Product Introduction
Quick Start
- Agent Development
- Importing a Git Repository
- Starting From a Template
- Direct Upload
- Start with AI
Framework Guide
- Agent
- Frontends
  - Vite
  - React
  - Vue
  - Hugo
  - Other Frameworks
- Backends
- Full-stack
  - Next.js
  - Nuxt
  - Astro
  - React Router
  - SvelteKit
  - TanStack Start
  - Vike
- Custom 404 Page
Project Guide
- Project Management
- edgeone.json
- Configuring Cache
- Building Output Configuration
- Error Codes
Build Guide
Deployment Guide
- Overview
- Create Deploys
- Manage Deploys
- Deploy Button
- Using Github Actions
- Using Gitlab CI/CD
- Using CNB Plugin
- Using IDE PlugIn
- Using CodeBuddy IDE
Domain Management
- Overview
- Custom Domain
- HTTPS Configuration
  - Overview
  - Apply for Free Certificate
  - Using Managed SSL Certificate
- Configure DNS CNAME Record
Observability
- Overview
- Metric Analysis
- Log Analysis
Functions
- Overview
- Edge Functions
- Cloud Functions
  - Overview
  - Node.js
  - Python
  - Go
Agents
- Overview
- Quick Start
- Conversation Storage
- Observability
- Sandbox Tool
  - Overview
  - Using the Agent Framework
  - Sandbox Atomic API
  - Network Search Tool
- Agent Authentication
Models
- Overview
- Models and Vendors
  - Overview
  - Using Vendor Keys
    - OpenAI
    - Anthropic
    - Google AI Studio
    - DeepSeek
    - MiniMax
    - Hunyuan
    - Zhipu
    - MoonShot AI
- FAQs
Storage
- Overview
- KV
- Blob
Middleware
AI-Native Development
- Skills
- MCP
Copilot
- Overview
- Quick Start
API Token
EdgeOne CLI
Message Notification
Integration Guide
- AI
  - Dialogue Large Models Integration
  - Large Models for Images Integration
- Database
  - Supabase Integration
  - Pages KV Integration
- Ecommerce
  - Shopify Integration
  - WooCommerce Integration
- Payment
  - Stripe Integration
  - Integrating Paddle
- CMS
  - WordPress Integration
  - Contentful Integration
  - Sanity Integration
  - Payload Integration
- Authentication
  - Supabase Integration
  - Clerk Integration
Best Practices
- Adding an AI Chat Assistant to a Website
- AI Dialogue Deployment: Deploy Project with One Sentence Using Skill
- Using General Large Model to Quickly Build AI Application
- Use the DeepSeek model to quickly build a conversational AI site
- Building an Ecommerce Platform with Shopify
- Building a SaaS Site Using Supabase and Stripe
- Building a Company Brand Site Quickly
- How to Quickly Build a Blog Site
Migration Guides
- Migrating from Vercel to EdgeOne Pages
- Migrating from Cloudflare Pages to EdgeOne Pages
- Migrating from Netlify to EdgeOne Pages
Troubleshooting
FAQs
Limits
Pricing
Contact Us
Release Notes

Using Gitlab CI/CD

By following the guidelines in this section, you can use GitLab CI/CD to automatically build and deploy your project to EdgeOne Makers.
Configure GitLab CI/CD Variables
1. Enter the GitLab warehouse page, click in the left menu Settings > CI/CD.
2. Unfold the Variables part and click Add variable.
3. Configure the following variables:
Key: EDGEONE_API_TOKEN
Value: API Token obtained from the EdgeOne Makers console
Type: Variable
Flags: Check Mask variable (hide variable value) and Protect variable (Option, only for protected branch usage)
Refer to the document API Token for EDGEONE_API_TOKEN retrieval.
Create a GitLab CI/CD Configuration File
Create a file named .gitlab-ci.yml in the root directory of your GitLab repository and add the following content:
stages:
  - deploy
﻿
variables:
  PROJECT_NAME: "my-project" # change to your project name
﻿
# Cache node_modules to speed up builds (Option)
cache:
  key: ${CI_COMMIT_REF_SLUG}
  paths:
    - node_modules/
﻿
# Scenario 1: Deploy preview URL before code merge (Preview)
# Trigger when a Merge Request is generated
deploy-preview:
  image: node:22.11.0 # Select a suitable Node.js version
  stage: deploy
  only:
    - merge_requests
  script:
    - npm install
    - npm run build
    - npx edgeone makers deploy -n $PROJECT_NAME -t $EDGEONE_API_TOKEN -e preview
﻿
# Scenario 2: Deploy trunk code change (Production)
# Trigger when code is pushed to the main branch
deploy-production:
  image: node:22.11.0 # Select a suitable Node.js version
  stage: deploy
  only:
    - main
  script:
    - npm install
    - npm run build
    - npx edgeone makers deploy -n $PROJECT_NAME -t $EDGEONE_API_TOKEN -e production
Configuration Instructions
EdgeOne CLI Deployment Command
npx edgeone makers deploy -n <project-name> -t <API_TOKEN> -e <environment>
Parameter
Description
-n <project name>
EdgeOne Makers project name. If the project does not exist, it will be created automatically.
-t <API_TOKEN>
EdgeOne Makers API Token
-e <environment>
Deployment environment, selectable preview (Preview) or production (Production)
Deployment Scenario Description
Scenario
Trigger Condition
Environment Parameter
Description
preview deployment
Merge Request
-e preview
Each time an MR is generated, an independent preview URL is created for code review to view the effect
production deployment
push to the main branch
-e production
After code merge, automatic deployment to the production environment is used for official release, with high traffic and stability requirements.
Workflow
After the configuration is complete, GitLab CI/CD will automatically execute the following process:
Preview Environment (Merge Request)
1. Trigger Condition: Automatically trigger when creating or updating a Merge Request.
2. Execution Process: Dependency installation → Build project → Deploy to preview environment
3. Result: Generate a preview URL, which can be viewed in the MR to see the effect.
Production Environment (Main Branch)
1. Trigger Condition: Automatically trigger when code is pushed to the main branch.
2. Execution Process: Dependency installation → Build project → Deploy to production environment
3. Result: Update the production environment site
View Deployment Result
1. On the GitLab warehouse page, click Build > Pipelines in the left menu to view the pipeline status.
2. Click on the pipeline to view stage execution details.
3. After a successful deployment, access the EdgeOne Makers console to view the deployed project and its access URL.
FAQs
Q: Pipeline Execution Failed with a Permission Error Notification?
A: Please check whether the EDGEONE_API_TOKEN variable is configured correctly and ensure the TOKEN is not expired.
Q: How to Deploy to an Existing Project?
A: Specify the name of an existing project using the -n parameter in the deployment command, and the deployment for that project will be updated automatically.
References
GitLab CI/CD official documentation

Configure GitLab CI/CD Variables
Create a GitLab CI/CD Configuration File
Configuration Instructions
- EdgeOne CLI Deployment Command
- Deployment Scenario Description
Workflow
- Preview Environment (Merge Request)
- Production Environment (Main Branch)
View Deployment Result
FAQs
- Q: Pipeline Execution Failed with a Permission Error Notification?
- Q: How to Deploy to an Existing Project?
References

Parameter	Description
`-n <project name>`	EdgeOne Makers project name. If the project does not exist, it will be created automatically.
`-t <API_TOKEN>`	EdgeOne Makers API Token
`-e <environment>`	Deployment environment, selectable `preview` (Preview) or `production` (Production)

Scenario	Trigger Condition	Environment Parameter	Description
preview deployment	Merge Request	`-e preview`	Each time an MR is generated, an independent preview URL is created for code review to view the effect
production deployment	push to the main branch	`-e production`	After code merge, automatic deployment to the production environment is used for official release, with high traffic and stability requirements.