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Each README is written for a human audience, explaining: - What the skill does (not what format it follows) - What benefit the user gets from installing it - Quick setup and usage patterns - When to load/trigger the skill - What scripts, references, and templates it ships data-scientist already had a README — left unchanged. 48 READMEs added across all skill and bundle directories.
LangChain — LLM Application Framework
An expert-level skill for building LLM-powered applications with LangChain — the most widely adopted LLM orchestration framework. LCEL chains, RAG pipelines, agents, LangSmith observability, and LangServe deployment.
Why Install This Skill
When your agent loads this skill, it becomes a LangChain expert who can:
- Build chains with LCEL —
prompt | model | parsercomposition with the Runnable interface - Create agents —
create_agentwith tools (not legacy AgentExecutor) - Implement RAG pipelines — document loading, splitting, embedding, retrieval, generation
- Add observability — LangSmith tracing for production debugging
- Deploy with LangServe — REST API deployment for production
What You Get
| Directory | Purpose |
|---|---|
SKILL.md |
Core principles, pipeline modes, where-to-start table, quick reference |
references/ |
LCEL reference, RAG strategies, agent patterns, LangSmith, LangServe, framework comparisons |
Framework Comparison
LangChain is the broadest LLM framework with 1000+ integrations. Its agents now run on LangGraph underneath. Use LangChain for rapid prototyping and broad integration support; drop to LangGraph when you need full state-machine control.
Requirements
Python 3.8+ with langchain, langchain-community, and provider-specific packages.