Design, validate, and benchmark reliable AI agent loops.
A product workbench for developers who need to choose an architecture, bound execution, verify outcomes, estimate cost, export code, and improve loops across tools and models.
Start from reusable production loop templates.
Templates are not recipes. Each one carries a loop graph, config, prompt, schemas, permissions, supported tools, test results, average cost, rating, and repository link.
The platform is the workflow.
Documentation supports acquisition and onboarding, but the core product helps a developer finish a loop design and know whether it can run safely.
Loop Validator
Import a loop config, code snippet, or Mermaid graph and get a structured safety report before it runs.
Loop Simulator
Preview likely rounds, verifier outcomes, recovery points, and estimated spend without calling a model.
Pattern Generator
Answer risk and verification questions, then generate a recommended agent-loop architecture.
Benchmark Database
Compare the same loops across Codex, Claude Code, LangGraph, and model providers with comparable metrics.
Find structural, cost, permission, and quality risks before runtime.
The validator turns loop diagrams and configs into actionable engineering feedback: missing stops, impossible branches, unsafe tools, weak verifiers, unbounded spend, and recovery gaps.
See what the loop will do round by round.
Start with static execution simulation and cost estimation. Later, connect real model APIs to replay runs, compare recovery strategies, and inspect each tool call.
Compare loops by success rate and average cost.
Run the same task suite across agents, frameworks, and models. Track success rate, average cost, average rounds, human intervention rate, and time to pass.
MVP scope: designer, validator, and template gallery.
The first version can run entirely in the browser: add nodes, connect edges, configure retry and stop policies, save JSON, export Mermaid, share a link, and validate common risks.