Jaeger

OpenTelemetry is an open-source, vendor-neutral observability framework for cloud-native software. It provides APIs, SDKs, automatic-instrumentation agents, and collector services for capturing distributed traces, metrics, logs, and contextual metadata such as baggage. Its context-propagation mechanism correlates telemetry across service boundaries and carries trace identity through an application’s request path, including standard HTTP communication. The OpenTelemetry Collector receives, processes, filters, and routes telemetry to observability backends such as Jaeger, Prometheus, commercial systems, or custom solutions. It supports native SDKs for more than 12 languages and is hosted as a graduated project by the Cloud Native Computing Foundation.

An open-source distributed tracing backend developed by Grafana Labs. Tempo ingests traces via Jaeger, Zipkin and OpenTelemetry (OTLP), stores trace data in scalable object stores (Amazon S3, Google Cloud Storage, Azure Blob Storage, MinIO) using a chunk-based, low-index architecture, and exposes query APIs compatible with Jaeger/OTLP with tight integration into Grafana for visualization. It is available under the Apache 2.0 license and can be run self-hosted (Docker, Kubernetes/Helm) or used as a managed service on Grafana Cloud.

MLflow is an open-source AI engineering platform for agents, large language models, and machine-learning models, developed in the MLflow repository. It provides experiment tracking for models, parameters, metrics, and evaluation results, along with production observability, evaluation, prompt versioning and optimization, and model lifecycle tools. Its agent and LLM observability captures application traces using OpenTelemetry and supports different LLM providers and agent frameworks; its AI Gateway provides an OpenAI-compatible interface for routing requests, managing rate limits and credentials, handling fallbacks, controlling costs, and applying guardrails. MLflow can be run as a server and accessed through Python, TypeScript/JavaScript, Java, and other programming languages, with integrations including OpenTelemetry and MCP.

Phoenix is an open-source AI observability platform developed by Arize for experimenting with, evaluating, and troubleshooting LLM applications and agents. It traces application runtime with OpenTelemetry-based instrumentation, supports response and retrieval evaluations, and provides versioned datasets, experiment tracking, prompt management, and a playground for comparing models, adjusting parameters, and replaying traced calls. Phoenix also includes PXI, an AI engineering agent for debugging traces and iterating on prompts, plus a remote MCP server for querying traces, datasets, and experiments from MCP clients. It can run locally, in containers, through Kubernetes and Helm, or in the cloud, and is distributed through the `arize-phoenix` Python package, Conda, Docker images, and related TypeScript and Python packages.

LocalJev is a TypeScript service that provides a local Jev-compatible decision API for applications using TypeSafe's typed decision interface. It exposes a POST /v1/systemone endpoint and translates typed Jev questions and application state into classification prompts for an OpenAI-compatible DiffusionGemma inference server. The service validates and retries malformed JSON, normalizes probability vectors, calculates Jev-compatible choices, expected scores, and entropy-based confidence, and returns the standard Jev response shape. Its probabilities are model-generated or self-reported rather than read directly from model logits, so the repository advises evaluating calibration for the intended workload. It runs with Bun and can use oMLX as the local inference server; the default LocalJev address is http://127.0.0.1:8080.