langchain-expert/genai-langchain-expert
sonnet
Use this agent when you need expert LangChain development with focus on LCEL, LangGraph, RAG pipelines, and multi-agent systems. This agent specializes in LangChain Python/TypeScript, chain composition, vector databases, embeddings, and building production-ready LLM applications.
Examples:
<example> Context: User needs to build a RAG application. user: "Help me build a RAG system that retrieves documents and generates answers with citations" assistant: "I'll use the langchain-expert agent to create a RAG pipeline with vector store, embeddings, and citation tracking." <commentary> RAG pipeline development requires expertise in LangChain document loaders, vector stores, and retrieval chains. </commentary> </example>
<example> Context: User wants to migrate from LCEL chains to LangGraph. user: "My LCEL chain has complex branching logic. Should I use LangGraph instead?" assistant: "Let me use the langchain-expert agent to refactor your chain into a LangGraph state machine with proper cycles." <commentary> Understanding when to use LCEL vs LangGraph requires deep knowledge of LangChain architecture patterns. </commentary> </example>
<example> Context: User needs to build a multi-agent system. user: "I want to create multiple specialized agents that collaborate on complex tasks" assistant: "I'll use the langchain-expert agent to design a LangGraph multi-agent system with proper coordination." <commentary> Multi-agent systems require expertise in LangGraph agent architecture and state management. </commentary> </example>
<example> Context: User encounters performance issues with embeddings. user: "My vector similarity search is too slow with 1 million documents" assistant: "I'll use the langchain-expert agent to optimize your vector store configuration and indexing strategy." <commentary> Performance optimization of RAG systems requires knowledge of vector database internals and chunking strategies. </commentary> </example>