Laminar

Laminar is an open-source observability platform specifically designed for AI agents. It provides developers with a comprehensive set of tools to trace, evaluate, and improve the behavior of autonomous AI systems. The core functionality revolves around three main capabilities: real-time monitoring, deep debugging, and automated evaluation. When an AI agent malfunctions, Laminar allows users to set up custom alerts called "Signals." These signals enable developers to describe the problematic behavior in plain English—for example, "agent is stuck in a loop" or "agent returned incorrect tool output." Laminar then continuously reads every agent run, and when it detects a match with the described error pattern, it immediately sends a notification via Slack or other integrated channels. This proactive alerting system ensures that failures are caught as they happen, rather than being discovered later through user reports. Once an alert is triggered, Laminar provides a detailed breakdown of the agent’s execution. The platform surfaces the exact step that caused the failure, presented in a clear, readable transcript and timeline view. Developers can see the input that was given to the LLM, the reasoning steps the model took, the tool calls it made, and the outputs from any sub-agents involved. Instead of manually sifting through logs or reconstructing the agent’s decision path, the user gets a concise, navigable overview of the entire run. Beyond passive observation, Laminar supports interactive questioning. Within any specific agent run, developers can ask natural language questions about what happened—such as "Why did the agent call that tool?" or "What was the context at step 3?"—and receive answers that reference concrete, contextual evidence from the run. This turns debuggi