RagaAI-Catalyst: Python SDK for Agent AI Observability, Monitoring and Evaluation Framework

by raga-ai-hub · Agent Tool · ★ 16.2k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install RagaAI-Catalyst

pip install ragaai-catalyst

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 73/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agentic-aiagentic-ai-developmentagentneoagentsai-agent-monitoringai-application-debuggingai-evaluation-toolsai-performance-optimizationai-tool-interaction-monitoringllm-testing

Quick Facts

Stars16,162
Forks3,567
LanguagePython
CategoryAgent Tool
LicenseApache-2.0
Quality Score73/100
Open Issues34
Last Updated2026-02-11
Created2024-08-26
Platformspython
Est. Tokens~3792k

About RagaAI-Catalyst

RagaAI Catalyst  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.

Compatible Skills

These tools work well together with RagaAI-Catalyst for enhanced workflows:

  • autogen — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (53%)
  • nexent — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)
  • ag2 — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)
  • PPTAgent — semantic(0.15)+complementary+same_lang+similar_pop+shared_platform (50%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

View on GitHub → Browse Agent Tool tools