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framework-selection

Guide AI framework selection among LangChain, LangGraph, and Deep Agents.

2|Updated Mar 7, 2026
Search Tags:#langchain#agent development#langgraph#deep agents#framework selection#ai architecture
One-click install
npx skills add https://github.com/Lincyaw/AgentM --skill framework-selection-lincyaw
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: framework-selection
Source: https://github.com/Lincyaw/AgentM/tree/main/.claude/skills/framework-selection
Command: npx skills add https://github.com/Lincyaw/AgentM --skill framework-selection-lincyaw

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

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

Core Features & Use Cases

  • Framework Guidance: Provides clear decision trees and profiles for LangChain, LangGraph, and Deep Agents.
  • Layered Architecture Explanation: Illustrates how the frameworks build upon each other.
  • Use Case: Before starting a new agent project, consult this skill to determine if a simple LangChain agent, a complex LangGraph workflow, or a feature-rich Deep Agent is the best fit for your requirements.

Quick Start

Use the framework-selection skill to determine the best framework for a task requiring planning, memory, and file 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 between LangChain and LangGraph for agent development?▼

Choose LangChain for simple agent tasks and LangGraph for complex control flow, planning, and memory. This framework selection process uses decision trees to map project requirements to the optimal architecture layer.

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

You need Deep Agents when your AI architecture requires advanced file management alongside complex control flow, planning, and memory. This framework selection evaluates layered architecture to determine if feature-rich Deep Agents fit your project.

What is the best way to map agent requirements to a framework layer?▼

The best way to map agent requirements is evaluating task complexity against framework profiles. This framework selection provides clear decision trees to match planning, memory, and file management needs with LangChain, LangGraph, or Deep Agents.

Does LangChain support complex control flow and stateful memory?▼

LangChain supports basic agent development but lacks native complex control flow. For stateful memory and intricate workflows, this framework selection guides you to LangGraph or Deep Agents for optimal architectural decisions.

How do these AI frameworks build upon each other in a layered architecture?▼

These AI frameworks build upon each other in a layered architecture where LangChain provides the foundation, LangGraph adds complex control flow, and Deep Agents integrates advanced file management and planning capabilities for feature-rich development.

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