OpenLIT provides an open-source platform for AI Engineering, focusing on LLM observability, GPU monitoring, and prompt management. It targets developers building generative AI applications, offering a streamlined workflow from experimentation to production with a single line of code integration. The platform aims to simplify AI development by providing comprehensive monitoring, versioning, and secure secret management.

How It Works

OpenLIT leverages OpenTelemetry-native SDKs to send traces and metrics to an OpenTelemetry Collector, which then stores the data in ClickHouse. The OpenLIT UI pulls this data from ClickHouse for visualization and analysis. This approach allows seamless integration with existing observability stacks and provides vendor-neutral, full-stack monitoring for LLMs, vector databases, and GPUs.

Quick Start & Requirements

• Install: pip install openlit
• Prerequisites: Docker for self-hosting the OpenLIT stack. Python 3.x.
• Setup: Deploy the OpenLIT stack using docker compose up -d. Integrate the SDK into your application with import openlit; openlit.init(). Configure the OTLP endpoint via code or environment variables.
• Links: Documentation, Quickstart, Python SDK, Typescript SDK

Highlighted Details

• OpenTelemetry-native observability for LLMs, GPUs, and vector databases.
• Features include analytics dashboards, cost tracking for custom models, exceptions monitoring, prompt management, and secure API key handling.
• Supports 50+ LLM providers, vector databases, agent frameworks, and GPUs.
• Includes "OpenGround" for LLM experimentation and comparison.

Maintenance & Community

• Active community with Slack and Discord channels for support and discussion.
• Regular updates to align with OpenTelemetry Semantic Conventions.
• GitHub issues available for bug reporting and feature requests.
• Follow on X: @openlit_io.

Licensing & Compatibility

• Licensed under the Apache-2.0 license.
• Permissive license suitable for commercial use and integration into closed-source applications.

Limitations & Caveats

The roadmap indicates features like "Auto-Evaluation Metrics Based on Usage" and "Search over Traces" are "Coming Soon," suggesting these capabilities are not yet fully implemented.