RagaAI Catalyst — LLM Eval + Tracing SDK

Practical Notes

• GitHub: 16,156 stars · 2,019 forks; pushed 2026-02-11 (verified via GitHub API).
• README installation is pip install ragaai-catalyst and config uses access_key / secret_key / base_url.
• README lists modules for evaluation, trace management, agentic tracing, prompt management, and guardrails.

Main

A practical way to adopt evaluation:

1. Define a “golden set” of prompts + expected behaviors, and keep it versioned.
2. Instrument tracing first, so every regression can be tied to a specific change (prompt/model/tooling).
3. Treat guardrails as tests: start with allowlists/denylists, then add heuristic checks and human review gates.
4. Track cost and latency next to quality; a “better” model that doubles latency may not be viable.

Make evals run on every release candidate, not just ad-hoc experiments.

FAQ

Q: Is it only for evaluation? A: No—README includes tracing, prompt management, and guardrail/red-teaming modules too.

Q: Do I need credentials? A: Yes—README config uses access and secret keys plus a base URL before operations.

Q: What should I measure first? A: Start with correctness and safety, then add latency and cost as first-class metrics.