Halist AI

Halist AI implements a model-agnostic conversation router that abstracts away differences between Claude, GPT-4, Llama, and other LLMs behind a single chat interface. The system maintains a unified conversation history and allows users to send the same prompt to multiple models simultaneously or sequentially, comparing outputs without context switching. This is achieved through a standardized message format that translates user input into provider-specific API schemas (OpenAI's chat completion format, Anthropic's messages API, etc.) and normalizes responses back to a common structure.

Halist AI provides an optional local processing mode where conversation history and user prompts are encrypted and stored on the user's device rather than transmitted to Halist's servers. The architecture uses client-side encryption (likely AES-256 or similar) to encrypt conversations before any network transmission, with decryption keys managed locally. When users opt for local-only mode, API calls to LLM providers (OpenAI, Anthropic) are routed directly from the client without intermediation, ensuring Halist servers never see the conversation content—only metadata like API usage.

Halist AI allows users to share conversations with others via shareable links or direct invitations, with granular access control (view-only, edit, comment). Shared conversations can be encrypted or public depending on user preference. The system supports role-based access (owner, editor, viewer) and time-limited sharing links that expire after a set duration. Shared conversations maintain a separate access log showing who accessed the conversation and when.

Halist AI automatically generates summaries of long conversations and extracts key topics/themes using NLP techniques (likely abstractive summarization via a smaller LLM or extractive methods). Summaries are generated on-demand or automatically for conversations exceeding a certain length, and are displayed in conversation metadata. Topic extraction identifies key concepts, entities, and themes discussed in the conversation for tagging and organization purposes.

Halist AI synchronizes conversations across desktop (Windows, macOS, Linux), mobile (iOS, Android), and web clients using a decentralized or hybrid sync architecture. Rather than forcing all data through Halist's servers, the system uses optional cloud sync (with encryption) or peer-to-peer sync via local network protocols (e.g., WebRTC, local network APIs). Users can choose to sync only specific conversations or devices, and the sync mechanism respects local-first principles—conversations are always stored locally first, with optional cloud backup for convenience.

Halist AI implements a freemium model with rate limits enforced at the API gateway level, tracking per-user token consumption and request counts across all model providers. Free tier users receive a monthly quota (e.g., 100K tokens or 50 requests) that resets on a calendar basis, while paid tiers unlock higher limits or unlimited access. The system uses a quota tracking service that monitors real-time consumption and blocks requests when limits are exceeded, with clear messaging about remaining quota and upgrade paths.

Halist AI provides a secure credential management system where users can add API keys for multiple LLM providers (OpenAI, Anthropic, local Ollama) through a unified settings interface. Keys are encrypted at rest using a user-specific encryption key derived from their account password, and are never logged or transmitted to Halist's servers in plaintext. The system supports both user-managed keys (users provide their own API keys) and Halist-managed keys (Halist provides shared API access with usage tracking). Each provider integration includes validation logic to test key validity before storing.

Halist AI allows users to export conversations in multiple formats (JSON, Markdown, PDF, plaintext) for archival, sharing, or migration to other platforms. The export system preserves conversation metadata (timestamps, model used, token counts) and supports selective export (single conversation or bulk export of all conversations). Exported files are generated client-side when possible to avoid transmitting conversation content to Halist servers, and include optional encryption for sensitive exports.

Halist AI integrates with Ollama, an open-source framework for running LLMs locally, allowing users to add self-hosted models (Llama 2, Mistral, etc.) to their model roster without API costs. The integration works by discovering Ollama instances on the local network or via explicit host configuration, and routing prompts to local models using Ollama's REST API. Users can mix local models with cloud-based providers in the same conversation, enabling cost optimization (use free local models for drafting, expensive cloud models for refinement).

Halist AI indexes all user conversations using both full-text search (keyword matching) and semantic search (embedding-based similarity) to enable fast retrieval of past conversations. The system builds a searchable index of conversation content, metadata (date, model used), and extracted topics/summaries. Users can search by keywords, natural language queries, or filters (date range, model used, conversation length). Semantic search uses embeddings (likely from a lightweight model like MiniLM or Sentence Transformers) to find conceptually similar conversations even if keywords don't match.

Halist AI allows users to tag conversations with custom labels (e.g., 'project-x', 'client-feedback', 'research') and organize them into folders or collections. Tags are user-defined and can be applied to multiple conversations, enabling flexible organization without rigid hierarchies. The system supports bulk tagging operations and tag-based filtering in search. Metadata like conversation title, description, and custom fields can be added to conversations for richer organization.

Halist AI provides analytics dashboards showing per-model performance metrics (response time, token usage, cost) and aggregated usage statistics. The system tracks metrics like average latency per model, total tokens consumed, estimated costs (if using user's own API keys), and success/failure rates. Analytics are computed from conversation metadata and API provider responses, and are displayed in charts and tables. Users can filter analytics by date range, model, or conversation type to understand usage patterns.