Overview
OpenLIT helps you elevate your LLM applications from development to debugging to production. It offers one-click observability across more than 20 GenAI tools, including OpenAI and LangChain. With just one line of code, it can collect and send GPU performance, costs, tokens, user activity, LLM traces, and metrics to any OpenTelemetry endpoint.
Features
- Advanced Monitoring of LLM and VectorDB Performance
- World's First OpenTelemetry-Native Collector for GPU Monitoring
- Cost Tracking for Custom and Fine-Tuned Models
- OpenTelemetry-Native & Vendor-Neutral SDKs allowing you to send data to any OpenTelemetry endpoint
Comments
Badges & Awards
Badges & Awards
Comments
Hi Fazier! I'm Patcher, the founder and maintainer of OpenLIT. Today, we're thrilled to introduce OpenLIT, the first truly open-source observability and analytics platform based on OpenTelemetry, designed to monitor the entire LLM Stack—from the LLM applications themselves down to the GPU infrastructure layer—with just one line of code. Building an MVP with LLMs is fast, but turning it into a polished product is hard. In our previous projects, we've spent countless hours dealing with common challenges that many LLM engineers face. Probabilistic outputs can be inaccurate or costly. High inference costs eat into budgets. Applications often suffer from high latency due to long response times from LLMs. Debugging complex LLM setups like chains, agents, and tools, is toughhhh. Plus, understanding user behavior from open-ended prompts and interactions is difficult. We've faced these challenges too and that's why we created OpenLIT—to support developers by providing crucial insights into production data to enhance LLM application performance (without sweating). Key Features: ⚡ Comprehensive Logging: Log full queries, errors, and metrics for every request. 🔍 Inspect & Debug: Visual UI to track token counts, compute costs, and latency over time. 👨👩👧👦 User Tracking: Monitor user interactions and gather feedback effectively. 🕹️ Prompt Playground: Test and optimize different prompts and LLMs. 🐾 Detailed Traces: Debug complex agent interactions easily. 🧪 Output Evaluations: Ensure high-quality, accurate responses. 💯 Seamless Integration: Export data to your existing observability stack without hassle. Why Choose OpenLIT? 🏗 Open Source: Integrates with 20+ AI tools, customizable, community-driven. 💰 Cost Efficiency: Automatically calculates costs for custom and fine-tuned models. 🧑💻 Self-Hosted: Full control with self-hosting, keeping your data private and secure. We'd love to hear your feedback! Check out our Quickstart Guide to get OpenLIT in action. If you like what you see, support us with a ⭐ star on https://github.com/openlit/openlit. 🥳 Let's chat in the comments—I’d love to discuss your needs and how OpenLIT can help!
Hi Fazier! I'm Patcher, the founder and maintainer of OpenLIT. Today, we're thrilled to introduce OpenLIT, the first truly open-source observability and analytics platform based on OpenTelemetry, designed to monitor the entire LLM Stack—from the LLM applications themselves down to the GPU infrastructure layer—with just one line of code. Building an MVP with LLMs is fast, but turning it into a polished product is hard. In our previous projects, we've spent countless hours dealing with common challenges that many LLM engineers face. Probabilistic outputs can be inaccurate or costly. High inference costs eat into budgets. Applications often suffer from high latency due to long response times from LLMs. Debugging complex LLM setups like chains, agents, and tools, is toughhhh. Plus, understanding user behavior from open-ended prompts and interactions is difficult. We've faced these challenges too and that's why we created OpenLIT—to support developers by providing crucial insights into production data to enhance LLM application performance (without sweating). Key Features: ⚡ Comprehensive Logging: Log full queries, errors, and metrics for every request. 🔍 Inspect & Debug: Visual UI to track token counts, compute costs, and latency over time. 👨👩👧👦 User Tracking: Monitor user interactions and gather feedback effectively. 🕹️ Prompt Playground: Test and optimize different prompts and LLMs. 🐾 Detailed Traces: Debug complex agent interactions easily. 🧪 Output Evaluations: Ensure high-quality, accurate responses. 💯 Seamless Integration: Export data to your existing observability stack without hassle. Why Choose OpenLIT? 🏗 Open Source: Integrates with 20+ AI tools, customizable, community-driven. 💰 Cost Efficiency: Automatically calculates costs for custom and fine-tuned models. 🧑💻 Self-Hosted: Full control with self-hosting, keeping your data private and secure. We'd love to hear your feedback! Check out our Quickstart Guide to get OpenLIT in action. If you like what you see, support us with a ⭐ star on https://github.com/openlit/openlit. 🥳 Let's chat in the comments—I’d love to discuss your needs and how OpenLIT can help!