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langchain

Compose LangChain v1 LCEL pipelines for RAG and tool-using agents.

15|2|Updated May 23, 2026
Search Tags:#langchain#rag#tool calling#structured output#langsmith#lcel
One-click install
npx skills add https://github.com/VKirill/antigravity-for-claude-code --skill langchain-vkirill
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain
Source: https://github.com/VKirill/antigravity-for-claude-code/tree/main/skills/langchain
Command: npx skills add https://github.com/VKirill/antigravity-for-claude-code --skill langchain-vkirill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

LangChain streamlines building reliable LLM applications by giving you reusable building blocks for prompts, tools, RAG, and agents, while reducing token cost and output brittleness.

Core Features & Use Cases

  • LCEL/Runnable composition: create end-to-end pipelines by wiring prompt | model | parser into traceable, batchable Runnables.
  • Typed structured outputs: generate validated Pydantic results via with_structured_output, avoiding fragile json parsing.
  • Tool calling & agents: expose Python tools to the model and run v1 agents via create_agent (LangGraph-backed).
  • RAG-ready retrieval: connect retrievers and vector stores into chains for grounded answers with optional compression/reranking.
  • Streaming & observability: stream tokens or semantic events and enable LangSmith tracing with run_name, tags, and metadata.

Quick Start

Use the langchain skill to build a LangChain v1 RAG chain for your question with typed structured output from the model.

Frequently Asked Questions about langchain

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a RAG pipeline with typed structured output in LangChain?▼

Build a RAG pipeline in LangChain by composing LCEL Runnables to wire retrievers and prompts into models, then use with_structured_output to enforce Pydantic schemas for validated, typed responses.

What is the best way to expose Python tools to LLM agents using LangChain?▼

Expose Python tools to LLM agents by decorating functions with @tool or using model.bind_tools, then orchestrate execution through the LangGraph-backed create_agent primitive for v1 tool-calling workflows.

How does LCEL Runnable composition work for streaming LLM responses?▼

LCEL Runnable composition chains prompt, model, and parser into traceable pipelines that support astream_events, enabling real-time token streaming and semantic event observation for interactive UIs.

Can I use LangSmith tracing with provider-agnostic models in LangChain?▼

LangSmith tracing integrates with provider-agnostic models initialized via init_chat_model by configuring run_name, tags, and metadata per run, providing first-class observability for LLM pipelines.

How do I migrate from deprecated LangChain v0 APIs to v1 invoke-based patterns?▼

Migrate from deprecated v0 APIs to v1 by replacing legacy chain calls with invoke-based patterns using LCEL Runnables, ensuring dependable execution and compatibility with current LangChain primitives.

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