Trace and evaluate Cursor agents with Langfuse, powered by ClickHouse

Langfuse’s open-source Observability plugin for Cursor records model calls, tool executions, token usage, latency, and related metadata as traces, enabling teams to analyze agent behavior, costs, and performance. Powered by ClickHouse, Langfuse v4 stores trace context alongside observations to support investigation of individual prompts and aggregate dashboards for usage, cost, latency, and evaluation scores across users, models, tags, and environments. The guide explains how to install and configure the plugin with Langfuse API keys, notes its privacy and logging settings, and describes how Cursor prompts are represented through LLM, tool, subagent, and context-compaction observations. It presents a workflow in which teams identify unusually expensive turns, detect recurring inefficiencies such as repeated file reads or failed shell-command retries, revise project instructions, and evaluate whether changes preserve task completion while reducing cost and latency. Langfuse can also support broader AI application development by turning production cases into evaluation datasets, comparing prompt, model, and tool changes before deployment, enforcing quality thresholds in CI, and feeding poorly scored production traces back into future experiments.

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