Overview:
OpenLIT is an open-source platform designed for AI engineering workloads, with a focus on Generative AI and LLMs. It provides a unified interface for observability, prompt management, secure API key handling, and LLM experimentation. The platform helps developers and teams move AI applications from testing to production by offering OpenTelemetry-native monitoring that covers LLMs, vector databases, and GPUs. It is suitable for developers building or operating AI features and applications.
Core Features:
OpenTelemetry-native Observability SDKs: Vendor-neutral SDKs for Python, TypeScript, and Go that send traces and metrics to existing observability tools.
Built-in Evaluation Types: 11 automated LLM-as-a-Judge evaluations for hallucination, bias, toxicity, safety, instruction following, completeness, conciseness, sensitivity, relevance, coherence, and faithfulness.
Rule Engine: Define conditional rules with AND/OR logic to match runtime trace attributes and dynamically retrieve contexts, prompts, and evaluation configs.
Prompt Management: Manage and version prompts using the Prompt Hub for consistent access across applications.
API Keys and Secrets Management: Centrally store and handle API keys and secrets to avoid insecure practices.
Fleet Hub for OpAMP Management: Centrally manage and monitor OpenTelemetry Collectors across infrastructure using the Open Agent Management Protocol (OpAMP) with TLS communication.
Use Cases:
Monitoring AI application health: Use the analytics dashboard to track metrics, costs, and user interactions for AI applications.
Evaluating LLM responses: Automatically assess model outputs for safety, relevance, and other quality criteria using built-in evaluation types.
Managing prompts across applications: Version and organize prompts in the Prompt Hub for consistent use across different environments.
Securing LLM API keys: Centralize storage and management of API keys and secrets to avoid insecure handling practices.
Why It Matters:
OpenLIT integrates observability, evaluation, prompt management, and secret handling into a single, self-hostable platform. Its use of OpenTelemetry-native SDKs means teams can send traces and metrics to their existing observability stacks without vendor lock-in. The platform also includes a rule engine for dynamic trace-based configuration and Fleet Hub for managing OpenTelemetry Collectors at scale, making it a practical choice for teams that need control over their AI engineering toolchain.
