openlit vs Tracely-ai
openlit is much bigger: 2.9k stars against 1.2k.
They split the axes: openlit leads on context depth and model freedom, Tracely-ai on noise control 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
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
- 2.9k
- Tracked growth
- +8.7%
- Maturity
- ●●●●●
- Last commit
- 2h ago
- Language
- TypeScript
- License
- Apache-2.0
- Cost to run
- Self-hosted; you pay for LLM/provider API usage.
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 | 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 | ●●●●● Diff 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 | ●●●●● 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-key | ●●●●● Multiple providers |
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)
- Model freedom: Bring-your-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 3/5)
- 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 and model freedom, Tracely-ai on noise control and 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: Self-hosted; optional provider API keys for LLM judges.
Full profiles: openlit/openlit and jwuthri-cloud/Tracely-ai. Everything else in Observability & tracing.