RongxinAI

Why ZhiYuan

• 🆓 Free to start — built-in ZhiYuan free model, no third-party API key needed (network required; limits shown in app). Bring your own provider anytime.
• 💻 Runs local models — browse a model marketplace, pick a GGUF model & quantization, tune context / GPU / threads, powered by llama.cpp runtime.
• 🛠️ Actually does the work — reads & writes files, runs commands, operates a browser; creates PPT, Word, PDF, Excel outputs.
• 💬 Lives in your chat apps — WeChat, WeCom, DingTalk, Feishu/Lark, QQ, email.
• 🧩 Extensible — Experts, Skills and MCP; scheduled recurring tasks.
• 🔒 Local first — sessions, config and task metadata stored locally; approval modes for tool execution.

Get started

1. Download the installer from the official website or GitHub Releases. GitHub Releases currently ships a Windows installer; macOS and Linux packages are unconfirmed—check the download page.
2. Open the app and chat with the built-in free model — no API key required.
3. Pick a project folder, describe your task, then follow progress and approve actions as it works.

Developers iterating on the UI can start with bun run web:preview after installation (Node 24.x, Bun 1.4.0). See Developer quick start for setup and desktop development commands.

The free model requires a network connection; availability and usage limits are shown in the app. You can also run from source on other platforms.

Use local models

Open the local inference workspace, browse the model marketplace, and choose a GGUF model and quantization suited to your hardware. Install the model and start its service. Adjust context length, GPU allocation, threads, and other options, then use a running local model for agent tasks.

Local models reduce reliance on cloud inference. Web search, model downloads, remote MCP services, and messaging channels still require their respective network services.

Data and permissions

Sessions, configuration, and task metadata are stored locally. The desktop execution environment accesses local files. When you use a cloud model or remote tool, the content needed for that request is sent to the corresponding service.

Tool execution follows the selected permission mode. Operations requiring approval display a request; automatic authorization allows some operations to run directly. Progress and results remain visible in the workspace.

How it compares

Star History

Choose your workspace appearance

ZhiYuan ships one registered theme plugin, Zenith, with paired light and dark appearances. Zenith is a premium futuristic design system: subtle glassmorphism, crisp typography, efficient spacing, and semantic tokens that define backgrounds, control recipes, and interaction states as a single package.

In Settings → Appearance, pick the Zenith preview card (shown when only one plugin is bundled) and choose light, dark, or system mode. The preview follows the selected mode. Configurations that still reference removed theme IDs (Codex, Daming Fenghua, Changan Fengwu, Weiyang Jinshi, and earlier styles) resolve to Zenith automatically.

To add another bundled theme later, start with the theme authoring guide and design specification. Theme plugins supply presentation data; shared components retain interaction and state ownership.

Developer quick start

Install Git, Node.js 24.x, and Bun 1.4.0, as pinned in package.json. Native dependencies may require Python and a C/C++ toolchain when prebuilt binaries are unavailable. See the contributing guide for Windows build requirements.

git clone https://github.com/rongxinzy/RongxinAI.git ZhiYuanAgent
cd ZhiYuanAgent
bun install
bun run web:preview

Open the Vite URL in a browser to iterate on the renderer with hot reload, without starting Electron. To verify real desktop and IPC flows, run bun run electron:dev; it prepares the channel and memory runtimes, then starts Vite and Electron. Initial runtime preparation requires network access. To use local inference, download the inference runtime for your host separately:

bun run llamacpp:runtime:download

To develop the enterprise client from the same clone, run:

bun run electron:dev:enterprise

This builds enterprise/, loads this worktree's extension, and starts the same Vite/Electron development flow. Start AEP separately; the default endpoint is http://localhost:8080. Set ZHIYUAN_AEP_BASE_URL to use another endpoint and optionally ZHIYUAN_AGENT_CONTROL_BASE_URL for split deployments. The command generates development configuration under the ignored .work/enterprise-dev/ directory. To use an existing configuration file instead, set ZHIYUAN_ENTERPRISE_CONFIG_DEV_PATH. Packaged applications always use their bundled configuration and ignore this development override. Rerun the command after changing enterprise sources to rebuild the extension and its renderer.

Useful commands

Packaging

Every installer bundles the pinned Magpie model gateway. Build it before packaging; this needs the exact Go SDK pinned in resources/magpie/upstream.json (currently Go 1.26.8), plus Git and tar. The builder rejects any other Go version and never downloads a toolchain:

bun run magpie:build --target <platform-arch> --go /path/to/go   # e.g. win32-x64, darwin-arm64, linux-x64, or all
bun run magpie:verify --target <platform-arch>

Packaging fails with an explicit error when the Magpie binary for the target is missing or does not match its manifest. See resources/magpie/README.md.