framework-selection

Choosing the right AI framework depends on task complexity, persistent memory needs, control flow requirements, and subagent delegation. This Skill evaluates these project parameters to map tasks to Deep Agents, LangGraph, LangChain, or combinations to guide your initialization.

You need LangGraph for workflow design when your project requires a graph-based subagent to handle complex control flows. This Skill identifies whether your task structure demands graph-based orchestration or if a single-purpose tool-based agent like LangChain is sufficient.

The best way to start an AI project with Deep Agents is to run this framework-selection process at project initialization. It determines whether a top-level orchestrator fits your task complexity and provides a recommended next-skills path for appropriate scaffolding.

Yes, you can layer or mix LangChain and LangGraph frameworks. This Skill provides a clear, start-to-finish rationale for when to layer or mix frameworks across complex workflows based on your specific task structure and subagent delegation requirements.

Yes, framework selection requires understanding your task complexity, persistent memory needs, control flow requirements, and subagent delegation needs. These factors determine whether you start with a top-level orchestrator, a graph-based subagent, or a single-purpose tool-based agent.

Using a framework-selection guide before building AI agents ensures you choose the optimal framework layer at project start. It guides subsequent skill selection and tooling decisions, preventing architectural mismatches between your task requirements and the chosen orchestration layer.