langchain

Build LLM applications with LangChain using prompts, chains, agents, memory, and retrieval.

44|9|Updated May 7, 2026

Search Tags:#prompt engineering #langchain #rag #tool calling #agents #memory #retrievers

Ready to run

npx skills add https://github.com/Omar-Obando/qwen-orchestrator --skill langchain-omar-obando

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain
Source: https://github.com/Omar-Obando/qwen-orchestrator/tree/main/skills/langchain
Command: npx skills add https://github.com/Omar-Obando/qwen-orchestrator --skill langchain-omar-obando

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

You need a reliable way to design LLM applications that go beyond single prompts—covering chains, tool-using agents, memory, and retrieval so outputs stay consistent and context-aware.

Core Features & Use Cases

LLM application building: Create prompt templates and compose them into chains for repeatable results.
Tool-using agents: Build agents that can call tools and external services to complete multi-step tasks.
Memory and conversational context: Add buffer, summary, or vector-store backed memory to maintain relevant history.
Retrieval-Augmented Generation (RAG): Implement document retrieval with embeddings and feed retrieved context into generation.
Prompt engineering patterns: Use few-shot and chat prompt structures, plus structured output parsing patterns for reliability.

Quick Start

Use the langchain skill when designing a LangChain-based app that needs tools, conversation memory, and RAG over your own documents.

Frequently Asked Questions about langchain

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

FAQPage Schema

How do I build a LangChain app with agents and RAG?▼

To build a LangChain app with agents and RAG, compose prompt templates into chains, implement tool-using agents for multi-step execution, and feed retrieved document embeddings into the generation workflow.

What's the best way to maintain conversational memory in LangChain?▼

Maintain conversational memory in LangChain by adding buffer, summary, or vector-store backed memory to your chains. This ensures the assistant retains relevant historical context across multi-turn interactions.

How do I implement retrieval-augmented generation pipelines using LangChain?▼

Implement retrieval-augmented generation pipelines in LangChain by using embeddings and retrievers to fetch relevant documents, then passing that retrieved context into the LLM generation step for grounded outputs.

Can I create tool-using agents in LangChain for multi-step tasks?▼

Yes, you can create tool-using agents in LangChain that call external services and tools to complete multi-step tasks. This allows the LLM to autonomously select and execute tools based on the task requirements.

What prompt engineering patterns work best for reliable LangChain chains?▼

For reliable LangChain chains, use few-shot and chat prompt structures alongside structured output parsing patterns. These prompt engineering techniques ensure consistent and repeatable results across your LLM applications.

Does LangChain work with LangGraph for agent execution?▼

Yes, LangChain integrates with LangGraph for multi-step agent execution. The framework's best-practice guidance is aligned to LangGraph documentation, ensuring robust orchestration of complex agent workflows.