)
-    app.agent_done.set()
-
-
-    app.agent_done.set()
-    ntro.end(stamp)
-

lib/agent_loop.py

@@ -1,12 +1,20 @@
+"""Agent loop terpusat — satu engine untuk semua interface (TUI, GUI, service).
+
+Dua boundary pemisah, supaya engine tidak pernah mengenal interface:
+
+  SessionStore  → ke mana pesan percakapan ditulis (list aktif + persist opsional)
+  AgentObserver → siapa yang melihat prosesnya (log, streaming, timing)
+
+Engine sengaja tetap sync (semua handler tool saat ini sync; MCP masih jauh).
+"""
+
 import json
-from datetime import datetime
+
 import config
 from lib.personality import PERSONALITY
 from tools.vision import ImagePayload
 from scripts import loggorilla
 
-def _ts():
-    return datetime.now().strftime('%H:%M:%S')
 
 def _cap_output(result):
     # Jaring pengaman ukuran output tool. Hormati semantics config:
@@ -19,6 +27,7 @@ def _cap_output(result):
         return result[:limit] + f"\n... [truncated: {omitted} chars omitted]"
     return result
 
+
 def execute_tool(tool_call, TOOL_HANDLERS):
     tname = tool_call['function']['name']
     targs = json.loads(tool_call['function']['arguments'])
@@ -39,15 +48,165 @@ def execute_tool(tool_call, TOOL_HANDLERS):
     except Exception as e:
         return _cap_output(f'Error executing tool: {str(e)}')
 
-def run_agent_loop(session, llm_client, TOOLS, TOOL_HANDLERS, max_iterations, on_tool_calls=None, tool_reminder=None):
+
+# --------------------------------------------------------------- contracts ---
+
+class SessionStore:
+    """Tujuan tulis untuk setiap pesan percakapan selama satu loop berjalan.
+
+    `messages`      state aktif yang dikirim ke LLM (list dict).
+    `on_append`     dipanggil setelah tiap pesan masuk — untuk persistensi
+                    (TUI/GUI/API via NeoSessionManager). Boleh None.
+    `keep_model_info`
+                    False (default): kunci `model_info` dibuang sebelum pesan
+                    masuk list, jadi payload ke provider identik dengan sebelum
+                    refactor. Interface yang menampilkan info model menyalakannya.
+    """
+
+    def __init__(self, messages, on_append=None, keep_model_info=False):
+        self.messages = messages
+        self._on_append = on_append
+        self.keep_model_info = keep_model_info
+
+    def append(self, message):
+        if not self.keep_model_info:
+            message.pop('model_info', None)
+        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):
+        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
+
+
+class AgentObserver:
+    """Hook tampilan. Semua no-op — subclass cukup menimpa yang dibutuhkan.
+
+    Engine tidak pernah mencetak atau menyentuh UI; seluruh efek visual hidup
+    di sini, sehingga interface baru tinggal membuat subclass sendiri.
+    """
+
+    # True bila observer membutuhkan delta streaming dari LLM client.
+    wants_streaming = False
+
+    def on_run_start(self):
+        pass
+
+    def on_run_end(self):
+        pass
+
+    def on_step_start(self, step):
+        pass
+
+    def on_response(self, model_info, warning):
+        """llm.chat() sudah kembali, sebelum engine memproses response."""
+        pass
+
+    def on_step_end(self, step):
+        """Satu iterasi selesai dan loop berlanjut ke iterasi berikutnya."""
+        pass
+
+    def on_stream_chunk(self, chunk):
+        pass
+
+    def on_tool_calls(self, content, names):
+        """Assistant menjadwalkan tool call; `content` narasi sebelumnya."""
+        pass
+
+    def on_tool_call(self, name, arguments):
+        pass
+
+    def on_final(self, result):
+        pass
+
+    def on_max_iterations(self, max_iterations=None):
+        pass
+
+
+class ConsoleObserver(AgentObserver):
+    """Observer default proses headless (service bot): log gorilla ke stdout.
+
+    `on_tool_calls` mengirim narasi assistant sebelum tool dijalankan —
+    dipakai Telegram/XMPP untuk mengirim pesan interim ke chat.
+    """
+
+    def __init__(self, on_tool_calls=None):
+        self._on_tool_calls = on_tool_calls
+
+    def on_step_start(self, step):
+        loggorilla.prcss("agent_loop", f"Step {step + 1} — calling LLM...")
+
+    def on_tool_calls(self, content, names):
+        loggorilla.fyinf("agent_loop", f"Using tools: {', '.join(names)}")
+        if self._on_tool_calls:
+            self._on_tool_calls(content)
+
+    def on_final(self, result):
+        # Content None = respons final kosong (mis. sesudah end_session):
+        # tidak ada yang dicetak, sama seperti sebelumnya.
+        if result.content is None:
+            return
+        if result.is_multimodal:
+            loggorilla.prcss("agent_loop", "Response generated (multimodal)")
+        else:
+            loggorilla.prcss("agent_loop",
+                             f"Response generated ({len(result.content)} chars)")
+
+    def on_max_iterations(self, max_iterations=None):
+        loggorilla.prcss("agent_loop",
+                         f"Max iterations ({max_iterations}) reached")
+
+
+# ------------------------------------------------------------------- loop ---
+
+def run_agent_loop(store, llm_client, TOOLS, TOOL_HANDLERS, max_iterations,
+                   observer=None, tool_reminder=None, disable_reasoning=None):
+    """Jalankan loop LLM <-> tool sampai ada jawaban final atau batas iterasi.
+
+    `store`    : SessionStore (bukan list mentah — biar persistensi eksplisit).
+    `observer` : None → ConsoleObserver (headless, tanpa streaming).
+    `disable_reasoning` None → ikut PERSONALITY (yaml karakter).
+
+    Return AgentResult. Engine tidak mengklasifikasikan konten sebagai error;
+    itu keputusan interface (lihat CursesObserver di interfaces/tui/agent.py).
+    """
+    if observer is None:
+        observer = ConsoleObserver()
+    if disable_reasoning is None:
+        disable_reasoning = PERSONALITY.disable_reasoning
+
     session_ended = False
+    model_info = {}
+    warning = None
+    stream_cb = observer.on_stream_chunk if observer.wants_streaming else None
+
+    observer.on_run_start()
 
     for step in range(max_iterations):
-        loggorilla.prcss("agent_loop", f"Step {step + 1} — calling LLM...")
+        observer.on_step_start(step)
-        
+
-        disable_reasoning = PERSONALITY.disable_reasoning
+        response = llm_client.chat(
-        
+            store.messages,
-        response = llm_client.chat(session.messages, tools=TOOLS, disable_reasoning=disable_reasoning, tool_reminder=tool_reminder)
+            tools=TOOLS,
+            on_stream_chunk=stream_cb,
+            disable_reasoning=disable_reasoning,
+            tool_reminder=tool_reminder,
+        )
+
+        # Model final diambil dari response (bukan state llm saat ini) supaya
+        # 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)
 
         if response.tool_calls:
             amsg = {
@@ -55,20 +214,20 @@ def run_agent_loop(session, llm_client, TOOLS, TOOL_HANDLERS, max_iterations, on
                 'content': response.content,
                 'tool_calls': response.tool_calls,
             }
-            session.messages.append(amsg)
+            if model_info:
+                amsg['model_info'] = model_info
+            store.append(amsg)
 
-            tnames = [tc['function']['name'] for tc in response.tool_calls]
+            observer.on_tool_calls(response.content or "",
-            loggorilla.fyinf("agent_loop", f"Using tools: {', '.join(tnames)}")
+                                   [tc['function']['name'] for tc in response.tool_calls])
-
-            if on_tool_calls:
-                on_tool_calls(response.content or "")
 
             for tc in response.tool_calls:
+                observer.on_tool_call(tc['function']['name'], tc['function']['arguments'])
                 if tc['function']['name'] == 'end_session':
                     session_ended = True
                 result = execute_tool(tc, TOOL_HANDLERS)
                 if isinstance(result, ImagePayload):
-                    session.messages.append({
+                    store.append({
                         'role': 'tool',
                         'tool_call_id': tc['id'],
                         'content': [
@@ -77,30 +236,53 @@ def run_agent_loop(session, llm_client, TOOLS, TOOL_HANDLERS, max_iterations, on
                         ]
                     })
                 else:
-                    session.messages.append({
+                    store.append({
                         'role': 'tool',
                         'tool_call_id': tc['id'],
                         'content': str(result),
                     })
-        else:
+            observer.on_step_end(step)
-            has_content = bool(response.content) or (isinstance(response.content, list) and len(response.content) > 0)
+            continue
-            if has_content:
-                session.messages.append({'role': 'assistant', 'content': response.content})
-                if isinstance(response.content, list):
-                    text_parts = [b.get('text', '') for b in response.content
-                                   if isinstance(b, dict) and b.get('type') == 'text']
-                    text = '\n'.join(text_parts) if text_parts else "[Image generated]"
-                    loggorilla.prcss("agent_loop", "Response generated (multimodal)")
-                    return text, session_ended
-                loggorilla.prcss("agent_loop", f"Response generated ({len(response.content)} chars)")
-                return response.content, session_ended
-            # Respons kosong: tetap bawa flag session_ended — bisa jadi
-            # end_session sudah dipanggil di iterasi sebelumnya.
-            return None, session_ended
 
-    loggorilla.prcss("agent_loop", f"Max iterations ({max_iterations}) reached")
+        has_content = bool(response.content) or (isinstance(response.content, list) and len(response.content) > 0)
-    session.messages.append({
+        if has_content:
+            is_multimodal = isinstance(response.content, list)
+            msg = {'role': 'assistant', 'content': response.content}
+            if model_info:
+                msg['model_info'] = model_info
+            store.append(msg)
+
+            if is_multimodal:
+                text_parts = [b.get('text', '') for b in response.content
+                               if isinstance(b, dict) and b.get('type') == 'text']
+                text = '\n'.join(text_parts) if text_parts else "[Image generated]"
+            else:
+                text = response.content
+
+            final = AgentResult(
+                content=text,
+                session_ended=session_ended,
+                model_info=model_info,
+                warning=warning,
+                is_multimodal=is_multimodal,
+            )
+            observer.on_final(final)
+            observer.on_run_end()
+            return final
+
+        # Respons kosong: tetap bawa flag session_ended — bisa jadi
+        # end_session sudah dipanggil di iterasi sebelumnya.
+        empty = AgentResult(content=None, session_ended=session_ended,
+                            model_info=model_info, warning=warning)
+        observer.on_final(empty)
+        observer.on_run_end()
+        return empty
+
+    store.append({
         'role': 'assistant',
         'content': 'Max iterations reached without final answer.',
     })
-    return None, session_ended
+    observer.on_max_iterations(max_iterations)
+    observer.on_run_end()
+    return AgentResult(content=None, session_ended=session_ended,
+                       model_info=model_info, warning=warning)

services/telegram_client.py

@@ -7,7 +7,7 @@ from datetime import datetime
 
 import config
 from services.session_manager import SessionManager
-from lib.agent_loop import run_agent_loop
+from lib.agent_loop import run_agent_loop, SessionStore, ConsoleObserver
 from lib import personality
 from lib import ragroleplay
 from scripts import loggorilla
@@ -259,10 +259,13 @@ class TelegramClient:
 
         tool_reminder = None
 
-        final_content, should_close = run_agent_loop(
+        outcome = run_agent_loop(
-            session, self._llm, self._TOOLS, self._TOOL_HANDLERS,
+            SessionStore(session.messages),
-            self._max_iterations, on_tool_calls=on_tool_calls, tool_reminder=tool_reminder
+            self._llm, self._TOOLS, self._TOOL_HANDLERS, self._max_iterations,
+            observer=ConsoleObserver(on_tool_calls=on_tool_calls),
+            tool_reminder=tool_reminder,
         )
+        final_content, should_close = outcome.content, outcome.session_ended
 
         def _pop_ghost(sentinel: str):
             # Hapus pesan assistant sentinel (NO-REPLY / END-SESSION) yang

services/xmpp_client.py

@@ -8,7 +8,7 @@ from datetime import datetime
 
 from slixmpp import ClientXMPP
 from services.session_manager import SessionManager
-from lib.agent_loop import run_agent_loop
+from lib.agent_loop import run_agent_loop, SessionStore, ConsoleObserver
 
 import config
 from tools.roleplayer import should_respond
@@ -624,10 +624,13 @@ class XMPPClient(ClientXMPP):
 
         tool_reminder = None
 
-        final_content, should_close = run_agent_loop(
+        outcome = run_agent_loop(
-            session, self._llm, self._TOOLS, self._TOOL_HANDLERS,
+            SessionStore(session.messages),
-            self._max_iterations, on_tool_calls=on_tool_calls, tool_reminder=tool_reminder
+            self._llm, self._TOOLS, self._TOOL_HANDLERS, self._max_iterations,
+            observer=ConsoleObserver(on_tool_calls=on_tool_calls),
+            tool_reminder=tool_reminder,
         )
+        final_content, should_close = outcome.content, outcome.session_ended
 
         if final_content is not None:
             if is_roleplay:
@@ -709,10 +712,13 @@ class XMPPClient(ClientXMPP):
 
         tool_reminder = None
 
-        final_content, should_close = run_agent_loop(
+        outcome = run_agent_loop(
-            session, self._llm, self._TOOLS, self._TOOL_HANDLERS,
+            SessionStore(session.messages),
-            self._max_iterations, on_tool_calls=on_tool_calls, tool_reminder=tool_reminder
+            self._llm, self._TOOLS, self._TOOL_HANDLERS, self._max_iterations,
+            observer=ConsoleObserver(on_tool_calls=on_tool_calls),
+            tool_reminder=tool_reminder,
         )
+        final_content, should_close = outcome.content, outcome.session_ended
 
         def _pop_ghost(sentinel: str):
             # Hapus pesan assistant sentinel (NO-REPLY / END-SESSION) yang