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).

The Best Agent Loop Is the One Your On-Call Engineer Can Explain at 2 A.M.

A lot of agent systems look impressive in a demo because they do many things at once: planning, tool use, self-critique, retries, fallbacks, maybe even a second model judging the first. Then the first real incident happens at 2 a.m., and the person on call cannot explain what state the loop is in or why it made the last decision. That is the moment when architectural taste stops being theoretical. A good production loop is explainable under pressure. You should be able to point to the current state, the last tool call, the retry count, the budget left, and the rule that decides the next step. If you cannot do that, the loop is carrying more hidden complexity than your team can safely operate. The boring-looking loop often wins in production because it is easier to replay, easier to instrument, and easier to hand off between humans. Reliability is not anti-ambition. It is what lets ambition survive contact with real traffic.