HertzBeat: An AI-Powered Open Source Real-time Observability System
This repository profile is provided by osrepos.com, an open source repository discovery platform.

Summary
HertzBeat is an advanced, AI-powered open source observability system designed for real-time monitoring. It offers unified collection, centralized alerting, and intelligent management for a wide range of IT infrastructure, eliminating the need for agents. This comprehensive solution simplifies observability requirements for modern IT environments.
Repository Information
Topics
Click on any tag to explore related repositories
Use at your own risk
OSRepos shares public repositories for knowledge and discovery only. Any installation, execution, configuration, or use of code from these repositories is the user's own responsibility. Always review the repository, source code, dependencies, licenses, and security implications before running or installing anything. OSRepos is not responsible for issues, damages, or losses resulting from third-party repositories.
Introduction
Apache HertzBeat™ is an innovative, AI-powered open source real-time observability system. It provides a unified platform for metrics and logs collection, centralized alerting, and intelligent management and analysis. Designed to be agentless, HertzBeat offers high-performance cluster capabilities and powerful custom monitoring, along with status page building features. It aims to simplify observability requirements for users by integrating various functionalities into one comprehensive solution.
Installation
Getting started with HertzBeat is straightforward, especially using Docker.
To deploy HertzBeat quickly with a single command:
docker run -d -p 1157:1157 -p 1158:1158 --name hertzbeat apache/hertzbeat
After deployment, you can access the web interface at http://localhost:1157. The default login credentials are admin/hertzbeat.
For more advanced deployments, including collector clusters, package installation, source code setup, or Kubernetes with Docker-compose/Helm charts, please refer to the official documentation.
Examples
HertzBeat supports a vast array of monitoring types through configurable YML templates. This flexibility allows users to easily adapt to new monitoring requirements. Some examples of supported monitoring include:
- Web & Network: Website, Port Telnet, Http Api, Ping Connect, SSL Certificate, DNS.
- Applications: JVM, SpringBoot2/3, Nginx, Tomcat, Kafka, Flink.
- Databases: MySQL, PostgreSQL, Redis, ElasticSearch, SQLServer, Oracle, MongoDB.
- Operating Systems: Linux, Windows, Ubuntu, CentOS.
- Cloud-Native: Kubernetes, Docker.
- Network Devices: CiscoSwitch, HuaweiSwitch.
This template-driven approach means you can collect virtually any metrics by simply configuring a YML file online.
Why Use HertzBeat
HertzBeat stands out with several compelling features:
- All-in-One Observability: It integrates collection, analysis, alerting, and notification into a single platform, enhanced with AI-powered interactions and built-in MCP Server capabilities.
- Agentless & Prometheus-Compatible: Offers unified metrics collection without requiring agents, and is compatible with Prometheus. It supports a wide range of services, databases, operating systems, and more.
- Unified Logging: Seamlessly integrates multiple log sources via the OTLP protocol.
- Advanced Alerting: Provides a unified alerting platform with flexible real-time and periodic threshold rules, grouping, silence, and suppression features.
- Diverse Notifications: Alerts can be distributed through various channels, including Email, Discord, Slack, Telegram, DingTalk, WeChat, Webhook, and SMS.
- Highly Customizable: Protocols like Http, Jmx, Ssh, Snmp, Jdbc are configurable, allowing users to define custom monitoring types easily through YML templates.
- Scalable & Performant: Supports horizontal expansion of multi-collector clusters, enabling monitoring across isolated networks and cloud-edge collaboration.
- Status Page: Offers powerful capabilities to build status pages, effectively communicating service health to users.
Links
- GitHub Repository: https://github.com/apache/hertzbeat
- Official Website: https://hertzbeat.apache.org
- Documentation: https://hertzbeat.apache.org/docs/
- Join Discord: https://discord.gg/Fb6M73htGr
- Subscribe Mailing Lists: Mail to
dev-subscribe@hertzbeat.apache.org
Related repositories
Similar repositories that may be relevant next.
Evidently: Open-Source ML and LLM Observability Framework
June 30, 2026
Evidently is an open-source Python library designed for evaluating, testing, and monitoring machine learning and large language model systems. It provides over 100 built-in metrics for various tasks, from data drift detection to LLM judges, supporting both tabular and text data. This framework helps ensure the quality and performance of AI-powered systems throughout their lifecycle.

LangWatch: The Platform for LLM Evaluations and AI Agent Testing
April 28, 2026
LangWatch is an open-source platform designed for end-to-end LLM evaluations and AI agent testing. It helps teams test, simulate, evaluate, and monitor LLM-powered agents both before release and in production. Built for robust regression testing, simulations, and production observability, LangWatch eliminates the need for custom tooling.

Langsmith-sdk: Client SDK for LLM Debugging, Evaluation, and Monitoring
March 18, 2026
The Langsmith-sdk provides client SDKs for interacting with the LangSmith platform, enabling robust debugging, evaluation, and monitoring of language models and intelligent agents. It offers native integrations with both LangChain Python and LangChain JS, making it an essential tool for LLM application development.

Karpor: Intelligence and Visualization for Kubernetes Clusters
December 29, 2025
Karpor is an open-source Kubernetes Visualization Tool designed to bring intelligence to your clusters. It provides advanced search, insightful analytics, and AI-powered operations for developers and platform engineering teams. Gain crucial visibility and streamline management across your Kubernetes environments with Karpor.
Source repository
Open the original repository on GitHub.
16 counted GitHub visits