Agent skill · magnus919
langchain
Expert skill for building LLM applications with LangChain — LCEL chains, RAG pipelines, agent orchestration, LangGraph integration, LangSmith observability, and production deployment via LangServe. Use when working with LangChain or comparing LLM application frameworks.
What it needs
About 5k tokens when loaded.
What this skill does
LangChain Expert Skill LangChain is an MIT-licensed Python framework for building LLM-powered applications. Since v1.0 (October 2025), it provides a layered architecture: high-level chain composition via LCEL (LangChain Expression Language), agent creation via createagent (running on the LangGraph runtime underneath), and production observability via LangSmith. With 1000+ integrations and 100K+ GitHub stars, it is the most widely adopted LLM orchestration framework. Key v1.0 change: All new LangChain agents run on the LangGraph runtime. AgentExecutor is in maintenance mode until December 2026. Use createagent for new agents. Drop to LangGraph directly when you need full state-machine control. ⚠️ CRITICAL: Do NOT use AgentExecutor for new code. It is in maintenance mode until December 2026. Use createagent(model, tools, prompt) instead — it generates a LangGraph state machine with streaming, persistence, and observability out of the box. Core Principles These principles govern every decision when building with LangChain. Read them before proceeding to the reference guides. 1. LCEL is the composition primitive. The pipe operator ( ) chains Runnables. Every component — prompt, model, parser, retriever — implements the Runnable interface. Build everything in LCEL. 2. Agents run on LangGraph. Since v1.0, createagent generates a LangGraph state machine underneath. You get streaming, persistence, and observability without writing graph code. Drop to LangGraph when you need branching, cycles, or human-in-the-loop. 3. RAG is a chain, not a framework. retriever prompt model parser is the canonical RAG pattern. Document loaders, splitters, and vector stores are all interchangeable components. 4. LangSmith is production observability. Enable tracing at startup. 89% of production teams use observability — without it, debugging agent behavior is guesswork. 5. The ecosystem is the moat. 1000+ integrations mean model providers, vector stores, and tools are swappable with one line. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills magnus919/langchain