Jan vs deepchat
Desktop AI assistants compared
jan and deepchat are cross-platform desktop apps for working with cloud and local AI models. jan emphasizes downloading and running models locally, custom assistants, and an OpenAI-compatible local API, while deepchat focuses on agent workflows, tools, Skills, and resumable sessions.
Jan: Run Local AI on Your Desktop with llama.cpp
Jan is a Tauri desktop app built on llama.cpp that brings open-source AI into an offline ChatGPT-style interface. It runs on Windows, macOS, and Linux, with options to use local models or connect to cloud AI services.
deepchat: Local-First AI Agent Desktop Client with Electron
DeepChat is a local-first AI agent desktop client built with Electron and Vue for Windows, macOS, and Linux. It brings model providers, agent tools, Skills, MCP, ACP agents, and remote messaging control into one app.
| Jan | deepchat | |
|---|---|---|
| Language | Rust | TypeScript |
| License | — | Apache-2.0 |
| Stars | 45k | 6.4k |
| Forks | 3.1k | 756 |
| Last analyzed | Oct 18, 2026 | Oct 18, 2026 |
Key differences
- jan can download models, including from Hugging Face, and offers an OpenAI-compatible local API at localhost:1337; deepchat connects to local Ollama models and does not list that API.
- deepchat includes resumable agent sessions, MCP tools, reusable Skills, compatible ACP agents, and messaging-based remote control; jan highlights custom assistants and MCP integration.
- jan is written in Rust and has a NOASSERTION license; deepchat is written in TypeScript and uses Apache-2.0.
- Both run on Windows, macOS, and Linux, but jan describes local inference alongside cloud connections, while deepchat describes a local-first workspace connecting cloud providers and Ollama.
- jan has 44.8k stars and 3.1k forks, compared with deepchat's 6.4k stars and 747 forks; both are unarchived.
Choose Jan if you…
- want to download and run models locally through a desktop interface.
- need an OpenAI-compatible local API for applications.
- want custom assistants and a choice between local models and listed cloud providers.
Choose deepchat if you…
- need to inspect and resume structured agent sessions.
- want MCP tools, reusable Skills, or compatible ACP agents in a desktop workflow.
- want to control sessions through configured messaging integrations or organize work across windows and tabs.
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.