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Jan: open-source local AI desktop app, README overview

Public record src_01M6VAE75CPQF3C84632CHKEGG, revision 1. Source and rights labels remain attached. Linked media has not been viewed by opening this page.

# Jan: open-source local AI desktop app, README overview

- id: src_01M6VAE75CPQF3C84632CHKEGG
- type: source · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T09 · tags: none
- updated: 2026-11-07T15:36:07.564Z
- url: https://veyrul.com/sources/src_01M6VAE75CPQF3C84632CHKEGG

The README presents Jan as an open-source desktop product for running LLMs locally with control and privacy, with optional cloud models.

Source: https://github.com/janhq/jan

What it supplies: The README presents Jan as an open-source desktop product for running LLMs locally with control and privacy, with optional cloud models.

Useful for: - Run Llama, Gemma or Qwen models from Hugging Face locally.
- Expose an OpenAI-compatible local API server for other apps.
- Create custom assistants and use MCP integrations.

First action: - Download the installer for your platform, or use the Microsoft Store or Flathub.
- Check the minimum specs for the model size you want.
- Add a local model or connect a cloud provider with your own key.

Prerequisites and limits: The README lists macOS 13.6+ with RAM guidance by model size (8GB for 3B, 16GB for 7B, 32GB for 13B), Windows 10+ with GPU support, and most Linux distributions.

- Larger models need more RAM, per the README's guidance.
- Build and troubleshooting details are in sections not summarised here.

Source-documented: Apache 2.0. Cloud providers and downloaded models have their own terms and costs.

Written from the README text only. Statements are as the README gives them; nothing was installed or run.
Based on opening, install, features, requirements and licence README text only; other sections were not read in full. Nothing was installed or run.

Source inspected: https://raw.githubusercontent.com/janhq/jan/879e6ae9cca4a38673fdb4093349a327e17ccbb1/README.md ; 7190 bytes ; sha256 297a156f1747488b907a98fb9182027ba3c535386b6827195528925abba80998 ; 5 exact pinned excerpts in the draft pin map

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  "revision": {
    "ref": "commit 879e6ae9cca4a38673fdb4093349a327e17ccbb1",
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    "source_url": "https://raw.githubusercontent.com/janhq/jan/879e6ae9cca4a38673fdb4093349a327e17ccbb1/README.md"
  },
  "checked_at": "2026-11-07T11:13:45.544Z",
  "parent_url": null,
  "usefulness": "- Run Llama, Gemma or Qwen models from Hugging Face locally.\n- Expose an OpenAI-compatible local API server for other apps.\n- Create custom assistants and use MCP integrations.",
  "source_type": "repository",
  "availability": "available",
  "dependencies": [],
  "source_owner": "Jan (declared repository namespace)",
  "next_check_at": null,
  "successor_url": null,
  "hardware_notes": null,
  "known_limitations": [
    "The README lists macOS 13.6+ with RAM guidance by model size (8GB for 3B, 16GB for 7B, 32GB for 13B), Windows 10+ with GPU support, and most Linux distributions.\n\n- Larger models need more RAM, per the README's guidance.\n- Build and troubleshooting details are in sections not summarised here.\n\nSource-documented: Apache 2.0. Cloud providers and downloaded models have their own terms and costs.\n\nWritten from the README text only. Statements are as the README gives them; nothing was installed or run.\nBased on opening, install, features, requirements and licence README text only; other sections were not read in full. Nothing was installed or run.",
    "Rendered README text of the exact archived capture at the pinned commit, read in full by the author; no media viewed or heard; nothing executed or installed.",
    "Source-documented steps; execution not performed. Internal affiliated review is not independent validation."
  ],
  "upstream_updated_at": null
}
```

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=permitted training=unknown · Only the team-authored original attributed summary and links are covered. Upstream papers, tutorials, code, models, weights and media retain their separate terms; no source-text/media redistribution right is implied.

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