LLM Application Development plugin: what it installs and how to add it

This plugin supports development of LLM applications using LangChain and LangGraph, including StateGraph workflows, memory, tool use, structured outputs and asynchronous execution. It covers RAG with hybrid search and reranking, embedding selection, vector similarity search and index tuning for systems such as Pinecone, Qdrant, Weaviate, Milvus and pgvector.

Use it when creating AI assistants, LangGraph agents, retrieval systems or production prompts. Projects need Python 3.11 or later, LangChain 1.2.0 or later and LangGraph 0.3.0 or later.

What's inside

• 8 skills: embedding strategies, hybrid search implementation, LangChain architecture, LLM evaluation, prompt engineering patterns, RAG implementation, similarity search patterns and vector index tuning
• 3 slash commands: /llm-application-dev:ai-assistant, /llm-application-dev:langchain-agent and /llm-application-dev:prompt-optimize
• 3 agents: ai-engineer, prompt-engineer and vector-database-engineer