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This skill helps you master production-grade LangChain orchestration across chains, agents, memory, and RAG patterns for scalable LLM applications.

This skill helps you build robust LangChain applications by guiding chains, agents, memory, and RAG patterns across production-ready components.

This skill provides a concise LangChain 1.0 quick reference for building agents, tools, memory, and middleware workflows.

Builds LLM applications with LangChain including chains, agents, memory, tools, and RAG pipelines. Use when users request "LangChain setup", "LLM chain", "AI workflow", "conversational AI", or "RAG pipeline".

This skill helps you implement Retrieval-Augmented Generation patterns with LangChain4j to build knowledge-rich QA and document-aware assistants.

This skill enables building agentic applications by defining tools and function calls with LangChain4j, enabling real-time API access and external integrations.

This skill helps you design production-grade AI agents with LangGraph, enabling graph-based state management, persistence, and reactive tool integration.

This skill helps you design production-grade multi-agent systems with LangGraph, enabling cost-optimized orchestration, state coordination, and scalable agent

Creates code-based evaluators for LangSmith-traced agents. Use when building custom evaluation logic, testing tool usage patterns, or scoring agent outputs…

This skill helps you debug LangChain and LangSmith agents by fetching recent traces and quickly analyzing execution patterns for insight.

This skill configures LangSmith tracing for LangChain and LangGraph apps, enabling observability, trace collection, and debugging to improve reliability.

This skill helps you build AI agents with true long-term memory using Letta to remember, learn, and improve across sessions.