Exposes Spark History Server metrics and metadata as tools for LLM-based analysis of Spark applications. It enables deep optimization of Spark jobs by providing access to job summaries, stage details, SQL execution plans, and executor performance.
Exposes Spark History Server data as tools for AI agents, enabling natural language querying of Spark applications, jobs, stages, and performance metrics.
Enables LLMs to analyze logs by extracting patterns, redacting secrets, and providing token-efficient summaries from files, Docker containers, or journald.
Provides tools to analyze test failures, cluster similar failures, and detect flaky tests from input or log files, helping QA teams debug and triage issues.
A terminal live-tail and a browser dashboard — one process, one event stream, served from localhost. Unified timeline across Claude Code, Codex, Gemini CLI, Cursor, Hermes, and OpenClaw. Token + cost accounting, compaction + anomaly detection, hybrid search, SVG call graphs, monaco-style diff attribution, agent-aware replay ("what would the agent say if I edited the prompt?"), policy editor, MCP s
Enables AI to automatically diagnose bugs by querying logs, tracing call chains, and analyzing code across multiple log platforms like Elasticsearch and Loki.
