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framework-selection

Select LangChain, LangGraph, or Deep Agents for AI project initialization.

36|27|Updated Jan 28, 2026
Search Tags:#langchain#multi-agent#orchestration#langgraph#ai-agent#framework-selection#deep-agents
One-click install
npx skills add https://github.com/caio-moliveira/ai-engineer-roadmap --skill framework-selection-caio-moliveira
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: framework-selection
Source: https://github.com/caio-moliveira/ai-engineer-roadmap/tree/main/.agents/skills/framework-selection
Command: npx skills add https://github.com/caio-moliveira/ai-engineer-roadmap --skill framework-selection-caio-moliveira

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The skill guides teams to pick the correct agent framework layer at project start, preventing misalignment between LangChain, LangGraph, and Deep Agents and ensuring the right tools and patterns are available from day one.

Core Features & Use Cases

  • Decision guide to determine when to use LangChain, LangGraph, or Deep Agents.
  • Clear rules for when to apply each layer, enabling smoother tool integration and orchestration.
  • Use case: starting a new AI agent project that needs a combination of planning, memory, and subagent coordination.

Quick Start

Run this skill at the outset of a LangChain/LangGraph/Deep Agents project to select the optimal framework before writing agent code.

Frequently Asked Questions about framework-selection

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

FAQPage Schema
When should I use LangChain vs LangGraph for multi-agent orchestration?▼

LangChain suits standard tool integration, while LangGraph handles complex multi-agent orchestration requiring explicit state management and cyclical planning workflows.

How do I select the right AI agent framework before starting project development?▼

Select an AI agent framework at project initialization by evaluating requirements for planning, memory, and subagent coordination to ensure proper tool availability from day one.

Does Deep Agents work with LangChain for subagent coordination?▼

Deep Agents integrates with LangChain and LangGraph layers to provide specialized subagent coordination, ensuring cross-layer compatibility and proper skill invocation sequencing during execution.

What is the best way to structure an AI agent project needing planning and memory?▼

The best way to structure an AI agent project is to determine the optimal framework layer at the outset, applying rules for LangChain, LangGraph, or Deep Agents based on orchestration complexity.

Why does framework selection matter at the start of an AI agent project?▼

Framework selection at project start prevents misalignment between LangChain, LangGraph, and Deep Agents, ensuring the right tools and orchestration patterns are available before writing agent code.

Are there limitations to choosing LangChain for complex multi-agent workflows?▼

Choosing LangChain for complex multi-agent workflows may limit advanced orchestration; LangGraph or Deep Agents provide better support for intricate subagent coordination and cyclical planning.

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