Jan
Open-source offline AI assistant with local models, agents, and total privacy.
Jan is the definitive offline AI app for developers who want privacy and customization without paying a monthly fee. Its agent features, local API, and steady stream of updates make it a strong choice, but it's not turnkey—you'll need to manage models and infrastructure yourself. Skip it if you want a polished, zero-setup ChatGPT experience.
Verified 2d ago · liveness 73/100 · cite: rightaichoice.com/tools/jan
- Privacy-focused individuals who want AI without internet or account—perfect for confidential work.
- Developers building local agents or using the OpenAI-compatible API for custom apps.
- Researchers running reproducible offline experiments with open models from HuggingFace.
- Power users bouncing between multiple models on their own hardware with a unified interface.
- Users who want a polished, ChatGPT-like experience with zero setup—Jan expects tinkering.
- Anyone with limited hardware (no GPU or under 8GB RAM) who can't run local models well.
- Organizations needing centralized billing, user management, or enterprise support.
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Skip Jan if you want a polished, zero-setup ChatGPT experience or lack the hardware (no GPU or under 8GB RAM) to run local models efficiently—it expects tinkering and technical expertise.
Jan is free and open-source, but you'll need to provide your own hardware—a capable GPU or at least 8GB of RAM—to run large models at usable speeds.
Jan is completely free and open-source—no subscription, no per-seat fees, and no usage caps. This makes it ideal for individuals and teams who want to avoid monthly costs of cloud AI assistants like ChatGPT Plus ($20/mo) or Claude Pro. However, you pay with your own hardware and time: you'll need a decent GPU or at least 8GB of RAM, and you'll manage models and infrastructure yourself. For privacy-focused developers and researchers, the cost savings and data control are unmatched.
In short
Jan — Open-source offline AI assistant with local models, agents, and total privacy. Best for Privacy-focused individuals who want AI without internet or account—perfect for confidential work., Developers building local agents or using the OpenAI-compatible API for custom apps., Researchers running reproducible offline experiments with open models from HuggingFace.. Free to use.
What's new in Jan
Checked 2 days agoAcross the latest 4 updates: 4 feature updates.
Jan v0.8.4: Native Web Search, Per-Model Chat Templates & a Backend Settings Store
Adds native web_search/web_fetch tools, per-model chat-template kwargs for llama.cpp, token counter for remote/MLX providers, and a backend-managed settings store.
Jan v0.8.3: Message Branching, Linux Custom Titlebar & HTML/SVG Artifacts
Introduces message version branching, a unified reasoning/tool timeline, HTML/SVG artifact previews, video input for vision models, and a custom Linux titlebar.
Jan v0.8.2: Faster Startup, AMD ROCm/HIP on Linux & Resumable Downloads
Adds AMD ROCm/HIP backend support on Linux, a phased-startup loader, pause/resume model downloads, and a safer default context size.
Jan v0.8.1: Anthropic-Compatible Custom Providers, Per-Message Errors & llama.cpp Settings Overhaul
Adds Anthropic-compatible custom providers, OS-native TLS trust, a sampler popover with capability gating, and a major llama.cpp settings overhaul.
What people actually say about Jan — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
45 mentions across 2 sources (Hacker News, Lemmy) · researched Jul 3, 2026.
- +100% offline AI with no cloud data leaks—strong privacy win.
- +Open-source Apache 2.0 license—full stack transparency and modification.
- +Supports multiple local and cloud model providers, no lock-in.
- +Built-in agent capabilities for autonomous file and calendar tasks.
- +Cross-platform desktop, web, CLI, and API server—flexible deployment.
- −No real user reviews or testimonials in public forums.
- −Community feedback is absent—key features unvalidated by users.
- −Local model performance depends heavily on user hardware.
- −Agent functionality likely still experimental and unstable.
- −Documentation and support may be insufficient for beginners.
- • Requires powerful local hardware; users may need to invest in GPU/CPU upgrades
- • Cloud provider API usage billed separately if using remote models
Viability Score
How well maintained and how widely used is Jan? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Offline AI chat with local models
- Native web search and web fetch tools (v0.8.4)
- Message branching with version history (v0.8.3)
- HTML/SVG artifact previews (v0.8.3)
- Video input for local vision models (v0.8.3)
- Multi-token prediction for compatible llama.cpp models (v0.8.0)
- Inline MCP tool approval with citation cards (v0.8.0)
- AMD ROCm/HIP backend support on Linux (v0.8.2)
- Anthropic-compatible custom providers (v0.8.1)
- Per-model chat template kwargs and token counter (v0.8.4)
- Backend-managed settings store (v0.8.4)
- Resumable model downloads (v0.8.2)
- Local API server compatible with OpenAI API spec
- Jan CLI for serving models and launching agents
- AI agents for file browsing, calendar management, and messaging via Slack/Discord/WhatsApp
About Jan
Jan is a free, open-source AI platform that runs entirely on your machine, giving you ChatGPT-like capabilities without the trade-offs of cloud dependence or data leaving your computer. With over 6.4 million downloads and 44.2K GitHub stars, it's a community-driven project built for privacy-conscious users, developers, and researchers who want full control over their AI. Jan supports open models from HuggingFace (Llama, Mistral, Qwen, DeepSeek, Gemma, Kimi) and also lets you plug in online providers like OpenAI, Anthropic, and Google when you need a hybrid setup. Its modular architecture includes the Jan Desktop app for macOS, Windows, and Linux, plus a CLI, a local API server compatible with OpenAI's spec, and the separately distributed Jan Agent for running on your own VM or container. The latest updates show steady evolution: v0.8.4 (July 2026) adds native web search and web fetch tools, per-model chat templates, a token counter, and a backend-managed settings store. v0.8.3 introduced message branching with version history, HTML/SVG artifact previews, and video input for local vision models. v0.8.2 brought AMD ROCm/HIP support on Linux and resumable downloads, while earlier versions added Anthropic-compatible providers, multi-token prediction, and inline MCP tool approval with citation cards. These features make Jan not just a chat client but a capable local AI workstation. What sets Jan apart is its agent ecosystem: agents can browse files, manage calendars, and send messages via Slack, Discord, or WhatsApp—all controlled locally. The memory feature is coming soon, promising context that carries over across sessions. Compared to ChatGPT or Claude, Jan gives you absolute data sovereignty, but it demands capable hardware (a decent GPU or at least 8GB of RAM) and a willingness to tinker. It's a direct alternative to Ollama or LM Studio, but more ambitious in scope, blending local inference with agent workflows and a pluggable backend.
Behind the Verdict
Jan has carved out a unique niche as a fully open-source, offline-first AI assistant. The project's latest versions (through v0.8.4, July 2026) show a commitment to serious engineering: native web search tools, message branching with version history, AMD ROCm/HIP support on Linux, and a local API server compatible with OpenAI's spec. For developers, this means you can run a private ChatGPT-like experience with the ability to swap models freely and even build custom agents that automate workflows (file browsing, calendar management, Slack/Discord/WhatsApp messaging). The agent ecosystem, Jan Agent, is distributed separately and can be run on your own VM or container, giving enterprise teams a path to decentralized AI. Where Jan shines: absolute data control, zero subscription costs, and a thriving open-source community (44.2K+ GitHub stars). It's ideal for privacy-sensitive professionals (lawyers, doctors, researchers) and developers who want to prototype against an OpenAI-compatible local endpoint. The recent addition of multi-token prediction and inline MCP approval shows the team is focused on performance and security. Where Jan falls short: it's not a polished consumer app. Setup requires technical expertise—you'll need to manage model downloads, hardware specs, and sometimes compile or configure backends. The 'Memory' feature is still 'Coming Soon,' so long-term context is not yet available. Artifact previews are limited to HTML/SVG. Performance on low-end hardware (no GPU or under 8GB RAM) will be frustrating. Also, there's no centralized billing or enterprise support, so large organizations may find it lacking. In short, Jan is a powerful tool for tinkerers and privacy advocates, but it's not a drop-in replacement for ChatGPT for the average user. It's a legitimate alternative to Ollama or LM Studio, with more ambition and a larger feature set, but it requires more effort to set up and maintain.
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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.
Need to analyze confidential case documents without sending data to the cloud.
Outcome: Installs Jan on a laptop with no internet connection, downloads a local Mistral model, and summarizes case files with full data sovereignty.
Prototyping an app that needs an OpenAI-compatible API endpoint for chat completions.
Outcome: Uses Jan's local API server to serve a Qwen model, tests the app locally, and avoids cloud API costs during development.
Wants to automate web research and compile a report from multiple sources.
Outcome: Deploys Jan Agent on a container, connects it to local models, and sets up a workflow that uses the built-in web search tool to gather and summarize data.
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.
- Use message branching to explore alternative responses and roll back to previous versions.
Models Under the Hood
as of 2026-09-01
Limitations
- Jan is an open-source AI assistant that can run offline with local models, so performance depends on your own hardware (RAM/GPU).
- The Memory feature is listed as 'Coming Soon' and is not yet available.
- Artifact previews are currently limited to HTML and SVG formats.
- Setup may require technical expertise to install and manage models.
as of 2026-08-31
Verification history
We have re-verified Jan 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
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 completely free and open-source—no subscription, no per-seat fees, and no usage caps. This makes it ideal for individuals and teams who want to avoid monthly costs of cloud AI assistants like ChatGPT Plus ($20/mo) or Claude Pro. However, you pay with your own hardware and time: you'll need a decent GPU or at least 8GB of RAM, and you'll manage models and infrastructure yourself. For privacy-focused developers and researchers, the cost savings and data control are unmatched.
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.
For a developer familiar with command-line tools, you can install Jan Desktop and download a small model (e.g., 7B) within 15 minutes. For less technical users, expect 30-60 minutes to get a large model running and configure settings. Running agents or the CLI may take another hour to set up. The exact time depends on your hardware and internet speed.
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 Ollama: Export your models and use Jan's HuggingFace integration to pull the same or different local models. Jan acts as a full UI with agent capabilities on top of local inference.
- →From LM Studio: You can import your existing GGUF models into Jan or point it to your local model folder. The transition is straightforward if you're comfortable with file paths.
- ↗To Ollama: If you prefer a lighter CLI-only tool, you can export your models and scripts, though Jan's agent features won't carry over.
- ↗To ChatGPT/Claude: If you need managed cloud AI with low setup, you can export your chat history from Jan (if available) and manually replicate workflows, but you'll lose offline/local control.
Integrations
Resources & Guides
Tutorials & Learning
Tools that pair well with Jan
Common stack mates teams adopt alongside Jan, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Jan vs Temporal Ai
Choose Jan if you need fully offline, privacy-first AI with local agents and model control — it's free and runs on your hardware. Choose Temporal AI if you're orchestrating mission-critical, durable workflows that must survive failures, especially for AI agents in production. They solve different problems: Jan is an AI endpoint; Temporal is the plumbing behind reliable execution.
Jan vs Spider Cloud
Choose Jan if you value total privacy and offline AI with customizable local models; choose Spider Cloud if you need fast, real-time web data for AI agents or RAG pipelines. They solve different problems — Jan is your private AI workstation, Spider Cloud is your web data pipeline.
Jan vs Presto Voice
If you need a local, privacy-first AI assistant for tinkering, coding, or research, Jan is the versatile free choice. For QSR chains automating drive-thru orders with proven ROI, Presto Voice delivers specialized voice AI with up to 95% automation. They serve completely different needs—pick based on your domain.
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Frequently Asked Questions
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