Bedah Repositori GitHub Trending yang Wajib Diketahui Developer

┌─────────────────────────────────────────┐
                    │      GitHub Trending Ecosystem          │
                    └────────────────────┬────────────────────┘
                                         │
        ┌────────────────────────────────┼────────────────────────────────┐
        ▼                                ▼                                ▼
 ┌──────────────┐                 ┌──────────────┐                 ┌──────────────┐
 │ vLLM / SGLang│                 │  Browser-Use │                 │   Langfuse   │
 ├──────────────┤                 ├──────────────┤                 ├──────────────┤
 │ Engine Level │                 │ Execution    │                 │ Observability│
 │ PagedAttention│                 │ Agentic Loop │                 │ OTEL Tracing │
 └──────────────┘                 └──────────────┘                 └──────────────┘

# Contoh konseptual: PagedAttention KV Cache allocation footprint
# Memori tidak lagi dialokasikan secara statis per panjang sekuens maksimum

from vllm import LLM, SamplingParams

sampling_params = SamplingParams(temperature=0.7, top_p=0.95, max_tokens=256)

# Engine secara otomatis memetakan physical memory blocks secara dinamis
llm = LLM(
    model="meta-llama/Llama-3.3-70B-Instruct",
    tensor_parallel_size=4, # Distribusi ke 4 GPU
    gpu_memory_utilization=0.90,
    max_num_seqs=256
)

prompts = ["Tuliskan arsitektur data pipeline berskala terabyte:"]
outputs = llm.generate(prompts, sampling_params)

Visual / DOM Parsing Workflow
 ┌──────────────┐       ┌─────────────────┐       ┌─────────────────┐
 │ Target Page  │ ────► │ Injected Script │ ────► │ Annotated Tree  │
 └──────────────┘       └─────────────────┘       └────────┬────────┘
                                                           │
 ┌──────────────┐       ┌─────────────────┐                │
 │ Action Exec  │ ◄──── │ Vision LLM Plan │ ◄──────────────┘
 └──────────────┘       └─────────────────┘

Trace: Customer Query(Root)
  │
  ├── Span 1: Semantic Cache Lookup(Hit: False, Latency: 12ms)
  ├── Span 2: Vector Embedding Generation(Latency: 85ms, Cost: $0.00001)
  ├── Span 3: Hybrid Search Retrieval(Retrieved: 5 chunks, Latency: 45ms)
  └── Span 4: LLM Generation / Stream(Provider: AiStudio.id, Latency: 620ms)

import os
import time
import json
from typing import List, Dict, Any
from openai import OpenAI

# Inisialisasi klien menggunakan gateway terpadu AiStudio.id
# Menggunakan standar protokol OpenAI-compatible dengan latensi optimal dan multi-model support
AI_GATEWAY_URL = os.getenv("AISTUDIO_BASE_URL", "https://api.aistudio.id/v1")
AI_GATEWAY_KEY = os.getenv("AISTUDIO_API_KEY", "sk-live-aistudio-sample-key")

client = OpenAI(
    base_url=AI_GATEWAY_URL,
    api_key=AI_GATEWAY_KEY,
    timeout=30.0,
    max_retries=3
)

class AgentTool:
    """Definisi struktur tool deterministik untuk pemanggilan fungsi."""
    @staticmethod
    def execute_system_query(command: str) -> str:
        # Simulasi eksekusi perintah terlindungi
        if "status" in command:
            return json.dumps({"status": "HEALTHY", "cpu_load": "18%", "active_workers": 12})
        return json.dumps({"status": "UNKNOWN_COMMAND"})

def run_agentic_pipeline(user_prompt: str):
    print(f"[+] Memulai Agen untuk tugas: '{user_prompt}'")
    start_time = time.time()
    
    # 1. Definisi Tools Schema
    tools = [
        {
            "type": "function",
            "function": {
                "name": "execute_system_query",
                "description": "Menjalankan pemantauan status kluster peladen.",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "command": {
                            "type": "string",
                            "description": "Perintah sistem yang akan dijalankan, misal: 'check_status'"
                        }
                    },
                    "required": ["command"],
                },
            },
        }
    ]

    messages: List[Dict[str, Any]] = [
        {
            "role": "system", 
            "content": "Anda adalah SRE Assistant berdaya analitis tinggi. Gunakan alat yang tersedia secara deterministik."
        },
        {"role": "user", "content": user_prompt}
    ]

    # 2. Pemanggilan Model Penalaran / Eksekusi melalui AiStudio Gateway
    # Router dapat secara fleksibel mengarahkan permintaan ke DeepSeek-R1, Claude 3.5, atau Llama 3.3
    response = client.chat.completions.create(
        model="deepseek-r1",
        messages=messages,
        tools=tools,
        tool_choice="auto",
        temperature=0.2
    )

    response_message = response.choices[0].message
    tool_calls = response_message.tool_calls

    # 3. Handling Tool Invocation Loop
    if tool_calls:
        messages.append(response_message)
        for tool_call in tool_calls:
            function_name = tool_call.function.name
            function_args = json.loads(tool_call.function.arguments)
            
            print(f"[*] Tool Execution: {function_name}({function_args})")
            
            if function_name == "execute_system_query":
                tool_output = AgentTool.execute_system_query(
                    command=function_args.get("command", "")
                )
                
                messages.append({
                    "tool_call_id": tool_call.id,
                    "role": "tool",
                    "name": function_name,
                    "content": tool_output,
                })

        # 4. Sintesis Akhir dari Respons
        final_response = client.chat.completions.create(
            model="deepseek-r1",
            messages=messages,
            temperature=0.3
        )
        
        latency = (time.time() - start_time) * 1000
        print(f"[✓] Siklus selesai dalam {latency:.2f}ms")
        print("\n=== RESPON AGEN ===")
        print(final_response.choices[0].message.content)
    else:
        print("[!] Agen tidak memerlukan pemanggilan tool.")
        print(response_message.content)

if __name__ == "__main__":
    run_agentic_pipeline("Tolong periksa status kluster sistem saat ini dan beri saya ringkasan kondisinya.")

┌────────────────────────────────────────┐
                  │          Client Applications           │
                  └───────────────────┬────────────────────┘
                                      │
                                      ▼
           ┌─────────────────────────────────────────────────────┐
           │              AiStudio.id API Gateway                │
           │  ┌─────────────────┬─────────────────┬───────────┐  │
           │  │ Unified Routing │ Multi-Model Fall│ Latency   │  │
           │  │ & Token Balance │ over & Circuit  │ Balancing │  │
           │  └─────────────────┴─────────────────┴───────────┘  │
           └──────────────────────────┬──────────────────────────┘
                                      │
        ┌─────────────────────────────┼─────────────────────────────┐
        ▼                             ▼                             ▼
 ┌──────────────┐              ┌──────────────┐              ┌──────────────┐
 │ DeepSeek-R1  │              │ Claude 3.5   │              │   Llama 3    │
 │ (Reasoning)  │              │ (Execution)  │              │  (Fallback)  │
 └──────────────┘              └──────────────┘              └──────────────┘

Checklist Audit Repositori
 ┌─────────────────────────────────────────────────────────────────┐
 │ [ ] 1. Rasio Bintang vs. Commit Berkelanjutan                   │
 │        (Hindari proyek dengan 1 commit raksasa lalu ditinggal)  │
 │                                                                 │
 │ [ ] 2. Mekanisme State Management Terukur                       │
 │        (Apakah agen mengontrol token context window dengan baik)│
 │                                                                 │
 │ [ ] 3. Kompatibilitas Abstraksi Provider                        │
 │        (Apakah arsitekturnya mengizinkan custom Base URL/Gateway)│
 │                                                                 │
 │ [ ] 4. Keamanan Lingkungan Eksekusi                             │
 │        (Tidak mengeksekusi `eval()` atau shell tanpa sandbox)   │
 └─────────────────────────────────────────────────────────────────┘