framework-selection

Name: framework-selection
Availability: InStock
Author: jxtngx

Evaluate project requirements to select LangChain, LangGraph, or Deep Agents.

2|Updated Apr 3, 2026

Search Tags:#architecture #langchain #framework #langgraph #agent-design #deep-agents

Ready to run

npx skills add https://github.com/jxtngx/dgx-lab --skill framework-selection-jxtngx

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: framework-selection
Source: https://github.com/jxtngx/dgx-lab/tree/main/.cursor/skills/framework-selection
Command: npx skills add https://github.com/jxtngx/dgx-lab --skill framework-selection-jxtngx

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill eliminates the confusion of choosing between LangChain, LangGraph, and Deep Agents by providing a structured decision framework to ensure you select the correct architectural layer for your project.

Core Features & Use Cases

Decision Table: A clear logic flow to determine if you need simple chains, complex graph control, or full-featured agent middleware.
Framework Profiles: Detailed breakdowns of when to use each layer and which specific skills to invoke next.
Mixing Layers: Guidance on how to combine frameworks, such as using Deep Agents as an orchestrator for specialized LangGraph subagents.

Quick Start

Invoke the framework-selection skill to analyze your project requirements and determine whether to use LangChain, LangGraph, or Deep Agents.

Frequently Asked Questions about framework-selection

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

FAQPage Schema

How do I choose between LangChain and LangGraph for my agent architecture?▼

To choose your agent architecture, evaluate whether your project needs simple chains best handled by LangChain, or complex graph control requiring LangGraph. A structured decision table maps specific project requirements to the correct framework layer.

When do I need Deep Agents middleware instead of LangChain or LangGraph?▼

You need Deep Agents middleware when your project requires full-featured agent orchestration rather than simple chains or complex graph control. This framework selection process ensures appropriate complexity management during initial architectural planning.

Can I mix agent frameworks like Deep Agents and LangGraph in the same project?▼

Yes, you can mix agent frameworks by using Deep Agents as an orchestrator for specialized LangGraph subagents. This layer mixing approach allows you to combine full-featured middleware with complex graph control.

What is the best way to plan AI agent architecture before implementing logic?▼

The best way to plan AI agent architecture is to apply a standardized decision-making process that evaluates project requirements against LangChain, LangGraph, and Deep Agents capabilities before implementing agent logic or middleware.

Does LangGraph replace LangChain for complex agent design?▼

LangGraph does not replace LangChain but provides complex graph control for agent design when simple chains are insufficient. The framework selection process determines if your task requires LangGraph's advanced capabilities or LangChain's simpler architecture.

Why does my agent framework selection fail when project complexity scales?▼

Agent framework selection fails when complexity scales if you mismatch architectural layers, such as using simple chains for tasks requiring complex graph control. Evaluating requirements against LangChain, LangGraph, and Deep Agents prevents this.