When an LLM agent fails, the trace is right there, the user turns, the tool calls, the responses, the final result. But knowing what happened and knowing why it failed are two different things. Most teams read traces manually, form a guess, and move on. Agent Failure Classifier is a CLI tool and Python library for post-hoc root cause analysis of failed or low-quality LLM agent runs. Feed it any agent trace and it classifies the failure into one of eight named failure modes, identifies the first turn where things went wrong, and produces a structured report with actionable fixes. The classifier combines eight fast rule-based detectors with an optional LLM-as-judge pass via OpenRouter. The rule-based layer is free, deterministic, and requires no network access. The LLM pass breaks ties and classifies traces the rules cannot resolve alone.…