Replay | Video to React Code & Agent Infrastructure

What

Replay is a modernization platform that converts screen recordings of existing software into React-based UI architecture, documentation assets, and supporting engineering outputs. It is aimed at teams working with legacy or business-critical systems, especially in industries such as financial services, healthcare, telecom, logistics, government, insurance, manufacturing, retail, and energy.

The core workflow appears to be: record a real user session, let Replay extract interface structure and workflow context, and receive componentized React code, a design-system-aligned component library, and related artifacts such as API contracts and tests. Based on the page, Replay is positioned as a faster alternative to manual reverse-engineering for legacy migration, UI reconstruction, and documentation.

Features

• Video-to-React reconstruction — Converts screen recordings into pixel-matched React architecture by analyzing layout, typography, and state changes across the recording.
• Reimagine pipeline — Offers a separate path to generate a more modernized UI output rather than only reproducing the legacy interface 1:1.
• Automatic component library creation — Catalogs UI elements such as buttons, inputs, and states into reusable, modular React components and design tokens.
• Flow mapping across screens — Tracks multi-page navigation and temporal state changes to help teams understand journeys and identify logic gaps beyond isolated screens.
• Agentic editor for global changes — Supports interactive component editing through a search/replace-style workflow intended to propagate changes without manually updating each instance.
• Automation and API access — Generates outputs such as API contracts, end-to-end tests, and technical debt audits, and exposes a headless API with REST and webhooks for agent-driven workflows.

Helpful Tips

• Validate against real business workflows — For legacy modernization tools, success depends on whether recorded sessions cover critical paths, edge cases, and exception handling, not just common screens.
• Check output boundaries early — The page emphasizes React generation, but teams should confirm how much backend logic, validation behavior, and non-visual business rules are actually inferred versus requiring manual follow-up.
• Use design-system inputs deliberately — If Figma or Storybook is available, aligning generated components to an existing design system can reduce downstream cleanup and improve adoption by frontend teams.
• Review security options by deployment model — Replay mentions configurable retention, PII detection in beta, on-premise deployment, and SOC 2-aligned controls in progress, so regulated teams should verify current availability and maturity before rollout.
• Pilot on a bounded migration slice — A practical evaluation approach is to test a representative workflow set from a legacy app and compare generated code quality, documentation usefulness, and regression risk against current manual methods.

OpenClaw Skills

Replay could fit well into the OpenClaw ecosystem as a source system for modernization agents and engineering workflow automation. A likely use case would be an OpenClaw skill that ingests Replay outputs, then chains follow-on tasks such as code review, migration planning, backlog generation, documentation enrichment, test orchestration, or handoff packaging for product, design, and engineering teams. The page explicitly mentions a headless API, REST, webhooks, and agent-ready infrastructure, which makes this kind of orchestration plausible.

A broader OpenClaw workflow could support professions such as enterprise architects, modernization leads, platform teams, and product owners managing legacy estates. For example, an OpenClaw agent could classify reconstructed screens by business domain, detect missing user journeys, generate implementation tickets, compare generated components to internal standards, and coordinate remediation loops. Native integration with OpenClaw is not stated on the page, so this should be treated as a likely orchestration pattern rather than a confirmed built-in connection.