The errors slice of observability, built for agents.

The scope difference

OpenObserve wants to be your whole observability stack. Agenterr takes only the errors slice: it reads your logs and turns them into grouped issues. If you find yourself searching logs just to answer "what's broken and is it new", Agenterr hands you that answer already computed.

The grouping difference

In a log platform, the same crash 4,000 times is 4,000 rows. Agenterr fingerprints errors into issues with counts, first-seen, and regression detection — resolved issues reopen when the error returns in a new release.

The agent difference

Agents work best with small, structured answers. Agenterr serves issues over MCP as its primary interface — one call returns what's open, how often, and since when, instead of pages of query results to wade through.

The performance difference

We loaded one real production day — 317,229 logs — into both systems and queried them the same way. Agenterr stores it in 9.4 bytes per record against OpenObserve's 10.9, and answers a scoped search in 10.9 ms against 37.0 ms, an unscoped search in 14.8 ms against 34.4 ms, and an aggregate by service in 0.17 ms against 20.3 ms.

Two caveats worth stating. OpenObserve's result cache flatters repeated queries by around 4×, so the harness pins use_cache=false for both. And OpenObserve ingests faster — 213k logs/s against 79k — because it acknowledges writes before they are durable, while agenterr fsyncs first. Methodology and a reproduction command are in the full report.

When OpenObserve is the right choice

You want metrics and traces alongside logs, SQL over your log data, or dashboards for a team. OpenObserve is a real platform at a fraction of the usual platform weight. Agenterr trades that breadth for issue grouping and agent access.