Auralog
AI on-call engineer for your production logs
Auralog launched on May 4, 2026 with 4 votes, #2,419 of 20,538 launches that month and more than 75% of that year's launches.
What it does
Auralog is a structured logging platform with an AI on-call engineer built in. Send logs, then let Claude, Codex, or Cursor investigate incidents over MCP. Search logs, pull related events, and open autofix PRs.
Structured production logging with fast search and read-only MCP access for Codex, Claude, Cursor, and internal agents. Start with logs, then add AI and autofix PRs.
Auralogs gives Cursor, Claude Code, and Codex read-only access to your real production logs over MCP, so they debug with real context instead of guessing. Stop pasting stack traces into chat. MCP/API read keys are scoped to one project and cannot mutate settings or source code. Drop-in logging for JavaScript, Python, Java, Swift, Go, Rust, C++, and Godot. AI analysis and autofix use the Anthropic or OpenAI key you configure. Auralogs does not resell inference. Start with structured logging and MCP. Connect a specific repo only when you want reviewed fix PRs. Built by engineers who wanted their own AI on-call to read real logs, not screenshots. Auralogs separates ingest keys from read keys…from auralogs.ai
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LogDNA – Easy logging in the cloud2016 · logdna.com · ▲196Mezmo runs agentic SRE on your own infrastructure: AURA is an open-source agent harness, and Mezmo's telemetry pipelines feed it in-stream operational context.
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Aura – a Rust agent that investigates and fixes production incidentsSep 2026 · github.com · ▲28AURA is a production-tested SRE agent platform you can deploy in minutes. AURA handles the guardrails, APIs, state management, streaming, and failure handling required to put AI to work safely on production infrastructure. - mezmo/aura
Aura IDEJun 2026 · github.com · ▲8Hi HN, I built Aura-IDE, a native desktop LLM coding harness for AI assisted software engineering. The idea is not just “chat with your codebase.” Aura wraps models in a structured engineering loop: repo awareness → Planner spec → Worker execution → surgical edits → validation → recovery → final receipt The Planner reads the project and writes a spec. The Worker executes that spec with filesystem tools, diff approval, terminal validation, and recovery behavior. The goal is to make ordinary models produce better code by giving them better process, context, and tools. The unusual part: Aura…

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