Alternatives
Products that do what BareAgent does
AI enabled docker monitoring and Incident management
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Trace, evaluate, and improve AI agents in production
Aug 2026 · telerik.com
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2013 · getanomalous.com
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Hi HN, we're Kiran and Vijay! Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability. Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS…
Jul 2026 · oodle.ai
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I started using Claude Code (claude --dangerously-skip-permissions) and Codex (codex --yolo) and realized I had no reliable way to know what they actually did. The agent's own output tells you a story, but it's the agent's story. logira records exec, file, and network events at the OS level via eBPF, scoped per run. Events are saved locally in JSONL and SQLite. It ships with default detection rules for credential access, persistence changes, suspicious exec patterns, and more. Observe-only – it never blocks. https://github.com/melonattacker/logira
Mar 2026 · github.com
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Mar 2026 · github.com
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Hello HN! I'm an Android OS engineer. I've worked with AOSP and Linux kernels all my career and always wondered about lack of sophisticated tools to debug and analyze system-level logs. Always had to resort to manually skimming through large log files to find something I needed to. With the rise of LLMs and the AI-age, I felt it was a great opportunity to build something for OS engineers, which is what led to logcat.ai! We are building the industry-first observability platform for system level intelligence. Think "Datadog for operating systems" instead of applications. Currently, we support…
2025 · logcat.ai
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I built an open-source AIOps MCP (Monitoring & Control Plane) that detects anomalies in logs using Isolation Forest. It accepts logs from agents, apps, or collectors, parses and extracts features, and identifies unusual patterns in real time. Alerts can be sent to Slack, Webhooks, or PagerDuty. It’s lightweight, easy to deploy with Kubernetes & Helm, and designed to plug into existing observability stacks. I built this to experiment with combining ML-based anomaly detection and flexible alerting for DevOps/SRE teams. Most AIOps platforms are either too heavyweight or closed-source — I…
2025 · github.com
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Mar 2026 · github.com
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