OpenLIT
An open-source platform for AI engineering with OpenTelemetry-native LLM observability, GPU monitoring, guardrails, evaluations, and prompt management.
What is OpenLIT?
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.
Overview
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.
Use Cases
Best For
- Monitoring LLM application performance and costs across multiple providers
- Implementing automated LLM evaluation and guardrails for production deployments
- Managing and versioning prompts across different AI applications
- Self-hosting an observability platform for AI/ML workloads
- Tracking GPU utilization and costs in AI inference pipelines
- Securely managing API keys and secrets for various LLM services
Not Ideal For
- Projects relying solely on cloud LLM platforms with integrated monitoring dashboards, as OpenLIT's value diminishes without multi-provider or custom model usage.
- Teams with entrenched, non-OpenTelemetry observability stacks that would require significant migration effort.
- Rapid prototyping or small-scale experiments where deploying Docker containers and ClickHouse is overly burdensome.
- Organizations needing out-of-the-box SaaS solutions without the overhead of self-hosting and maintenance.
Pros & Cons
Pros
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.
Cons
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.
Frequently Asked Questions
Related Projects
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bitnet.cppOfficial inference framework for 1-bit LLMs
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