{"categories":[{"label":"Monitoring","url":"https://skillfed.io/packages/category/system-monitoring"}],"enrichment":{"capability":"Defines semantic conventions and standardized attributes for tracing AI applications, particularly LLM invocations, retrieval operations, and tool usage, for use with OpenTelemetry-compatible backends.","skillfed_tags":["llm-observability","opentelemetry","ai-tracing"],"use_cases":["Standardize trace attributes when instrumenting LLM applications with OpenTelemetry for observability.","Ensure consistent semantic meaning of trace events across different AI frameworks and backends.","Build custom instrumentation libraries that emit traces following OpenInference conventions.","Integrate with Arize Phoenix or other OpenTelemetry backends using a common trace schema.","Document and enforce tracing standards for retrieval-augmented generation (RAG) and agent workflows."],"what_it_does":"OpenInference Semantic Conventions is a specification library that standardizes how AI application traces\u2014particularly LLM calls, vector store retrievals, and external tool invocations\u2014are represented and labeled. It works alongside OpenTelemetry to provide a common vocabulary for observability across AI systems, enabling consistent tracing whether you use Arize Phoenix, Arize AX, or any other OpenTelemetry-compatible backend.\n\nThe package itself is a pure Python module with no external runtime dependencies, making it lightweight to integrate into instrumentation libraries and AI frameworks. It defines the attribute names, semantic meanings, and conventions that other OpenInference instrumentation packages (for LangChain, LlamaIndex, OpenAI, and others) use when emitting trace data. If you are building or using an AI application that needs observability, this package provides the foundational schema that ensures your traces are interpreted consistently across tools.","worth_installing":"Yes, if you are building or instrumenting AI applications and need a shared semantic vocabulary for tracing. It is lightweight, actively maintained, and essential infrastructure for any project using OpenInference instrumentation libraries or OpenTelemetry with AI workloads. No security vulnerabilities and permissive licensing. Install it as a dependency of instrumentation packages rather than directly unless you are defining custom tracing logic."},"id":"openinference-semantic-conventions","links":{"html":"https://skillfed.io/packages/openinference-semantic-conventions","md":"https://skillfed.io/packages/openinference-semantic-conventions.md","pypi":"https://pypi.org/project/openinference-semantic-conventions/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"openinference-semantic-conventions","python_support":"supports_current","summary":"OpenInference Semantic Conventions"},"popularity":{"monthly_downloads":8531636,"position":1610,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.1.32"}