Add ntro timing utility and integrate into agent loop

scripts/ntro.py

@@ -0,0 +1,12 @@
+import time
+
+def start():
+    return {"_start": time.perf_counter_ns()}
+
+def end(stamp):
+    elapsed_ns = time.perf_counter_ns() - stamp["_start"]
+    elapsed_ms = elapsed_ns / 1_000_000
+    print(f"[ntropy] {elapsed_ms:.2f}ms")
+    stamp["_start"] = time.perf_counter_ns()
+    return elapsed_ms
+

tui/agent.py

@@ -0,0 +1,129 @@
+#  agent.py — Agent loop dan tool execution.
+#  submit() adalah entry point: membaca input buffer, mengirim ke LLM,
+#  memproses tool calls, dan menampilkan hasil di log.
+
+import json
+from datetime import datetime
+from .render import draw
+from scripts import ntro
+
+
+def log(app, role, text):
+    # Simpan item ke app.log untuk di-render oleh render.py
+    app.log.append({
+        "role": role,
+        "text": text,
+        "time": datetime.now().strftime("%H:%M"),
+    })
+
+
+def submit(app, stdscr):
+    # Kirim query dari input buffer ke LLM.
+    # Loop sampai LLM mengembalikan final answer (tanpa tool_calls)
+    # atau mencapai max_iterations.
+
+    query = "\n".join(app.input_buffer).strip()
+    if not query:
+        return
+
+    log(app, "user", query)
+    # Reset input buffer
+    app.input_buffer = [""]
+    app.input_line = 0
+    app.input_col = 0
+    app.scroll = 999999  # scroll ke paling bawah
+    app.processing = True
+    draw(app, stdscr)
+    stdscr.refresh()
+
+    app.messages.append({"role": "user", "content": query})
+
+    stamp = ntro.start()
+
+    for step in range(app.agent_max_iterations):
+        stamp_step = ntro.start()
+        log(app, "system", f"  step {step + 1} \u2014 LLM...")
+        app.scroll = 999999
+        draw(app, stdscr)
+        stdscr.refresh()
+
+        response = app.llm.chat(app.messages, tools=app.TOOLS)
+
+        # Hapus "step N — LLM..." log, ganti dengan hasil aktual
+        app.log.pop()
+
+        if response.tool_calls:
+            # LLM meminta menjalankan tool(s)
+            amsg = {
+                "role": "assistant",
+                "content": response.content,
+                "tool_calls": response.tool_calls,
+            }
+            app.messages.append(amsg)
+            if response.content and response.content.strip():
+                log(app, "ai", response.content)
+                app.scroll = 999999
+                draw(app, stdscr)
+                stdscr.refresh()
+            for tc in response.tool_calls:
+                tname = tc["function"]["name"]
+                log(app, "system", f"  \u2192 {tname}")
+                app.scroll = 999999
+                draw(app, stdscr)
+                stdscr.refresh()
+                execute_tool(app, tc)
+        else:
+            # Final answer — tidak ada tool_calls
+            if response.content:
+                app.messages.append({
+                    "role": "assistant",
+                    "content": response.content,
+                })
+                log(app, "ai", response.content)
+            log(app, "sep", "")
+            app.processing = False
+            app.scroll = 999999
+            draw(app, stdscr)
+            stdscr.refresh()
+            ntro.end(stamp)
+            return
+        ntro.end(stamp_step)
+
+    # Timeout — max iterations tercapai tanpa final answer
+    log(app, "error", "Max iterations reached without final answer.")
+    app.messages.append({"role": "assistant",
+                          "content": "Max iterations reached without final answer."})
+    app.processing = False
+    ntro.end(stamp)
+
+
+def execute_tool(app, tool_call):
+    # Dispatch tool_call ke handler yang terdaftar di TOOL_HANDLERS.
+    # search_code dan git_operation butuh penanganan argumen khusus.
+    tname = tool_call["function"]["name"]
+    targs = json.loads(tool_call["function"]["arguments"])
+    handler = app.TOOL_HANDLERS.get(tname)
+
+    if not handler:
+        result = f"Tool {tname} not found"
+    else:
+        try:
+            if tname == "search_code":
+                result = handler(
+                    pattern=targs["pattern"],
+                    search_type=targs["search_type"],
+                    path=targs.get("path", "."),
+                )
+            elif tname == "git_operation":
+                result = handler(args=targs["args"])
+            else:
+                result = handler(**targs)
+        except Exception as e:
+            result = f"Error executing tool: {str(e)}"
+
+    # Hasil tool disimpan ke messages agar bisa dikirim balik ke LLM
+    app.messages.append({
+        "role": "tool",
+        "tool_call_id": tool_call["id"],
+        "content": str(result),
+    })