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 — Design AI Agent Loops Visually

Blog

In-depth articles on AI agent Loop Engineering

2026-07-305 min

Designing Agent Loops That Fail Gracefully: Error Handling Patterns

Practical patterns for adding retry logic, fallback branches, and structured error reporting to your agent loop designs.

2026-07-296 min

Error Handling Patterns for Agent Loops: Retry, Fallback, and Circuit Breaker

A practical guide to implementing error handling in agent loops using retry logic, fallback paths, and circuit breaker patterns.

2026-07-285 min

Versioning Your Agent Loops: Why Design History Matters

Agent loops evolve through many iterations. Without versioning, you lose the ability to understand why a design decision was made or roll back when a change breaks something.

2026-07-275 min

When to Extract a Sub-Loop vs. Inline It: A Decision Framework

A practical framework for deciding whether an agent sub-task should be its own reusable loop or stay inline in the parent workflow.

2026-07-266 min

Designing Agent Loops That Fail Gracefully: A Pattern Catalog

Four patterns for building agent loops that degrade predictably when tools timeout, schemas shift, or context overflows.

2026-07-257 min

Token Budget Planning for Agent Loops: A Practical Framework

How to estimate, allocate, and monitor token budgets across multi-node agent loops before they burn through your API credits.

2026-07-206 min

Design Failure Paths on the Canvas Before You Export the Loop

Treat timeouts, schema failures, and optional nodes as first-class edges—not footnotes you discover in production.

2026-07-205 min

Design Human-Review Gates That Pause the Loop Without Stalling It

Place approval nodes where judgment is cheap to request and expensive to undo, then keep a clear resume path on the canvas.

2026-07-186 min

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

A visual workflow makes failure modes inspectable: timeouts, schema mismatches, and retry budgets become nodes you can point to instead of buried log lines.

2026-07-156 min

Stop Building Agent Loops That Only Work When Everything Goes Right

Most agent demos assume clean inputs and responsive tools. Production assumes the opposite. Here is how to design loops that degrade gracefully instead of falling over.

2026-07-125 min

How to Choose ReAct vs Plan-Execute for Your First Agent Loop

Start with ReAct for tool-using assistants. Move to Plan-Execute when tasks need explicit multi-step planning and clearer budgets.

2026-07-126 min

Visual Agent Loop Design vs Hand-Written Graphs

Use a visual loop builder to choose patterns and communicate them before coding. Hand-write graphs when you need deep framework-specific customization.

2026-07-158 min

Why Most Agent Loops Break in Production — It's Usually Not the Model's Fault

Production failures in agent systems rarely come from the LLM making a bad call. They come from unwritten assumptions about tool reliability, state, and error handling.

2026-07-157 min

The Best Agent Loop Is the One Your On-Call Engineer Can Explain at 2 A.M.

A production agent loop does not need to look clever. It needs to be legible under stress, especially when something expensive has gone wrong.

2026-06-229 min

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

Manual testing of AI agents doesn't scale. Learn how to build systematic evaluation pipelines with assertion-based tests, LLM-as-judge scoring, and production monitoring.

2026-06-218 min

Designing Fault-Tolerant AI Agent Systems: Patterns from Production

Production agent systems fail in predictable ways. Learn the retry, fallback, circuit-breaker, and human-in-the-loop patterns that keep agents running reliably.

2026-06-085 min

Why ReAct is Still the Foundation

Understanding the Thought-Action-Observation loop.

2026-06-047 min

Orchestrator-Workers vs Plan-and-Execute

A practical comparison of two powerful agent patterns.

2026-05-286 min

Building Reliable Agent Loops: Lessons from Anthropic

Key takeaways from Anthropic Building Effective Agents guide.