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).

LoopCraft Introduces Built-in A/B Testing for Agent Loops

We are excited to launch LoopCraft A/B Testing — a built-in experimentation framework for AI agent loops. Define two agent configurations (different prompts, models, or loop patterns), set your evaluation criteria, and LoopCraft runs both configurations against the same inputs. The dashboard shows side-by-side comparisons with statistical significance indicators — win rate, average latency, token cost per run, and success rate. Bayesian analysis determines when a winner is statistically significant, so you're not fooled by small sample sizes. Export test reports as PDF for stakeholder reviews. You can test any component in isolation: prompts, model selection (GPT-4o vs Claude vs Gemini), loop patterns (ReAct vs Plan-Execute), tool configurations, or even entire agent pipelines. A/B Testing is available on Team and Enterprise plans.