One agent for the open web
Checkout, a job application, a multi-step wizard, reading one page to write on another — no integration, no scripts.
OpenSidebar is an open-source Chrome extension that puts an autonomous agent in your side panel — describe a task in plain English and it sees the page, clicks, types, and carries multi-step work across tabs to done.
Bring your own API key. No subscription, no telemetry, no backend of ours — the extension runs in your browser and talks only to the model providers you configure.
The Chrome Web Store listing is on the way. Until then, build from source and load the unpacked extension — the steps below are the entire process.
A signed listing so anyone can add OpenSidebar straight from Chrome.
Coming soon# clone & build git clone https://github.com/krisshkodrani/OpenSidebar cd OpenSidebar corepack enable corepack pnpm install corepack pnpm run dist # then in Chrome chrome://extensions → Developer mode → Load unpacked → select dist/
Requires Node.js 22+ and a supported provider API key.
Every clip is a real session the agent drove end to end, captured as it ran. Scenes are trimmed and captioned for length — nothing is mocked up or re-enacted.
Checkout, a job application, a multi-step wizard, reading one page to write on another — no integration, no scripts.
The same agent drives ServiceNow — creating incidents, ordering from the catalog, filtering lists. One agent, every workflow.
Leave it watching a page and it re-checks every few seconds — in this run it flagged “back in stock” within seconds of the flip.
Paste a key, pick your models, go. Fireworks, OpenRouter, Moonshot, or Xiaomi — nothing hardcoded.
Three acts — the open web, staying in control, and the ServiceNow finale. Narrated.
Real sessions, unscripted — edited only for pacing and captions. Executor: Kimi K2.7 Code (vision) · Planner: GLM 5.2 · Judge: GPT-OSS 120B · Fireworks AI
A vision model works from the live screenshot and DOM — reads charts, zooms into fine print, and handles pages that defeat text-only bots.
Clicking, typing, and scrolling, plus file upload and download, tab and window management, and structured read-outs of tables, chart labels, and active filters — the agent gets data out of pages, not just actions into them.
Approvals are on by default, and consequential actions — submitting a job application, sending a message — always pause for you. When the agent works from a draft you approved, the final submit is checked field-by-field against that draft; any mismatch stops and shows you the difference before anything is sent.
A planner decomposes the task, an executor drives it, and a verifier confirms the result — high-risk completions get a second opinion from a dedicated judge model. Stuck runs escalate to a stronger model instead of burning turns.
A local profile you review yourself, per-site skills you record once and reuse, and checkpoints that survive restarts (kept for 24 hours). Sensitive fields are consent-gated per task and encrypted with a key that never leaves your machine.
Point it at a page and it re-checks every few seconds, comparing what actually rendered — then tells you when a price, a status, or a listing changes.
Add a Groq API key in settings and voice input transcribes your words straight into the composer (Whisper large-v3-turbo) — describe the task out loud and let it run.
Every session produces a full-fidelity local trace. Keys live in Chrome storage, and traffic goes from your browser to the providers you configure — your LLM provider, plus Groq if you enable voice — and nowhere else.
Hybrid stacks add DeepSeek-, Cerebras-, and Groq-served models. Your API keys stay in Chrome storage; page context goes only to the providers you configure. No analytics, no tracking, no hosted relay, no OpenSidebar servers.
The repo ships a full observability workspace: replay any run as a story — plan, nodes, turns, verification — with the judge model's per-criterion reasoning and cost on high-risk completions, then record whether the outcome was actually right. Traces never leave your machine.