Jango

Jango is a macOS application for testing multi-user applications with AI participants instead of people. It gives your app a cast of AI users, and each cast member gets its own isolated browser, its own account, a goal and a memory. You point Jango at your development URL and watch those participants sign in, navigate your app, fill forms and take actions in real time — from posting in a social feed, to placing test orders in a sandbox order book, to updating shared tasks. Jango is built for developers and teams who need to exercise the parts of an application that only appear when more than one person is involved, and it runs on macOS for both Apple silicon and Intel Macs. The problem Jango addresses is that a large class of application behaviour simply cannot be tested alone. Messaging and communities, marketplaces and order books, and teams and collaboration all depend on other people showing up, holding their own accounts and doing something at the same time as someone else. Coordinating a group of testers for every change is slow and impractical, particularly for solo developers who are building these features without a testing team. Even when testers are available, the context around a test — who the participants were, what they did and what the app showed — tends to be lost and has to be rebuilt the next day. Jango is designed to remove the wait and to keep that context instead of discarding it. At the centre of the product is the cast. Each participant receives its own account and an isolated browser session, so buyers, sellers, teammates and market participants can hold separate identities inside the same running application. You assign roles and goals — a buyer places a test order, a seller lists an item, a teammate updates a task — and the participants act through their own browsers to accomplish them. Cast members are not limited to chatting: they operate your web app the way a user would, navigating pages, clicking controls, filling forms, selecting options and uploading supplied images, with actions depending on the controls Jango can observe in your app. The product is explicit that these are AI participants acting through real browser sessions rather than real people, and that they help you exercise interactions and explore scenarios rather than replace research with real users. Setting up a scenario follows three explicit steps. First you give Jango a place to go: you add your development URL and test accounts, and each participant gets its own isolated browser. Next you choose who shows up, assigning roles and goals such as a buyer placing a test order, a seller listing an item or a teammate updating a task, and each participant acts through their own browser. Finally you join in and check both sides — you use your app alongside the participants, inspect their screens, pause when something breaks and save the cast for your next change. You can log into your app as yourself while the cast uses its assigned accounts, give directions to participants, pause them or take control of any user's screen. The documentation shows the same idea through the CLI, where a command can direct a participant, for example asking one participant to invite another to a group. Every run is designed to leave something useful behind. Jango keeps three kinds of context you would otherwise rebuild tomorrow: known identities (roles, relationships and encrypted login state), observed app controls (reusable hints with execution history) and run evidence (activity, observable checks and comparisons). A run can therefore leave behind a repeatable situation, a navigation hint backed by an actual action, and evidence that an expected message appeared. At the end of a session Jango produces a report containing actions, errors and screenshots, and paid plans add multi-user checks and saved checkpoints. Casts themselves can be saved, so the same participants, accounts and goals can be brought back for the next change to the app. Jango is meant to fit where you already build. You can use the dashboard, launch from your terminal with the Jango CLI, or give your coding assistant access through MCP, and the cast and its memory stay together across those entry points. For AI sessions you can connect OpenAI, Anthropic or Vercel AI Gateway. You can bring your own AI key — in which case your AI provider bills you directly and Jango charges no extra on any plan — or buy prepaid managed AI credits from Account settings, with no subscription needed. The application ships with Node.js and Chromium bundled, along with the browsers and tools Jango needs, so no separate Node or Playwright installation is required. Updates are handled automatically on macOS: Jango checks for and downloads them in the background and installs them when you restart immediately or quit later, saving your workspace before installation, with a manual Help → Check for updates option available as well. The outcome for users is being able to test the parts of an app that need other people without waiting for other people. Instead of scheduling testers, you define a cast once and reuse it: casts can be saved for the next change, and run evidence and checkpoints carry forward, so the situation you created becomes repeatable rather than something you rebuild each time. Because participants each have a separate browser and account, you can observe both sides of an interaction at once — a buyer and a seller, or a group of collaborators — pause when something breaks and inspect the exact screen where it happened. Jango states plainly what it does not replace: AI participants help you exercise interactions and explore scenarios, but they are not a substitute for research with real users. Jango's published use cases cover social app testing (invitations, conversations, community roles and shared activity, exercised alongside your own test account), chat app testing (messaging and group chat flows without coordinating a group of testers, by directing AI participants in separate browsers and joining the conversation yourself), collaboration testing (team invitations, shared tasks and role-based workflows in collaborative web apps without gathering a testing team), and order books and marketplaces (sandbox order books and marketplace workflows where separate AI participants navigate pages, fill forms and submit test orders). It also publishes a guide for the solo developer: a practical workflow for testing social and collaborative apps alone using separate accounts, purposeful scenarios, AI participants and checks across browsers. The homepage illustrates an order book scenario in which a buyer selects Buy, enters three units at $100 and submits a limit order, a seller offers two units at $99 and checks the resulting fill, and a market participant places another test order and reviews the updated book. Jango is aimed at developers and teams building multi-user web applications, and it is positioned especially for solo developers testing social and collaborative apps without a testing team. It is available for macOS on Apple silicon and Intel, with a separate build for each, and the version listed on the site is 1.1.1, signed with the company's Apple developer certificate and notarised by Apple. Pricing has three tiers. Free costs $0 and includes three participants per session, one project, your own AI key and managed AI credits at cost plus 20%. Pro costs $9 per month and is described as a founding price that stays the same for as long as you subscribe; it includes 12 participants per session, unlimited projects, multi-user checks and saved checkpoints, and managed AI credits at cost plus 10%. Enterprise is custom priced for larger casts, participant limits set with you, custom limits, invoiced billing, negotiated managed AI rates, support terms and direct support. Jango also documents its limits and data handling: use test accounts in apps you control, and note that canvas-only apps, popup sign-in flows and CAPTCHA may need a different integration. Your account keeps projects, evidence and browser checkpoints in the cloud, browsers run on your computer, sign-in credentials use your operating system's credential protection, relevant page text, goals and participant memories are sent to your selected AI provider, and browser destinations are restricted to your configured app origins. In short, Jango replaces the wait for other people with a reusable cast of AI participants that each hold their own browser and account, pursue the goals you give them inside your development URL, and leave behind evidence, memory and reports you can build on.