⚡ Zaltech

Zaltech is a full-stack framework for building AI-native, cross-provider applications that run across ChatGPT, OpenAI, Gemini, Claude, and future MCP-compatible platforms. It standardizes how you build tools, UI, and integrations on top of the Model Context Protocol (MCP) and ChatGPT Apps SDK / Apps SDK UI—so you stop rewriting the same glue code for every provider and every app.

🚀 Coming Soon (early 2026): Zaltech framework will be publicly available in 2026.

Why Zaltech Exists

Today, building real AI applications means dealing with:

Fragmented provider SDKs
Custom MCP servers per project
One-off UI bridges for every platform
No standard way to ship interactive AI apps
Rewriting the same transport, auth, and rendering logic

Zaltech unifies all of that into one consistent, standards-aligned developer surface.

What Zaltech Gives You

Core Platform Capabilities

Category	What You Get
MCP-First Architecture	Native implementation of MCP primitives: tools, resources, prompts, and structured output using FastMCP from the official Python SDK.
Cross-Provider Runtime	One app runs across hosts; OpenAI is fully supported today. Gemini, Claude, and others follow as their MCP support stabilizes.
Native AI UI System	Production-grade widgets that look native inside ChatGPT and other AI hosts. Prebuilt layouts, themes, and UI primitives with zero design setup.
Interactive UI over MCP	Embeddable widgets rendered via MCP resources using React, Remote DOM, and iframe strategies.
Feature-Based Architecture	Every capability is a vertical slice (tool + logic + UI). Clean domain-driven separation, no fragile layer coupling.
Streaming-First Transport	Built around Streamable HTTP and SSE with stateless scaling and JSON output by default.
Authentication Out of the Box	OAuth-ready, token-based auth, secure MCP auth flows, one-command setup.
Observability Built In	Structured logs, request tracing, progress notifications, and production-safe error visibility.
MCP Inspector (First-Class)	Visual testing of tools, prompts, resources, logs, execution flow, and capability negotiation using MCP Inspector.
Local Visual UI Testing	Storybook-style isolated environment for building widgets without live LLM calls.
Web + Native Embedding	Same AI UI runs inside chat clients, dashboards, and native/hybrid apps.
One-Command DX & Deploy	One command to run locally. One command to deploy. No manual infra orchestration.

Provider Support

Status	Provider	Notes
✅ Active	OpenAI	Full production support today.
🟡 Next	Gemini	Planned via MCP abstraction.
🟡 Next	Claude	Planned via MCP abstraction.
🟡 Next	Other MCP Hosts	Any MCP-speaking platform becomes pluggable.

What You Build With Zaltech

ChatGPT Apps
Gemini Extensions
Claude MCP Tools
All kinds of AI Native Apps

All from the same codebase. Build once deploy everywhere.

What Makes It Different

Zaltech targets the emerging category of AI-native applications—apps that are triggered, controlled, and rendered directly inside LLM platforms via MCP and embedded UI systems.

Architecture & Feature Model

Zaltech is feature-first across the entire stack.
Each feature is a vertical slice with:

MCP layer – tools, schemas, UI resources
App layer – business logic, provider routing, AI calls
UI layer – embedded widgets for AI hosts

Features are:

Self-contained
Reusable
Enable/disable per deployment
Provider-agnostic
Domain-driven by design

Vision

Zaltech’s goal is simple:

Make building AI-native applications as standard as building web apps.

The web had React.
AI apps will have Zaltech.

Community

💬 Join the Community: Join our Discord to stay updated and get notified when new updates are released.

Built by Mahmoud Zalt at Sista AI.

Mahmoudz/zaltech