framework-selection

Guide framework layer selection for LangChain, LangGraph, or Deep Agents projects.

Updated Aug 27, 2026

Search Tags:#workflow #langchain #langgraph #ai-agent #framework-selection #deep-agents

Ready to run

npx skills add https://github.com/infantesromeroadrian/arca-claude-code --skill framework-selection-infantesromeroadrian

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: framework-selection
Source: https://github.com/infantesromeroadrian/arca-claude-code/tree/main/skills/framework-selection
Command: npx skills add https://github.com/infantesromeroadrian/arca-claude-code --skill framework-selection-infantesromeroadrian

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Choosing the correct AI framework layer at the start of a LangChain/LangGraph/Deep Agents project can be ambiguous and error-prone. This Skill provides a structured decision surface to select the most appropriate framework upfront.

Core Features & Use Cases

Decision guidance to identify when to use Deep Agents, LangGraph, or LangChain based on task requirements.
Clear framework profiles and recommended next skills to invoke, enabling smooth mixing of layers.
Practical workflows showing how to bootstrap projects and align tooling with architecture goals.

Quick Start

At project startup, load this skill first to choose the appropriate framework by following the decision guide.

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 Deep Agents, LangGraph, and LangChain for my AI project?▼

Choosing between Deep Agents, LangGraph, and LangChain depends on task complexity: use LangChain for simple single-purpose tool usage, LangGraph for complex control flow, and Deep Agents for open-ended multi-step needs. This skill provides structured decision guidance to map requirements to the correct framework layer.

When should I use LangGraph instead of LangChain for my AI agent workflow?▼

Use LangGraph instead of LangChain when your AI agent workflow requires complex control flow. LangChain is better suited for simple single-purpose tool usage, while LangGraph handles state management and intricate routing logic for multi-step agent tasks.

Do I need Deep Agents for open-ended multi-step AI tasks?▼

Yes, Deep Agents are designed for open-ended multi-step needs. If your AI project involves unpredictable task execution paths requiring high autonomy, this framework layer provides the necessary architecture compared to simpler LangChain or LangGraph setups.

What is the best way to bootstrap a LangChain or LangGraph project?▼

The best way to bootstrap a LangChain or LangGraph project is to load this framework-selection skill first at project startup. It aligns tooling with architecture goals and provides practical workflows to identify the correct framework layer upfront before development.

Can I mix Deep Agents, LangGraph, and LangChain layers in the same project?▼

Yes, you can mix framework layers in the same project. This skill provides clear framework profiles and recommended next skills to invoke, enabling smooth mixing of Deep Agents, LangGraph, and LangChain layers to align tooling with your architecture goals.