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), 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 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 e...