Jango

Jango is a macOS app for testing the parts of your application that need more than one person. Rather than coordinating colleagues or paid testers, Jango gives your app a cast of AI participants. Each cast member has its own account, its own goal and its own isolated browser, so it signs in to your app as a distinct user and interacts with the other participants in real time. You point Jango at your development URL, decide who shows up, and watch the scenario play out. You can direct the cast, join in as yourself, or take control of any participant's screen. When the run ends, Jango leaves a report of actions, errors and screenshots. It is aimed at developers building social feeds, marketplaces, sandbox order books and collaborative workflows. Multi-user features are hard to test alone. The interactions that make a product interesting — two accounts trading on an order book, a group chat where messages arrive from several people, a team app where one user invites another and a third updates a shared task — all require other people to be present at the same time, using separate accounts, in the same app. That coordination is slow and unreliable. Testers have to be scheduled, briefed and kept in sync, and by the time the scenario is reproduced tomorrow the context has to be rebuilt from scratch. Jango targets exactly that gap: test apps with people, without the wait. The product's own framing is that you build the interactions that need other people, and Jango exists to exercise them. Setting up a scenario in Jango starts with the app itself. You add your development URL and your test accounts, and each participant receives its own isolated browser. From there you assign roles and goals: a buyer places a test order, a seller lists an item, a teammate updates a task. Because every participant acts through a separate browser session, the app sees several independent users rather than several tabs inside one signed-in session. In the illustrative order book example on the site, Rin acts as a buyer and selects Buy, enters three units at $100 and submits a limit order; Theo acts as a seller, offers two units at $99 and checks the resulting fill; Morgan acts as a market participant, places another test order and reviews the updated book. Each of those steps runs from a separate account inside the same shared application. Once a run is underway, Jango is built to be watched and steered rather than left alone. You can use your app alongside the participants, inspecting their screens as they go. You can pause when something breaks. You can direct participants with instructions — the site's CLI example shows a direction such as asking Maya to invite Alex to the group — or take manual control of any user's screen. Cast members do more than chat: each operates your web app through its own browser, navigating, clicking controls, filling forms, selecting options and uploading supplied images. What a participant can do depends on the controls Jango can observe in your app, and goals are used to exercise posts, profiles, shared tasks, marketplace listings or a sandbox order book. Jango's runs are designed to leave something useful behind. The app keeps an app memory built on three kinds of context. Known identities capture roles, relationships and encrypted login state, so the cast can be brought back with the accounts and relationships it already had. Observed app controls are reusable hints backed by an execution history, giving the cast a navigation hint grounded in an actual action rather than an assumption. Run evidence covers activity, observable checks and comparisons — evidence that an expected message appeared, for example. Together these let you save a repeatable situation instead of rebuilding context the next day. Pro plans also add multi-user checks and saved checkpoints. Architecturally, Jango splits work between your machine and your account. Browsers run locally on your computer, and Node.js and Chromium are bundled, so no separate Node or Playwright installation is required. Your account keeps projects, evidence and browser checkpoints in the cloud, and sign-in credentials use your operating system's credential protection. Browser destinations are restricted to your configured app origins. For intelligence, you can connect your own AI key — described as bring your own key — or buy managed AI credits. Providers named in the content are OpenAI, Anthropic and Vercel AI Gateway. Relevant page text, goals and participant memories are sent to the AI provider you select. Jango runs on macOS for both Apple silicon and Intel, ships as version 1.1.1, checks for updates automatically and installs them when you restart or quit. The practical benefit is that a multi-user scenario becomes something you can run on demand, alone, rather than something you have to schedule. You get a group of participants that sign in with separate accounts and act at the same time, a live view you can interrupt the moment something looks wrong, and evidence afterwards — actions, errors and screenshots — that documents what happened. Because casts, identities and observed controls are saved, the same situation can be repeated on your next change. Jango is explicit that its AI participants are not real people: they act through real browser sessions to help you exercise interactions and explore scenarios, and they do not replace research with real users. The site lists several concrete workflows. Social app testing exercises social app interactions with AI participants — invitations, conversations, community roles and shared activity — alongside your own test account. Chat app testing covers messaging and group chat flows without coordinating a group of testers, with AI participants directed in separate browsers while you join the conversation yourself. Collaboration testing explores team invitations, shared tasks and role-based workflows in collaborative web apps without gathering a testing team. Order books and marketplaces exercise sandbox order books and marketplace workflows with separate AI participants that navigate pages, fill forms and submit test orders. There is also a guide for the solo developer, describing a practical workflow for testing social and collaborative apps alone: separate accounts, purposeful scenarios, AI participants and checks across browsers. Jango is aimed at developers — particularly solo developers and small teams — testing real multi-user flows in apps they control. It fits where you build: the dashboard, a terminal launch, or a coding assistant given access through MCP, with a workspace API and portable casts keeping the cast and its memory together. The CLI example starts a cast with node ./jango-cli.mjs start --live --ai byok, and directions can be sent with node ./jango-cli.mjs direct . Pricing starts free: $0 with three participants per session, one project, your own AI key and managed AI credits at cost plus 20%. Pro is $9 per month at a founding price and includes twelve participants per session, unlimited projects, multi-user checks and saved checkpoints, and managed AI credits at cost plus 10%. Enterprise is custom, with participant limits set with you, invoiced billing, negotiated managed AI rates and direct support. Managed AI credits are prepaid and can be bought on any plan, while bringing your own key is available on every plan. Supported today are separate browser sessions, saved casts and memory, uploads, live views and manual control, plus CLI and coding-assistant integrations. Canvas-only apps, popup sign-in flows and CAPTCHA may need a different integration. Jango's value proposition is straightforward: it lets one developer run the multi-user parts of an application with a cast of AI participants that behave like separate signed-in users, watch and steer them live, and keep the memory and evidence afterwards. If a feature only reveals itself when more than one person is using the app, Jango is built to put those people there without the wait.