langchain

Build LLM applications with multi-provider support, ReAct agents, and RAG pipelines.

1|Updated Apr 30, 2026
One-click install
npx skills add https://github.com/wpsadi/stock-agent --skill langchain-wpsadi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain
Source: https://github.com/wpsadi/stock-agent/tree/main/.agents/skills/langchain
Command: npx skills add https://github.com/wpsadi/stock-agent --skill langchain-wpsadi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langchain, langchain-core, langchain-openai, langchain-anthropic, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill allows users to quickly build and deploy LLM-powered applications with a wide range of features and integrations.

Core Features & Use Cases

  • Multi-Provider Support: Integrates with OpenAI, Anthropic, Google, and more, providing flexibility in LLM choice.
  • ReAct Agents: Empowers the creation of agents that can use tools and reason.
  • RAG Pipelines: Supports Retrieval-Augmented Generation for enhanced context-based responses.
  • Use Case: Create a chatbot that can answer customer queries with confidence by accessing relevant information and generating responses in real-time.

Quick Start

To install and start using LangChain, run the following command:

pip install langchain

Frequently Asked Questions about langchain

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

FAQPage Schema
How do I build LLM applications with RAG pipelines and ReAct agents?▼

You can build LLM applications with RAG pipelines and ReAct agents using this Skill, which simplifies integrating complex language understanding, retrieval-augmented generation, and tool-based reasoning. It requires Python 3.10+ and various third-party libraries.

Does this Skill support integrating multiple LLM providers like OpenAI and Anthropic?▼

Yes, this Skill supports integrating multiple LLM providers including OpenAI and Anthropic. It provides flexibility in LLM choice by offering dedicated dependencies like langchain-openai and langchain-anthropic for seamless application development.

What Python version is required to set up RAG pipelines for AI application development?▼

Python 3.10 or higher is required to set up RAG pipelines for AI application development. You must also install the necessary third-party libraries like langchain and langchain-core to construct and deploy the LLM-powered features.

How does a ReAct agent framework work for answering customer queries?▼

A ReAct agent framework works by empowering the creation of agents that can reason and use an extensive library of tools. This allows applications to access relevant information and generate real-time responses with confidence.

Are there limitations when using multiple LLM providers for AI application development?▼

Limitations when using multiple LLM providers for AI application development include the necessity of managing distinct dependencies like langchain-openai and langchain-anthropic. You must ensure your Python 3.10+ environment supports these third-party libraries simultaneously.