Tiledesk lets you build AI-powered conversational agents with a visual drag-and-drop designer, then deploy them simultaneously across web chat, WhatsApp, Telegram, Facebook Messenger, Instagram, email, and Slack without rebuilding per channel. The no-code Design Studio combines LLM-powered conversations with conditional logic, external API calls, and branching dialogue trees that work identically everywhere. Multi-RAG knowledge management separates content into isolated repositories with hybrid search combining traditional full-text retrieval and semantic understanding via Qdrant vector embeddings, enabling accurate answers even when users phrase queries in unexpected ways. LLM integration supports GPT-4, GPT-4o, Anthropic Claude, and any model exposing an OpenAI-compatible API including locally hosted models via Ollama for complete data sovereignty. The human-in-the-loop system provides seamless escalation from AI agents to live support staff with full conversation context preserved, while multi-agent workflows enable complex orchestration where specialized bots collaborate on different aspects of a customer interaction. REST and MQTT APIs power integration with external systems and webhook-triggered automations, and pre-designed templates accelerate common scenarios. Docker Compose deployment starts the full stack including server, dashboard, messaging engine, and widget. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Most AI chat integrations bolt an LLM onto a text box and call it customer support; AgentDesk builds the entire helpdesk around AI agents that handle first responses, escalate to humans when they cannot answer, and track follow-up work through integrated ticket workflows. Each AI agent binds to a specific LLM configuration, one or more knowledge bases, a set of callable Skills, and MCP tool integrations, creating specialized support personas for different product lines or departments. When a customer message arrives, the agent retrieves relevant FAQ entries and document chunks through RAG-powered vector search (Qdrant), then passes retrieved context through an Answerability Gate that determines whether the knowledge base can support a reliable answer; if not, it triggers a fallback response and recommends human handoff instead of hallucinating. Human agents pick up escalated conversations in a dedicated workspace that preserves the full AI conversation history, customer profile, conversation tags, and linked tickets, so they never start from scratch. The ticket system converts conversations into tracked work items with category assignment, status flows, progress notes, and team routing. An admin dashboard provides user management, role-based access control, team scheduling, and automatic conversation assignment rules. Model configuration supports any OpenAI-compatible provider, letting you wire in LLMs, embedding models, and reranking models with per-model context limits, timeout settings, and rate controls. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.