langchain

Build and orchestrate LLM agents and RAG pipelines across providers.

Updated Apr 12, 2026

Search Tags:#langchain #llm #rag #build #agents #memory #tool-calling

Ready to run

npx skills add https://github.com/dotlab-hq/torque --skill langchain-dotlab-hq

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain
Source: https://github.com/dotlab-hq/torque/tree/main/.agents/skills/langchain
Command: npx skills add https://github.com/dotlab-hq/torque --skill langchain-dotlab-hq

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

LangChain provides a framework to build and orchestrate LLM-powered agents, chains, memory, and RAG pipelines across providers like OpenAI, Anthropic, and Google. It enables rapid prototyping and production-grade deployments for chatbots, QA systems, and autonomous workflows.

Core Features & Use Cases

ReAct-style reasoning and tool calling
Memory management and context handling
Vector store integration and LangSmith observability
Use cases include building chatbots, QA systems, and multi-tool agents
Integrates with multiple providers to switch models and tools seamlessly

Quick Start

Create a simple tool-using agent with a local LLM and run a sample task to observe agent reasoning and actions.

Frequently Asked Questions about langchain

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

FAQPage Schema

How do I build LLM-powered agents with tool calling and memory?▼

You can build LLM-powered agents with tool calling and memory using LangChain to orchestrate ReAct-style reasoning, context handling, and multi-provider model integrations for autonomous workflows.

What is the best way to build a RAG pipeline for a QA system?▼

The best way to build a RAG pipeline for QA systems is using LangChain, which provides vector store integration and orchestration for rapid prototyping and production deployments.

Can I use LangChain to switch between OpenAI, Anthropic, and Google models?▼

Yes, LangChain integrates with multiple providers like OpenAI, Anthropic, and Google, allowing you to switch models and tools seamlessly across your LLM applications.

Does LangChain support LangSmith observability for production deployments?▼

LangChain supports LangSmith observability, enabling you to monitor and trace LLM-powered agents and RAG pipelines during production-grade deployments.

When do I need ReAct-style reasoning for my LLM chatbot?▼

You need ReAct-style reasoning when building LLM chatbots that require autonomous decision-making, allowing the agent to dynamically call tools and manage memory context.

What are the limitations of building autonomous workflows with local LLMs?▼

Limitations of building autonomous workflows with local LLMs involve managing context handling and memory, though LangChain helps mitigate this with its robust framework for tool calling.