AI AGENTS

RagaAI-Catalyst

by RagaAI

agentlead-sourcegithub-agent-framework-repositoriessource:github.comAI evaluationLLMRAGagentsobservabilitymonitoringtracingdebuggingPython SDK

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Overview

Automated AI evaluation and Test-and-Fix platform for LLM, RAG, and agentic applications.

Details

RagaAI Catalyst is presented by RagaAI as a platform to test, trace, and tune LLM responses for LLM, RAG, and agentic workflows. The documentation describes it as an automated AI evaluation and Test-and-Fix platform with observability, evaluation, prompt management, and guardrail features. The GitHub project describes a Python SDK for Agent AI observability, monitoring, and evaluation, including agent, LLM, and tool tracing, multi-agent debugging, a self-hosted dashboard, timeline views, execution graph views, and advanced analytics.

When to Use

Use for evaluating, observing, and improving LLM, RAG, or agentic applications. Use when you need tracing and debugging for agents, LLM calls, tools, or multi-agent systems. Use when you want to flag safety and cost risks in LLM, RAG, and agentic workflows.

Getting Started

Visit the RagaAI Catalyst product page to understand the platform positioning and supported workflows.
Read the RagaAI Catalyst documentation for setup and feature guidance.
Review the RagaAI-Catalyst GitHub repository if you want the Python SDK and project-level implementation details.

Key Features

•Automated AI evaluation and Test-and-Fix workflow for LLM, RAG, and agentic applications.
•Observability, evaluation, prompt management, and guardrail features documented by RagaAI.
•Agent, LLM, and tool tracing for monitoring and debugging agentic systems.
•Self-hosted dashboard with timeline and execution graph views described in the GitHub project.
•Advanced analytics for agent AI observability and monitoring.

Capabilities

•LLM evaluation
•RAG evaluation
•agent observability
•agent tracing
•tool tracing
•multi-agent debugging
•prompt management
•guardrails

Last updated May 31, 2026