The article also provides a guide on getting started with Laminar, either through their managed platform or self-hosting with Docker compose. It explains how to instrument Python code using Laminar, including automatic instrumentation of LLM calls and a simple `@observe()` decorator for tracing inputs/outputs of functions. Users can also send events in two ways and create Laminar pipelines in the UI to manage chains of LLM calls. The article concludes by directing readers to client libraries and documentation for further learning.
Key takeaways:
- Laminar is an open-source platform for observability, analytics, and prompt chains for complex LLM apps, comparable to DataDog and PostHog.
- It offers OpenTelemetry-based instrumentation, semantic events-based analytics, and is built for scale with a modern stack including Rust, RabbitMQ, Postgres, and Clickhouse.
- Laminar can be easily started with a free tier on their managed platform or self-hosted with Docker compose.
- Laminar provides automatic instrumentation for LLM calls and a simple '@observe()' decorator for tracing inputs/outputs of functions. It also allows for the creation and management of Laminar pipelines in the UI.