openlit vs Tracely-ai
openlit is much bigger: 2.7k stars against 1.2k. Over the days we have tracked them openlit moved +9.5% and Tracely-ai +18.6%, so Tracely-ai is growing faster right now.
They split the axes: openlit leads on context depth, Tracely-ai on 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
OpenLIT provides an OpenTelemetry-native, open-source AI observability platform combining traces, built-in evaluations, a rule engine, prompt management and guardrails in one stack.
- Stars
- 2.7k
- Tracked growth
- +9.5%
- Maturity
- ●●●●●
- Last commit
- 1d ago
- Language
- TypeScript
- License
- Apache-2.0
- Cost to run
- Self-hosted; you pay for LLM/provider API usage.
Promotes real failing production traces into hermetic, replayable regression cases that run offline in CI and block PRs, instead of relying on hand-authored datasets.
- Stars
- 1.2k
- Tracked growth
- +18.6%
- Maturity
- ●●●●●
- Last commit
- 5h ago
- Language
- Python
- License
- MIT
- Cost to run
- CI replay $0; live judges use your model API key.
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 | openlit | 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. | ●●●●● Full-stack traces | ●●●●● Per-run only |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Rule engine filters | ●●●●● Clustering & gating |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Rules & prompts | ●●●●● Rich rule & prompts |
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-key | ●●●●● Bring-your-key |
Setup ease What it takes to get a first useful run out of it. | ●●●●● Docker + config | ●●●●● One-command demo |
Which one to pick
Pick openlit if…
Open-source, self-hostable — choose OpenLIT when you need vendor-neutral, full-stack LLM observability plus integrated evaluations, rule-driven guardrails, and prompt/version management.
- Context depth: Full-stack traces (5/5 against 1/5)
Pick Tracely-ai if…
Production-driven tests — pick Tracely when you want failing runs promoted from real traces into deterministic, offline regression cases that gate PRs.
- Setup ease: One-command demo (5/5 against 3/5)
What people want from each one
openlit/openlit
Hacker News: Show HN: OpenLIT – Open-Source LLM Observability with OpenTelemetry drew 62 points and 22 comments.
Questions people ask
Is openlit better than Tracely-ai?
They split the axes: openlit leads on context depth, Tracely-ai on setup ease. openlit is worth picking when open-source, self-hostable — choose OpenLIT when you need vendor-neutral, full-stack LLM observability plus integrated evaluations, rule-driven guardrails, and prompt/version management.
Which of openlit and Tracely-ai keeps my code private?
openlit: Self-hostable (4/5). Tracely-ai: Self-hostable (4/5).
What does each one cost to run?
openlit: Self-hosted; you pay for LLM/provider API usage.. Tracely-ai: CI replay $0; live judges use your model API key..
Full profiles: openlit/openlit and Jwuthri/Tracely-ai. Everything else in Observability & tracing.