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).

Agent Evaluation at Scale: Beyond 'Vibe Checks' to Systematic Testing

The dirty secret of AI agent development: most teams evaluate agents by eyeballing a handful of outputs and saying 'looks good.' This 'vibe check' approach breaks down catastrophically at scale. A systematic evaluation pipeline has three layers. Layer 1 — Unit Tests: Assertion-based checks for deterministic behaviors. Did the agent call the right tool? Did it respect the max token budget? Did it stay within the allowed action space? These should run on every commit. Layer 2 — LLM-as-Judge: For non-deterministic outputs, use a stronger model (or a different model) to score outputs on rubrics like accuracy, completeness, and safety. Define clear scoring criteria with examples at each level. Layer 3 — Production Monitoring: Track success rate, latency p50/p95/p99, token cost, tool call error rate, and user feedback signals. Set alerts on statistically significant degradations. At LoopCraft, our A/B Testing feature (launched today) lets you run controlled experiments comparing agent configurations across all three layers. The key insight: evaluation is not a one-time gate before deployment — it's a continuous feedback loop that runs alongside production.