AgentOps vs Traceloop
A side-by-side comparison of AgentOps and Traceloop, two Observability tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | AgentOps | Traceloop |
|---|---|---|
| Category | Observability | Observability |
| Pricing | FREEMIUM | FREEMIUM |
| License | Open core | Open core |
| Deployment | Hybrid | Hybrid |
| Platforms | Web, API | Web, API |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | AgentOps | Traceloop |
The honest brief
AgentOps
Purpose-built for multi-step agents — session replay with time-travel debugging and per-run cost tracking, not just flat LLM-call logging.
- Open-source MIT SDK, two-line setup
- 400+ LLM and framework integrations
- Records every LLM call, tool use, decision
- Agent benchmarking and evaluation
- Free tier to start
- Python/TypeScript SDK-centric
- Full analytics rely on the hosted dashboard
- Younger than general-purpose APM tools
Traceloop
Pure OpenTelemetry: OpenLLMetry emits standard OTel spans, so traces flow to Datadog/Honeycomb, not a locked-in store.
- Built on open OpenTelemetry standard
- OpenLLMetry SDK is open source
- Exports to any OTel backend
- No proprietary data lock-in
- Instruments LLM, vector-DB, frameworks
- Hosted dashboard less rich than rivals
- Relies on your existing OTel stack
- Smaller eval tooling than competitors