npm install -g tingly-box@latest   # --registry=<mirror> works here too
tb start   # tb = tingly-box; runs in the background (--no-daemon for foreground)
tb open    # open the web UI
update: reinstall, then restart to apply
npm install -g tingly-box@latest
tb restart

if any trouble, please check tingly-box output, or call for an issue to help.

# One command: fetch, restart the server in the background, migrate and open the web UI
(a golang binary release; npx wraps the cli for convenience)
npx tingly-box@latest
or -y for convenience
npx -y tingly-box@latest
the binary for your platform comes from npm too (no GitHub download),
so an npm mirror is all you need for CN (one of below)
npx --registry=https://registry.npmmirror.com -y tingly-box@latest
npx --registry=https://mirrors.huaweicloud.com/repository/npm/ -y tingly-box@latest
npx --registry=http://mirrors.tencent.com/npm/ -y tingly-box@latest

# MacOS & Linux
Install Node.js LTS via nvm
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/master/install.sh | bash
Restart terminal or load nvm
source ~/.nvm/nvm.sh
Install Node.js LTS
nvm install --lts
Verify installation
node -v
npx -v

# Windows
Powershell with Winget
winget install OpenJS.NodeJS.LTS
Restart terminal, then verify installation
node -v
npx -v
Or download and install Node.js LTS manually:
https://nodejs.org/en/download

mkdir tingly-data
docker run -d \
  --name tingly-box \
  -p 12580:12580 \
  -v pwd/tingly-data:/home/tingly/.tingly-box \
  ghcr.io/tingly-dev/tingly-box

• Claude Code (support 1-click config)
• OpenCode (support 1-click config)
• Xcode (require manual config)
• ……

We've provided detailed config guide in application

!Agent Integration Demo

Give your whole team one endpoint backed by centrally managed model rules. Each member gets a dedicated Team Key, and usage is tracked per user.

1. Open Web UI like http://localhost:12580
2. Navigate to Team, configure the model rules, then hand out keys via Team Keys
3. Track per-user consumption under Dashboard → Team usage

!Team Usage Demo

Route image generation and editing through the same gateway. Point any OpenAI-compatible image client at http://localhost:12580/tingly/imagegen and pick the upstream image models with rules, like any other scenario. Try prompts right away in Image → Playground.

!Image Playground Demo

!Image API Demo

DeepSeek is optimized for mainstream agent workflows, offering broad compatibility across protocol adapters, agent clients, extended context, vision, web search, and cache optimization.

| Module | Status | What It Solves | | -------------------------------- | ----------: | --------------------------------------------------------------------- | | Model List | ✅ Supported | Keeps the official model list up to date in real time | | Protocol Adaptation | ✅ Supported | Supports official Anthropic/OpenAI APIs with bidirectional conversion | | Reasoning Capability | ✅ Supported | Provides compatibility with Thinking workflows | | Cache Hit Optimization | ✅ Supported | Improves cache hit rates for DeepSeek requests | | Vision Proxy | ✅ Supported | Enables DeepSeek to understand and process images | | Web Search | ✅ Supported | Calls official Web tools through the Anthropic endpoint | | 1M Context Window | ✅ Supported | Enables one-click setup for 1M context | | Codex Adaptation | ✅ Supported | Ensures compatibility with mainstream agent workflows | | Claude Code / Desktop Adaptation | ✅ Supported | Ensures compatibility with mainstream agent workflows |

Supports one-click configuration where available. For applications that require manual setup, detailed in-app configuration guides are provided.

Any compatible application is ready to use.

Detailed configuration guides are available inside each application.

Tingly Box now supports remote control through popular IM platforms. Interact with your AI agents remotely without direct server access.

Supported Platforms

• ✅ Telegram
• ✅ DingTalk
• ✅ Feishu
• ✅ Lark
• ✅ Weixin
• ✅ WeCom

1. Open Web UI like http://localhost:12580
2. Turn on Projects under Power-ups in the gear menu at the bottom of the left rail, then open IM in the rail
3. Configure your preferred IM platform bot
4. Start interacting with your agents remotely

• Execute tasks and queries from your phone or any device
• Team collaboration with shared agent access
• Monitor and control agents while away from your workstation

from openai import OpenAI
client = OpenAI(
    api_key="your-tingly-model-token",
    base_url="http://localhost:12580/tingly/openai/v1"
)
response = client.chat.completions.create(
    model="tingly-gpt",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response)

from anthropic import Anthropic
client = Anthropic(
    api_key="your-tingly-model-token",
    base_url="http://localhost:12580/tingly/anthropic"
)
response = client.messages.create(
    model="tingly",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)
print(response)

Tingly Box proxies requests transparently for SDKs and CLI tools.

You can also add OAuth providers (like Claude Code) and use your existing quota in any OpenAI-compatible tool:

# 1. Add Claude Code via OAuth in Web UI (http://localhost:12580)
2. Configure your tool with Tingly Box endpoint

Requests route through your OAuth-authorized provider, using your existing Claude Code quota instead of requiring a separate API key.

This works with any tool that supports OpenAI-compatible endpoints: Cherry Studio, VS Code extensions, or custom AI agents.

!OAuth Provider Demo

Already running your own inference server? Tingly Box connects to it like any other provider and exposes it through the same unified gateway.

Supported out of the box

• vLLM
• SGLang
• Ollama
• LM Studio
• LocalAI
• Jan

Quick Setup

1. Open Web UI like http://localhost:12580
2. Click Connect AI → pick your engine from the Self-hosted section (default URL and key are pre-filled)
3. Test Connection, then save — it's now usable like any other provider

Launch the web management interface:

npx tingly-box@latest

Then open http://localhost:12580 in your browser.

!Dashboard

Prerequisites

| Tool | Version | Install | | ------- | ------- | --------------------------------------------------------------------------------------------- | | Go | 1.26+ | https://go.dev/doc/install | | Node.js | 20+ | https://nodejs.org/ | | pnpm | latest | npm install -g pnpm | | task | latest | go install github.com/go-task/task/v3/cmd/task@latest or https://taskfile.dev/installation/ |

Tip: you can also copy individual shell commands out of Taskfile.yml and run them directly if you prefer not to install task.

1. Clone and init submodules

git clone https://github.com/tingly-dev/tingly-box.git
cd tingly-box
git submodule update --init --recursive

2. Frontend development

The frontend is a React + Vite app located in frontend/. You can work on it independently of the Go backend.

Login token: the frontend requires an auth token to log in. When the backend starts it prints the full login URL (e.g. Web UI: http://localhost:12580/login/). If you need to retrieve it later, run:

> tingly-box token view auth --reveal

Mock mode – no running backend required, uses built-in fixture data:

task web:mock
or directly:
cd frontend && pnpm install && pnpm dev:mock

Open http://localhost:9245 in your browser. Use the token obtained above to log in.

Dev mode – proxies API calls to a local backend (start the backend first, see step 3):

task web
or directly:
cd frontend && pnpm install && pnpm dev

3. Backend development

Run the Go server (hot-reload via go run):

task start
or directly:
go run ./cli/tingly-box --verbose start --debug --port 12580 --browser=false

Open http://localhost:12580 in your browser (serves the last built frontend bundle).

4. Full build (frontend + backend binary)

Builds the frontend, embeds it into the binary, then compiles Go:

task build

The output binary is written to ./build/tingly-box.

5. GUI binary (Wails) (optional)

The GUI build is not yet publicly released. Skip this step unless you are specifically working on the desktop app.

task wails:build

Other useful commands

task swagger        # Regenerate openapi.json from Go source
task codegen        # Regenerate frontend API client from openapi.json
task go:test        # Run Go unit tests