agentops vs Tracely-ai
agentops is much bigger: 5.9k stars against 1.2k.
They split the axes: agentops leads on model freedom, Tracely-ai on noise control and customization and setup ease.
Stars and commit dates come from our own daily tracking. The six axes are read off each project's documentation by our review pipeline, so they describe what a project says about itself, not what we measured in its code.
Where they stand today
Provides end-to-end agent session replays, LLM cost tracking and debugging across many agent frameworks with minimal instrumentation.
- Stars
- 5.9k
- Tracked growth
- +3%
- Maturity
- ●●●●●
- Last commit
- 118d ago
- Language
- Python
- License
- MIT
- Cost to run
- AgentOps API key for hosted dashboard; can self-host
Promotes real production traces into hermetic, replayable regression tests and CI gates instead of relying on hand-authored datasets.
- Stars
- 1.2k
- Tracked growth
- not tracked long enough
- Maturity
- ●●●●●
- Last commit
- 1d ago
- Language
- Python
- License
- MIT
- Cost to run
- Self-hosted; optional provider API keys for LLM judges
Six axes, head to head
Each axis runs 0 to 5. The label under a score is what that project's own docs claim, not a category average.
| Axis | agentops | Tracely-ai |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Runtime tracing | ●●●●● Diff only |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● None mentioned | ●●●●● Clustering & gating |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Config options | ●●●●● Custom evaluators & flows |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Self-hostable | ●●●●● Self-hostable |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Bring-your-own-key | ●●●●● Multiple providers |
Setup ease What it takes to get a first useful run out of it. | ●●●●● API key + init | ●●●●● One-command demo |
Which one to pick
Pick agentops if…
Easy integration — one-line initialization (agentops.init) yields session replays, LLM call tracking, and cost analytics across multiple agent frameworks, with an option to self-host.
- Model freedom: Bring-your-own-key (5/5 against 3/5)
Pick Tracely-ai if…
Production-driven testing — freeze failing production traces into deterministic, offline regression cases and block PRs that would reintroduce them.
- Noise control: Clustering & gating (5/5 against 1/5)
- Customization: Custom evaluators & flows (5/5 against 3/5)
- Setup ease: One-command demo (5/5 against 3/5)
What people want from each one
AgentOps-AI/agentops
Questions people ask
Is agentops better than Tracely-ai?
They split the axes: agentops leads on model freedom, Tracely-ai on noise control and customization and setup ease. agentops is worth picking when easy integration — one-line initialization (agentops.init) yields session replays, LLM call tracking, and cost analytics across multiple agent frameworks, with an option to self-host.
Which of agentops and Tracely-ai keeps my code private?
agentops: Self-hostable (4/5). Tracely-ai: Self-hostable (4/5).
What does each one cost to run?
agentops: AgentOps API key for hosted dashboard; can self-host. Tracely-ai: Self-hosted; optional provider API keys for LLM judges.
Full profiles: AgentOps-AI/agentops and jwuthri-cloud/Tracely-ai. Everything else in Observability & tracing.