CHAI - Multi-Provider AI Chat Platform

Overview

VorteChat is a ChatGPT-style AI chat interface that supports multiple AI providers in one unified platform. Instead of being locked into a single model, users can switch between different providers such as OpenAI, Anthropic, Google, Groq, and more—depending on their needs.

The goal is to provide a flexible, developer-friendly chat interface that makes it easy to experiment with LLMs, compare answers, and use whichever model suits a task best.

Key Features

• Multi-provider model support
  Integrates multiple major AI providers:

  • OpenAI / ChatGPT
  • Anthropic
  • Google Gemini
  • Groq (LLaMA, Mixtral, etc.)
  • Any model that supports OpenAI-compatible API

• Unified chat interface
  A clean, simple ChatGPT-like UI where users can switch models instantly without losing context.

• Model comparison mode
  Users can generate responses from multiple providers side-by-side to evaluate:

  • speed
  • reasoning quality
  • coding accuracy
  • creativity

• History & workspace management
  Automatic saving of conversations, with folders/workspaces for organization.

• System prompts & presets
  Users can create and save custom personas, instructions, and pre-made prompt templates.

• Streaming responses
  Messages stream in real-time like ChatGPT for a smooth experience.

• Cost control (if connected via user API keys)
  Users can plug in their own provider API keys to control usage and cost more transparently.

Why I Built It

Different AI models have different strengths:

• Some are better for coding.
• Some are better for reasoning.
• Some are extremely fast.
• Some are much cheaper.

I built VorteChat so I can personally use the best LLM for each task without switching apps. This turned into a full tool that others can benefit from as well.

Technical Overview

• Modern full-stack implementation using:
  • Next.js or Laravel API backend (depending on deployment)
  • Streaming responses through server-sent events or WebSockets
  • Provider-agnostic request layer for easy model expansion
• Modular architecture allows adding new LLMs with minimal code changes.
• Secure handling of API keys; user keys never stored unless explicitly enabled.

Future Roadmap

• Multi-agent mode (ask several models collaboratively)
• Plugin & tools support (browsing, code execution)
• Team accounts and shared workspaces
• Rate-limit an