ChatGPT Next Web

Abstracts multiple LLM providers (OpenAI GPT-4, Anthropic Claude, custom endpoints) behind a single unified chat interface. Implements provider-agnostic message routing that translates user inputs into provider-specific API schemas, handles authentication via environment variables or user-provided API keys, and manages response normalization across different model output formats. Supports streaming responses and fallback provider selection.

Provides a Vercel deployment template that auto-configures environment variables, serverless function routing, and static asset hosting with zero manual infrastructure setup. Uses Vercel's GitHub integration to enable one-click deployment from the repository, automatically sets up API key environment variables through Vercel's dashboard, and handles CORS configuration for cross-origin API calls. Includes pre-built deployment scripts that validate configuration before deployment.

Implements streaming response handling that displays LLM output token-by-token as it arrives from the API, rather than waiting for the complete response. Uses server-sent events (SSE) or WebSocket connections to receive streamed tokens, renders each token incrementally to the DOM, and handles edge cases like partial markdown or LaTeX expressions. Provides visual feedback (typing indicator, cursor animation) while streaming is in progress.

Allows users to create alternative conversation branches at any point (e.g., 'what if I asked this differently?'), maintaining a tree structure of conversation paths. Implements a visual tree navigator showing all branches and allowing users to switch between them, compare branches side-by-side, or merge branches. Each branch maintains its own message history and can be edited independently. Supports undo/redo within a branch and restoration of previous conversation states.

Provides dark and light theme options with automatic detection of system color scheme preferences (via prefers-color-scheme media query). Implements theme switching via UI toggle with persistence to local storage, and supports custom color palette configuration. Uses CSS variables for theme colors, enabling runtime theme switching without page reload. Includes accessibility features like high-contrast mode and adjustable font sizes.

Parses and renders user and assistant messages using a markdown processor (likely remark/rehype stack) that supports GitHub-flavored markdown, inline LaTeX expressions (via KaTeX), and syntax-highlighted code blocks. Implements client-side rendering with language detection for code blocks, automatic line numbering, and copy-to-clipboard functionality. Handles edge cases like nested code blocks and mixed markdown/LaTeX content without rendering conflicts.

Implements a conversation compression strategy that summarizes older messages or extracts key context when conversation history exceeds a configurable token threshold. Uses the LLM itself to generate summaries of earlier exchanges, then replaces the original messages with compressed summaries in the context window. Maintains a configurable compression ratio and allows users to manually trigger compression or set automatic thresholds. Preserves conversation continuity by keeping recent messages uncompressed.

Provides a built-in library of pre-written prompt templates (system prompts, role-play scenarios, task-specific instructions) with support for variable placeholders (e.g., {{topic}}, {{language}}) that users can customize before sending. Implements a template management UI for creating, editing, and organizing templates, and allows users to export/import templates as JSON or share via URL. Templates are stored locally in browser storage or synced to a backend if authentication is enabled.

Implements PWA standards (service worker, manifest.json, offline caching) to enable installation as a native-like app on desktop and mobile devices. Caches static assets and recent conversations locally, allowing users to view chat history offline. Uses service worker to intercept network requests and serve cached content when offline, with graceful degradation when API calls fail. Supports app shortcuts and push notifications if configured.

Allows users to export conversations in multiple formats (JSON, Markdown, PDF) and import previously exported conversations to restore state. Implements format-specific serialization that preserves message metadata (timestamps, model used, token counts) in JSON exports, converts to readable markdown for sharing, and generates formatted PDFs for archival. Supports batch export of multiple conversations and selective export of conversation segments.

Implements a responsive web UI using CSS media queries and flexible layouts that adapt to different screen sizes (mobile <768px, tablet 768-1024px, desktop >1024px). Uses a mobile-first design approach with touch-optimized controls, collapsible sidebars, and adaptive message layouts. Built with a modern frontend framework (React or Vue) that handles state management across different viewport sizes and maintains conversation continuity when resizing.

Implements client-side full-text search across conversation history using a lightweight indexing approach (likely simple string matching or a library like Lunr.js). Allows users to search by keywords, filter by date range, model used, or conversation topic, and displays search results with highlighted matches. Search is performed locally in the browser without sending conversation data to external services, preserving privacy.

Allows users to define custom system prompts that set the LLM's behavior, tone, and role for a conversation (e.g., 'You are a Python expert', 'Respond in Shakespearean English'). Implements a system prompt editor with preset templates and the ability to save custom system prompts for reuse. System prompts are prepended to the conversation context and can be modified mid-conversation. Supports variable substitution in system prompts (e.g., {{user_name}}, {{language}}).