Laminar is an open-source observability platform designed to help developers build and monitor reliable AI agents. It provides tools for tracing, evaluating, and analyzing AI agent performance in production.
Key Features:
- Tracing: Comprehensive tracing of AI agents, including support for popular LLM frameworks and SDKs like LangChain, CrewAI, and OpenAI.
- Evaluation: Tools for creating and running evaluations to improve agent performance, with zero boilerplate evaluation SDK.
- Analysis: Built-in SQL editor for analyzing traces, metrics, and events, enabling custom dashboards and dataset creation.
- Open-Source: Fully open-source, allowing self-hosting or cloud usage, with a transparent and extensible architecture.
- Scalability: Rust-powered and optimized for performance, capable of ingesting millions of traces per day.
- Browser Agent Observability: Automatically captures browser window recordings synced with agent traces.
Use Cases:
- Monitoring AI agents in production to understand failure modes.
- Evaluating and iterating on prompts and models to improve performance.
- Debugging long-running agents in real-time.
- Tracking custom metrics and events to gain insights into agent behavior.
- Building high-quality evaluation datasets efficiently.
