# 0a. 🐧 🍏 1-Line Quick Install (Linux / macOS / Alpine)
$ curl -fsSL https://saur.run/install.sh | bash

# 0b. 🪟 1-Line Quick Install (Windows PowerShell)
PS> irm https://saur.run/install.ps1 | iex

# 1. ⚡ 1-Line Auto-Registration (via GitLab Access Token glpat-...)
$ saur --token="glpat-YOUR_TOKEN" --project="my-org/my-project"

# 2. ⚡ Direct Runner Token (glrt-...)
$ saur --token="glrt-YOUR_RUNNER_TOKEN"

# 3. 🧙 Interactive 2-Question Setup Wizard
$ saur init

# 4. 🤖 Interactive Runner + AI Studio (:8080 & Telegram)
$ saur --token="glrt-YOUR_TOKEN" --studio

# 🐍 Python (pip install openai)
from openai import OpenAI

client = OpenAI(base_url="https://saur.run/v1", api_key="saur_local")
stream = client.chat.completions.create(
    model="google-antigravity",
    messages=[{"role": "user", "content": "Привет! Как работает Scale-to-Zero?"}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

# 🦕 Node.js / TypeScript (npm i openai)
import OpenAI from 'openai';

const client = new OpenAI({ baseURL: 'https://saur.run/v1', apiKey: 'saur_local' });
const res = await client.chat.completions.create({
  model: 'google-antigravity',
  messages: [{ role: 'user', content: 'Привет!' }],
});
console.log(res.choices[0].message.content);

<script src="https://saur.run/widget.js" data-agent-id="consultant" data-title="ИИ-Консультант"></script>

# .gitlab-ci.yml
# 💡 Best Practice: Prompts configured in Settings > CI/CD > Variables
variables:
  AI_AUTO_REPAIR_PROMPT: "You are an autonomous DevOps SRE engineer. Analyze the test failure log, inspect the repository diff, locate the root cause bug, and output a clean unified git patch to fix it."

ai_auto_repair:
  stage: test
  when: on_failure
  tags: [saur.run]
  script:
    - |
      curl -s -X POST https://saur.run/v1/chat/completions \
        -H "Content-Type: application/json" \
        -d "{
          \"model\": \"google-antigravity\",
          \"messages\": [
            {\"role\": \"system\", \"content\": \"$AI_AUTO_REPAIR_PROMPT\"},
            {\"role\": \"user\", \"content\": \"CI Job '\''$CI_JOB_NAME'\'' failed on commit '\''$CI_COMMIT_SHA\''. Analyze build log and provide root cause analysis and code fix.\"}
          ]
        }"

# 1. Start local LLM (Ollama / DeepSeek R1 / Llama 3)
$ ollama run deepseek-r1:8b

# 2. Point Saur Agent Gateway to your local engine
$ saur --token="glrt-YOUR_TOKEN" --studio

💬 1. ИИ-Консультант Чат-Виджет (widget.js)

Легкий встраиваемый чат-виджет (<10 КБ) для консультирования пользователей по базе знаний. Живой стенд: https://saur.run/widget-demo ↗.

<!-- 1-Line Embed on any website -->
<script src="https://saur.run/widget.js" data-agent-id="consultant" data-title="ИИ-Консультант Saur" data-color="#10b981"></script>

🛡️ 2. Web Component Телеметрии и Раннера (saur-widget.js)

Отказоустойчивый Web Component (<4 КБ) с Shadow DOM, Circuit Breaker и Stale-While-Revalidate кэшем. Интерактивное демо: https://saur.run/widget/demo.html ↗.

<!-- Web Component Embed -->
<script async defer src="https://saur.run/widget/saur-widget.js"></script>
<saur-widget mode="card" runner-id="fra-spot-1"></saur-widget>