Dagger Engine

intermediate

Programmable CI/CD engine that runs pipelines in containers.

Overview

Dagger Engine is a programmable CI/CD engine developed by the team behind Docker Compose that revolutionizes pipeline development by treating infrastructure as code. Unlike traditional CI/CD systems that rely on YAML configurations, Dagger allows developers to write pipelines using familiar programming languages like Go, Python, TypeScript, and Node.js, with all operations running in isolated containers for maximum portability and reproducibility. This containerized deployment of Dagger Engine creates a persistent service that can execute CI/CD pipelines defined through code rather than configuration files. The engine leverages Docker's container runtime to provide a consistent execution environment while maintaining intelligent caching mechanisms that dramatically reduce pipeline execution times. By mounting the Docker socket, the engine can spawn and manage containers for each pipeline step while preserving cache layers between runs. Development teams seeking to eliminate CI/CD vendor lock-in and infrastructure engineers wanting to standardize pipeline execution across different environments will find this setup particularly valuable. The containerized Dagger Engine enables organizations to run identical pipelines locally during development and in production CI systems, eliminating the common 'works on my machine' problem that plagues traditional CI/CD workflows while providing the flexibility to define complex build, test, and deployment logic using actual programming languages.

Key Features

Code-based pipeline definitions using Go, Python, TypeScript, or Node.js SDKs instead of YAML
Automatic build cache optimization with persistent volume storage for faster subsequent runs
Container-native execution ensuring identical pipeline behavior across development and production
Direct Docker daemon integration through socket mounting for container orchestration
Multi-language SDK support enabling teams to use their preferred programming language
Intelligent dependency resolution and parallel execution of pipeline steps
Built-in secrets management and secure handling of sensitive pipeline data
Real-time pipeline execution monitoring through the exposed web interface on port 8080

Common Use Cases

1Replacing Jenkins, GitLab CI, or GitHub Actions with vendor-agnostic pipeline execution
2Standardizing CI/CD processes across multiple cloud providers and on-premises infrastructure
3Local development environments where developers need to test CI/CD pipelines before committing
4Microservices architectures requiring complex multi-service build and deployment coordination
5Organizations migrating from traditional CI/CD systems to infrastructure-as-code approaches
6Container-based applications needing sophisticated multi-stage build processes with advanced caching
7Development teams wanting to version control their CI/CD logic alongside application code

Prerequisites

Docker Engine 20.10 or newer with privileged container support enabled
Minimum 4GB RAM and 20GB disk space for container operations and build caching
Host system with /var/run/docker.sock accessible for container management
Network access to registry.dagger.io for pulling the engine image
Dagger CLI installed on development machines for pipeline development and testing
Basic programming knowledge in Go, Python, TypeScript, or Node.js for pipeline creation

For development & testing. Review security settings, change default credentials, and test thoroughly before production use. See Terms

docker-compose.yml

1services: 
2  dagger-engine: 
3    image: registry.dagger.io/engine:latest
4    container_name: dagger-engine
5    restart: unless-stopped
6    privileged: true
7    ports: 
8      - "8080:8080"
9    volumes: 
10      - dagger_cache:/var/lib/dagger
11      - /var/run/docker.sock:/var/run/docker.sock
12
13volumes: 
14  dagger_cache:

.env Template

.env

1# Install Dagger CLI: curl -L https://dl.dagger.io/dagger/install.sh | sh

Usage Notes

1Docs: https://docs.dagger.io/
2Install CLI: curl -L https://dl.dagger.io/dagger/install.sh | sh
3SDKs available for Go, Python, TypeScript, Node.js
4Define pipelines as code: dagger.Container().From('alpine').WithExec([...])
5Cache automatically shared across pipeline runs
6Run same pipeline locally and in CI - no YAML required

Quick Start

terminal

1# 1. Create the compose file
2cat > docker-compose.yml << 'EOF'
3services:
4  dagger-engine:
5    image: registry.dagger.io/engine:latest
6    container_name: dagger-engine
7    restart: unless-stopped
8    privileged: true
9    ports:
10      - "8080:8080"
11    volumes:
12      - dagger_cache:/var/lib/dagger
13      - /var/run/docker.sock:/var/run/docker.sock
14
15volumes:
16  dagger_cache:
17EOF
18
19# 2. Create the .env file
20cat > .env << 'EOF'
21# Install Dagger CLI: curl -L https://dl.dagger.io/dagger/install.sh | sh
22EOF
23
24# 3. Start the services
25docker compose up -d
26
27# 4. View logs
28docker compose logs -f

One-Liner

Run this command to download and set up the recipe in one step:

terminal

1curl -fsSL https://docker.recipes/api/recipes/dagger-engine/run | bash

Troubleshooting

Permission denied when accessing Docker socket: Ensure the container has privileged access and Docker socket is properly mounted
Engine fails to start with 'bind: address already in use': Check if port 8080 is occupied by another service and modify port mapping if needed
Pipeline execution fails with 'no space left on device': Increase Docker disk space allocation or clean up unused images and volumes
Cache misses despite unchanged code: Verify dagger_cache volume is properly mounted and persistent across container restarts
SDK connection timeouts: Ensure Dagger Engine container is running and accessible on the configured port before executing pipeline code
Container spawn failures during pipeline execution: Check Docker daemon health and available system resources for nested container creation

Community Notes

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