GitLab-CICD-Agent

Name: GitLab-CICD-Agent
Availability: InStock
Author: Shalwin

🚀 agentic-cicd: Automate GitLab CI/CD with Natural Language + AI

“Automate your GitLab CI/CD workflows using natural language — powered by AI and Google Cloud.”

✨ Overview

agentic-cicd is an intelligent DevOps agent that allows users to automate complex GitLab CI/CD workflows simply by describing what they want in natural language.

It bridges natural language commands and real infrastructure changes by using an AI-driven multi-agent system that understands, generates, pushes, and monitors CI/CD pipelines on GitLab.

Whether you're a developer looking to set up a CI/CD pipeline or an ops engineer handling deployments — this tool simplifies GitLab automation through LLMs + agents + MCP tool servers.

📟️ Demo

Live Application youtube demo

🧠 Key Features

🔐 GitLab OAuth Integration – Connect your GitLab account securely.

💬 Natural Language Input – Describe what you want, like:

Set up CI to run tests on push and deploy to GKE when main branch is updated.

🤖 Multi-Agent AI Engine – Understands the request and plans accordingly.
⚙️ Pipeline & Infra Generation – Generates .gitlab-ci.yml, Dockerfile, Terraform, Helm config, etc.
↻ Push + Merge Request – Pushes the generated files to your GitLab repo and opens an MR.
🔍 Pipeline Monitoring – Optionally track status of the build, test, and deployment stages.
🧹 MCP Server/Client Architecture – Modular tool abstraction to interface with GitLab API via agents.

🧹 Architecture

↻ System Flow

🧠 Multi-Agent Architecture – LangGraphJS Supervisor Pattern

This project follows the LangGraphJS Multi-Agent Supervisor Pattern to automate GitLab CI/CD workflows using natural language commands.

🧑‍⚖️ 1. Supervisor Agent

The central decision-maker.
Receives natural language input from the user.
Maintains shared memory and execution state across agents.
Dynamically delegates to sub-agents using a graph-based control flow.

🔄 Agent Workflow Graph

The Supervisor uses LangGraph to define a directed graph of agents, where edges represent conditional flows based on task results.

🤖 Sub-Agents

Each agent handles a distinct responsibility within the pipeline.

🧭 `PlanAgent`

“Understand what needs to be done.”

Parses user intent and decomposes it into logical subtasks.
Understands GitLab CI/CD terminology and translates user commands accordingly.
Outputs a plan with goals such as:
- Generate .gitlab-ci.yml
- Create Dockerfile
- Define cloud deployment strategy (e.g., GKE)

🧑‍💻 `CodeAgent`

“Write the code/configuration needed.”

Converts the structured plan into CI/CD artifacts such as:
- .gitlab-ci.yml
- Dockerfile
- Terraform / Helm configurations
Adapts generation based on selected cloud providers (GCP, AWS, etc.)
Can scaffold standard GitLab CI templates dynamically.

🧪 `TestAgent`

“Validate the configuration before use.”

Runs tests on generated configuration to ensure correctness.
Performs:
- YAML linting / CI syntax validation
- Dry-run of GitLab CI if applicable
- Optional LLM-assisted analysis of pipeline logic

💬 `ChatAgent`

“Explain what’s happening or answer user queries.”

Maintains conversation with the user.
Can answer questions like:
- "What is this pipeline doing?"
- "Why did my build fail?"
Pushes updates about:
- MR creation
- Pipeline execution
- Deployment status
Can re-prompt for missing details if required

🔧 GitLab MCP Tool Server (Tool Use via API)

Agents like CodeAgent and ChatAgent interact with GitLab via a dedicated Tool Server (GitLab MCP Server).

🛠 MCP GitLab Tool Server

A REST API wrapper around GitLab’s official APIs.
Allows fine-grained agent control over GitLab actions like:
- Getting project info
- Creating branches/commits
- Listing and triggering pipelines
- Monitoring job statuses
- Opening Merge Requests

🤝 MCP Client

Each agent uses an MCP Client to communicate with the Tool Server.
Benefits:
- Keeps logic clean and separated from direct API calls.
- Enables observability, tracing, and debugging of agent decisions.
- Makes actions reversible and replicable.

🔄 Feedback Loop

User Input

“Set up CI to run tests on push and deploy to GKE when main is updated.”
Supervisor Flow
- PlanAgent → Understands the request and breaks into subgoals.
- CodeAgent → Generates .gitlab-ci.yml, Dockerfile, etc.
- TestAgent → Validates the generated files.
- GitLab MCP Server → Applies the changes via GitLab API (opens MR).
- ChatAgent → Informs the user and monitors progress.
Pipeline Execution
- GitLab CI is triggered.
- Status is periodically updated to the user.

✅ Why LangGraphJS?

Structured Reasoning
Flow is modeled as a directed graph for better traceability and control.
Shared Memory Across Agents
All agents work with a centralized state context.
Retries and Fallbacks
Automatically retry failed tasks or re-ask user when needed.
Plug-and-Play Agents
Each agent is modular, testable, and can be extended independently.

✨ The result is a natural-language-driven GitLab CI/CD automation system powered by intelligent agents, tailored infrastructure, and real-time feedback – all orchestrated via LangGraph.

🔐 Authorization:

Uses the user’s OAuth token to act on their behalf.
Each API call is scoped by the GitLab OAuth access token.

💽 Tech Stack

Layer	Tech
Frontend	React, React Router, Vite, Tailwind
Backend	Node.js, Express, Supabase
AI Agents	LangGraphJS, LangChainJS, Gemini/GPT
Auth	GitLab OAuth 2.0
Infra Tools	Docker, Terraform, Helm (generated)
Hosting	Vercel (frontend), Render (backend)

🚀 Getting Started

1. Clone the Repository

git clone https://github.com/your-org/agentic-cicd.git
cd agentic-cicd

2. Frontend Setup

cd frontend
npm install

Create a .env file:

VITE_BACKEND_URL=https://your-backend-url.onrender.com
VITE_GITLAB_CLIENT_ID=your_gitlab_client_id
VITE_REDIRECT_URI=https://your-frontend-url.vercel.app/oauth/callback

npm run dev

3. Backend Setup

cd backend
npm install

.env file:

GITLAB_CLIENT_ID=your_gitlab_client_id
GITLAB_CLIENT_SECRET=your_gitlab_client_secret
GITLAB_REDIRECT_URI=https://your-frontend-url.vercel.app/oauth/callback

SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_key

GOOGLE_API_KEY=your_gemini_api_key
TAVUS_API_KEY=your_tavus_key

Build and Run:

npm run build
npm run start

🧪 Usage

Go to the app homepage.
Click "Connect GitLab" and authorize access.

Enter your command like:

Set up CI to run tests on push and deploy to GKE when main branch is updated.

Agent will:
- Understand your intent
- Generate .gitlab-ci.yml and Dockerfile
- Push to a new branch
- Open a merge request
- Trigger a pipeline
Watch pipeline logs via GitLab or frontend.

☁️ Deployment Guide

Frontend (Vercel)

Link GitHub Repo → Vercel Dashboard
Set environment variables in Settings
Deploy

Backend (Render)

Create new Web Service → Connect to GitHub
Build Command: npm run build
Start Command: node dist/index.js
Add your environment variables

❗ Troubleshooting

Issue	Fix
`CORS error`	Ensure backend sets correct `Access-Control-Allow-Origin`
`OAuth redirect 404`	Check GitLab app redirect URI matches deployed frontend
`Failed to fetch`	Backend must be live + CORS enabled
`503 MCP Init`	GitLab token expired or MCP endpoint down
`CI config invalid`	Re-run or correct command syntax

🤝 Contributing

We welcome PRs and ideas!

# Fork -> Clone -> Create Branch -> Code -> Commit -> PR

Please open issues for:

Bugs
New agent tasks
GitLab features you'd like automated

📄 License

💡 Inspired By

🗂️ Folder Structure (Simplified)

agentic-cicd/
│
├── frontend/
│   ├── App.jsx
│   ├── pages/
│   └── components/
│
├── backend/
│   ├── agents/
│   ├── routes/
│   |── services/
|-─ mcp-server/
│
├── .env.example
└── README.md

📬 Contact

For inquiries or collaborations: 📧 shalwinsanju.25cs@licet.ac.in 🐦 Twitter: @samshalwin