DeepEval OpenTelemetry Export

Use this skill to instrument an AI application, an LLM app, agent, RAG pipeline, or chatbot, with raw OpenTelemetry so its traces land in Confident AI's Observatory. No deepeval package is needed, it works with any OTLP-capable OpenTelemetry SDK. The job is exactly two things: export to the correct Confident AI OTLP endpoint, and set the confident.* attributes Confident AI reads off each span.

Scope: AI Applications Only

This skill instruments AI applications only. The confident.* attributes and span types, agent, llm, retriever, tool, describe AI components, and Confident AI's Observatory is built to evaluate and monitor AI behavior.

Instrument only the AI parts of the system: agent loops and planning, LLM calls, retrieval / vector search, and tool calls. Do not apply confident.* attributes to non-AI software (web servers, CRUD backends, database layers, infrastructure) or to non-AI spans inside an otherwise-AI app, that data does not belong in Confident AI and will not render meaningfully. If the target has no LLM, agent, retrieval, or tool-calling component, this skill does not apply.

When to Use vs the deepeval Skill

Use this skill for vendor-neutral OTLP export to Confident AI, pointing an OpenTelemetry exporter at Confident AI and setting confident.* attributes.

Use the deepeval skill when the user wants to build a Python pytest eval suite, generate datasets or goldens, write metrics, run deepeval test run, or instrument with the deepeval SDK's @observe decorator. The two skills are complementary, not alternatives.

Prerequisites

• A Confident AI account and a CONFIDENT_API_KEY.
• An OpenTelemetry SDK for the application's language. For Python: opentelemetry-sdk and opentelemetry-exporter-otlp-proto-http.
• The Confident AI OTLP endpoint accepts HTTP only, never gRPC.

How It Works