{"categories":[{"label":"Monitoring","url":"https://skillfed.io/packages/category/system-monitoring/3"}],"enrichment":{"capability":"OpenLIT provides OpenTelemetry-native auto-instrumentation for monitoring LLM applications, vector databases, and GPU usage, sending traces and metrics to observability platforms.","skillfed_tags":["llm-observability","opentelemetry","auto-instrumentation"],"use_cases":["Monitor token usage and costs across multiple LLM providers in a single application to track GenAI spending.","Trace LLM request latency and errors through LangChain or LlamaIndex pipelines to debug slow or failing AI workflows.","Instrument vector database queries (ChromaDB, Pinecone) to correlate retrieval performance with LLM response quality.","Export GPU metrics (NVIDIA, AMD) alongside LLM traces to correlate compute resource usage with inference performance.","Send LLM observability data to existing monitoring tools for unified monitoring of AI and traditional infrastructure.","Track custom and fine-tuned model costs using custom pricing files for accurate budget forecasting."],"what_it_does":"OpenLIT is an OpenTelemetry-native SDK that automatically instruments LLM applications, vector databases, and GPU operations to generate traces and metrics without code changes. It works by wrapping calls to LLM providers (OpenAI, Anthropic, Cohere, etc.), vector databases (ChromaDB, Pinecone, Qdrant, etc.), and AI frameworks (LangChain, LlamaIndex, Haystack, etc.), capturing request/response data, latency, token usage, and costs. The generated telemetry follows OpenTelemetry semantic conventions and can be exported to any OTLP-compatible backend.\n\nThe package is designed for developers building GenAI applications who need observability without modifying their application logic. It handles the complexity of instrumenting multiple LLM providers and frameworks through a single initialization call. The 26 runtime dependencies include the OpenTelemetry SDK, instrumentation modules for common web frameworks (FastAPI, Flask, Django, etc.), and client SDKs for major LLM and cloud providers (OpenAI, Anthropic, boto3). This breadth of dependencies means the package pulls in significant infrastructure but also ensures broad compatibility out of the box.","worth_installing":"Yes. OpenLIT is actively maintained, has low install friction, carries a permissive Apache-2.0 license, and solves a real problem\u2014observability for LLM applications\u2014with minimal code changes. The broad instrumentation coverage and OpenTelemetry-native design make it a practical choice for production GenAI monitoring. No known vulnerabilities as of 2026-08-14. Install if you need LLM observability and already use or plan to adopt OpenTelemetry."},"id":"openlit","links":{"html":"https://skillfed.io/packages/openlit","md":"https://skillfed.io/packages/openlit.md","pypi":"https://pypi.org/project/openlit/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-03","license_spdx":null,"license_treatment":"permissive","name":"openlit","python_support":"supports_current","summary":"OpenTelemetry-native Auto instrumentation library for monitoring LLM Applications and GPUs, facilitating the integration of observability into your GenAI-driven projects"},"popularity":{"monthly_downloads":412347,"position":6848,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.45.0"}