Refrain – Generate browser automations with AI, replay them without AI

Hacker News

Hey HN, I'm timakin. Refrain is a CLI that uses an AI agent to generate browser automation runbooks, then replays them deterministically with no LLM calls. You give it a URL and a goal ("log in and export the sales dashboard"). An AI agent explores the site in a headless browser, records each action, and outputs a YAML runbook. After that, the runbook replays on its own. When UIs change and selectors break, multi -stage recovery pipeline kicks in — tries deterministic fixes first, falls back to AI only when needed, then patches the runbook so the next run is deterministic again. I built this because I was using AI agents for recurring browser tasks but couldn't justify the per-run cost and non-determinism. Traditional RPA didn't fit either — I wanted something I could npx and iterate on in my editor. `npx @refrainai/cli generate --url https://example.com --goal "..." --context context.md --output runbook.yaml` `npx @refrainai/cli execute --runbook runbook.yaml --context context.md` Runbooks are plain YAML — version-controllable, reviewable in PRs. BYOK (Anthropic, OpenAI, Google, Azure, Bedrock, Vertex). LP: https://therefrain.ai Docs: https://docs.therefrain.ai/

Visit product View on Hacker NewsMar 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, google · Missing: mac, macos, cursor

96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: pipe, io · Missing: https docs, excited, just released

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: recurring · Missing: arr, mrr, revenue

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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