Jan
Open-source AI desktop app that runs 100% offline with local models and agents.
Jan is the best option for anyone who wants total privacy and control over their AI. The agent features and local API server make it a powerful tool for developers. Beginners may find the learning curve steep, but the community and open-source nature compensate.
- Privacy-conscious users who want AI without internet or account
- Developers and tinkerers who want local customization and CLI control
- Researchers needing reproducible, offline experiments with open models
- Power users running multiple models on their own hardware
- Users who want a polished, ChatGPT-like experience out of the box without setup
- Those with limited hardware (no GPU or RAM <8GB) who cannot run local models
- Teams needing centralized billing and user management (no enterprise features)
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Skip Jan if you need a plug-and-play ChatGPT alternative without hardware prerequisites or setup effort.
Running larger models (e.g., 30B parameters) requires a powerful GPU with at least 16GB VRAM, which can cost $1,000+.
Jan is free and open-source (Apache 2.0). There are no paid tiers, making it cost-effective for individuals and small teams. However, hardware costs can offset savings for heavy users. Competitors like ChatGPT ($20/mo) offer less privacy but require no local hardware.
In short
Jan — Open-source AI desktop app that runs 100% offline with local models and agents. Best for Privacy-conscious users who want AI without internet or account, Developers and tinkerers who want local customization and CLI control, Researchers needing reproducible, offline experiments with open models. Free to use.
What's new in Jan
Checked 11 days agoAcross the latest 4 updates: 4 feature updates.
Jan v0.8.3: Message Branching, Linux Custom Titlebar & HTML/SVG Artifacts
Adds navigable message branching, custom Linux titlebar, unified reasoning timeline, interactive HTML/SVG previews, and video input for local vision models.
Jan v0.8.2: Faster Startup, AMD ROCm/HIP on Linux & Resumable Downloads
Adds AMD ROCm/HIP backend support on Linux, faster startup with deferred work, and pause/resume model downloads.
Jan v0.8.1: Anthropic-Compatible Custom Providers, Per-Message Errors & llama.cpp Overhaul
Adds Anthropic-compatible custom providers, OS-native TLS trust, sampler popover, persistent per-message errors, and major llama.cpp settings overhaul.
Jan v0.8.0: Multi-Token Prediction, llama.cpp Router Mode & Inline MCP Approval
Ships Multi-Token Prediction for llama.cpp models, unified llama.cpp router process, inline MCP tool approval with citation cards, and bulk model deletion.
Viability Score
How likely is Jan to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- 100% offline AI chat with local models
- Message branching with navigable version history
- AI agents for file browsing, calendar management, messaging via Slack/Discord/WhatsApp
- Local API server compatible with OpenAI's API spec
- Multi-token prediction for faster inference (llama.cpp compatible models)
- Video input support for local vision models
- Interactive HTML and SVG artifact previews (experimental)
- Model download management (pause/resume) and bulk deletion
- Custom providers with Anthropic-compatible endpoints
- OpenClaw integration for CLI-based interactions
- Jan CLI for serving models and launching agents from terminal
- MCP tool approval with inline citation cards
- AMD ROCm/HIP backend support on Linux
- Memory system (coming soon) that carries context across sessions
- Consumer-grade privacy: no account required, no data sent to cloud
About Jan
Jan is an open-source, privacy-focused AI platform that runs entirely on your local machine, with no account or internet required. It combines a desktop app (macOS, Windows, Linux), web interface, CLI, and local API server, giving you full ownership of your AI stack. Built-in foundation models are optimized for reasoning, agents, and deep research, and you can plug in any open model from HuggingFace or connect to cloud providers like OpenAI, Anthropic, and Google. Key features include message branching with navigable version history, AI agents that autonomously browse files, manage calendars, and act via Slack/Discord/WhatsApp, video input for local vision models, and interactive HTML/SVG artifact previews. With over 5.9 million downloads, 43.2K GitHub stars, and an Apache 2.0 license, Jan is a serious alternative for power users who want to avoid vendor lock-in and keep data private. Unlike ChatGPT or Claude, Jan offers full offline capability and deep customization, but requires more setup and local hardware.
Behind the Verdict
Jan provides genuine offline AI capability for users who value privacy and control over convenience. With frequent updates (v0.8.3 as of June 2026) adding message branching, video input, and AMD ROCm/HIP support on Linux, it's maturing rapidly. Strengths include a broad model compatibility (HuggingFace, OpenAI, Anthropic, etc.), a local API server, and autonomous agents. Weaknesses: local models require substantial hardware (16GB+ RAM for 7B+ models), setup is non-trivial, and agent features are still nascent. Best for developers and privacy enthusiasts; not for those seeking a polished ChatGPT-like experience out of the box.
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Real-world workflow fit
Concrete scenarios for the personas Jan actually fits — and what changes day-one when you adopt it.
You're traveling with a laptop and want to draft sensitive documents without sending data to the cloud. You install Jan, download a 7B model (e.g., Mistral 7B), and start chatting offline instantly.
Outcome: You get local AI assistance with full privacy and zero internet dependency.
You need an OpenAI-compatible API endpoint for your app but want to keep data local. You enable Jan's API server, set it to run a Llama 3 model, and point your app to http://localhost:1337.
Outcome: Your app now has a local AI backend with standard OpenAI API calls, no external calls or costs.
You want to benchmark reasoning capabilities across several open models. You use Jan to quickly download (with pause/resume) and switch between DeepSeek, Qwen, and Gemma, running the same prompt on each.
Outcome: You get side-by-side model comparisons without cloud costs, in a reproducible local environment.
Use Cases
- Run completely offline AI chat on a laptop while traveling to avoid data exposure.
- Automate research tasks using autonomous agents that browse the web and compile reports.
- Serve a local OpenAI-compatible endpoint for custom applications and prototyping.
- Swap between different open models to compare reasoning capabilities without cloud costs.
- Integrate AI into your development workflow via Jan CLI and MCP servers.
- Build privacy-aware AI tutoring tools by leveraging local vision and text models.
Models Under the Hood
as of 2026-07-15
Limitations
- Jan's local models are constrained by your hardware: larger models (e.g., 30B+) need significant RAM and GPU.
- The agentic features (file reading, calendar management) are still nascent and may not work reliably with all providers.
- The 'Memory' feature is listed as coming soon and is not yet available.
- Artifact previews are experimental and limited to HTML/SVG for now.
as of 2026-07-06
Where the pricing makes sense
The company stage and team size where Jan's pricing actually pencils out — and where peers do it cheaper.
Jan is free and open-source (Apache 2.0). There are no paid tiers, making it cost-effective for individuals and small teams. However, hardware costs can offset savings for heavy users. Competitors like ChatGPT ($20/mo) offer less privacy but require no local hardware.
Setup time & first value
How long it actually takes to get something useful out of Jan — broken out by persona, not the marketing-page minute.
5–15 minutes for download and basic local model setup (e.g., Mistral 7B). Developer setup (CLI, API server) takes another 10–30 minutes. Hardware prerequisites: 8GB+ RAM for 7B models, GPU recommended for larger models.
Switching to or from Jan
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From ChatGPT: export chat history via OpenAI dashboard (if needed, Jan imports conversations manually).
- →From Ollama: Jan supports llama.cpp compatible models; you can reuse model files.
- →From LM Studio: Jan provides a similar local API server and model management, with added agent features.
- ↗To Ollama: Export custom model files; Ollama supports same llama.cpp models.
- ↗To ChatGPT: No direct export; copy-paste conversations manually.
- ↗To LM Studio: LM Studio can load the same local models; export settings via config files.
Integrations
Resources & Guides
Official links
Tools that pair well with Jan
Common stack mates teams adopt alongside Jan, with the specific reason each pairing earns its keep.
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Frequently Asked Questions
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