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).

Visual Workflow Debugging: Seeing Agent Loop Failures Before They Reach Production

Agent loops fail for ordinary reasons: a tool times out, a payload drifts from the expected schema, a retry budget silently burns through overnight. In a hand-written graph those failures hide in logs. In a visual workflow they become inspectable nodes and edges. LoopCraft is built around that idea. You design the loop as a diagram, attach timeouts and validators to each step, and export to LangGraph, OpenAI Agents SDK, AutoGen, or CrewAI only after the control flow is clear. The 2026-07-18 update focuses on making failure paths first-class in the canvas: mark a step as optional, attach a fallback branch, and keep the on-call story short enough to explain at 2 a.m. Visual debugging is not a substitute for production tracing, but it is the cheapest place to catch the loops that only work when everything goes right.