phoenix and openinspector

While both tools provide observability for LLM interactions, Phoenix offers a comprehensive AI observability and evaluation platform that could incorporate the localized, lightweight interception and tracing provided by OpenInspector as a data source or pre-processing layer, suggesting a **complementary** relationship.

phoenix

Verified

openinspector

Experimental

Maintenance 25/25

Adoption 25/25

Maturity 25/25

Community 19/25

Maintenance 13/25

Adoption 0/25

Maturity 9/25

Community 0/25

Stars: 8,847

Forks: 753

Downloads: 1,013,605

Commits (30d): 330

Language: Jupyter Notebook

License: —

Stars: —

Forks: —

Downloads: —

Commits (30d): 0

Language: TypeScript

License: Apache-2.0

No risk flags

No Package No Dependents

About phoenix

Arize-ai/phoenix

AI Observability & Evaluation

Provides OpenTelemetry-based tracing, LLM-powered evaluation, versioned datasets, and experiment tracking across LLM frameworks (LangGraph, LlamaIndex, Claude/OpenAI agent SDKs) and providers. Features a web UI with prompt optimization playground, dataset management, and call replay capabilities. Runs locally, in notebooks, or containerized with Helm support, and integrates via auto-instrumentation through the OpenInference standard.

About openinspector

as32608/openinspector

A lightweight, local-first observability proxy and dashboard designed to intercept, log, and trace LLM interactions. OpenInspector acts as a transparent middleman, offering full visibility into agentic workflows, tool executions, and latency metrics without requiring you to change a single line of your application code.

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