Is LoopCraft free to use?

Yes. LoopCraft offers a free tier for designing and exporting AI agent loop patterns. A Pro plan is available for advanced features.

What is Loop Engineering?

Loop Engineering is designing the control flow for AI agents: how they plan actions, observe results, and iterate. LoopCraft includes 11 pre-built patterns based on research from Anthropic, OpenAI, and others — including ReAct, Plan-Execute, Evaluator-Optimizer, Multi-Agent, and more. Design loops visually and export to LangGraph, OpenAI Agents SDK, AutoGen, or CrewAI.

Which frameworks can I export to?

LoopCraft exports to LangGraph (Python), OpenAI Agents SDK (TypeScript), AutoGen (Python), and CrewAI (Python). Each export produces working code you can run directly in your project.

Do I need to write code?

No. LoopCraft is a visual builder — you design loop patterns with a drag-and-drop interface and configure nodes without writing code. Code export to LangGraph, OpenAI SDK, AutoGen, and CrewAI is generated automatically and is optional.

What loop patterns are supported?

LoopCraft supports 11 agent loop patterns: ReAct (think-act-observe cycle), Prompt Chaining (sequential steps with quality gates), Routing (classify and direct inputs), Parallel Sectioning (independent subtasks in parallel), Parallel Voting (multiple runs for confidence), Orchestrator-Workers (dynamic task decomposition), Evaluator-Optimizer (generate-evaluate loop), Plan-and-Execute (plan then execute with replanning), ReWOO (variable passing between tasks), LLM Compiler (DAG-based parallel execution, 3.6x speedup), and Multi-Agent (collaborative agents with roles).

Why ReAct is Still the Foundation

The ReAct pattern (2022) remains the most fundamental building block of AI agent design, and understanding why is the fastest way to reason about every newer framework. ReAct stands for Reasoning + Acting: the model interleaves a Thought step (what should I do next?), an Action step (call a tool or emit an output), and an Observation step (read the result). This Thought-Action-Observation loop runs until the task is complete. The idea seems simple, but the consequences are deep. The loop forces the model to separate reasoning from action. When the reasoning is written down, you can see whether the model is chasing a sensible hypothesis. When the action result comes back, the observation gives the model real feedback from the environment instead of asking it to improvise in a vacuum. That feedback loop is what turns a chat model into an agent: it can try, observe, and course-correct. Modern frameworks like LangGraph and the OpenAI Agents SDK still use ReAct as their default execution loop. They add orchestration, state, and tool management on top, but the core rhythm is the same: think, act, observe, repeat. When you read a framework's agent implementation, you are almost always reading a polished ReAct loop. The practical lesson for builders: start with ReAct and only add complexity when a simpler pattern is demonstrably insufficient. Teams often jump straight to sophisticated graph structures, multi-agent topologies, and long-term memory before they have verified that a plain ReAct loop fails on a concrete example. In most production agents, the bottleneck is tool quality and observation handling, not the loop topology. Iterate on the tools, make observations structured, and keep the loop legible. LoopCraft's visual builder makes the ReAct loop explicit by default, so you can see and test each Thought-Action-Observation step before adding orchestration patterns on top. Start there. Available at https://getloopcraft.com.