📄 中文摘要
与 Chatwoot 的联合创始人 Pranav 的对话揭示了 LLM 可观测性面临的挑战。在生产环境中构建、调试和改进 AI 代理的过程变得复杂，存在多种竞争标准的默认库。许多声称基于 OpenTelemetry 的库，如 OpenInference，并未严格遵循其规范，这给用户在提高其技术栈的可观测性时带来了问题。此次讨论强调了在将 LLM 功能应用于实际产品时，遵循统一标准的重要性。
LLM 可观测性在实际应用中的重要性 - 为什么 OpenTelemetry 应该成为标准
出处: LLM Observability in the Wild - Why OpenTelemetry should be the Standard
发布: 2026年2月25日
📄 English Summary
LLM Observability in the Wild - Why OpenTelemetry should be the Standard
A recent conversation with Pranav, co-founder of Chatwoot, highlighted the challenges surrounding LLM observability. Building, debugging, and improving AI agents in production can quickly become complicated due to the presence of multiple competing standards for default libraries. Many libraries, such as OpenInference, which claim to be based on OpenTelemetry, do not strictly adhere to its conventions. This inconsistency poses problems for users aiming to enhance observability across their tech stack. The discussion underscores the importance of adhering to a unified standard when shipping LLM features into real products.