RagaAI Catalyst
StaleDescription
RagaAI Catalyst is an observability, monitoring, and evaluation framework for Agent AI, supporting agent/LLM/tool tracing, multi-agent debugging, and self-hosted dashboard analytics.
Key Features
- Comprehensive LLM project management: project management, dataset management, evaluation, tracing, and prompt management in one platform
- Agentic Tracing module: tracks agent/LLM/tool call full chains, monitors token usage and execution patterns
- Built-in evaluation metrics (Faithfulness, Hallucination, etc.) with custom thresholds and batch evaluation support
- Synthetic data generation capability for automatic creation of test datasets for model evaluation
- Guardrail management and red-teaming to ensure LLM application security and reliability
Use Cases
💡 Evaluating RAG application answer faithfulness and hallucination rates for continuous retrieval quality improvement
💡 Monitoring multi-agent system runtime status, tracking each agent's decision-making process and tool calls
💡 Managing prompt versions and performing A/B tests to compare different prompting strategies
💡 Discovering security vulnerabilities and potential risks in LLM applications through red-teaming
Strengths & Limitations
✅ Strengths
- • High community interest (16.2k stars)
- • Permissive open-source license (Apache-2.0)
- • Established track record (2 years in production)
- • Responsive to issues, low backlog
⚠️ Limitations
- • No updates in over 6 months
Categories
Quick Start
Install: pip install ragaai-catalyst; generate Access Key and Secret Key in profile settings; initialize RagaAICatalyst instance with credentials; create project with catalyst.create_project(); record traces via Tracer; add evaluation metrics with Evaluation class and retrieve results.