Redis instrumentation adapter for the OpenTelemetry framework

REST Client instrumentation adapter for the OpenTelemetry framework

Sidekiq instrumentation adapter for the OpenTelemetry framework

Sinatra instrumentation adapter for the OpenTelemetry framework

Resource detection helpers for OpenTelemetry

Baggage Span Processor for the OpenTelemetry framework

Container resource detection helpers for OpenTelemetry

AWS resource detector for OpenTelemetry

Azure resource detection helpers for OpenTelemetry

Google Cloud Platform resource detection helpers for OpenTelemetry

XRay Remote Sampler for the OpenTelemetry framework

A Rails engine for browser-based health probes and uptime monitoring via Prometheus metrics

OpenC3 provides all the functionality needed to send commands to and receive data from one or more embedded systems referred to as "targets". Out of the box functionality includes: Telemetry Display, Telemetry Graphing, Operational and Test Scripting, Command Sending, Logging, and more.

Provides DSPy::Observability, AsyncSpanProcessor, and ObservationType so instrumentation can be enabled independently from the main DSPy gem.

Registers the Langfuse OpenTelemetry exporter with DSPy::Observability so spans flow to Langfuse when the required environment variables are present.

vmpooler provides configurable pools of instantly-available (running) virtual machines

An opinionated web framework build on web standards

A small, lightweight and extensible DynDNS server written with Ruby and Rack.

llm.rb is a runtime for building AI systems that integrate directly with your application. It is not just an API wrapper. It provides a unified execution model for providers, tools, MCP servers, streaming, schemas, files, and state. It is built for engineers who want control over how these systems run. llm.rb stays close to Ruby, runs on the standard library by default, loads optional pieces only when needed, and remains easy to extend. It also works well in Rails or ActiveRecord applications, where a small wrapper around context persistence is enough to save and restore long-lived conversation state across requests, jobs, or retries. Most LLM libraries stop at request/response APIs. Building real systems means stitching together streaming, tools, state, persistence, and external services by hand. llm.rb provides a single execution model for all of these, so they compose naturally instead of becoming separate subsystems.

A service that uses a relational database inserts messages/events into an outbox table as part of the local transaction.