Tracea

What it does

Agents fail silently. You fire one off, it runs, nothing comes back - no trace, no cost data, no idea which call broke. Tracea captures every tool call, LLM response, and cost spike. Automatic RCA tells you exactly why it failed. YAML detection rules catch loops, spikes, and silent errors before they hit production. Self-hosted. One Docker command. No data leaves your network. Company Brain turns every session into team memory - agents start smarter each run.

Trace every LLM call, tool call, cost spike, error, and decision path, then turn finished sessions into searchable team memory. Most observability tools are cloud SaaS — your sessions, costs, and errors transit their infrastructure. Tracea runs entirely on yours. Zero vendor lock-in. No per-event pricing. No black-box rules you can't inspect or version-control. Route issues to Slack, email, or any HTTP webhook. Tracea catches behavioral failures your logs never will. Tracea detected an infinite_loop pattern in ops-agent — the same tool was called 14 times in a single session without a terminal state. { "type" : "issue.detected" , "severity" : "high" , "issue_type" : "rate_limit" ,…from tracea.dev

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  Trace-AIOct 2025 · trace-ai.dev · ▲146

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  Progress AI ObservabilityAug 2026 · telerik.com · ▲168

  Hi HN! We’re the team behind Fiddler, and we recently built an Agent Cache feature. It works by capturing an LLM request/response pair and, once caching is enabled, serving subsequent matching calls locally so duplicate requests never hit the provider. It’s meant to reduce three pains that show up together in agent development: token cost, feedback-loop latency, and output non-determinism. We’d love to hear your thoughts.

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  Oodle.ai – $10 per million agent tracesJul 2026 · oodle.ai · ▲31

  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…

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  traceAIApr 2026 · github.com · ▲273

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  RetraceJul 2026 · retraceai.tech · ▲101

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  Needle: We Distilled Gemini Tool Calling into a 26M Model▲776

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