RagaAI-Catalyst vs Tracely-ai
RagaAI-Catalyst is much bigger: 16.2k stars against 1.2k. Over the days we have tracked them RagaAI-Catalyst moved +0.5% and Tracely-ai +21.2%, so Tracely-ai is growing faster right now.
Tracely-ai leads on setup ease. RagaAI-Catalyst does not take any axis by a clear margin.
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
Combines agent/LLM tracing, evaluation, guardrails and red‑teaming with a self‑hosted dashboard and execution-timeline analytics for multi-agent debugging.
- Stars
- 16.2k
- Tracked growth
- +0.5%
- Maturity
- ●●●●●
- Last commit
- 208d ago
- Language
- Python
- License
- Apache-2.0
- Cost to run
- Requires RagaAI account; external LLM usage billed to your provider.
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
- +21.2%
- Maturity
- ●●●●●
- Last commit
- 1d 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 | RagaAI-Catalyst | 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. | ●●●●● Diff/file only | ●●●●● Per-run only |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Configurable thresholds | ●●●●● Clustering & gating |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Custom 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. | ●●●●● API key + config | ●●●●● One-command demo |
Which one to pick
Pick RagaAI-Catalyst if…
Comprehensive observability — pick this when you need end-to-end tracing, evaluation and guardrails for agentic systems with an optional self-hosted dashboard and red‑teaming tools.
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
raga-ai-hub/RagaAI-Catalyst
Questions people ask
Is RagaAI-Catalyst better than Tracely-ai?
Tracely-ai leads on setup ease. RagaAI-Catalyst does not take any axis by a clear margin. RagaAI-Catalyst is worth picking when comprehensive observability — pick this when you need end-to-end tracing, evaluation and guardrails for agentic systems with an optional self-hosted dashboard and red‑teaming tools.
Which of RagaAI-Catalyst and Tracely-ai keeps my code private?
RagaAI-Catalyst: Self-hostable (4/5). Tracely-ai: Self-hostable (4/5).
What does each one cost to run?
RagaAI-Catalyst: Requires RagaAI account; external LLM usage billed to your provider.. Tracely-ai: CI replay $0; live judges use your model API key..
Full profiles: raga-ai-hub/RagaAI-Catalyst and Jwuthri/Tracely-ai. Everything else in Observability & tracing.