RagaAI-Catalyst

RagaAI-Catalyst is a LLM observability tool - Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic system, self-hosted dashboard and advanced analytics with timeline and execution graph view.

Overview

RagaAI Catalyst is a comprehensive platform designed to enhance the management and optimization of LLM projects. It offers a wide range of features, including project management, dataset management, evaluation management, trace management, prompt management, synthetic data generation, and guardrail management. These functionalities enable you to efficiently evaluate, and safeguard your LLM applications.

Before using RagaAI Catalyst, you need to set up your credentials. You can do this by setting environment variables or passing them directly to the 'RagaAICatalyst' class:

1. Navigate to your profile settings 2. Select "Authenticate" 3. Click "Generate New Key" to create your access and secret keys

Note: Authetication to RagaAICatalyst is necessary to perform any operations below.

For more detailed information on Dataset Management, including CSV schema handling and advanced usage, please refer to the Dataset Management documentation.

For more detailed information on Trace Management, please refer to the Trace Management documentation.

The Agentic Tracing module provides comprehensive monitoring and analysis capabilities for AI agent systems. It helps track various aspects of agent behavior including:

The module includes utilities for cost tracking, performance monitoring, and debugging agent behavior. This helps in understanding and optimizing AI agent performance while maintaining transparency in agent operations.

For more detailed information on Trace Management, please refer to the Agentic Tracing Management documentation.

RagaAI-Catalyst is open-source, written primarily in Python, with 16,168 GitHub stars under the Apache 2.0 license. The latest release is v2.2.4 (2025-06-23).

Key capabilities

From the project's documentation:

• LLM interactions and token usage
• Tool utilization and execution patterns
• Network activities and API calls
• User interactions and feedback
• Support for multiple LLM providers (OpenAI, XAI, ..)
• Built-in and custom detectors

Install

A quick way to get started (always check the official docs for the latest):

pip install ragaai-catalyst

How it fits a local-AI stack

RagaAI-Catalyst runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Browse the full tools directory for alternatives.

Sources

• Source code & docs: raga-ai-hub/RagaAI-Catalyst
• Official website: https://catalyst.raga.ai/

Stats from GitHub, 2026-06-08.