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framework-selection

Select among LangChain, LangGraph, and Deep Agents based on task requirements.

Updated Mar 7, 2026
Search Tags:#developer tools#langchain#langgraph#deep agents#framework selection#ai architecture
One-click install
npx skills add https://github.com/tivon-x/deep-research --skill framework-selection-tivon-x
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: framework-selection
Source: https://github.com/tivon-x/deep-research/tree/main/.agents/skills/framework-selection
Command: npx skills add https://github.com/tivon-x/deep-research --skill framework-selection-tivon-x

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps developers select the most appropriate AI framework (LangChain, LangGraph, or Deep Agents) for their project, ensuring efficient development and optimal architecture.

Core Features & Use Cases

  • Framework Guidance: Provides clear decision trees and profiles for LangChain, LangGraph, and Deep Agents.
  • Layered Architecture Explanation: Clarifies how the frameworks build upon each other.
  • Use Case: A new AI project requires planning, memory, and on-demand skills. This Skill guides the developer to choose Deep Agents and suggests the next skills to invoke.

Quick Start

Use the framework-selection skill to determine the best AI framework for a task requiring complex control flow and state management.

Frequently Asked Questions about framework-selection

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

FAQPage Schema
How do I choose the right AI framework for orchestration and state management?▼

To choose the right AI framework for orchestration and state management, evaluate your task requirements against the layered profiles of LangChain, LangGraph, and Deep Agents. This Skill provides decision trees to match your architectural needs to the correct framework layer.

What is the difference between LangChain, LangGraph, and Deep Agents?▼

The difference between LangChain, LangGraph, and Deep Agents lies in their layered architecture. LangChain handles basic orchestration, LangGraph adds complex control flow and state management, and Deep Agents provide planning, memory, and on-demand skill delegation.

When do I need Deep Agents instead of LangGraph for my AI architecture?▼

You need Deep Agents instead of LangGraph when your AI architecture explicitly requires advanced planning, persistent memory, and on-demand skill delegation. This Skill guides you to select Deep Agents and suggests the next architectural steps to invoke.

Does LangGraph support complex control flow for AI projects?▼

Yes, LangGraph supports complex control flow for AI projects. This Skill clarifies how LangGraph builds upon LangChain to provide the state management and control flow logic required for advanced task orchestration.

What are the limitations of using LangChain for complex AI planning?▼

The limitations of using LangChain for complex AI planning include a lack of built-in state management and advanced skill delegation. This Skill helps you identify these constraints and guides you to upgrade to LangGraph or Deep Agents for robust architectural control.

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