raga-ai-hub/ragaai-catalyst

Analysis updated 2026-06-24

★ 16,156PythonAudience · developerComplexity · 3/5Setup · moderate

Mindmap

mindmap
  root((RagaAI-Catalyst))
    Inputs
      LLM app traces
      Datasets CSV
      Prompt versions
      API keys
    Outputs
      Evaluation metrics
      Trace timelines
      Guardrail blocks
    Use Cases
      Monitor agentic AI apps
      Score RAG quality
      Debug tool calls
      Generate synthetic eval data
    Tech Stack
      Python
      RagaAI platform
      OpenAI

mindmap root((RagaAI-Catalyst)) Inputs LLM app traces Datasets CSV Prompt versions API keys Outputs Evaluation metrics Trace timelines Guardrail blocks Use Cases Monitor agentic AI apps Score RAG quality Debug tool calls Generate synthetic eval data Tech Stack Python RagaAI platform OpenAI

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What do people build with it?

USE CASE 1

Add tracing to a LangChain or LlamaIndex agent and watch tool calls in a dashboard.

USE CASE 2

Score a RAG pipeline on faithfulness and hallucination across a CSV dataset.

USE CASE 3

Version prompts and roll back to a previous version when a regression appears.

USE CASE 4

Generate synthetic test data and run red-team probes against a chatbot before launch.

What is it built with?

PythonOpenAIRagaAI Platform

How does it compare?

	raga-ai-hub/ragaai-catalyst	aidc-ai/pixelle-video	free-tv/iptv
Stars	16,156	16,152	16,174
Language	Python	Python	Python
Setup difficulty	moderate	moderate	easy
Complexity	3/5	3/5	1/5
Audience	developer	general	general

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires a RagaAI platform account and access/secret keys, the dashboard is needed to view traces.

In plain English

RagaAI Catalyst is a Python toolkit for developers who build applications powered by large language models (LLMs, AI systems like ChatGPT that understand and generate text). It helps you observe, monitor, and evaluate how those AI applications are actually performing in practice. The core idea is visibility: when you deploy an AI-powered app, things can go wrong in subtle ways, the AI might give inaccurate answers, make up facts (called "hallucination"), or behave unpredictably across different requests. Catalyst helps you catch and debug these issues by recording detailed traces of what happened during each interaction, including which tools the AI called, how many tokens were used, and what the AI decided at each step. Beyond monitoring, it includes tools for evaluating AI output quality against metrics like Faithfulness (did the response match the source material?) and Hallucination (did the AI invent information?). You can upload datasets, run evaluations, and compare results over time through a self-hosted dashboard. It also includes prompt management (storing and versioning the instructions you send to the AI), synthetic data generation for testing, and guardrails to block unsafe or off-topic responses. The toolkit is installed as a Python package and connects to a RagaAI platform account via API keys. It is aimed at developers and ML engineers building and maintaining multi-agent AI systems who need structured tooling to understand and improve their applications' behavior.

Copy-paste prompts

Prompt 1

Add ragaai-catalyst Tracer to my existing Python RAG app that uses OpenAI, wrap the main retrieve plus generate function with init_tracing.

Prompt 2

Write a script that loads my eval.csv into RagaAI Catalyst as a dataset and runs Faithfulness and Hallucination metrics with gpt-4o-mini.

Prompt 3

Show how to fetch a versioned prompt by name from PromptManager and substitute variables before calling OpenAI.

Prompt 4

Set up a guardrail in RagaAI Catalyst to block responses containing PII for my customer support bot.

Prompt 5

Compare RagaAI Catalyst against LangSmith and Arize Phoenix for a small Python team building one agent.

Frequently asked questions

What is ragaai-catalyst?

Python SDK for tracing, evaluating, and monitoring LLM and multi-agent applications, with metrics like faithfulness and hallucination plus a self-hosted dashboard.

What language is ragaai-catalyst written in?

Mainly Python. The stack also includes Python, OpenAI, RagaAI Platform.

How hard is ragaai-catalyst to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is ragaai-catalyst for?

Mainly developer.

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