{"categories":[{"label":"Monitoring","url":"https://skillfed.io/packages/category/system-monitoring"}],"enrichment":{"capability":"Adds distributed tracing to HuggingFace Transformers library calls, capturing prompts, completions, and embeddings as OpenTelemetry spans for observability and debugging.","skillfed_tags":["observability","llm-tracing","huggingface"],"use_cases":["Debug LLM application behavior by inspecting prompts and completions in distributed traces","Monitor text generation performance and latency across your application infrastructure","Correlate Transformers calls with upstream requests and downstream database operations in end-to-end traces","Audit model inputs and outputs for compliance or quality evaluation in production systems","Reduce trace verbosity in privacy-critical deployments by disabling content logging"],"what_it_does":"This package wraps HuggingFace Transformers library calls with OpenTelemetry instrumentation, automatically capturing text generation and embedding operations as distributed traces. Once initialized with a single call to `TransformersInstrumentor().instrument()`, it intercepts Transformers API calls and records them as spans containing prompts, completions, and embeddings\u2014useful for debugging model behavior, tracking inference patterns, and correlating LLM calls with the rest of your application's trace data.\n\nBy default, all content (prompts and outputs) is logged to span attributes for full visibility. For privacy-sensitive applications or to reduce trace size, you can disable content logging via the `TRACELOOP_TRACE_CONTENT` environment variable. The package requires Python 3.10 or later and depends on opentelemetry-api, opentelemetry-instrumentation, opentelemetry-semantic-conventions-ai, and opentelemetry-semantic-conventions.","worth_installing":"Yes, if you use HuggingFace Transformers and already have OpenTelemetry infrastructure in place. The package is actively maintained, has no known vulnerabilities, installs with low friction, and solves a real observability gap for LLM applications. Start with it if you need visibility into Transformers calls; skip it if you have no tracing backend or don't use Transformers."},"id":"opentelemetry-instrumentation-transformers","links":{"html":"https://skillfed.io/packages/opentelemetry-instrumentation-transformers","md":"https://skillfed.io/packages/opentelemetry-instrumentation-transformers.md","pypi":"https://pypi.org/project/opentelemetry-instrumentation-transformers/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-10","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"opentelemetry-instrumentation-transformers","python_support":"supports_current","summary":"OpenTelemetry transformers instrumentation"},"popularity":{"monthly_downloads":6847828,"position":1835,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.62.3"}