Magai

Sends a single user prompt simultaneously to multiple AI APIs (ChatGPT, Claude, Bard, etc.) and aggregates responses in a unified interface. Magai maintains separate API connections to each provider's endpoint, handles authentication via user-supplied API keys, and orchestrates concurrent requests to minimize latency while collecting all responses for side-by-side comparison.

Stores, organizes, and retrieves user-created prompt templates with variable substitution and tagging. Templates are persisted in user account storage (likely cloud-backed), support parameterization via placeholder syntax (e.g., {{variable}}), and enable one-click execution across all connected AI models with consistent formatting and context injection.

Provides a free tier with limited API query allowances (likely 5-10 queries per day or per month) and premium features gated behind a subscription. Free tier includes core functionality (multi-model comparison, conversation history, templates) but with reduced query limits and no advanced features (bulk export, advanced analytics, team sharing). Limits are enforced server-side and reset on a daily or monthly cadence.

Maintains persistent conversation threads across multiple AI models, storing message history, metadata (timestamps, model used, token counts), and enabling retrieval of past exchanges. Conversations are indexed by user account and searchable, allowing users to resume multi-turn dialogues with context preservation across sessions without re-prompting.

Renders responses from multiple AI models in a single viewport using a multi-column or tabbed layout, allowing users to read and compare outputs without switching windows or tabs. The interface handles variable response lengths, formatting preservation (code blocks, lists, etc.), and provides UI controls for copying, editing, or re-running queries against individual models.

Provides a secure credential storage and management system for API keys from multiple AI providers (OpenAI, Anthropic, Google, etc.). Keys are encrypted at rest, scoped to the user account, and injected into API requests at runtime without exposing them to the client-side application. Supports key rotation, revocation, and per-provider rate limiting configuration.

Automatically extracts and displays metadata about each AI response, including token count, generation time, model version, and estimated cost. Provides basic quality signals (e.g., response length, presence of code blocks) to help users evaluate response utility without manual inspection. Metrics are computed server-side and cached for performance.

Allows users to edit a previously-submitted prompt and re-execute it against selected AI models without losing conversation context. Edited prompts are tracked with version history, and users can compare responses from the original and edited prompts side-by-side. Re-execution targets specific models (e.g., 'run against Claude only') or all connected models.

Provides one-click copying of individual model responses or bulk export of entire conversations to clipboard, files, or external formats (Markdown, PDF, JSON). Supports selective export (e.g., 'export only Claude responses') and preserves formatting, metadata, and conversation structure in exported output.

Provides UI controls to select which AI models to query for a given prompt, enable/disable specific models mid-conversation, and switch between models for follow-up questions. Model selection is persistent per conversation but can be changed at any point. Supports dynamic model availability (e.g., disabling a model if API key is invalid or quota exceeded).

Allows users to organize conversations into folders or collections, apply custom tags or labels, and search/filter conversations by metadata. Conversations are indexed by user account and support full-text search across conversation content. Tags are user-defined and can be applied retroactively to existing conversations.