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 adds dry-run simulation with cost estimation for agent loops (2026-08-17)

Starting 2026-08-17, LoopCraft ships dry-run simulation with cost estimation. Instead of burning real API calls to find out whether a loop's branching logic works, you can now run the whole definition against stubbed tool responses and inspect every decision the loop makes. A dry run executes the loop exactly as production would — nodes fire, conditions evaluate, branches take their paths — but every tool node returns a canned response you define in the simulation panel. The trace view plays the run back step by step, showing which condition each branch evaluated and why the loop ended where it ended, which makes unreachable branches and inverted conditions obvious at a glance. The simulation also computes a per-branch estimate of model invocations, token usage, and cost, so you can compare design alternatives before committing to one. Pair a dry run with the cost budget guard, and you can prove a loop stays inside budget before it ever runs live. Dry-run simulation is available on all plans. Try it at https://getloopcraft.com. Feedback: sapsap@qq.com.