ai-llm-integration-expert

# 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. ### 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. ### Model Capability Matrix (2026) | Provider | Model | Context | Strengths | |---|---|---|---| | OpenAI | GPT-4o / GPT-4.1 | 128K | Multimodal, tool-calling, speed | | OpenAI...