Process your telemetry data with Grepr pipelines

Grepr pipelines orchestrate data processing flows that ingest your telemetry data, such as logs and Application Performance Monitoring (APM) data produced by agents and collectors, transform and enrich the data, and deliver the resulting data to your observability platform.

An example of a pipeline is a log or trace reduction pipeline that reduces the volume of duplicate, low-value events by creating summarized events that retain the context of the original events.

Trace reduction pipelines

Trace reduction pipelines process the APM data produced by your applications and agents by applying configured transformations, sampling to create representative subsets of the data, and forwarding the results to your observability platform. Grepr uses a novel approach to identify and sample every path through your system. Sampling reduces the volume and cost of data sent to your observability platform, while the complete set of raw traces is retained in a Grepr data lake. If you use Datadog, you can also derive and forward metrics from your trace data to track trends across your traces without forwarding every trace. See Use a trace reduction pipeline for your APM data.

Agent sessions pipelines

Agent sessions pipelines process the telemetry produced by coding agents such as Claude Code, Codex, and OpenCode. A coding agent reports its conversation as OpenTelemetry log events and its execution as OpenTelemetry spans, and neither signal is readable as a session on its own. An agent sessions pipeline reads both signals, reconstructs each run into a single session, and stores it in a Grepr data lake, so you can read the transcript, measure the tokens and cost the run consumed, and find the runs that failed. See Capture and explore agent sessions.