OpenLIT

<# OpenLIT: OpenTelemetry-native GenAI and LLM Application Observability #>

OpenLIT is an open-source observability platform designed for GenAI and LLM applications. It helps you understand, monitor, and optimize AI-powered workloads by providing end-to-end tracing, exception monitoring, cost analysis, prompt management, secret management, and seamless integration with OpenTelemetry. It supports running locally (docker-compose) and can be hosted yourself, offering transparency into what your code does and how it performs across different providers and models.

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What OpenLIT Provides

• End-to-end tracing of requests across different providers to improve performance visibility
• Detailed span tracking for response time and efficiency
• OpenTelemetry-native instrumentation for AI apps
• Cost tracking and detailed reporting to aid budgeting and decision-making
• Automatic exception monitoring and detailed stack traces to diagnose issues
• Integration with Traces to capture exceptions within request flows
• A playground to compare LLMs side-by-side on performance, cost, and metrics
• Centralized prompt repository with versioning and dynamic variables
• Secrets management (Vault Hub) with secure storage, access, and environment integration
• Easy-to-use SDK integrations for Python and TypeScript
• Real-time data streaming for quick visualization and decision making
• Observability platform integrations (Datadog, Grafana Cloud, etc.)
• Open source: easy onboarding with docker-compose and self-hosting capabilities

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How to Use OpenLIT

1. Run locally with Docker:

• docker-compose up -d

1. Initialize the OpenLIT client in your project:

• Add openlit.init() to start collecting data from your LLM application

1. Instrument your code to emit traces, metrics, and logs via OpenTelemetry-compatible mechanisms
2. Explore:

• Side-by-side LLM comparisons in OpenLIT PlayGround
• Cost analysis dashboards and detailed reports
• Centralized prompts and secrets management

1. Connect to your favorite observability tools (Datadog, Grafana Cloud) for export and visualization

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Core Features

• End-to-end tracing of requests across LLM providers via OpenTelemetry
• Detailed span tracking for performance and efficiency analysis
• Automatic exception monitoring with detailed stack traces
• Side-by-side LLM comparison in PlayGround (performance, cost, metrics)
• Centralized prompt repository with versioning and dynamic variable substitution
• Secrets management via Vault Hub with secure storage and environment integration
• Environment-aware secret access and secure key management
• Real-time data streaming for immediate visibility into AI workloads
• OpenTelemetry-native integration for seamless instrumentation
• Cost analysis and comprehensive reporting for budget optimization
• Easy onboarding: self-hosted solution with docker-compose
• Observability platform integrations for export to Datadog, Grafana Cloud, etc.

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How It Works

• Instrument your LLM applications with OpenTelemetry and OpenLIT SDKs (Python/TypeScript)
• Collect traces, metrics, logs, and secrets to obtain full observability across models, prompts, and calls
• Use the PlayGround to compare models on real-time performance and cost
• Store prompts and secrets securely; substitute variables at runtime
• Export data to your preferred observability stack for dashboards and alerts

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Safety and Privacy

• OpenLIT is an open-source tool designed for transparency; ensure you do not log sensitive data in traces or prompts
• Use Vault Hub and secure environment handling to protect credentials and API keys
• Follow best practices for data minimization and access controls when instrumenting your applications

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Getting Started Quick Reference

• Start locally: docker-compose up -d
• Initialize: openlit.init() in your app
• Instrument: add tracing and metrics via OpenLIT/OpenTelemetry
• Analyze: use PlayGround and cost reports to optimize
• Secure: manage secrets with Vault Hub and environment variables

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Target Audience

• AI engineers and MLOps teams building GenAI/LLM-powered apps
• Teams needing end-to-end observability, cost control, and reliability for AI workloads
• Open-source enthusiasts seeking a transparent, self-hosted observability solution