Search Results for "metrics"
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Showing 1004 open source projects for "metrics"
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Metrics
Capturing JVM and application-level metrics
...The central library for Metrics is metrics-core, which provides some basic functionality. The starting point for Metrics is the MetricRegistry class, which is a collection of all the metrics for your application (or a subset of your application). Each metric is associated with a MetricRegistry, and has a unique name within that registry. A gauge is the simplest metric type. -
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kube-state-metrics
Add-on agent to generate and expose cluster-level metrics
kube-state-metrics (KSM) is a simple service that listens to the Kubernetes API server and generates metrics about the state of the objects. (See examples in the Metrics section below.) It is not focused on the health of the individual Kubernetes components, but rather on the health of the various objects inside, such as deployments, nodes and pods. kube-state-metrics is about generating metrics from Kubernetes API objects without modification. ... -
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Prometheus Redis Metrics Exporter
Prometheus Exporter for Redis Metrics. Supports Redis 2.x, 3.x, 4.x, 5
Prometheus exporter for Redis metrics. Supports Redis 2.x, 3.x, 4.x, 5.x, 6.x, and 7.x. To have instances in the drop-down as human readable names rather than IPs, it is suggested to use instance relabelling. The Prometheus docs have a very informative article on how multi-target exporters are intended to work. Run the exporter with the command line flag --redis.addr= so it won't try to access the local instance every time the /metrics endpoint is scraped. -
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Telegraf
The plugin-driven server agent for collecting and reporting metrics
Telegraf is an agent for collecting, processing, aggregating, and writing metrics. Design goals are to have a minimal memory footprint with a plugin system so that developers in the community can easily add support for collecting metrics. Telegraf is plugin-driven and has the concept of 4 distinct plugin types. Input Plugins collect metrics from the system, services, or 3rd party APIs. Processor Plugins transform, decorate, and/or filter metrics. -
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Amazon CloudWatch Agent
CloudWatch Agent enables you to collect and export host-level metrics
CloudWatch Agent enables you to collect and export host-level metrics and logs on instances running Linux or Windows server. The Amazon CloudWatch Agent is software developed for the CloudWatch Agent. Collect more system-level metrics from Amazon EC2 instances across operating systems. The metrics can include in-guest metrics, in addition to the metrics for EC2 instances. The additional metrics that can be collected are listed in Metrics Collected by the CloudWatch Agent. ... -
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promptfoo
Evaluate and compare LLM outputs, catch regressions, improve prompts
Ensure high-quality LLM outputs with automatic evals. Use a representative sample of user inputs to reduce subjectivity when tuning prompts. Use built-in metrics, LLM-graded evals, or define your own custom metrics. Compare prompts and model outputs side-by-side, or integrate the library into your existing test/CI workflow. Use OpenAI, Anthropic, and open-source models like Llama and Vicuna, or integrate custom API providers for any LLM API. -
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Zabbix plugin for Grafana
Zabbix plugin for Grafana dashboard
Grafana Zabbix is a plugin that integrates Zabbix monitoring with Grafana, enabling advanced visualization and analysis of Zabbix metrics using Grafana's powerful dashboard features. -
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Intel PCM
Intel® Performance Counter Monitor (Intel® PCM)
Intel® Performance Counter Monitor (Intel® PCM) is an application programming interface (API) and a set of tools based on the API to monitor performance and energy metrics of Intel® Core™, Xeon®, Atom™ and Xeon Phi™ processors. PCM works on Linux, Windows, Mac OS X, FreeBSD, DragonFlyBSD and ChromeOS operating systems. -
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Uptrace
Open source APM: OpenTelemetry traces, metrics, and logs
Uptrace is an open-source APM tool that supports distributed tracing, metrics, and logs. You can use it to monitor applications and set up automatic alerts to receive notifications via email, Slack, Telegram, and more. Uptrace is an open-source APM that supports OpenTelemetry tracing, metrics, and logs. You can use it to monitor applications and set up alerts to receive notifications via email, Slack, Telegram, and more. -
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TorchMetrics
Machine learning metrics for distributed, scalable PyTorch application
TorchMetrics is a collection of 80+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. Your data will always be placed on the same device as your metrics. You can log Metric objects directly in Lightning to reduce even more boilerplate. The module-based metrics contain internal metric states (similar to the parameters of the PyTorch module) that automate accumulation and synchronization across devices! -
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SigNoz
SigNoz is an open-source APM. It helps developers monitor their apps
...SigNoz helps developers monitor applications and troubleshoot problems in their deployed applications. SigNoz uses distributed tracing to gain visibility into your software stack. Visualise Metrics, Traces and Logs in a single pane of glass. You can see metrics like p99 latency, error rates for your services, external API calls and individual end points. You can find the root cause of the problem by going to the exact traces which are causing the problem and see detailed flamegraphs of individual request traces. Run aggregates on trace data to get business relevant metrics. ... -
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pgwatch2
PostgreSQL metrics monitor/dashboard
Flexible self-contained PostgreSQL metrics monitoring/dashboarding solution. Supports monitoring PG versions 9.0 to 16 out of the box. Pgwatch2 is a PostgreSQL monitoring tool designed to track database performance and usage, providing insights for database optimization. -
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Swarmpit
Lightweight mobile-friendly Docker Swarm management UI
Swarmpit is an open-source, self-hosted web dashboard that simplifies Docker Swarm cluster operations. It offers management of stacks, services, secrets, volumes, networks, and allows secure team access. It visualizes real-time CPU/memory/disk metrics, supports private registry integration, and streamlines deployment flows. The interface is mobile-friendly and privacy-focused—collecting no telemetry. -
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MetricFlow
MetricFlow allows you to define, build, and maintain metrics in code
MetricFlow is an open-source semantic layer engine designed to help organizations define, manage, and query business metrics in a consistent, governed way. It works alongside a data stack—typically built with dbt—and allows you to express metrics as YAML‐based definitions tied to semantic models and dimension tables, rather than embedding logic ad-hoc across many dashboards or scripts. When a user or tool requests a metric (e.g., “monthly revenue by region”), MetricFlow generates optimized, warehouse-specific SQL to compute that metric, handling joins, filters, time grains, offsets, and other complexities under the hood. ... -
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TorchMetrics AI
Machine learning metrics for distributed, scalable PyTorch application
TorchMetrics is a collection of 100+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. -
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Grafana Mimir
Grafana Mimir provides long-term storage for Prometheus
Grafana Mimir is an open-source, horizontally scalable, long-term storage solution for Prometheus metrics. Built by Grafana Labs, Mimir is designed to handle massive volumes of time-series data efficiently while maintaining high availability and reliability. It enables organizations to scale their Prometheus infrastructure without the typical limitations of single-server setups. Mimir is used to power Grafana Cloud Metrics and is built to be fully compatible with Prometheus, allowing easy integration into existing monitoring workflows. -
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Grafana Alloy
OpenTelemetry Collector distribution with programmable pipelines
Grafana Alloy is an open source OpenTelemetry Collector distribution with built-in Prometheus pipelines and support for metrics, logs, traces, and profiles. Grafana Alloy is Grafana Labs’ distribution of the OpenTelemetry Collector. It is an OTLP-compatible collector with built-in Prometheus optimizations that also support signals across metrics, logs, traces, and profiles. Alloy was started at Grafana Labs and announced at GrafanaCON in 2024. The mission of the project is to create the best “big tent” collector that’s compatible with the most popular open-source observability ecosystems and includes enterprise-grade features to simplify operating at scale in a modern cloud-native infrastructure. -
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MCP Monitor
A system monitoring tool that exposes system metrics
The MCP System Monitor is a tool that exposes system metrics via the Model Context Protocol (MCP), allowing Large Language Models (LLMs) to retrieve real-time system information through an MCP-compatible interface. -
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PHP Font Lib
A library to read, parse, export and make subsets of different fonts
This library can be used to read TrueType, OpenType (with TrueType glyphs), WOFF font files. Extract basic info (name, style, etc). Extract advanced info (horizontal metrics, glyph names, glyph shapes, etc). Make an Adobe Font Metrics (AFM) file from a font. You can find a demo GUI. This project was initiated by the need to read font files in the DOMPDF project. -
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APM Server
APM Server
...Elastic APM also automatically collects unhandled errors and exceptions. Errors are grouped based primarily on the stack trace, so you can identify new errors as they appear and keep an eye on how many times specific errors happen. Metrics are another vital source of information when debugging production systems. Elastic APM agents automatically pick up basic host-level metrics and agent-specific metrics, like JVM metrics in the Java Agent, and Go runtime metrics in the Go Agent. -
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PhpMetrics
Beautiful and understandable static analysis tool for PHP
PhpMetrics is a powerful static analysis tool for PHP code that provides metrics on code quality, complexity, maintainability, and architecture. It generates comprehensive reports in HTML format, visualizing metrics like cyclomatic complexity, lines of code, and coupling between classes. PhpMetrics helps developers identify potential code issues, optimize performance, and maintain high-quality codebases. -
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Prom-Client
Prometheus client for node.js
Prom-Client is a Prometheus client for Node.js that allows developers to instrument their applications and expose metrics, facilitating the monitoring of application performance and resource usage. -
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OpenTelemetry Collector distributions
OpenTelemetry Collector Official Releases
High-quality, ubiquitous, and portable telemetry to enable effective observability. OpenTelemetry is a collection of APIs, SDKs, and tools. Use it to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) to help you analyze your software’s performance and behavior. Create and collect telemetry from your services and software, then forward it to a variety of analysis tools. OpenTelemetry integrates with many popular libraries and frameworks, and supports code-based and zero-code instrumentation. -
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django-prometheus
Export Django monitoring metrics for Prometheus.io
Export Django monitoring metrics for Prometheus.io. This library provides Prometheus metrics for Django-related operations. Prometheus uses Histogram based grouping for monitoring latencies. You can define custom buckets for latency, adding more buckets decreases performance but increases accuracy. SQLite, MySQL, and PostgreSQL databases can be monitored. Just replace the ENGINE property of your database, replacing django.db.backends with django_prometheus.db.backends. ... -
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OpenJarvis
Personal AI, On Personal Devices
...Developed as part of the Intelligence Per Watt research initiative, it focuses on improving the efficiency and practicality of on-device AI systems. The framework provides shared primitives for building local-first agents, along with evaluation tools that measure performance using metrics such as energy consumption, latency, cost, and accuracy. OpenJarvis integrates with local inference engines like Ollama, vLLM, SGLang, and llama.cpp to run language models directly on personal hardware. It also includes a learning loop that allows models to improve over time using locally generated interaction traces. By prioritizing local execution and efficiency, OpenJarvis aims to provide a foundation for privacy-preserving personal AI assistants.
