At a Glance:
OpenLIT is an open-source AI engineering platform providing OpenTelemetry-native observability for LLMs, vector databases, and GPUs, along with prompt management, 11 built-in evaluation types, a rule engine, and a vault for API keys, deployable via Docker.
Overview:
OpenLIT is an open-source platform that simplifies AI development workflows, especially for Generative AI and LLMs. It integrates OpenTelemetry-native observability across 50+ LLM providers, AI frameworks, and vector databases with a single line of code, enabling full-stack monitoring. Beyond tracing and metrics, OpenLIT centralizes prompt versioning, secret management, and LLM experimentation. It provides tools for automated evaluation against 11 criteria like hallucination and toxicity, and a rule engine to dynamically manage runtime configurations. The platform supports Python, TypeScript, and Go SDKs and can be self-hosted using Docker or Kubernetes, offering a unified interface to move from testing to production.
Key Decision Points:
Observability Standard: Uses a vendor-neutral, OpenTelemetry-native approach for traces and metrics, not a proprietary format, aligning with existing observability tools.
Self-Hosted Deployment: Deployed as a self-hosted stack using Docker or Kubernetes with Helm, keeping observability data and management components within your own infrastructure.
Core Management Hub: Beyond observability, it centralizes prompt versioning (Prompt Hub), API key storage (Vault), and LLM testing (OpenGround), reducing the need for separate tools.
Automated Quality Guardrails: Includes 11 built-in, LLM-as-a-Judge evaluation types for detecting hallucination, bias, safety issues, and more, which can be dynamically applied via the rule engine.
Infrastructure Management: Includes Fleet Hub for centrally managing OpenTelemetry Collectors using the OpAMP protocol with TLS, intended for monitoring the observability layer itself.
Core Features:
OpenTelemetry-native Observability SDKs: Vendor-neutral SDKs for Python, TypeScript, and Go that auto-instrument over 50 LLMs, frameworks, and vector databases to capture traces and metrics.
Analytics Dashboard: A web-based dashboard to monitor AI application health, cost, performance metrics, and user interactions.
11 Built-in Evaluation Types: Automated context-aware evaluation for hallucination, bias, toxicity, safety, faithfulness, relevance, and other criteria.
Rule Engine: A conditional system using AND/OR logic on trace attributes to dynamically fetch contexts, prompts, and evaluation configurations at runtime.
Prompt Hub: A centralized tool for managing and versioning prompts to ensure consistency across applications.
Fleet Hub for OpAMP Management: Central management and monitoring of OpenTelemetry Collectors across infrastructure with secure TLS communication.
Use Cases:
AI Application Developers: Instrument applications to gain full-stack visibility into LLM calls, vector database queries, and associated costs during development and production.
AI Engineers Testing Models: Use OpenGround to experiment with and compare different LLMs, and run the 11 evaluation types to automatically check responses for quality issues like hallucination and bias.
Infrastructure Teams: Deploy the self-hosted stack and use Fleet Hub to centrally manage and monitor a fleet of OpenTelemetry Collectors using the OpAMP protocol.
Platform Engineers: Securely manage API keys and prompt versions across teams in a central Vault and prompt Hub, and enforce dynamic AI guardrails with the rule engine.
Open-Source Alternative Value:
As a self-hosted, open-source platform, OpenLIT gives developers direct control over their AI observability and engineering stack. It integrates with the existing OpenTelemetry ecosystem using vendor-neutral semantic conventions, avoiding lock-in to a proprietary observability backend. The platform combines tracing, evaluations, and key resources like prompts and secrets management into a single deployable unit, consolidating capabilities that might otherwise require multiple separate tools. This structure allows teams to build and monitor an AI infrastructure layer with the ability to customize cost tracking for fine-tuned models.
