# Technical Documentation: janhq/jan > ℹ️ **Provenance:** Multi-Tier Ingestion · [CodeWiki Reference](https://codewiki.google/github.com/janhq/jan) ### Overview (README.md) # Jan - Open-source ChatGPT replacement github jan banner

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Getting Started - Community - Changelog - Bug reports

Jan is bringing the best of open-source AI in an easy-to-use product. Download and run LLMs with **full control** and **privacy**. ## Installation

Get it from Microsoft Store Get it on Flathub

The easiest way to get started is by downloading one of the following versions for your respective operating system:
Platform Download
Windows jan.exe
macOS jan.dmg
Linux (deb) jan.deb
Linux (AppImage) jan.AppImage
Linux (Arm64) How-to
Download from [jan.ai](https://jan.ai/) or [GitHub Releases](https://github.com/janhq/jan/releases). ## Features - **Local AI Models**: Download and run LLMs (Llama, Gemma, Qwen, GPT-oss etc.) from HuggingFace - **Cloud Integration**: Connect to GPT models via OpenAI, Claude models via Anthropic, Mistral, Groq, MiniMax, and others - **Custom Assistants**: Create specialized AI assistants for your tasks - **OpenAI-Compatible API**: Local server at `localhost:1337` for other applications - **Model Context Protocol**: MCP integration for agentic capabilities - **Privacy First**: Everything runs locally when you want it to ## Build from Source For those who enjoy the scenic route: ### Prerequisites - Node.js ≥ 20.0.0 - Yarn ≥ 4.5.3 - Make ≥ 3.81 - Rust (for Tauri) - (macOS Apple Silicon only) MetalToolchain `xcodebuild -downloadComponent MetalToolchain` ### Run with Make ```bash git clone https://github.com/janhq/jan cd jan make dev ``` This handles everything: installs dependencies, builds core components, and launches the app. **Available make targets:** - `make dev` - Full development setup and launch - `make build` - Production build - `make test` - Run tests and linting - `make clean` - Delete everything and start fresh ### Manual Commands ```bash yarn install yarn build yarn dev ``` ### Building on Windows Run `make dev` from **Git Bash** (installed with Git for Windows) — make dispatches its recipes through `sh`, so a plain `cmd.exe` won't work. You do **not** need a "Native Tools Command Prompt for VS 2022". The bundled llama.cpp engine builds with Ninja + `clang-cl`, and `clang-cl` locates the MSVC toolchain and Windows SDK on its own. What has to be installed (and on `PATH` for `ninja`/`clang-cl`/`cmake`): - Visual Studio 2022 Build Tools (MSVC x64 workload + Windows SDK) - LLVM (provides `clang-cl`) - Ninja - CMake - CUDA Toolkit — only for `JAN_ENGINE_VARIANT=cuda12`/`cuda13` builds Engine variants are picked with `JAN_ENGINE_VARIANT` (tokens: `cpu`, `vulkan`, `metal`, `cuda12`, `cuda13`, `hip`/`rocm`, joined by `-`), e.g.: ```bash make dev JAN_ENGINE_VARIANT=cuda13 ``` **"nvcc fatal : Could not open output file ...fattn-...cu.obj.d"** during `tauri-plugin-llamacpp(build)` means the build path crossed Windows' 260-character `MAX_PATH` limit — nvcc does not honor the long-path opt-in. The build script now detects this and automatically relocates the llama.cpp build tree to a short directory under `%LOCALAPPDATA%\jan-engine`. If you hit path-length errors anyway, set `JAN_ENGINE_BUILD_DIR` to a short path (e.g. `C:\jb`) or move the checkout closer to the drive root. ## System Requirements **Minimum specs for a decent experience:** - **macOS**: 13.6+ (8GB RAM for 3B models, 16GB for 7B, 32GB for 13B) - **Windows**: 10+ with GPU support for NVIDIA/AMD/Intel Arc - **Linux**: Most distributions work, GPU acceleration available For detailed compatibility, check our [installation guides](https://jan.ai/docs/desktop/mac). ## Troubleshooting If things go sideways: 1. Check our [troubleshooting docs](https://jan.ai/docs/desktop/troubleshooting) 2. Copy your error logs and system specs 3. Ask for help in our [Discord](https://discord.gg/FTk2MvZwJH) `#🆘|jan-help` channel ## Contributing Contributions welcome. See [CONTRIBUTING.md](CONTRIBUTING.md) for the full spiel. > **Note:** Please [sign your commits](CONTRIBUTING.md#signed-commits) so we can verify your contributions. ## Links - [Documentation](https://jan.ai/docs) - The manual you should read - [API Reference](https://jan.ai/api-reference) - For the technically inclined - [Changelog](https://jan.ai/changelog) - What we broke and fixed - [Discord](https://discord.gg/FTk2MvZwJH) - Where the community lives ## Contact - **Bugs**: [GitHub Issues](https://github.com/janhq/jan/issues) - **Business**: hello@jan.ai - **Jobs**: hr@jan.ai - **General Discussion**: [Discord](https://discord.gg/FTk2MvZwJH) ## License Apache 2.0 - Because sharing is caring. ## Acknowledgements Built on the shoulders of giants: - [Llama.cpp](https://github.com/ggerganov/llama.cpp) - [Tauri](https://tauri.app/) - [Scalar](https://github.com/scalar/scalar) --- ### README (`docs/README.md`) # Website & Docs This website is built using [Nextra](https://nextra.site/), a modern static website generator. ### Information Architecture We try to **keep routes consistent** to maintain SEO. - **`/docs/`**: Product documentation (Jan Desktop, Jan Agent). - **`/handbook/`**: Handbook pages. - **`/research/`**: Research content. - **`/changelog/`**: A list of changes made to the Jan application with each release. - **`/blog/`**: The blog index; individual posts live under `/post/`. - **`/download/`**: Download page. - **`/support/`**: Support page. - **`/privacy/`**: Privacy policy. - **`/tokamak/`**: Tokamak landing page. ## How to Contribute Refer to the [Contributing Guide](https://github.com/janhq/jan/blob/main/CONTRIBUTING.md) for more comprehensive information on how to contribute to the Jan project. ### Pre-requisites and Installation - [Node.js](https://nodejs.org/en/) (version 20.0.0 or higher) - [yarn](https://yarnpkg.com/) (version 4.x; the repo pins 4.5.3 via corepack) #### Installation ```bash cd jan/docs yarn install yarn dev ``` This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server. #### Build ```bash yarn build ``` This command generates static content into the `out` directory (`next.config.mjs` sets `output: 'export'`) and can be served using any static contents hosting service. ### Deployment There is no `yarn deploy` script; the `jan-docs.yml` workflow builds the site and publishes `out` to Cloudflare Pages. ### Preview URL, Pre-release and Publishing Documentation - When a pull request is created, the preview URL will be automatically commented on the pull request. - The documentation will then be published to [https://jan.ai/](https://jan.ai/) when the pull request is merged to `main`. --- ### Src Pages Index (`docs/src/pages/index.mdx`) --- title: "Jan - Open-Source ChatGPT Replacement" description: "Jan is an open-source alternative to ChatGPT. Run open-source AI models locally or connect to cloud models like GPT, Claude and others." keywords: [ Jan, Customizable Intelligence, LLM, local AI, privacy focus, free and open source, private and offline, conversational AI, no-subscription fee, large language models, architecture, ] --- import Home from "@/components/Home" export const getStaticProps = async() => { const resReleaseLatest = await fetch('https://api.github.com/repos/janhq/jan/releases/latest') const resRelease = await fetch('https://api.github.com/repos/janhq/jan/releases?per_page=500') const resRepo = await fetch('https://api.github.com/repos/janhq/jan') const repo = await resRepo.json() const latestRelease = await resReleaseLatest.json() const release = await resRelease.json() return { props: { ssg: { stars: repo.stargazers_count || 44000, // placeholder before the client-side useGitHubStars fetch resolves lastRelease: latestRelease, release: release, lastVersion: latestRelease.tag_name || 'v.0.0.1' }, }, } } --- ### Src Pages Docs Index (`docs/src/pages/docs/index.mdx`) --- title: Jan Docs description: References for Jan Desktop and Jan Agent. --- import DocsHome from "@/components/DocsHome" --- ### Src Pages Handbook Index (`docs/src/pages/handbook/index.mdx`) --- title: "Jan Team Handbook" description: "Jan is a general purpose AI agent that works on its own to get things done." --- # Jan Handbook > Jan's Handbook is inspired by [Posthog](https://posthog.com/handbook) and [Gitlab](https://handbook.gitlab.com/). Thanks for showing us the way. ## Welcome Welcome to [Jan](https://jan.ai)! This handbook explains what Jan is, what we stand for, and how you can be part of it. Jan is built in public by a team that believes AI should get things done, not just talk about getting things done. We're open source, we move fast, and we build alongside the people using what we make. This handbook is a living thing. It grows as Jan grows - new ideas, new tools, new ways to make AI that actually works for you. ## Overview Jan is a general purpose AI agent that works on its own to get things done. Give Jan something to work on, maybe research for a brief, a report you have been putting off, or an analysis you need to pull together, and it will run for as long as needed. It could be five hours or ten. Jan figures it out, does the work, and finishes the job. Think of it like this: you hand off the task, Jan handles it, and you get on with your day. Jan is open source and built by a vibrant global community. Anyone can use it, contribute to it, or host it themselves. Jan gets it done. That is the whole idea. --- ### Src Pages Research Index (`docs/src/pages/research/index.mdx`) --- title: Research description: Model reports from the Jan family — Jan-v3.5-4B, Jan-Code-4B, Jan-v3-4B, Jan-v2-VL, Jan-v1, Jan Nano, and Lucy. --- import Research from "@/components/Research" --- ### Src Pages Docs Agent Index (`docs/src/pages/docs/agent/index.mdx`) --- title: Overview description: Jan Agent is a terminal AI agent that reads, writes, and runs code in your project - powered by the models you already configured. keywords: [ Jan, Jan AI, Jan Agent, agents, autonomous agent, terminal agent, coding agent, CLI agent, local AI agent, MCP, ] --- import { Callout, Tabs } from 'nextra/components' import { Link } from 'nextra-theme-docs' import { DocCard, DocCards } from '@/components/DocCard' import { Rocket, Terminal, Wrench, Settings, Users, SquareTerminal } from 'lucide-react' # Overview Jan Agent is an AI agent that lives in your terminal. You give it a task in plain language, and it reads files, edits them, runs commands, and searches the web until the work is done - asking for your approval before anything touches your machine. It uses the providers you have already configured in Jan Desktop, so if you're a Jan user there is nothing new to sign up for. ```bash jan ``` That single command opens the interactive console in whatever directory you're in. ## Jan Agent and Jan Desktop They're two front ends onto the same stack, built for different jobs. Jan Desktop is where you run models on your own hardware and work in a GUI. Jan Agent is where you point a model at a folder and let it work. | | Jan Desktop | Jan Agent | | --- | --- | --- | | Interface | Desktop app | Terminal, interactive or headless | | Where models run | Locally via llama.cpp or MLX, or a cloud provider | Cloud providers, or any OpenAI-compatible endpoint | | Works on | Conversations, projects, and files you attach | A project folder on disk | | Files and shell | Through MCP servers you add | Built in - read, write, edit, and run commands, each gated by approval | | MCP servers | Yes | Yes, the same ones | | Skills and memory | Yes | Yes, the same ones | Neither replaces the other. A common setup is Jan Desktop serving a local model, with Jan Agent pointed at it - see [running against local models](/docs/agent/providers#your-own-hardware). Provider settings, MCP servers, [skills, and memory](/docs/agent/skills) are all shared, so anything you set up in one is already there in the other. ## Install Jan Agent is a preview. The installer on `dev` pulls from the `agent-nightly` channel - expect nightly-quality builds. ```bash curl -fsSL https://delta.jan.ai/jan-cli/install-jan-agent.sh | bash ``` ```powershell irm https://delta.jan.ai/jan-cli/install-jan-agent.ps1 | iex ``` Installs to `%USERPROFILE%\.local\bin`. To also add it to your user `PATH`: ```powershell & ([scriptblock]::Create((irm https://delta.jan.ai/jan-cli/install-jan-agent.ps1))) -AddToPath ``` Then check it landed: ```bash jan --version ``` The [Quickstart](/docs/agent/quickstart) covers connecting a provider and finishing a first task. Looking for the desktop app instead? Download Jan Desktop. ## What it can do - **Read and change a project** - open files, make targeted edits, create new ones. - **Run commands** - build, test, grep, and read the output to decide what to do next. - **Search the web** - look things up mid-task when the answer isn't in your project. - **Use your MCP servers** - every connector configured in Jan Desktop is available here too. - **Follow your project's skills** - reusable procedures it loads when a task calls for them. - **Remember across sessions** - durable facts about the project, kept as files you can review. - **Delegate** - split large jobs across [subagents](/docs/agent/subagents) that work in parallel. - **Keep working toward a goal** - `/goal` keeps it going until a condition you set is true. Jan Agent ships no inference engine of its own, so a model always runs somewhere else. That can be a cloud provider, or your own machine - point it at any OpenAI-compatible endpoint, such as Jan Desktop's [local API server](/docs/desktop/api-server), with `jan config set --base-url`. ## Get started }>Install Jan Agent, connect a provider, and finish your first task. }>Slash commands, keyboard shortcuts, and how a session works. }>What the agent can do, and how approval keeps you in control. }>Providers, `agent.toml`, and per-project instructions. }>Delegate parts of a task to agents that run in parallel. }>Every flag and subcommand, including headless runs for scripts and CI. ## Staying in control The agent edits files and runs commands without stopping to ask. File paths are checked against the project root, and anything in `[tools] deny` is refused outright - but shell commands are not sandboxed by default, so one runs with your own access. Three ways to change that: - **Plan mode** (`jan --plan`) makes the session read-only. The agent investigates and proposes, but cannot change anything. - **Safe mode** (`jan --safe`) asks before every write and shell command, instead of auto-approving them. - **Sandbox** (`jan --sandbox`) confines shell commands to the project with an OS sandbox. Off by default on the CLI, always on in the desktop app. See [Tools & permissions](/docs/agent/permissions) for the full picture. --- ### Src Pages Docs Agent Quickstart (`docs/src/pages/docs/agent/quickstart.mdx`) --- title: QuickStart description: Install Jan Agent, connect a provider, and finish your first task in the terminal. keywords: [ Jan, Jan Agent, agents, autonomous agent, local AI agent, quickstart, install, terminal, CLI, ] --- import { Callout, Steps, Tabs } from 'nextra/components' import { DocCard, DocCards } from '@/components/DocCard' import { SquareTerminal, Wrench, Settings, Users } from 'lucide-react' # QuickStart Get Jan Agent running and finish a real task in a few minutes. You need a terminal and an API key for one model provider. ### Install Nightly Jan Agent is a preview. The installer on `dev` pulls from the `agent-nightly` channel - expect nightly-quality builds. ```bash curl -fsSL https://delta.jan.ai/jan-cli/install-jan-agent.sh | bash ``` Installs to `~/.local/bin` by default. Override it with `JAN_INSTALL_DIR=/usr/local/bin`, and make sure the directory is on your `PATH`. ```powershell irm https://delta.jan.ai/jan-cli/install-jan-agent.ps1 | iex ``` Installs to `%USERPROFILE%\.local\bin`, which isn't on your `PATH` by default. To install and add it to your user `PATH` in one go, then open a new terminal: ```powershell & ([scriptblock]::Create((irm https://delta.jan.ai/jan-cli/install-jan-agent.ps1))) -AddToPath ``` Check it worked: ```bash jan --version ``` Already have the Jan repo checked out? `scripts/install-jan-agent.sh --source` builds from your checkout instead of downloading. Source builds don't self-update, because the update channel is stamped in by CI. ### Connect a provider Jan Agent has no local inference engine - it calls a remote provider. Point it at one: ```bash jan config set --provider anthropic --api-key sk-ant-... ``` Or sign in to Tokamak, which needs no key of your own: ```bash jan login ``` Confirm what's configured (keys are redacted): ```bash jan config list ``` Already using Jan Desktop? Jan Agent picks up the providers you configured there, so you can skip this step entirely. ### Open the console Change into the project you want to work on, then: ```bash jan ``` You get an interactive console: type a message, press Enter. The agent works in the directory you launched it from. ### Give it a task Start with something small and verifiable: ``` Read the README and tell me how to run the tests ``` It will read files and answer. Nothing has changed on disk yet - reading is free. Now ask for a change: ``` Add a --version flag to the CLI and update the README to document it ``` This time the agent asks before it writes: ``` write src/cli.rs y yes, once a yes, and don't ask again this session n no ``` Press y to allow it once, a to stop being asked for that kind of call, or n to refuse. You stay in the loop for every write and every shell command. ### Watch it work While the agent runs you'll see each tool call as it happens, and a receipt when the turn ends: ``` Worked for 12s • 14:02 • ↑ 43K ↓ 1.1K • 45.6/s ``` That's how long the turn took, when it finished, context sent, tokens produced, and throughput. Press Esc at any point to cancel the current turn. Press Ctrl+D to quit. ## Pick up where you left off Sessions are saved per project. To continue the most recent one: ```bash jan -c ``` Or choose from a list inside the console with `/resume`. ## Try these next Three things worth doing on your first day: **Explore before changing.** Start read-only so the agent can investigate without touching anything: ```bash jan --plan ``` It will study the project and propose a plan. Type `/plan exit` when you're ready to let it act. **Set a finish line.** Instead of babysitting turn by turn, tell it what "done" means: ``` /goal all tests in tests/auth pass ``` The agent keeps working and re-checking until that holds. **Seed a task from the shell.** Handy in a script or an alias: ```bash jan --task "fix the failing test in tests/auth" ``` ## Where to go next }>Every slash command and keyboard shortcut. }>What the agent can reach, and how to widen or narrow it. }>Per-project instructions, models, and budgets. }>Split a big job across agents running in parallel. ## Troubleshooting **`jan: command not found`** - the install directory isn't on your `PATH`. The installer prints a note when it detects this. Add `~/.local/bin` to your shell profile. On Windows, re-run the installer with `-AddToPath` (see [Install Nightly](#install-nightly)) and open a new terminal. If the installer warns that another `jan` comes first on your `PATH`, remove the entry it names. **"no providers configured"** - run `jan config set --provider --api-key `, or `jan login` for Tokamak. `jan config path` prints the file it reads. **The agent won't edit anything** - you're in plan mode. Type `/plan exit`. **Model errors or an unexpected model** - `/model` inside the console lists what's available and switches on the spot. `jan config list` shows which providers are wired up. --- ### Src Pages Docs Desktop Index (`docs/src/pages/docs/desktop/index.mdx`) --- title: Overview description: Jan is a local, privacy-first AI — a Claude-class alternative you own and control keywords: [ Jan, Jan AI, Claude alternative, local AI, private AI, self-hosted AI, foundation models, AI ownership, open superintelligence, AI ecosystem, llama.cpp, Model Context Protocol, MCP, GGUF models, large language model, LLM, ] --- import { Callout } from 'nextra/components' import { DocCard, DocCards } from '@/components/DocCard' import { Rocket, Package, Bot, Plug, Monitor, Globe, Server, Brain, Terminal, Layers, MessageCircle, Hammer } from 'lucide-react' # Overview **Jan 0.8.4 is now live!** Native web search and fetch tools, per-model chat-template kwargs for llama.cpp, a token counter for remote and MLX providers, settings and credentials moved into a backend store, and a batch of chat, MCP, provider, and streaming fixes. [See the full changelog.](/changelog/2026-07-21-jan-v0.8.4) Jan is an open-source replacement for Claude and ChatGPT. It comes with built-in foundation models and an application ecosystem that runs entirely on your hardware, keeping your data private and giving you full ownership over your AI. ## Get Started }>Install Jan on macOS, Windows, or Linux and chat with AI in minutes — no account needed. }>Jan ships with foundation models built for reasoning, agents, and deep research — ready to run on your computer. }>Give Jan a task and it works on it autonomously — files, commands, and web research. }>Autonomous AI agents that run on your hardware — read files, manage calendars, and take actions via WhatsApp, Discord, or Slack. }>Connect Jan with Claude Code, OpenClaw, MCP servers, and more. ## Ecosystem }>macOS, Windows, and Linux apps with full offline support. }>Jan in the browser — a direct alternative to chatgpt.com. }>Run a local OpenAI-compatible API server on your machine. }>Optimized for reasoning, agents, and deep research — built to match frontier AI capabilities and small enough to run on your personal computer. }>Serve local models and launch autonomous agents from your terminal using Jan's local AI. }>The infrastructure layer for building AI products on top of Jan. (Coming Soon) ## Community }>Join the Jan community — get help, share your setup, and talk local AI. }>Ask questions, share setups, and discuss local AI on r/askjan. ## Principles - [Open source](https://www.redhat.com/en/blog/open-source-culture-9-core-principles-and-values): [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) licensed, built in public. - No [vendor lock-in](https://en.wikipedia.org/wiki/Vendor_lock-in): Switch freely between local and frontier models. - [Right to Repair](https://en.wikipedia.org/wiki/Right_to_repair): Inspect, audit, and modify your AI stack. ## Acknowledgements Jan exists because we've borrowed, learned, and built on the work of others. - [llama.cpp](https://github.com/ggerganov/llama.cpp) and [GGML](https://github.com/ggerganov/ggml) for efficient inference - [r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/) for ideas, feedback, and debate - [Model Context Protocol](https://modelcontextprotocol.io) for MCP integrations - The open-source community for contributions, bug reports, and improvements --- ### Src Pages Docs Desktop Quickstart (`docs/src/pages/docs/desktop/quickstart.mdx`) --- title: QuickStart description: Get started with Jan and start chatting with AI in minutes. keywords: [ Jan, local AI, LLM, chat, threads, models, download, installation, conversations, ] --- import { Callout, Steps } from 'nextra/components' import { Link } from 'nextra-theme-docs' import { DocCard, DocCards } from '@/components/DocCard' import { FolderOpen, Bot, Cpu, Plug, Brain, Hammer } from 'lucide-react' # QuickStart Get up and running with Jan in minutes. This guide will help you install Jan, download a model, and start chatting immediately. ### Install Jan 1. Download Jan 2. Install the app ([Mac](/docs/desktop/install/mac), [Windows](/docs/desktop/install/windows), [Linux](/docs/desktop/install/linux)) 3. Launch Jan ### Getting Ready Jan automatically downloads its default foundation model on first launch. Once the download completes, you're ready to chat — no setup required. ### Start Chatting Type your message and start chatting. Jan is ready to go. Try asking: - "Explain quantum computing in simple terms" - "Help me write a Python function to sort a list" - "What are the pros and cons of electric vehicles?" Jan has built-in web search and fetch tools, so you can ask about the latest news and current events without any extra setup. If you'd rather use a different provider, you can also configure the [Exa](https://exa.ai) MCP server under **Settings > MCP Servers** with your own API key. ### Download More Models The **Hub** is where you browse and download local models. Each model shows whether it fits your hardware, so you can pick one your machine can run comfortably. 1. Open the **Hub** from the left sidebar 2. Browse or search for a model, then click **Download** 3. Once downloaded, select it from the model selector in any chat Want help choosing? See [Manage Models](/docs/desktop/manage-models) and the [local engine guide](/docs/desktop/local-engine/llama-cpp). ## Local or Cloud Jan runs models two ways, and you can mix both in the same app: - **Local models** run entirely on your machine — private, offline, no API key. This is the default. - **Cloud models** run on a provider's servers (OpenAI, Anthropic, Google, and more). They need an API key but let you use the largest frontier models without local hardware. ### Connect a Cloud Provider 1. Navigate to **Settings** > **Model Providers** 2. Select a provider (e.g. **OpenAI** or **Anthropic**) and paste your API key 3. Select one of its models from the model selector and start chatting Running your own server or using a provider Jan doesn't list? Connect any OpenAI- or Anthropic-compatible endpoint — see [Custom Endpoints](/docs/desktop/remote-models/custom-endpoint). See all [Cloud Providers](/docs/desktop/remote-models/openai) for per-provider setup. ## App Features Jan is more than a chat interface. Organize your work with Projects, build custom Assistants, run autonomous Agents, and connect external tools via MCP. }>Hand Jan a task it works through on its own — in a sandbox or directly on your project. }>Organize conversations with shared instructions and files. }>Set up a personal AI assistant tailored to how you work. }>Autonomous AI that reads files, manages calendars, and takes actions. }>Extend your AI with web search, code execution, databases, and more. ## Models Beyond the default model, Jan comes with a range of foundation models for different tasks — from image understanding and reasoning to coding. }>Optimized for code generation and reasoning tasks. }>General purpose instruction-following model. }>Vision-language model with strong reasoning capability for image understanding. --- ### Public Robots (`docs/public/robots.txt`) # * User-agent: * Allow: / # Host Host: https://jan.ai/ # Sitemaps Sitemap: https://jan.ai/sitemap.xml --- ### Src Pages App (`docs/src/pages/_app.mdx`) import '@/styles/main.scss' import '@code-hike/mdx/styles.css' import { Fragment } from "react" import Script from "next/script" export default function App({ Component, pageProps }) { return (