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Framework Selection

Guide LangChain framework layer selection using a decision tree.

1.1k|86|Updated Jan 22, 2026
Search Tags:#architecture#langchain#langgraph#deep agents#framework selection
One-click install
npx skills add https://github.com/langchain-ai/langchain-skills --skill framework-selection
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Framework Selection
Source: https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/framework-selection
Command: npx skills add https://github.com/langchain-ai/langchain-skills --skill framework-selection

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you decide whether to use LangChain, LangGraph, or Deep Agents for your project, ensuring you select the most appropriate framework layer for your specific needs.

Core Features & Use Cases

  • Framework Guidance: Provides a decision tree to guide you through selecting LangChain, LangGraph, or Deep Agents.
  • Layered Architecture Explanation: Clarifies how LangChain, LangGraph, and Deep Agents build upon each other.
  • Use Case: You're starting a new AI project and are unsure whether to build a simple tool-using agent, a complex workflow with loops, or a planning agent with memory. This skill will guide you to the correct starting point.

Quick Start

Consult the framework selection skill to determine the best LangChain framework for your project.

Frequently Asked Questions about Framework Selection

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

FAQPage Schema
When should I choose LangGraph over LangChain for my AI agent?▼

Choose LangGraph over LangChain when your AI agent requires complex control flow with state management and loops, whereas LangChain is suited for simple tool execution workflows.

How do I decide if my project needs Deep Agents?▼

You need Deep Agents when your architecture requires advanced planning, file management, and on-demand skill loading that exceeds basic LangChain or LangGraph capabilities.

What is the difference between LangChain and LangGraph framework layers?▼

LangChain and LangGraph represent layered architecture where LangChain handles simple tool execution, while LangGraph builds upon it to manage complex workflows with state.

Can I use LangChain for an agent with memory and planning capabilities?▼

LangChain handles simple tool execution, so for advanced planning and memory you should use Deep Agents, which build on LangGraph to load skills on demand and manage files.

What's the best way to select the right LangChain framework for a new project?▼

The best way to select a LangChain framework is using a decision tree evaluating task requirements, matching simple execution to LangChain, state management to LangGraph, and planning to Deep Agents.

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