OpenLIT is an open-source, self-hosted tool for developing AI apps. It allows for LLM experimentation, prompt management, secure API key handling, and protection against prompt injection. It also offers OpenTelemetry-native observability for the GenAI stack.
Hello PH!!! I'm Aman, the maintainer of OpenLIT, and I'm thrilled to announce our second launch—OpenLIT 2.0! 🚀
With this version, we're enhancing our open-source, self-hosted AI Engineering and analytics platform to make it even more powerful and effortless to integrate. We understand the challenges of evolving an LLM MVP into a robust product—high inference costs, debugging hurdles, security issues, and performance tuning can be hard AF. OpenLIT is designed to provide essential insights and ease this journey for all of us developers.
Here's what's new in OpenLIT 2.0:
- ⚡ OpenTelemetry-native Tracing and Metrics
- 🔌 Vendor-neutral SDK for flexible data routing
- 🔍 Enhanced Visual Analytical and Debugging Tools
- 💭 Streamlined Prompt Management and Versioning
- 👨👩👧👦 Comprehensive User Interaction Tracking
- 🕹️ Interactive Model Playground
- 🧪 LLM Response Quality Evaluations
As always, OpenLIT remains fully open-source (Apache 2) and self-hosted, ensuring your data stays private and secure in your environment while seamlessly integrating with over 30 GenAI tools in just one line of code.
Check out our Quickstart Guide (https://docs.openlit.io/latest/q...) to see how OpenLIT 2.0 can streamline your AI development process.
If you're on board with our mission and vision, we'd love your support with a ⭐ star on GitHub (https://github.com/openlit/openlit).
I'm here to chat and eager to hear your thoughts on how we can continue improving OpenLIT for you! 😊
