# RagaAI Catalyst — LLM Eval + Tracing SDK

> RagaAI Catalyst is a Python SDK for managing LLM projects with evaluation, dataset management, trace/agentic tracing, and prompt/guardrail workflows.

## Install

Copy the content below into your project:

## Quick Use

```bash
pip install ragaai-catalyst

python - <<'PY'
from ragaai_catalyst import RagaAICatalyst

c = RagaAICatalyst(access_key="YOUR_ACCESS_KEY", secret_key="YOUR_SECRET_KEY", base_url="BASE_URL")
print(c)
PY
```

## Intro

RagaAI Catalyst is a Python SDK for managing LLM projects with evaluation, dataset management, trace/agentic tracing, and prompt/guardrail workflows.

- **Best for:** Teams that need repeatable evals, tracing, and guardrails for production LLM apps
- **Works with:** Python; your Catalyst credentials (access/secret keys) per README; integrates with LLM pipelines
- **Setup time:** 15–45 minutes

## 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.

## Source & Thanks

> Source: https://github.com/raga-ai-hub/RagaAI-Catalyst
> License: Apache-2.0
> GitHub stars: 16,156 · forks: 3,607

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## 快速使用

```bash
pip install ragaai-catalyst

python - <<'PY'
from ragaai_catalyst import RagaAICatalyst

c = RagaAICatalyst(access_key="YOUR_ACCESS_KEY", secret_key="YOUR_SECRET_KEY", base_url="BASE_URL")
print(c)
PY
```

## 简介

RagaAI Catalyst 是面向 LLM 项目的 Python SDK：覆盖评测、数据集管理、Trace/Agentic tracing、提示词管理与 guardrail 流程，适合把实验变成可复现的工程实践。

- **适合谁：** 需要把评测、追踪与护栏工程化的 LLM 团队
- **可搭配：** Python；按 README 配置 access/secret keys；可嵌入 LLM pipeline
- **准备时间：** 15–45 分钟

## 实战建议

- GitHub：16,156 stars · 2,019 forks；最近更新 2026-02-11（GitHub API 验证）。
- README 用 `pip install ragaai-catalyst` 安装，并通过 `access_key` / `secret_key` / `base_url` 配置。
- README 覆盖评测、Trace 管理、Agentic tracing、提示词管理与 guardrail 等模块。

## 主要内容

更务实的落地方式：

1. 先做“金标集”：固定一组 prompt 与期望行为，并版本化管理。
2. 优先接入 tracing：任何回归都能定位到某次变更（prompt/model/tooling）。
3. 把 guardrail 当测试：先用白/黑名单，再逐步叠加启发式检查与人工审核闸门。
4. 同步跟踪成本与延迟；质量提升但延迟翻倍的方案未必可用。

让评测跑在每次发布候选上，而不是只做临时实验。

### FAQ

**它只做评测吗？**
答：不是。README 还包含 tracing、提示词管理、guardrail/red-teaming 等模块。

**必须要账号凭据吗？**
答：需要。README 配置要求 access/secret keys 与 base_url 才能执行操作。

**第一步该测什么？**
答：先测正确性与安全性，再把延迟与成本纳入同等重要的指标。

## 来源与感谢

> Source: https://github.com/raga-ai-hub/RagaAI-Catalyst
> License: Apache-2.0
> GitHub stars: 16,156 · forks: 3,607


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Source: https://tokrepo.com/en/workflows/ragaai-catalyst-llm-eval-tracing-sdk
Author: Agent Toolkit