OpenInference

OpenInference is an open-source set of OpenTelemetry-complementary semantic conventions, instrumentation libraries, and plugins for tracing AI applications. Its specification models LLM invocations and surrounding context such as vector-store retrieval and external tool use in a transport- and file-format-agnostic schema, while its instrumentations normalize traces from model, agent, and application frameworks across Python, JavaScript, Java, and Go. The project also defines conventions for real-time audio and voice-agent traces, allowing audio, transcripts, and tool calls to be represented in a queryable session trace. It is natively supported by Arize Phoenix and Arize AX and can send data to any OpenTelemetry-compatible backend.

An OpenInference instrumentation tool for capturing voice-agent audio and trace data and sending it to a compatible backend for analysis. It supports tracing real-time audio with OpenInference semantic conventions across providers, including session data such as audio, transcripts, traces, interruptions, latency, and tool calls.

Arize Phoenix is an open-source AI observability and evaluation platform developed by Arize AI for tracing, evaluating, and debugging AI agents and LLM applications. It collects traces of LLM requests and the surrounding application execution, displays them in a trace UI, and supports evaluation storage, datasets, experiments, annotations, and result comparisons. Phoenix can use OpenInference telemetry to record execution paths across multiple application steps, helping identify failures in retrieval, tool execution, or model responses; it also supports code evaluations and LLM-as-a-judge evaluations. It can run locally or be self-hosted in another environment.

Arize AX is an AI engineering platform from Arize AI for observing and evaluating AI agents, including voice agents. It provides a unified session and trace view that combines audio, transcripts, latency metrics, tool calls, and evaluation results, allowing failures such as dead air, interruptions, transcription errors, and incorrect tool actions to be investigated together. The platform supports tracing real-time audio with OpenInference semantic conventions, audio-native evaluations for factors such as sentiment, latency, interruptions, and task success, and agent experiments that test fixes against failed traces. It is available through the Arize AX product interface and a terminal-based setup.

Arize AI is an AI-agent observability, evaluation, tracing, and experimentation platform for diagnosing production behavior, identifying issues, and testing changes. It stores production traces, evaluations, annotations, datasets, and experiments, with instrumentation adapters for multiple frameworks and LLM providers. It combines audio, transcripts, and traces in a session view for investigating voice-agent problems such as latency, interruptions, transcription errors, and incorrect tool actions. Its audio workflows use OpenInference semantic conventions to trace real-time audio across providers, run evaluations for sentiment, latency, interruptions, and task success, and attach evaluation results to spans. Its Phoenix tooling includes code-mode capabilities such as Bash, a Python sandbox, SQL, and user-interface control, allowing agents to search for and compose tools in code rather than placing complete tool catalogs and raw outputs in their context. Its CLI and skills can bring spans and evaluation results into a coding agent's workspace, where the agent can inspect supervisor, specialist-agent, model, and tool calls, and create or use evaluators.