An open-source platform for AI engineering with OpenTelemetry-native LLM observability, GPU monitoring, guardrails, evaluations, and prompt management.
OpenLIT is an open-source platform for AI engineering that provides comprehensive observability, monitoring, and management tools for LLM applications. It solves the problem of fragmented AI development workflows by integrating observability, evaluations, guardrails, and prompt management into a single platform. With OpenTelemetry-native instrumentation, it offers full-stack monitoring across LLM providers, vector databases, and GPUs.
AI engineers, ML developers, and DevOps teams building and deploying production LLM applications who need observability, evaluation, and management capabilities. It's particularly valuable for teams using multiple LLM providers and AI frameworks.
Developers choose OpenLIT because it provides a unified, open-source platform with vendor-neutral OpenTelemetry integration, eliminating lock-in to proprietary observability tools. Its comprehensive feature set—from observability to evaluations and prompt management—reduces the need for multiple disparate tools.
Open source platform for AI Engineering: OpenTelemetry-native LLM Observability, GPU Monitoring, Guardrails, Evaluations, Prompt Management, Vault, Playground. 🚀💻 Integrates with 50+ LLM Providers, VectorDBs, Agent Frameworks and GPUs.
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Auto-instruments over 50 LLM providers, AI frameworks, and vector databases with a single line of code, as listed in the extensive supported integrations table.
Offers 11 automated evaluation types including hallucination and bias detection, using LLM-as-a-Judge for context-aware assessment to ensure response quality.
Adheres to OpenTelemetry semantic conventions, providing vendor-neutral observability that avoids lock-in to proprietary tools and supports existing ecosystems.
Includes Prompt Hub for versioning prompts, Vault for secure API key management, and Fleet Hub for monitoring OpenTelemetry collectors across infrastructure.
Requires self-hosting via Docker Compose or Kubernetes, involving multiple services like ClickHouse, which adds operational overhead and resource commitment.
The roadmap shows key features like auto-evaluation metrics and human feedback are still 'Coming Soon,' indicating gaps in current capabilities for advanced use cases.
Only provides SDKs for Python, TypeScript, and Go, excluding support for other common languages like Java or C#, which may limit adoption in polyglot environments.