Kun vs deepchat

Local-first AI agent workspaces compared

Kun and deepchat are TypeScript desktop applications for working with AI models and agents. Kun centers on a shared desktop and terminal workspace for tasks across coding, writing, design, and research, while deepchat focuses on a desktop client for model access, agent tools, session history, and remote messaging control.

Kundeepchat
LanguageTypeScriptTypeScript
LicenseNOASSERTIONApache-2.0
Stars6.3k6.4k
Forks595756
Last analyzedOct 4, 2026Oct 18, 2026

Key differences

  • Kun shares a runtime between its desktop GUI and terminal TUI; deepchat is described as an Electron and Vue desktop client for Windows, macOS, and Linux.
  • Kun includes coding, design, document work, Rooms for multi-agent collaboration, and scheduled automation; deepchat focuses on model sessions, reusable Skills, MCP, and ACP agents.
  • Kun’s project metadata lists its license as NOASSERTION, while its README describes PolyForm Noncommercial terms and separate written authorization for commercial use; deepchat uses Apache-2.0, which permits commercial use subject to its terms and notices.
  • Kun’s listed model options include compatible services and self-hosted models; deepchat specifically supports cloud providers and local Ollama models.
  • Deepchat records sessions using Tape history and offers request traces and token budgets; Kun ties planning, tool use, approvals, and evidence to tasks.
  • Deepchat supports remote session control through messaging apps including Telegram, Discord, and WeChat iLink; Kun describes local desktop and terminal interfaces.

Choose Kun if you…

  • need a shared desktop and terminal workspace for agent tasks and approvals.
  • want to connect design work with implementation or coordinate delegated work in isolated Git worktrees.
  • need agent workflows for coding, writing, research, or repeatable automation in one workspace.
Read the Kun analysis →

Choose deepchat if you…

  • want a desktop client that combines cloud models, local Ollama models, and ACP agents.
  • need inspectable session history, request traces, or token budgets for long-running tasks.
  • want to manage desktop sessions remotely through supported messaging apps.
Read the deepchat analysis →

Compare the numbers side by side, or add more projects

This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.