Latitude LLM

Most agent observability tools give you logs. Latitude gives you a structured account of what a conversation was actually about — escalations, tool failures, retries, trust breaks, and the moments a user gave up. The core workflow is: instrument with the Latitude SDK or point an existing OpenTelemetry pipeline at the platform, let it capture full multi-turn traces including messages, costs, and tool calls, then use the conversation intelligence layer to surface patterns across those sessions automatically.

The differentiating feature is failure mode clustering. Instead of asking you to triage individual bad traces, Latitude groups similar failures into a single issue with trend data, affected user counts, and a lifecycle — so you are reviewing a pattern, not a log file. From any clustered issue, you can generate an automated evaluation grounded in real examples, attach it to a golden dataset built from validated production traces, and get alerted via Slack, email, or webhook the next time that pattern resurfaces after a deploy.

Latitude fits teams shipping multi-turn agents who need production visibility without committing to a proprietary data format. The MIT license and self-hosted option mean your traces stay in your infrastructure if compliance requires it — the vendor confirms SOC 2 and GDPR support for the hosted version. Where it breaks: Latitude is an observability and evaluation platform, not a builder or orchestrator. Teams that want one tool to both monitor agents and construct or run them will need a second system alongside it.

On the integration side, the vendor describes an MCP server that gives agents direct access to the Latitude workspace, and a GitHub-hosted skill for coding agents to add tracing automatically. Human annotation is available inline on any trace or span, turning reviewer feedback into structured signal that feeds back into search and evaluation pipelines.