agentops vs RagaAI-Catalyst
RagaAI-Catalyst is much bigger: 16.2k stars against 5.8k. Over the days we have tracked them agentops moved +3.6% and RagaAI-Catalyst +0.5%, so agentops is growing faster right now.
RagaAI-Catalyst leads on noise control and customization. agentops 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
Provides end-to-end agent session replays, LLM cost tracking and debugging across many agent frameworks with minimal instrumentation.
- Stars
- 5.8k
- Tracked growth
- +3.6%
- Maturity
- ●●●●●
- Last commit
- 75d ago
- Language
- Python
- License
- MIT
- Cost to run
- AgentOps API key for hosted dashboard; can self-host
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.
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 | RagaAI-Catalyst |
|---|---|---|
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/file only |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● None mentioned | ●●●●● Configurable thresholds |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Config options | ●●●●● Custom rules & 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-own-key | ●●●●● Bring-your-key |
Setup ease What it takes to get a first useful run out of it. | ●●●●● API key + init | ●●●●● API key + config |
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.
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.
- Noise control: Configurable thresholds (3/5 against 1/5)
- Customization: Custom rules & prompts (5/5 against 3/5)
What people want from each one
AgentOps-AI/agentops
raga-ai-hub/RagaAI-Catalyst
Questions people ask
Is agentops better than RagaAI-Catalyst?
RagaAI-Catalyst leads on noise control and customization. agentops does not take any axis by a clear margin. 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 RagaAI-Catalyst keeps my code private?
agentops: Self-hostable (4/5). RagaAI-Catalyst: Self-hostable (4/5).
What does each one cost to run?
agentops: AgentOps API key for hosted dashboard; can self-host. RagaAI-Catalyst: Requires RagaAI account; external LLM usage billed to your provider..
Full profiles: AgentOps-AI/agentops and raga-ai-hub/RagaAI-Catalyst. Everything else in Observability & tracing.