RagaAI-Catalyst

🥇Gold

RagaAI-Catalyst is a powerful Python SDK designed for monitoring and evaluating AI agents. It offers comprehensive features like tracing, debugging, and advanced analytics, enabling developers to optimize multi-agent systems effectively.

16,1013,6640Updated Today

Intermediate15 minutes to implementai-agents

Saves ~120 min per use

Quick InstallView Source

claude install raga-ai-hub/RagaAI-Catalyst

Works with:

ChatGPTGitHub Copilot

Overview

About This Skill

Use Cases

Monitor the performance metrics of AI agents in real-time to ensure optimal operation.

Debug complex multi-agent systems by tracing interactions and identifying issues.

Visualize execution timelines to understand the flow and timing of agent interactions.

Evaluate the effectiveness of large language model interactions for better output quality.

Best For

RevOpsGrowth

Setup & Installation

Quick Install

Terminal

claude install raga-ai-hub/RagaAI-Catalyst

Alternative Install (Git Clone)

git clone https://github.com/raga-ai-hub/RagaAI-Catalyst

Requirements

Claude Code or compatible AI agent
Works with: ChatGPT, GitHub Copilot

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Using RagaAI-Catalyst, I want to monitor the performance of my AI agents in [INDUSTRY] at [COMPANY]. Please provide insights on the debugging process and any advanced analytics I should focus on.

Example Output

### Performance Insights for AI Agents

**Company:** Tech Innovations Inc.  
**Industry:** Financial Services  

After implementing RagaAI-Catalyst, we observed the following performance metrics:
- **Agent Response Time:** 200ms average
- **Error Rate:** 2.5% during peak hours
- **Debugging Insights:** The primary source of errors was traced back to the data preprocessing module, which failed to handle null values effectively.

### Recommendations:
- **Enhance Data Validation:** Implement stricter checks for null values in the preprocessing stage.
- **Monitor Load Patterns:** Utilize RagaAI-Catalyst's analytics to identify peak usage times and optimize agent performance accordingly.

By focusing on these areas, we anticipate a significant reduction in error rates and improved response times.