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Found 144 Skills
LangChain LLM application framework with chains, agents, RAG, and memory for building AI-powered applications
Deploy and operate production agent servers with LangSmith Deployment. Use when work involves choosing Cloud vs Hybrid/Self-hosted-with-control-plane vs Standalone, preparing/validating langgraph.json, creating deployments or revisions, rolling back revisions, wiring CI/CD to control-plane APIs, configuring environment variables and secrets, setting monitoring/alerts/webhooks, or troubleshooting deployment/runtime/scaling issues for LangChain/LangGraph applications.
You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.
Integration patterns for LangChain4j with Spring Boot. Auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications.
Initialize, validate, and troubleshoot Deep Agents projects in Python or JavaScript using the `deepagents` package. Use when users need to create agents with built-in planning/filesystem/subagents, configure middleware/backends/checkpointing/HITL, migrate from `create_react_agent` or `create_agent`, scaffold projects with repo scripts, validate agent config files, and confirm compatibility with current LangChain/LangGraph/LangSmith docs.
Comprehensive guide for building production-grade LLM applications using LangChain's chains, agents, memory systems, RAG patterns, and advanced orchestration
Configure LangChain local development workflow with hot reload and testing. Use when setting up development environment, configuring test fixtures, or establishing a rapid iteration workflow for LangChain apps. Trigger with phrases like "langchain dev setup", "langchain local development", "langchain testing", "langchain development workflow".
Document Q&A with RAG using Supabase pgvector store.
Implement LangChain rate limiting and backoff strategies. Use when handling API quotas, implementing retry logic, or optimizing request throughput for LLM providers. Trigger with phrases like "langchain rate limit", "langchain throttling", "langchain backoff", "langchain retry", "API quota".
INVOKE THIS SKILL when you need human-in-the-loop approval, custom middleware, or structured output. Covers HumanInTheLoopMiddleware for human approval of dangerous tool calls, creating custom middleware with hooks, Command resume patterns, and structured output with Pydantic/Zod.
Configure LangChain4J vector stores for RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.
Model Context Protocol (MCP) server implementation patterns with LangChain4j. Use when building MCP servers to extend AI capabilities with custom tools, resources, and prompt templates.