How easy is it to integrate OpenLIT?

Integration is very simple - you just need to add one line of code 'openlit.init()' to start collecting data from your LLM application. It offers seamless integration with native OpenTelemetry support.

What are the main features of OpenLIT?

OpenLIT's main features include: application and request tracing, exceptions monitoring, cost tracking, prompt management with versioning, secure secrets management through Vault Hub, LLM comparison tools through Openground, and OpenTelemetry-native observability for LLMs, vector databases, and GPUs.

Is OpenLIT privacy-focused?

Yes, OpenLIT is privacy-focused. Users can see exactly what the code does, and they have the option to self-host the platform for additional privacy control.

What kind of monitoring capabilities does OpenLIT provide?

OpenLIT provides comprehensive monitoring including end-to-end request tracing, detailed span tracking, automatic exception monitoring with stacktraces, cost tracking, and real-time data streaming. It also supports integration with popular observability platforms like Datadog and Grafana Cloud.

How does OpenLIT handle prompt management?

OpenLIT offers a centralized prompt repository that allows for organized storage, versioning (with major, minor, and patch updates), and usage of prompts with dynamic variables. It supports creating drafts and customizing prompts using specific variables with the {{variableName}} convention.

OpenLIT 使い方

WebsiteFreeAI DevOps Assistant Monitor & Log Management

OpenLIT is an open-source, OpenTelemetry-native observability tool designed specifically for GenAI and LLM applications, offering comprehensive monitoring, prompt management, and evaluation capabilities with just one line of code integration.

詳細情報

OpenLITの使い方

Install OpenLIT: Install OpenLIT using either npm ('npm i openlit') for TypeScript/JavaScript projects or pip ('pip install openlit') for Python projects

Initialize OpenLIT: Add 'openlit.init()' to your code to start collecting observability data from your LLM application. You can configure options like OTLP endpoint and application name during initialization

Deploy OpenLIT Backend: Deploy the OpenLIT backend using either Docker Compose ('docker-compose up -d') for quick setup or Helm charts for Kubernetes deployment. This sets up the UI and required backend services

Configure Secret Management: Use OpenLIT Vault to securely store and manage API keys and other sensitive credentials. Access the Vault through the UI or SDK to store/retrieve secrets

Set Up Prompt Management: Use the Prompt Hub in the UI to create, version and manage your prompts. You can use variables in prompts with {{variableName}} syntax for dynamic content

Monitor Application: Access the OpenLIT dashboard to view traces, metrics, costs and exceptions. You can analyze LLM performance, track errors, and monitor GPU usage

Test LLMs with OpenGround: Use the OpenGround playground to experiment with and compare different LLM providers side-by-side based on performance, cost and other metrics

Set Up Evaluations: Configure evaluations for hallucination, bias and toxicity detection using the evals module to assess LLM outputs quality

Export Data: Optionally configure connections to send OpenLIT's observability data to external tools like Prometheus, Grafana or other OpenTelemetry-compatible platforms

OpenLITのよくある質問

OpenLIT is an open-source platform for AI engineering that simplifies AI development workflow, especially for Generative AI and LLMs. It provides observability, prompt management, and secrets handling capabilities with OpenTelemetry-native support.