Skip to content

Chat & Agents#

Most AI tools are synchronous, single-model, and ephemeral. You prompt, you wait, the session ends, you start over.

LIT is built differently. Conversations are persistent. Multiple AI models can participate. Agents run while you sleep and post results to channels you check when it suits you. Your context belongs to you — not to any provider — and it compounds over time.

This is the collaboration model, not the tool model. For data scientists, it means training deep learning models entirely from chat — designing architectures, running experiments, debugging failures — with no terminal and no context juggling. For everyone else, it means AI that keeps up with how real work actually gets done.

Core#

The day-to-day workspace: chat interface, channels, and multi-model support. Where most of your interaction with AI happens.

  • Chat Interface — streaming responses, voice input, mobile-ready
  • Channels & DM — persistent, async project workspaces with compounding context
  • Multi-Model — switch providers without losing history; multiple models in one channel

Bring Your AI to Work#

AI as an organizational participant, not a personal assistant. Team channels, shared resources, agent security, and enterprise identity.

Autonomous#

Agents that work without you. Scheduled tasks, heartbeat agents, self-improving loops.

Transparency#

Full visibility into every AI decision. Transcripts, timestamps, session management, safe mode.