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).

Building Reliable Agent Loops: Lessons from Anthropic

Anthropic's guide to building effective agents distills years of production experience into three principles that are easy to quote and easy to violate. Understanding them explains most of the difference between agent systems that work and agent systems that merely demo well. Principle one: maintain simplicity. Anthropic's consistent advice is to start with the simplest solution that could work - often a plain LLM with good prompting and retrieval - and to add agentic loops, tools, and multi-agent topologies only when a concrete failing case demonstrates the need. Most teams invert this: they build a sophisticated architecture first and then discover that prompt quality was the real bottleneck all along. Every layer of complexity is a new surface for bugs, latency, and cost, so each one must earn its place. Principle two: prioritize transparency. Agents fail in non-obvious ways, and the only reliable way to debug them is to be able to see what they were doing. Show planning steps explicitly, log tool calls with their inputs and outputs, and make the reasoning visible before it becomes a decision. A transparent agent is not just easier to debug; it is easier to trust, which matters whenever the system touches real users or real money. Principle three: craft the agent-computer interface carefully. Tools are the agent's hands, and the quality of the tool interface determines the quality of the agent. Anthropic emphasizes writing clear tool descriptions, returning structured data instead of prose, validating inputs, and testing tools the way you would test any library. A well-designed tool that returns predictable JSON beats a clever tool that returns free-form text, every time. At LoopCraft, these three principles are baked into every pattern template: the visual builder keeps loops simple by default, every node logs its inputs and outputs for transparency, and tool nodes are defined with explicit schemas and descriptions. The goal is not to restrict what you can build, but to make the simplest correct design the easiest one to reach. Available at https://getloopcraft.com.