Coroot is an open-source observability platform that collects metrics, logs, traces, and profiles and then goes a vital step further: it turns raw telemetry into explanations and next steps. The project, hosted at coroot/coroot, brings a modern stack together, eBPF-based auto-instrumentation, Prometheus, and ClickHouse, to give teams a service map, SLO-aware summaries, distributed tracing, log clustering, and continuous profiling without heavy manual setup.
coroot
Organization
Key features at a glance
Out of the box, Coroot covers the core observability loop from detection to diagnosis, with sensible defaults and minimal configuration.
- Zero-instrumentation observability: eBPF-based auto-discovery and request capture mean you can see traffic and dependencies without code changes ((eBPF, 2023)).
- Service Map: an interactive graph of applications with health overlays implemented in the frontend as ServiceMap.vue.
- Logs, traces, and profiles: ClickHouse-backed log search, OpenTelemetry-compatible tracing, and one-click continuous profiling for code-level answers ((OpenTelemetry, 2024)).
- Built-in expertise: automated inspections detect common issues and tie alerts to SLO context, reducing alert fatigue.
- Cost monitoring: cloud cost attribution down to the application without granting cloud-account access ((Coroot Docs, 2025)).
A problem worth solving
Collecting telemetry is easy; turning it into insight is not. Many teams accumulate dashboards and queries but still struggle to answer the core questions quickly: What is red? Why is it red? What should we do?
Coroot addresses that gap by combining auto-discovery and predefined inspections with a consistent data model of services, nodes, and dependencies. The result is a clickable picture of your system and a focused health summary that scales to hundreds of services without drowning you in charts (Coroot Docs, 2025).
Why it stands out
What I like most is the end-to-end flow: deploy the agents, point Coroot at Prometheus and ClickHouse, and the system begins explaining itself. The Service Map renders upstream/downstream relationships and highlights outliers on hover.
The Application Health Summary surfaces what matters: SLOs, error spikes, latency regressions, and deployment impacts and the UI makes it natural to jump from logs to traces to profiles. It feels opinionated in a good way.
Under the hood
Coroot's backend is written in Go and exposes a clean HTTP API. The entry point in main.go wires together configuration, storage (SQLite or Postgres), a Prometheus query cache, a ClickHouse-backed log/trace/profile collector, and a Gorilla Mux router.
Key subsystems live in clearly named packages: collector/ (ingest endpoints and TTLs), model/ (domain types like Application, Node, SLO, Trace), watchers/ (deployments, incidents), and timeseries/ (aggregation utilities). The UI is a Vue 2 app with Vuetify and CodeMirror, built to static assets by front/package.json and served by the Go process.
/* simplified from main.go (Apache-2.0) */
router := mux.NewRouter()
r := router.PathPrefix(cfg.UrlBasePath).Subrouter()
// ingest endpoints (ClickHouse-backed)
r.HandleFunc("/v1/metrics", coll.Metrics)
r.HandleFunc("/v1/traces", coll.Traces)
r.HandleFunc("/v1/logs", coll.Logs)
r.HandleFunc("/v1/profiles", coll.Profiles)
// project & app APIs
r.HandleFunc("/api/project/{project}/overview/{view}", a.Auth(a.Overview)).Methods(http.MethodGet)
r.HandleFunc("/api/project/{project}/app/{app}/tracing", a.Auth(a.Tracing)).Methods(http.MethodGet)
r.HandleFunc("/api/project/{project}/app/{app}/logs", a.Auth(a.Logs)).Methods(http.MethodGet)
// static UI (built from front/src)
r.PathPrefix("/static/").Handler(http.StripPrefix(cfg.UrlBasePath, http.FileServer(utils.NewStaticFSWrapper(static))))Two design choices stand out. First, Prometheus queries are cached on disk via cache/, smoothing restarts and limiting backend churn. Second, ClickHouse powers fast text and pattern search for logs and anchors trace storage; the code depends on official/native clients in go.mod and uses schema-aware inserts for speed ((ClickHouse, 2025)). On the frontend, the Service Map calculates levels and draws SVG arrows with status-aware styling, which keeps the graph readable even as it scales.
What it can replace in your observability stack
For many teams, Coroot can stand in for the APM layer found in commercial suites. Its OpenTelemetry-compatible tracing, eBPF-powered request capture, service map, and SLO-aware incidents cover the core workflows of tools like Datadog APM (Datadog, 2025), New Relic APM (New Relic, 2025), and Dynatrace APM (Dynatrace, 2025), while remaining vendor-neutral thanks to OTEL.
On logs, Coroot stores events in ClickHouse and adds pattern clustering and logs-to-traces correlation. That means many teams can reduce or remove paid log modules in suites such as Datadog Logs (Datadog, 2025), Splunk Observability Cloud (Splunk, 2025), or Elastic Observability (Elastic, 2025) for application log search and troubleshooting.
Coroot's one-click continuous profiling addresses a category often sold as an add-on (for example, Datadog Code Profiling (Datadog, 2025)). Under the hood, the Go code references the Pyroscope delta profiler library (Grafana, 2024), which enables low-overhead CPU and memory sampling and makes comparison views practical.
While SLO tracking and incident aggregation reduce the need for separate SLO modules bundled into commercial suites (for example, Datadog SLOs (Datadog, 2025)).
Finally, Coroot also includes cost monitoring at the application level, which can offset add-on costs in those platforms and complements existing Prometheus setups without replacing them (Coroot Docs, 2025). For a deeper cost perspective, the team outlines the on-prem economics in their essay on being "140x cheaper than Datadog" (Coroot, 2024).
Use cases in the wild
Coroot shines for teams that need quick, explainable answers without weeks of instrumentation. Typical scenarios include: investigating a latency spike across services and linking a deployment to an SLO regression; correlating an error pattern in logs with an outlier trace and a hot path in a profile; or mapping a legacy service's inbound and outbound calls after a lift-and-shift.
In Discussions and Issues, users ask about ClickHouse tuning on low-memory hosts, missing metrics post-install, and handling very large traces, all practical concerns for production (Coroot Discussions, 2025; Coroot Issues, 2024).
Community and contribution
The repository is active and approachable. The Contributing guide shows how to run the Vue builder and Go backend locally, and includes a Docker workflow. There's a Slack, a Discussions forum, and a steady stream of Issues around integrations (Redis, MongoDB), configuration-by-code requests, and observability UX. Good first contributions often land in the model and watchers packages, documentation, and UI refinements.
Usage and license terms
Coroot Community Edition is licensed under the Apache License 2.0 (LICENSE). You can use, modify, and distribute the software, including commercially, provided you include the license and notices and honor the patent and trademark terms. The software is offered without warranty or liability. If you need SSO, RBAC, and AI-assisted RCA, those are available in Coroot Enterprise as a commercial offering (Coroot, 2025).
Impact and what's next
The impact of eBPF + OpenTelemetry + columnar log storage is clear: you get near-real-time topology and rich context with less toil. From the codebase, the building blocks are there for scaling: on-disk query caching, ClickHouse for high-cardinality logs, and a clean separation between ingest, model, and presentation.
Based on user feedback, future work could focus on large-trace UX, declarative configuration for SLOs and integrations, and more guardrails for ClickHouse on tiny nodes. The project's open foundations make it a strong fit for teams migrating off proprietary APMs while keeping the door open to standards like OpenTelemetry.
About Coroot, Inc.
Coroot, Inc. builds an observability platform that emphasizes fast setup, vendor-neutral instrumentation, and explainable results. The company offers a Community Edition on GitHub and an Enterprise Edition with advanced features like AI-powered Root Cause Analysis, SSO, and RBAC, plus support and onboarding services. The website highlights quick deployment, cost efficiency, and clear system maps aimed at helping DevOps and SRE teams focus on outcomes rather than dashboard archaeology (Coroot, 2025).
Closing thoughts
Coroot is a rare blend of pragmatic engineering and thoughtful UX: the Go backend is straightforward to read, the Vue frontend is purpose-built for navigable context, and the defaults push you toward answers.
If you're exploring open-source observability, start with the Quick Start, click around the live demo, and then peek into main.go, model/, and ServiceMap.vue to see how the pieces fit. It's a solid foundation with meaningful headroom
Coroot: Open-Source Observability That Turns Telemetry Into Decisions