OpenLIT

Free

Open-source GenAI and LLM observability platform native to OpenTelemetry with traces and metrics. #opensource

FreeFree tier

Type

Open Source

Company

OpenLIT (project)

Visit Website GitHub (40 stars)

About OpenLIT

OpenLIT is an open-source platform for AI engineering, purpose-built for Generative AI and LLM applications. It provides OpenTelemetry-native observability with a single line of code, enabling full-stack monitoring of LLMs, vector databases, and GPUs. The platform streamlines LLM experimentation, prompt versioning and management, secure API key handling, and includes built-in guardrails, evaluations (11 types like hallucination, bias, toxicity), a rule engine with AND/OR logic, cost tracking for custom and fine-tuned models, exceptions monitoring, and an analytics dashboard. OpenLIT integrates with over 50 LLM providers, vector databases, agent frameworks, and GPUs, and follows OpenTelemetry Semantic Conventions for vendor-neutral telemetry.

Key Features

Analytics Dashboard for monitoring health, performance, costs, and user interactions

OpenTelemetry-native observability SDKs for Python, TypeScript, and Go

11 built-in evaluation types including hallucination, bias, toxicity, safety, instruction following, completeness, conciseness, sensitivity, relevance, coherence, and faithfulness

Rule Engine with AND/OR logic to match runtime trace attributes and dynamically retrieve contexts, prompts, and evaluation configs

Cost tracking for custom and fine-tuned models using custom pricing files

Exceptions monitoring dashboard for quick issue resolution

Prompt Management with versioning via Prompt Hub

API Keys and Secrets Management for secure credential handling

Integration with 50+ LLM providers, vector databases, agent frameworks, and GPUs

Vault and Playground features for secure storage and experimentation

Pros & Cons

Pros

Open-source and vendor-neutral with OpenTelemetry-native instrumentation
Single line of code setup for observability
Comprehensive monitoring covering LLMs, vector databases, and GPUs
Built-in automated LLM-as-a-Judge evaluations for safety and quality
Flexible rule engine for dynamic context and prompt retrieval
Cost tracking for custom and fine-tuned models
Actively follows OpenTelemetry Semantic Conventions and community standards
Integrates with over 50 LLM providers and popular frameworks

Cons

Requires familiarity with OpenTelemetry concepts and observability backends
Self-hosted solution, requiring deployment and maintenance effort
Learning curve for initial setup and configuration of the full stack

Best For

Monitoring and optimizing LLM application performance and costsExperimenting with different LLMs and prompts during developmentEvaluating LLM outputs for safety, bias, toxicity, and instruction followingManaging and versioning prompts across teams and environmentsSecurely handling API keys and secrets for AI servicesTracking GPU utilization and debugging issuesTransitioning AI applications from testing to production with observability

FAQ

What is OpenLIT?

OpenLIT is an open-source platform for AI engineering, providing OpenTelemetry-native observability, guardrails, evaluations, prompt management, and more for Generative AI and LLM applications.

What programming languages are supported?

OpenLIT offers SDKs for Python, TypeScript, and Go.

Can I use OpenLIT with my existing observability tools?

Yes, OpenLIT is vendor-neutral and sends traces and metrics to any OpenTelemetry-compatible backend.

Is OpenLIT free?

Yes, OpenLIT is open-source and free to use under its license.

What types of evaluations does OpenLIT support?

It includes 11 built-in evaluation types: hallucination, bias, toxicity, safety, instruction following, completeness, conciseness, sensitivity, relevance, coherence, and faithfulness.

How do I get started?

Visit the GitHub repository (github.com/openlit/openlit) for documentation, quickstart guides, and SDK references.