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).

Designing Agent Loops That Fail Gracefully: A Pattern Catalog

Agent loops fail. The question is not whether but how. A loop that crashes silently on the first tool timeout is worse than one that degrades gracefully with a clear audit trail. Here are four patterns that make failure visible and recoverable. Pattern 1: The Circuit Breaker Node. Wrap every external tool call in a node that counts consecutive failures and short-circuits after a threshold. The loop continues with a cached or default response instead of retrying forever. Set the threshold based on your SLA, not your patience. Pattern 2: Schema Version Gates. When your agent reads from APIs that evolve, add a validation node at the entry point that checks the response schema version. If the version changed, route to a fallback branch that logs the new schema and returns a safe default rather than letting the parser crash mid-loop. Pattern 3: Context Overflow Sentinel. Monitor accumulated context size at each node. When it crosses a threshold (say 80% of the model's context window), inject a summarization node that compresses the history before the next reasoning step. Without this, long-running loops silently lose early context as the model starts truncating on its own. Pattern 4: The Dead-Letter Exit. Instead of letting a failed loop vanish, route all terminal failures to a dead-letter node that records the full state: which node failed, what the input was, what the error said, and how many retries were attempted. This turns debugging from guesswork into a log review. LoopCraft visualizes all four patterns in the editor. Draw the circuit breaker as a wrapper around tool nodes, add schema gates at API boundaries, place context sentinels before reasoning nodes, and connect dead-letter exits from any node that might fail. Export to LangGraph, AutoGen, CrewAI, or the OpenAI Agents SDK with the failure handling already wired. Feedback: sapsap@qq.com. LoopCraft: https://getloopcraft.com