ai-llm-integration-expert · diff
git:20260816.d23736e to git:20260907.0ae92d0
135 added, 137 removed. Audit A to A.
---
name: ai-llm-integration-expert
- description: "Expert guide for integrating Large Language Models (LLMs), Model Context Protocol (MCP), RAG architecture, vector databases, and AI agents / Panduan ahli untuk integrasi LLM, Model Context Protocol (MCP), arsitektur RAG, vector database, dan agen AI."
+ description: "Expert guide for integrating Large Language Models (LLMs), Model Context Protocol (MCP v1.x), hybrid reasoning models, RAG architecture, vector databases, and AI agents / Panduan ahli untuk integrasi LLM, Model Context Protocol (MCP), model hybrid reasoning, arsitektur RAG, vector database, dan agen AI."
author: "vibes-plug-swarm"
---
# AI & LLM Integration Expert (2026 Edition)
[English](#english) | [Bahasa Indonesia](#bahasa-indonesia)
---
<a name="english"></a>
## English
### Description
- Production-grade guidelines for integrating AI, Model Context Protocol (MCP), and Large Language Models (LLMs) into modern applications. Covers RAG pipelines, vector embeddings, real-time token streaming, agentic tool execution, memory architectures, prompt caching, and multi-model orchestration.
+ Production-grade guidelines for integrating AI, Model Context Protocol (MCP), hybrid reasoning models, and Large Language Models (LLMs) into modern software architectures. Covers hybrid reasoning token streaming, Streamable HTTP MCP transports, agentic memory architectures, native context caching, RAG pipelines, and multi-model orchestration.
### Trigger Conditions
- - Integrating frontier models: OpenAI GPT-4o / GPT-4.1 / GPT-5, Anthropic Claude 3.7 / Claude 4 Sonnet/Opus, Google Gemini 3.1 Pro/Flash/Ultra, or open-source models (DeepSeek-V3/R2, Llama 4, Qwen 3).
- - Implementing Model Context Protocol (MCP) server or client integrations (MCP 1.9+).
- - Building AI chatbots, copilots, or autonomous AI agent workflows (LangGraph, OpenAI Agents SDK, Google ADK, Mastra.ai, Vercel AI SDK 4.x/5.x).
- - Building agentic memory systems (short-term, long-term, episodic) using Mem0, MemGPT, or Supabase-backed memory stores.
- - Implementing RAG with vector databases (Supabase pgvector HNSW, Qdrant, Pinecone, Weaviate).
- - Handling real-time AI token streaming via SSE, Web Streams, or WebSockets.
- - Designing AI agents with tool-calling, computer use (browser/OS automation), or code interpreter capabilities.
+ - Integrating frontier reasoning models: Anthropic Claude 3.7 Sonnet (Hybrid/Extended Thinking), Google Gemini 3.8 Flash / 3.1 Pro (Thinking Mode), OpenAI o1 / o3 / o3-mini / GPT-4.5 / GPT-4o, DeepSeek-R1 / V3, or open-source weights (Llama 4, Qwen 2.5/3 Coder).
+ - Implementing Model Context Protocol (MCP) server or client integrations with Streamable HTTP transport or MCP Sampling.
+ - Building AI chatbots, copilots, or autonomous AI agent workflows (LangGraph, OpenAI Agents SDK, Google ADK, Mastra.ai, Vercel AI SDK 5.x/6.x).
+ - Managing streaming reasoning tokens (`<think>` chunks) separately from final output in user interfaces.
+ - Implementing hybrid RAG with vector databases (Supabase pgvector HNSW, Qdrant, Pinecone) and cross-encoder rerankers.
+ - Building agentic memory systems (short-term, long-term semantic, episodic) using Mem0 or vector stores.
+ - Implementing cost optimization with provider-native Context Caching (Gemini `cachedContent`, Anthropic ephemeral prompt cache, OpenAI prefix cache).
### Model Capability Matrix (2026)
- | Provider | Model | Context | Strengths |
- |---|---|---|---|
- | OpenAI | GPT-4o / GPT-4.1 | 128K | Multimodal, tool-calling, speed |
- | OpenAI | GPT-5 | 1M+ | Reasoning, agentic tasks |
- | Anthropic | Claude 4 Sonnet | 200K | Coding, long documents, Computer Use |
- | Anthropic | Claude 4 Opus | 200K | Complex reasoning, deep analysis |
- | Google | Gemini 3.1 Flash | 2M | Speed, cost efficiency, multimodal |
- | Google | Gemini 3.1 Pro | 2M | Code, reasoning, deep research |
- | Meta | Llama 4 Scout/Maverick | 512K | Open-source, self-hosted |
- | DeepSeek | V3 / R2 | 128K | Code, math, cost-efficient |
- ### Core Architecture Guidelines
+ | Provider | Model | Context | Reasoning Type | Primary Strength |
+ |---|---|---|---|---|
+ | Anthropic | Claude 3.7 Sonnet | 200K | Hybrid Thinking (Standard + Extended) | Code generation, complex reasoning, Computer Use |
+ | Anthropic | Claude 3.5 / 4 Opus | 200K | Deep Deliberation | Deep architectural synthesis, policy analysis |
+ | Google | Gemini 3.8 Flash | 1M–2M | Flash Thinking (Configurable Budget) | Ultra-low latency, multimodal live, high-frequency loops |
+ | Google | Gemini 3.1 / 3.5 Pro | 2M | Extended Reasoning | Needle-in-a-haystack, long-context repos, deep research |
+ | OpenAI | o3 / o3-mini | 200K | Native Test-Time Reasoning | Math, competitive coding, formal logic verification |
+ | OpenAI | GPT-4.5 / GPT-4o | 128K | Direct Instruction & Fast Tooling | Low-latency voice, structured JSON, tool-calling |
+ | DeepSeek | DeepSeek-R1 | 128K | Open Reasoning (Distill & MoE) | State-of-the-art open weights reasoning, math, coding |
+ | DeepSeek | DeepSeek-V3 | 128K | General Multimodal / Text | High throughput, extremely cost-efficient coding |
+ | Qwen | Qwen 2.5 / 3 Coder | 128K | Open Source Code Specialist | Self-hosted coding agent, local copilot integration |
- #### 1. Model Context Protocol (MCP) Integration (v1.9+)
- Standardize agent-tool interactions using MCP. The 2026 standard adds Streamable HTTP transport alongside stdio/SSE:
- - MCP Servers: Expose tools, resources, and prompt templates over JSON-RPC 2.0.
- - Streamable HTTP: New default transport for cloud-hosted MCP servers — supports bidirectional streaming without long-polling.
- - Security & Scope: Validate all incoming parameters with Zod schemas. Enforce strict authorization boundaries before executing tool calls.
- - Tool namespacing: Use `domain/action` naming (`files/read`, `db/query`) to avoid collisions in multi-server environments.
+ ### Core Architecture Guidelines
- #### 2. AI Agents SDK Landscape (2026)
- Choose the right agentic framework based on your needs:
- - OpenAI Agents SDK: GPT-5 native agents. Handoffs, guardrails, tracing built-in.
- - Google ADK: Gemini-powered agents. Multi-agent, streaming, Vertex AI integration.
- - LangGraph: Complex stateful workflows. Graph-based, human-in-the-loop, any LLM.
- - Mastra.ai: TypeScript-first agents. Built-in memory, evals, RAG.
- - Vercel AI SDK 5.x: Streaming UI + agents. RSC streaming, multi-provider, tool-calling.
+ #### 1. Hybrid Reasoning & Streaming Token Handling
+ Modern frontier models emit internal thinking/reasoning tokens during test-time compute:
+ - **Thinking Token Separation**: Separate internal `<think>` or reasoning chunks from conversational output. Stream reasoning into collapsible UI accordion blocks while presenting clean output to the user.
+ - **Vercel AI SDK 5.x/6.x Integration**:
+ ```typescript
+ import { streamText } from 'ai';
+ import { anthropic } from '@ai-sdk/anthropic';
- #### 3. Agentic Memory Architecture
- Production AI agents require persistent memory across sessions:
- - Short-term (In-context): Pass recent conversation turns in the prompt window.
- - Long-term (Vector Store): Store user preferences and past interactions as embeddings in pgvector or Qdrant; retrieve with semantic similarity.
- - Episodic (Episodic Memory): Use Mem0 or MemGPT to give agents human-like recall — automatically summarizes and indexes past interactions.
- - Structured (Knowledge Graph): Use a graph DB (Neo4j, Kuzu) for entity relationships and factual memory.
+ const result = streamText({
+ model: anthropic('claude-3-7-sonnet-20250219'),
+ providerOptions: {
+ anthropic: {
+ thinking: { type: 'enabled', budgetTokens: 4096 },
+ },
+ },
+ prompt: 'Refactor this distributed consensus engine...',
+ });
- #### 4. Advanced RAG (Retrieval-Augmented Generation) Pipeline
- Build a production-grade RAG pipeline with hybrid search:
- 1. Ingestion: Chunk documents (500-1000 tokens, 10% overlap, respect semantic headings).
- 2. Embedding: Use `text-embedding-3-large` (OpenAI), `gemini-embedding-004` (Google), or `nomic-embed-text` (open-source).
- 3. Storage & Hybrid Search: PostgreSQL pgvector (HNSW index) or Qdrant. Combine vector cosine similarity with BM25 full-text search for hybrid retrieval.
- 4. Reranking: Apply cross-encoder reranker (Cohere Rerank 3, FlashRank) on top-K results.
- 5. Generation: Inject top contextual snippets into the system prompt with source citations.
+ // Access reasoning stream alongside main text
+ for await (const part of result.fullStream) {
+ if (part.type === 'reasoning') {
+ process.stdout.write(`[Thinking]: ${part.textDelta}`);
+ } else if (part.type === 'text-delta') {
+ process.stdout.write(part.textDelta);
+ }
+ }
+ ```
- #### 5. Prompt Caching (Cost Optimization)
- Reduce costs by 80-90% on repeated long prompts using provider-native caching:
- - Anthropic: Automatic prompt caching for prompts > 1024 tokens (marked with `cache_control: {"type": "ephemeral"}`).
- - OpenAI: Automatic prefix caching for prompts > 1024 tokens in GPT-4o and later models.
- - Google: Context caching in Gemini API via `cachedContent` for large system prompts or documents.
+ #### 2. Model Context Protocol (MCP) — Streamable HTTP & Sampling
+ Standardize all agent-tool and agent-host communications using MCP specifications:
+ - **Streamable HTTP Transport**: Modern cloud deployments use Streamable HTTP (bidirectional JSON-RPC streaming over HTTP POST/SSE hybrid) instead of fragile stdio connections.
+ - **MCP Sampling**: Allow MCP servers to request LLM completions back from the host client, enabling nested agentic tools without distributing API keys to tool servers.
+ - **Authorization & Security**: Enforce OAuth 2.1 scoped bearer tokens on remote MCP endpoints. Validate all incoming tool inputs using strict Zod schemas.
- #### 6. Streaming Responses & Vercel AI SDK 5.x
- - Backend (Next.js/Bun/FastAPI): Pipe stream chunks via Web Streams API or SSE.
- - Frontend (React): Use Vercel AI SDK `useChat` / `useCompletion` for zero perceived latency.
- - RSC Streaming: Use AI SDK 5.x `streamUI` to stream React components from the server — agent-rendered UI.
+ #### 3. Agentic Memory Architecture
+ Production AI agents require multi-layered memory:
+ - **In-Context Working Memory**: Pass recent turn messages within the prompt window.
+ - **Long-Term Semantic Memory**: Embed user preferences and facts using pgvector (HNSW index) or Qdrant; perform cosine similarity searches.
+ - **Episodic Memory (Mem0 / MemGPT)**: Automatically synthesize session checkpoints and index past user decisions for human-like recall.
+ - **Knowledge Graph Memory**: Maintain structured entity-relationship triples (using Graph DB or relational junction tables) for multi-hop relationship retrieval.
- #### 7. Computer Use & Browser Agents
- Modern agents can control browsers and operating systems:
- - Anthropic Computer Use: Claude 4 can take screenshots and control mouse/keyboard.
- - Browser-Use: Open-source library for browser-controlling agents with any LLM.
- - Playwright MCP Server: Expose a full browser to AI agents via MCP tools (`navigate`, `click`, `screenshot`).
+ #### 4. Advanced RAG & Late Chunking Pipeline
+ Build high-precision RAG pipelines:
+ 1. **Document Ingestion**: Chunk semantically (500–1000 tokens) with 10% overlap, or apply *Late Chunking* (chunking after full document contextual embedding).
+ 2. **Embeddings**: Utilize `text-embedding-3-large`, `gemini-embedding-004`, or local `bge-large-en-v1.5`.
+ 3. **Hybrid Search**: Combine dense vector cosine similarity with sparse BM25 / PostgreSQL `tsvector` queries.
+ 4. **Cross-Encoder Reranking**: Reorder top-K candidates using Cohere Rerank 3 or FlashRank before feeding into the prompt.
+ 5. **Context Window vs RAG Decision**: If document sets fit comfortably under 200k tokens and are queried repeatedly, prefer **Native Context Caching** over RAG chunking to eliminate retrieval boundary errors.
- #### 8. Structured Output & Tool Calling
- - Use native Tool/Function Calling APIs with strict JSON schemas via Zod (`zodResponseFormat`).
- - Prefer structured output mode over prompt engineering for JSON — guaranteed schema conformance.
- - Support reasoning models (DeepSeek-R2, Gemini 3.1 Pro Thinking, Claude 4 extended thinking) by separating internal `<think>` tokens from client-facing output.
+ #### 5. Native Context Caching (Cost & Latency Optimization)
+ Leverage provider-native context caching for large, repeated context (>32k tokens):
+ - **Anthropic**: Use ephemeral prompt caching with `cache_control: { type: "ephemeral" }` on system prompts and tools.
+ - **OpenAI**: Take advantage of automatic prefix caching for matching prompt prefixes >1024 tokens.
+ - **Google Gemini**: Explicitly create and reuse cached content via `cachedContent` API for huge repositories, reducing costs up to 90%.
- #### 9. Cost & Rate Limit Management
- - Monitor token consumption per session; store metrics in telemetry tables.
- - Implement semantic caching using Redis + vector similarity to skip redundant model queries.
- - Use batch APIs (OpenAI Batch, Anthropic Message Batches) for async, high-volume workloads at 50% cost reduction.
+ #### 6. Structured Output & Guardrails
+ - Utilize native Structured Outputs (`response_format: { type: "json_schema" }`) guaranteed by model token-level grammar masks.
+ - Use Zod schemas to define tool parameters and final structured entities.
+ - Implement rate limiting, circuit breakers, and semantic caching (Redis / Upstash vector cache) to prevent runaway recursive tool loops.
## Orchestration & Integration
- - **`mcp-server-architect`**: Delegate custom MCP server creation and architecture.
- - **`multi-agent-orchestration`**: Delegate complex agent state graphs and swarm workflows.
- - **`ai-prompt-engineering-expert`**: Delegate advanced prompt design and evals.
- - **`ai-cost-token-optimizer`**: Delegate API cost optimization and prompt caching strategies.
- - **`vector-db-rag-expert`**: Delegate pgvector indexing and hybrid search tuning.
- - **`doku-mcp-server`**: Delegate payment integration tools via MCP.
- - **`gemini-agent-booster`**: Delegate Gemini long-context optimization.
- - **`zero-to-prod-orchestrator`**: Executes this skill during Phase 4 or after AI decision lock in `brainstorming`.
+ - **`mcp-server-architect`**: Delegate custom MCP server implementation, schema definitions, and transport adapters.
+ - **`multi-agent-orchestration`**: Delegate complex multi-agent state graphs, swarm workflows, and supervisor patterns.
+ - **`gemini-agent-booster`**: Delegate Gemini 3.x long-context optimization, Multimodal Live API, and thinking budget controls.
+ - **`ai-prompt-engineering-expert`**: Delegate advanced prompt design, few-shot calibration, and system prompt evals.
+ - **`ai-cost-token-optimizer`**: Delegate API cost optimization, model routing, and token budget management.
+ - **`vector-db-rag-expert`**: Delegate pgvector HNSW indexing and hybrid retrieval fine-tuning.
+ - **`zero-to-prod-orchestrator`**: Executes this skill during Phase 4 architecture and implementation.
---
<a name="bahasa-indonesia"></a>
## Bahasa Indonesia
### Deskripsi
- Panduan tingkat produksi untuk mengintegrasikan AI, Model Context Protocol (MCP), dan LLM ke dalam aplikasi modern. Mencakup pipeline RAG, streaming token real-time, eksekusi tool agen AI, arsitektur memori, prompt caching, dan orkestrasi multi-model.
+ Panduan tingkat produksi untuk mengintegrasikan AI, Model Context Protocol (MCP), model hybrid reasoning, dan Large Language Models (LLM) ke dalam arsitektur perangkat lunak modern. Mencakup penanganan streaming token penalaran, transport MCP Streamable HTTP, arsitektur memori agentik, context caching native, pipeline RAG, dan orkestrasi multi-model.
### Kondisi Pemicu
- - Mengintegrasikan model frontier: GPT-4o / GPT-4.1 / GPT-5, Claude 3.7 / Claude 4 Sonnet/Opus, Gemini 3.1 Pro/Flash/Ultra, atau model open-source (DeepSeek-V3/R2, Llama 4, Qwen 3).
- - Mengimplementasikan server/klien MCP (v1.9+ dengan Streamable HTTP transport).
- - Membangun chatbot AI, copilot, atau alur kerja agen otonom (LangGraph, OpenAI Agents SDK, Google ADK, Mastra.ai, Vercel AI SDK 4.x/5.x).
- - Membangun sistem memori agentik (jangka pendek, jangka panjang, episodik) menggunakan Mem0, MemGPT, atau pgvector.
- - Mengimplementasikan RAG dengan vector database (Supabase pgvector HNSW, Qdrant, Pinecone).
- - Menangani streaming token AI real-time via SSE, Web Streams, atau WebSockets.
- - Merancang agen AI dengan tool-calling, Computer Use (otomatisasi browser/OS), atau code interpreter.
-
- ### Panduan Arsitektur Inti
+ - Mengintegrasikan model frontier reasoning: Anthropic Claude 3.7 Sonnet (Hybrid/Extended Thinking), Google Gemini 3.8 Flash / 3.1 Pro (Thinking Mode), OpenAI o1 / o3 / o3-mini / GPT-4.5 / GPT-4o, DeepSeek-R1 / V3, atau bobot open-source (Llama 4, Qwen 2.5/3 Coder).
+ - Mengimplementasikan server atau klien Model Context Protocol (MCP) dengan transport Streamable HTTP atau MCP Sampling.
+ - Membangun chatbot AI, copilot, atau workflow agen otonom (LangGraph, OpenAI Agents SDK, Google ADK, Mastra.ai, Vercel AI SDK 5.x/6.x).
+ - Mengelola streaming token penalaran (`<think>`) secara terpisah dari output akhir pada tampilan antarmuka pengguna.
+ - Mengimplementasikan RAG hibrida dengan database vektor (Supabase pgvector HNSW, Qdrant, Pinecone) dan reranker cross-encoder.
+ - Membangun sistem memori agentik (jangka pendek, semantik jangka panjang, episodik) menggunakan Mem0 atau vector store.
+ - Menerapkan optimasi biaya dengan Context Caching native provider (Gemini `cachedContent`, Anthropic ephemeral prompt cache, OpenAI prefix cache).
- #### 1. Integrasi MCP (v1.9+)
- Standarisasi interaksi agen-tool menggunakan MCP. Standar 2026 menambahkan Streamable HTTP transport — transport default baru untuk MCP server yang di-host di cloud, mendukung streaming dua arah tanpa long-polling. Validasi parameter masuk dengan skema Zod dan tegakkan batas otorisasi ketat.
+ ### Matriks Kapabilitas Model (2026)
- #### 2. Ekosistem Agents SDK (2026)
- Pilih framework agentik yang tepat:
- - OpenAI Agents SDK: Agen berbasis GPT-5. Handoffs, guardrails, tracing bawaan.
- - Google ADK: Agen bertenaga Gemini. Multi-agen, streaming, integrasi Vertex AI.
- - LangGraph: Alur kerja stateful kompleks. Berbasis graph, human-in-the-loop, LLM apa saja.
- - Mastra.ai: Agen TypeScript-first. Memori bawaan, evaluasi, RAG.
- - Vercel AI SDK 5.x: Streaming UI + agen. RSC streaming, multi-provider, tool-calling.
+ | Provider | Model | Konteks | Tipe Penalaran | Keunggulan Utama |
+ |---|---|---|---|---|
+ | Anthropic | Claude 3.7 Sonnet | 200K | Hybrid Thinking (Standar + Diperpanjang) | Generasi kode, penalaran kompleks, Computer Use |
+ | Anthropic | Claude 3.5 / 4 Opus | 200K | Deliberasi Mendalam | Sintesis arsitektur mendalam, analisis kebijakan |
+ | Google | Gemini 3.8 Flash | 1M–2M | Flash Thinking (Budget Terkonfigurasi) | Latensi ultra-rendah, multimodal live, loop cepat |
+ | Google | Gemini 3.1 / 3.5 Pro | 2M | Extended Reasoning | Needle-in-a-haystack, repo konteks panjang, riset |
+ | OpenAI | o3 / o3-mini | 200K | Test-Time Reasoning Native | Matematika, competitive coding, verifikasi logika |
+ | OpenAI | GPT-4.5 / GPT-4o | 128K | Instruksi Langsung & Tooling Cepat | Suara latensi rendah, JSON terstruktur, tool calling |
+ | DeepSeek | DeepSeek-R1 | 128K | Open Reasoning (Distill & MoE) | Penalaran open weights terdepan, matematika, koding |
+ | DeepSeek | DeepSeek-V3 | 128K | Teks & Multimodal Umum | Throughput tinggi, sangat hemat biaya untuk coding |
+ | Qwen | Qwen 2.5 / 3 Coder | 128K | Spesialis Kode Open Source | Agen coding mandiri / self-hosted, copilot lokal |
- #### 3. Arsitektur Memori Agentik
- - Jangka Pendek (In-context): Teruskan riwayat percakapan terbaru di prompt window.
- - Jangka Panjang (Vector Store): Simpan preferensi pengguna dan interaksi lampau sebagai embedding di pgvector atau Qdrant.
- - Episodik (Episodic Memory): Gunakan Mem0 atau MemGPT untuk recall mirip manusia — merangkum dan mengindeks interaksi otomatis.
- - Terstruktur (Knowledge Graph): Gunakan graph DB (Neo4j, Kuzu) untuk relasi entitas.
+ ### Panduan Arsitektur Inti
- #### 4. Pipeline RAG Lanjutan
- Bangun pipeline RAG tingkat produksi:
- 1. Ingestion: Chunking dokumen (500-1000 token, 10% overlap).
- 2. Embedding: Gunakan `text-embedding-3-large`, `gemini-embedding-004`, atau `nomic-embed-text`.
- 3. Penyimpanan & Pencarian Hibrida: pgvector (HNSW) atau Qdrant. Gabungkan kesamaan kosinus vektor dengan pencarian teks penuh BM25.
- 4. Reranking: Terapkan cross-encoder reranker (Cohere Rerank 3, FlashRank).
- 5. Generation: Injeksi snippet konteks terbaik ke system prompt dengan sitasi sumber.
+ #### 1. Hybrid Reasoning & Penanganan Token Streaming
+ Model frontier modern menghasilkan token pemikiran (*thinking tokens*) selama proses penalaran:
+ - **Pemisahan Token Pemikiran**: Pisahkan blok `<think>` dari output teks akhir. Alirkan proses pemikiran ke dalam accordion UI yang dapat dibuka/tutup, sementara output utama disajikan dengan bersih kepada user.
+ - **Integrasi Vercel AI SDK 5.x/6.x**: Gunakan event listener `reasoning` pada stream untuk menangkap proses pemikiran model secara terpisah dari `text-delta`.
- #### 5. Prompt Caching (Optimasi Biaya)
- Kurangi biaya 80-90% dengan caching prompt panjang:
- - Anthropic: Caching otomatis untuk prompt > 1024 token.
- - OpenAI: Prefix caching otomatis di GPT-4o ke atas.
- - Google: Context caching di Gemini API via `cachedContent`.
+ #### 2. Model Context Protocol (MCP) — Streamable HTTP & Sampling
+ Standarisasi seluruh komunikasi agen-ke-tool dan agen-ke-host menggunakan spesifikasi MCP:
+ - **Streamable HTTP Transport**: Standar cloud modern yang mendukung streaming dua arah JSON-RPC via HTTP POST/SSE hybrid tanpa ketergantungan koneksi stdio lokal.
+ - **MCP Sampling**: Memberikan izin kepada server MCP untuk meminta inferensi LLM kembali ke klien host, memungkinkan alat agentik modular tanpa membagikan API key sensitif.
+ - **Keamanan & Otorisasi**: Wajibkan bearer token berbasis OAuth 2.1 pada endpoint MCP publik dan validasi skema input dengan Zod.
- #### 6. Streaming & Vercel AI SDK 5.x
- - Backend: Alirkan chunk via Web Streams API atau SSE.
- - Frontend: Gunakan `useChat` / `useCompletion` untuk latensi nol yang dirasakan.
- - RSC Streaming: Gunakan `streamUI` di AI SDK 5.x untuk stream komponen React dari server.
+ #### 3. Arsitektur Memori Agentik
+ - **Memori Kerja In-Context**: Teruskan histori interaksi terbaru di jendela konteks.
+ - **Memori Semantik Jangka Panjang**: Simpan fakta dan preferensi pengguna di pgvector (indeks HNSW) atau Qdrant dengan pencarian kesamaan kosinus.
+ - **Memori Episodik (Mem0 / MemGPT)**: Sintesiskan ringkasan sesi secara berkala agar agen memiliki daya ingat historis jangka panjang.
+ - **Memori Graph Pengetahuan**: Hubungkan entitas relasional dalam basis data graf untuk kueri multi-langkah (*multi-hop reasoning*).
- #### 7. Computer Use & Browser Agents
- Agen modern dapat mengontrol browser dan OS:
- - Anthropic Computer Use: Claude 4 dapat mengambil screenshot dan mengontrol mouse/keyboard.
- - Browser-Use: Pustaka open-source untuk kontrol browser dengan LLM apa saja.
- - Playwright MCP Server: Beri agen kontrol atas browser melalui tool MCP.
+ #### 4. Pipeline RAG Lanjutan & Late Chunking
+ 1. **Ingesti Dokumen**: Bagi dokumen secara semantik (500–1000 token, 10% overlap) atau gunakan metode *Late Chunking*.
+ 2. **Embedding**: Gunakan `text-embedding-3-large` atau `gemini-embedding-004`.
+ 3. **Pencarian Hibrida**: Kombinasikan dense vector cosine similarity dengan sparse keyword BM25 / PostgreSQL full-text search.
+ 4. **Cross-Encoder Reranking**: Susun ulang kandidat terbaik menggunakan Cohere Rerank 3 atau FlashRank sebelum diteruskan ke system prompt.
+ 5. **Keputusan Cache vs RAG**: Jika dokumen stabil dan berada di bawah 200k token, utamakan **Context Caching Native** daripada RAG chunking untuk menghindari hilangnya konteks di perbatasan potongan teks.
- #### 8. Output Terstruktur & Tool Calling
- - Gunakan API Tool/Function Calling native dengan skema JSON ketat via Zod (`zodResponseFormat`).
- - Utamakan mode structured output untuk jaminan kesesuaian skema JSON.
- - Dukung model reasoning (DeepSeek-R2, Gemini 3.1 Pro Thinking, Claude 4) dengan memisahkan token internal `<think>` dari output klien.
+ #### 5. Context Caching Native (Optimasi Biaya & Latensi)
+ - **Anthropic**: Terapkan prompt caching ephemeral dengan `cache_control: { type: "ephemeral" }`.
+ - **OpenAI**: Manfaatkan prefix caching otomatis untuk teks berulang >1024 token.
+ - **Google Gemini**: Buat objek cache eksplisit via API `cachedContent` untuk repositori kode besar guna menghemat hingga 90% biaya input token.
- #### 9. Manajemen Biaya & Rate Limit
- - Pantau konsumsi token per sesi di tabel telemetri.
- - Terapkan semantic caching dengan Redis + vektor untuk melewati query duplikat.
- - Gunakan Batch API untuk workload async bervolume tinggi dengan pengurangan biaya 50%.
+ #### 6. Output Terstruktur & Guardrails
+ - Manfaatkan mode Structured Outputs native model dengan skema Zod untuk menjamin integritas JSON.
+ - Terapkan rate limiting, circuit breaker, dan semantic caching (Redis / Upstash) untuk mencegah pemanggilan tool secara rekursif tak berujung.
## Integrasi Orkestrasi
- - **`mcp-server-architect`**: Delegasikan pembuatan dan arsitektur custom MCP server.
- - **`multi-agent-orchestration`**: Delegasikan alur state graph agen kompleks dan swarm workflows.
- - **`ai-prompt-engineering-expert`**: Delegasikan desain prompt lanjutan dan evaluasi.
- - **`ai-cost-token-optimizer`**: Delegasikan optimasi biaya API dan strategi prompt caching.
- - **`vector-db-rag-expert`**: Delegasikan indexing pgvector dan tuning pencarian hibrida.
- - **`doku-mcp-server`**: Delegasikan integrasi tool pembayaran via MCP.
- - **`gemini-agent-booster`**: Delegasikan optimasi long-context Gemini.
- - **`zero-to-prod-orchestrator`**: Mengeksekusi skill ini pada Fase 4 atau setelah finalisasi keputusan AI di `brainstorming`.
+ - **`mcp-server-architect`**: Delegasikan pembuatan server MCP kustom, definisi skema, dan transport adapter.
+ - **`multi-agent-orchestration`**: Delegasikan alur kerja graph multi-agen, topologi swarm, dan pattern supervisor.
+ - **`gemini-agent-booster`**: Delegasikan optimasi long-context Gemini 3.x, Multimodal Live API, dan kontrol thinking budget.
+ - **`ai-prompt-engineering-expert`**: Delegasikan desain prompt lanjutan, kalibrasi few-shot, dan evaluasi prompt.
+ - **`ai-cost-token-optimizer`**: Delegasikan optimasi biaya API, routing model cerdas, dan token budget.
+ - **`vector-db-rag-expert`**: Delegasikan tuning indeks HNSW pgvector dan pencarian hibrida.
+ - **`zero-to-prod-orchestrator`**: Mengeksekusi skill ini pada Fase 4 perancangan arsitektur dan implementasi.