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megaease/easegress

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5,873 stars·495 forks·Go·Apache-2.0·19 viewsmegaease.com/easegress↗

Easegress

Easegress is a cloud native API gateway, Kubernetes ingress controller, and service mesh control plane. It serves as a centralized entry point for managing network traffic, providing a dedicated control plane and sidecar data plane to handle the lifecycle and interception of requests between distributed services.

The project functions as an AI model gateway that proxies requests to large language model providers and translates request formats for compatibility. It is an extensible proxy that allows for custom request and response handling through a filter pipeline implemented via WebAssembly code.

The system covers broad traffic management capabilities, including multi-protocol routing for HTTP, MQTT, and WebSockets, as well as load balancing and service discovery. It implements stability protections such as circuit breakers and rate limits, and supports deployment strategies like canary and blue-green releases.

Observability is integrated through OpenTelemetry for distributed request tracing and traffic performance monitoring. Security is managed via token and signature validation and SSL/TLS certificate management.

Features

  • Ingress Controllers - Serves as a Kubernetes ingress controller to manage external access and route traffic into the cluster.
  • Centralized API Gateways - Provides a centralized entry point for routing incoming network requests to various backend microservices.
  • AI Gateways - Provides a unified interface for routing requests to multiple AI model providers while maintaining format compatibility.
  • Control and Data Plane Separation - Separates network configuration and management in the control plane from the packet forwarding in the data plane.
  • Ingress Gateways - Implements specialized ingress gateways for managing external entry points into Kubernetes clusters.
  • Proxy Filters - Supports custom request and response handling through a filter pipeline implemented via WebAssembly code.
  • Service Mesh Control Planes - Implements a centralized management system for configuring and orchestrating service-to-service network policies.
  • Service Meshes - Provides an infrastructure layer to manage service-to-service communication, observability, and security.
  • Dynamic Service Discovery - Maps logical service names to physical network addresses in real-time to enable dynamic microservices communication.
  • Model Request Proxies - Acts as a gateway that intercepts and routes API requests to various large language model providers.
  • System Stability Protections - Prevents cascading failures by implementing rate limiting, circuit breakers, and execution timeouts.
  • Content-Aware Routing Rules - Implements routing rules that inspect request headers, paths, and IP addresses to direct traffic.
  • Request Token Validators - Validates tokens and digital signatures on every request to enforce access control and security permissions.
  • API Access Security - Secures API access through token validation, digital signatures, and stability mechanisms like rate limiting.
  • Multi-Protocol Proxies - Functions as a network intermediary supporting multiple protocols including HTTP, MQTT, and WebSockets.
  • Request Processing Filters - Executes custom request and response processing logic using portable WebAssembly binary modules.
  • Deployment Strategies - Supports various deployment patterns including blue-green and canary releases to manage application rollout.
  • Canary Deployment Controllers - Implements controllers to manage incremental traffic shifts for safe canary deployments of API updates.
  • Traffic Load Balancers - Distributes network traffic across multiple service instances using round-robin and sticky session policies.
  • Multi-Protocol Traffic Orchestration - Routes and manages traffic across multiple protocols including HTTP, MQTT, and WebSockets.
  • Zero-Downtime Deployments - Ensures continuous service availability during updates through health-check-gated traffic switching.
  • Zero-Downtime Upgrades - Updates service binaries and configurations in place to maintain active network connections without downtime.
  • API Orchestration - Provides capabilities to aggregate and manage concurrent requests across multiple external service APIs into single responses.
  • Cloud Network Orchestration - Directs network data flow within cloud environments to ensure efficient connectivity between distributed components.
  • Circuit Breakers - Prevents cascading failures by automatically stopping traffic to unhealthy backend services.
  • Traffic Pipeline Filters - Implements WebAssembly-based custom filters within the traffic pipeline for request transformation and routing.
  • SSL/TLS Certificate Management - Manages the lifecycle and deployment of SSL/TLS certificates to secure network traffic.
  • Dynamic Service Discovery - Dynamically locates and tracks available backend service instances using external service registries.
  • Configuration Hot-Reloading - Refreshes routing rules and system settings in place without requiring a full process restart.
  • Distributed Tracing - Generates vendor-neutral distributed traces using unique identifiers to analyze request execution flow across services.
  • Request Traffic Monitors - Tracks request volume, processing duration, and error rates to monitor the health of backend servers.
  • OpenTelemetry - Integrates with OpenTelemetry to export trace spans to OTLP-compatible backends for distributed tracing.
  • OpenTelemetry SDKs - Integrates OpenTelemetry SDKs to generate and export vendor-neutral observability data and distributed traces.
  • Infrastructure and Containers - A cloud-native traffic orchestration and management system.
  • API Gateways - Cloud-native traffic orchestration and gateway system.
  • API Gateways and Proxies - Traffic orchestration system for cloud-native architectures.

Star history

Star history chart for megaease/easegressStar history chart for megaease/easegress

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Frequently asked questions

What does megaease/easegress do?

Easegress is a cloud native API gateway, Kubernetes ingress controller, and service mesh control plane. It serves as a centralized entry point for managing network traffic, providing a dedicated control plane and sidecar data plane to handle the lifecycle and interception of requests between distributed services.

What are the main features of megaease/easegress?

The main features of megaease/easegress are: Ingress Controllers, Centralized API Gateways, AI Gateways, Control and Data Plane Separation, Ingress Gateways, Proxy Filters, Service Mesh Control Planes, Service Meshes.

Which projects share features with megaease/easegress?

Projects with overlapping indexed features include: alibaba/higress — Higress is an AI API gateway and cloud-native traffic manager that functions as a Kubernetes ingress controller. It… apache/apisix — This project is a high-performance, distributed API gateway designed to manage, secure, and observe traffic for… higress-group/higress — Higress is an AI-native and cloud-native API gateway that routes, secures, and optimizes traffic between clients and… linkerd/linkerd2 — This project is a service mesh platform designed to manage, secure, and observe service-to-service communication… easegress-io/easegress — Easegress is a cloud-native traffic orchestration platform that functions as a multi-protocol API gateway and service… yudaocode/springboot-labs — SpringBoot-Labs is a collection of educational resources and reference implementations for Java backend architecture…

Projects sharing features with Easegress

These projects share indexed features with Easegress. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • alibaba/higressalibaba avatar

    alibaba/higress

    7,558View on GitHub↗

    Higress is an AI API gateway and cloud-native traffic manager that functions as a Kubernetes ingress controller. It provides a centralized system for routing, securing, and optimizing traffic directed toward large language models, AI agents, and microservice architectures. The project distinguishes itself through deep AI orchestration, including the ability to host and manage Model Context Protocol servers that transform REST APIs into tools for AI agents. It features specialized AI infrastructure for model request proxying, protocol translation across multiple providers, and semantic-based c

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  • apache/apisixapache avatar

    apache/apisix

    16,767View on GitHub↗

    This project is a high-performance, distributed API gateway designed to manage, secure, and observe traffic for microservices, serverless functions, and artificial intelligence model providers. It functions as a dynamic service proxy and cloud-native ingress controller, centralizing policy enforcement and traffic routing through a unified configuration interface that synchronizes state across multiple nodes in real time. The platform distinguishes itself through a highly extensible architecture that utilizes a high-performance scripting engine to execute modular logic directly within the requ

    Luaapiapi-gatewayapi-management
    View on GitHub↗16,767
  • higress-group/higresshigress-group avatar

    higress-group/higress

    8,727View on GitHub↗

    Higress is an AI-native and cloud-native API gateway that routes, secures, and optimizes traffic between clients and large language model services. It functions as a centralized entry point for microservices, serving as both a Kubernetes ingress controller and an AI gateway orchestrator. The project distinguishes itself by managing traffic across multiple AI providers using a unified protocol, incorporating token-aware rate limiting and response caching to optimize model inference. It coordinates communication between AI models and external tools to provide real-time context and data, while a

    Go
    View on GitHub↗8,727
  • linkerd/linkerd2linkerd avatar

    linkerd/linkerd2

    11,424View on GitHub↗

    This project is a service mesh platform designed to manage, secure, and observe service-to-service communication within Kubernetes clusters. It functions as a control plane that orchestrates transparent sidecar proxies, which intercept and manage network traffic to provide reliable connectivity for microservices. By automating the injection of these proxies, the platform ensures that infrastructure-level policies are applied consistently across all workloads without requiring manual configuration changes. The platform distinguishes itself through its focus on zero-trust security and cross-clu

    Gocloud-nativegolangkubernetes
    View on GitHub↗11,424
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