ConfigsApr 16, 2026·3 min read

Easegress — Cloud-Native Traffic Orchestration System

Easegress is a high-performance, cloud-native traffic orchestration platform written in Go that provides API gateway, load balancing, service mesh sidecar, and pipeline-based request processing with built-in resilience patterns.

AI Open Source
AI Open Source · Community
TL;DR
Easegress routes and load-balances HTTP traffic through composable filter pipelines with built-in resilience patterns and Raft-based HA.
§01

What it is

Easegress is a cloud-native traffic orchestration system written in Go. It functions as an API gateway, load balancer, and service mesh sidecar, processing requests through composable filter pipelines. Each pipeline chains together filters for routing, rate limiting, authentication, and request transformation.

Easegress targets platform engineers and DevOps teams who need fine-grained control over HTTP traffic. Its declarative YAML configuration and CLI tool (egctl) make it accessible to teams already familiar with Kubernetes-style resource definitions.

§02

How it saves time or tokens

Instead of stitching together separate tools for rate limiting, load balancing, canary releases, and request rewriting, Easegress consolidates them into a single binary. The pipeline model means you define traffic rules once in YAML and apply them across environments. Its distributed architecture uses Raft consensus, so you avoid building your own HA layer.

§03

How to use

  1. Install Easegress using the official install script or download the binary directly from the GitHub releases page.
  2. Start the server with easegress-server and use egctl to create pipeline resources via YAML manifests.
  3. Define an HTTP server listener on your desired port and bind it to your pipeline.
§04

Example

# Install Easegress
bash -c "$(curl -kfsSL https://raw.githubusercontent.com/easegress-io/easegress/main/scripts/install.sh)"

# Start server
easegress-server

# Create a proxy pipeline
cat <<EOF | egctl create -f -
name: demo-pipeline
kind: Pipeline
flow:
  - filter: proxy
filters:
  - name: proxy
    kind: Proxy
    pools:
      - servers:
          - url: http://httpbin.org
EOF

# Bind to port 10080
egctl create httpserver --port 10080 --rules demo-pipeline
curl http://localhost:10080/get
§05

Related on TokRepo

§06

Common pitfalls

  • Easegress uses its own filter pipeline model, not standard middleware chains. Read the filter documentation before assuming behavior from other gateways.
  • Raft consensus requires at least three nodes for production HA. A single-node setup works for development but has no failover.
  • YAML pipeline definitions are order-sensitive. Filter execution follows the flow array sequence exactly.

Frequently Asked Questions

What is the difference between Easegress and Nginx or Envoy?+

Easegress uses a composable filter pipeline model rather than static configuration blocks. You chain filters like rate limiting, authentication, and proxying in a declarative YAML flow. It also includes built-in Raft consensus for distributed HA without external dependencies.

Can Easegress run as a Kubernetes ingress controller?+

Yes. Easegress supports running as a Kubernetes ingress controller, reading Ingress resources and routing traffic accordingly. It can also run as a standalone gateway or service mesh sidecar.

What traffic patterns does Easegress support?+

Easegress supports canary deployments with weighted backends, blue-green routing, A/B testing, circuit breaking, rate limiting, and request mirroring. All patterns are configured through the pipeline filter system.

Is Easegress suitable for production use?+

Easegress is designed for production with Raft-based distributed consensus, health checks, and automatic failover. However, you need at least three nodes for proper HA in production environments.

What programming language is Easegress written in?+

Easegress is written entirely in Go, which gives it a single-binary deployment model, low memory footprint, and high concurrency handling through goroutines.

Citations (3)
Related on TokRepo
DevOps AI toolsAPI tools on TokRepoFeatured workflows

Discussion

Sign in to join the discussion.
No comments yet. Be the first to share your thoughts.

Related Assets

Hugging Face Tokenizers — Fast Text Tokenization for ML Pipelines

Hugging Face Tokenizers is a Rust-powered tokenization library with Python bindings that implements BPE, WordPiece, Unigram, and SentencePiece tokenizers with training and encoding speeds of gigabytes per second, used as the backbone for Transformers model tokenization.

AI Open Source

Cleanlab — Find and Fix Label Errors in Any ML Dataset

Cleanlab is a data-centric AI Python library that automatically detects label errors, outliers, and data quality issues in classification and regression datasets, helping improve model accuracy by cleaning training data rather than tuning models.

AI Open Source

Hugging Face Datasets — Access and Process ML Datasets at Scale

Hugging Face Datasets is a Python library for efficiently loading, processing, and sharing machine learning datasets with Apache Arrow-backed memory mapping, streaming support, and access to thousands of community datasets on the Hub.

AI Open Source
◈Home🔍Search👤Me