Why Zango
Zango is not a library, a thin wrapper, or a frontend framework. It is a vertically integrated backend + frontend + operations platform for building Agentic AI business applications, multi-tenant by default, enterprise-ready by default, with a package ecosystem and a native AI module.
Every internal tool and client portal built on a generic stack hits the same wall before the real product work begins: OTP and 2FA delivery, session management, role-based access that stays maintainable, password policy, SAML for enterprise clients, audit trails for compliance, account lockout. Teams lose weeks here.
In Zango, every item on that list is a toggle or a config field, and the zango-app-developer plugin can set them for you through the platform API. The developer never writes an auth flow. The infrastructure is solved before a single line of business logic exists, so your time goes to the product, not the plumbing.
Who it's for
Two audiences, one platform. Django developers who want to stop rebuilding tenancy, auth, and access control on every project. And product managers and technical leads who have been burned before, who need internal tools and client-facing portals shipped without losing a sprint to session-management bugs and access-control edge cases.
Multi-tenant SaaS, built in
A single codebase, one Docker deployment, hundreds of customers. Each tenant gets its own isolated
database schema, no WHERE tenant_id = ?, no leakage. Tenant context is set
automatically from the request hostname.
Auth: configured, not coded
OTP, MFA, SAML 2.0, OAuth, session timeout, concurrent-session control, account lockout, password policy, per-role overrides. Every one is a toggle or config field. Zero auth code, ever.
Workflow lifecycle
Statuses, transitions, conditions, done methods, tags, the full record lifecycle, audit-logged.
Native AI module
Agents, tools, prompts, with contextual access to tenant data, workflow state, and audit history.
Compliance-ready
Access logs, audit logs, policy expiry, release fixtures, the things regulators ask for, on from day one.
Why "Agentic AI" is the point
A capable AI agent needs more than a model, it needs context and the authority to act safely. Because Zango already provides multi-tenancy, workflow state, permissions, audit history, secrets, and background processing, the AI module inherits all of it. Tools run in the correct tenant schema, agent endpoints are policy-gated like any other view, and an agent invoked inside a background task becomes an autonomous worker. The AI is powerful because the platform under it is complete.