# LLM Application Development Plugin for Claude Code Build production-ready LLM applications, advanced RAG systems, and intelligent agents with modern AI patterns. ## Version 2.0.0 Highlights - **LangGraph Integration**: Updated from deprecated LangChain patterns to LangGraph StateGraph workflows - **Modern Model Support**: Claude Opus 4.6/Sonnet 4.6/Haiku 4.5 and GPT-5.2/GPT-5-mini - **Voyage AI Embeddings**: Recommended embedding models for Claude applications - **Structured Outputs**: Pydantic-based structured output patterns ## Features ### Core Capabilities - **RAG Systems**: Production retrieval-augmented generation with hybrid search - **Vector Search**: Pinecone, Qdrant, Weaviate, Milvus, pgvector optimization - **Agent Architectures**: LangGraph-based agents with memory and tool use - **Prompt Engineering**: Advanced prompting techniques with model-specific optimization ### Key Technologies - LangChain 1.x / LangGraph for agent workflows - Voyage AI, OpenAI, and open-source embedding models - HNSW, IVF, and Product Quantization index strategies - Async patterns with checkpointers for durable execution ## Agents | Agent | Description | | -------------------------- | -------------------------------------------------------------------------- | | `ai-engineer` | Production-grade LLM applications, RAG systems, and agent architectures | | `prompt-engineer` | Advanced prompting techniques, constitutional AI, and model optimization | | `vector-database-engineer` | Vector search implementation, embedding strategies, and semantic retrieval | ## Skills | Skill | Description | | ------------------------------ | ----------------------------------------------------------- | | `langchain-architecture` | LangGraph StateGraph patterns, memory, and tool integration | | `rag-implementation` | RAG systems with hybrid search and reranking | | `llm-evaluation` | Evaluation frameworks for LLM applications | | `prompt-engineering-patterns` | Chain-of-thought, few-shot, and structured outputs | | `embedding-strategies` | Embedding model selection and optimization | | `similarity-search-patterns` | Vector similarity search implementation | | `vector-index-tuning` | HNSW, IVF, and quantization optimization | | `hybrid-search-implementation` | Vector + keyword search fusion | ## Commands | Command | Description | | -------------------------------------- | ------------------------------- | | `/llm-application-dev:langchain-agent` | Create LangGraph-based agent | | `/llm-application-dev:ai-assistant` | Build AI assistant application | | `/llm-application-dev:prompt-optimize` | Optimize prompts for production | ## Installation ```bash /plugin install llm-application-dev ``` ## Requirements - LangChain >= 1.2.0 - LangGraph >= 0.3.0 - Python 3.11+ ## Changelog ### 2.0.0 (January 2026) - **Breaking**: Migrated from LangChain 0.x to LangChain 1.x/LangGraph - **Breaking**: Updated model references to Claude 4.6 and GPT-5.2 - Added Voyage AI as primary embedding recommendation for Claude apps - Added LangGraph StateGraph patterns replacing deprecated `initialize_agent()` - Added structured outputs with Pydantic - Added async patterns with checkpointers - Fixed security issue: replaced unsafe code execution with AST-based safe math evaluation - Updated hybrid search with modern Pinecone client API ### 1.2.2 - Minor bug fixes and documentation updates ## License MIT License - See the plugin configuration for details.