AI Frameworks · raga-ai-hub

RagaAI-Catalyst

RagaAI Catalyst is a Python SDK for monitoring, debugging, and evaluating AI agent systems and LLM applications. It provides tracing, multi-agentic system debugging, self-hosted dashboards, and advanced analytics with execution graph visualization.

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Source: GitHub — github.com/raga-ai-hub/RagaAI-Catalyst

16.1k

GitHub stars

3.6k

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	raga-ai-hub/RagaAI-Catalyst
Owner	raga-ai-hub
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	16.1k
Forks	3.6k
Open issues	34
Latest release	v2.2.4 (2025-06-23)
Last updated	2026-02-11
Source	https://github.com/raga-ai-hub/RagaAI-Catalyst

What RagaAI-Catalyst is

Python-based observability framework offering agent/LLM/tool tracing, multi-span debugging, prompt management, evaluation metrics (Faithfulness, Hallucination), synthetic data generation, guardrails, and red-teaming capabilities. Integrates via environment variables and API keys; supports decorators for auto-instrumentation (@trace_llm, @trace_tool, @trace_agent).

Quickstart

Get the RagaAI-Catalyst source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/raga-ai-hub/RagaAI-Catalyst.git2cd RagaAI-Catalyst3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-Agent System Debugging

Track interactions across multiple agents, tools, and LLM calls with timeline and execution graph views to identify bottlenecks, failures, and token usage patterns in complex agentic workflows.

RAG Application Evaluation

Systematically evaluate retrieval-augmented generation pipelines using built-in metrics (Faithfulness, Hallucination) against datasets, with schema mapping and threshold-based result filtering for quality gates.

LLMOps CI/CD Integration

Integrate trace recording, metric evaluation, and prompt versioning into CI/CD pipelines for continuous monitoring of LLM app performance, cost tracking, and automated performance regression detection.

Implementation considerations

Requires RagaAI backend account and API credentials (access_key, secret_key, base_url); credential generation via UI before SDK use.
Schema mapping is mandatory for evaluation (map dataset columns to 'prompt', 'response', 'context', 'expected_response'). Misalignment will cause metric failures.
Agentic tracing via decorators (@trace_agent, @trace_tool, @trace_llm) requires wrapping function definitions; auto-instrumentation via init_tracing() available but verify coverage in your codebase.
Metric evaluation calls external LLM providers (e.g., gpt-4o-mini on OpenAI); budget LLM costs and latency (each metric run incurs API calls per row in dataset).
Dataset creation from CSV requires pre-defined schema. Ensure CSV columns align with supported schema elements before bulk imports.

When to avoid it — and what to weigh

Fully Offline / Air-Gapped Deployment Required — The SDK requires authentication via access/secret keys and external API calls (OpenAI, etc.) for metric evaluation. A self-hosted dashboard is offered, but core evaluation depends on external LLM calls.
Zero Third-Party Dependencies Constraint — The SDK integrates with LiteLLM, OpenAI, and other providers. If your security policy forbids external integrations or dynamic dependency chains, evaluate carefully.
Real-Time Stream Processing at Scale — Designed for project/experiment-based workflows with batch dataset evaluation. Not optimized for streaming terabyte-scale real-time analytics; designed for structured evaluation cycles.
Non-Python Tech Stack — Python SDK only. If your agent or LLM infrastructure is built in Go, Rust, Node.js, or Java, integration requires custom HTTP/API adapters or wrapper code.

License & commercial use

Licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved license allowing commercial use, modification, and redistribution with attribution and liability/warranty disclaimers.

Apache-2.0 license permits commercial use, proprietary modifications, and internal deployments. However, RagaAI Catalyst is a client SDK that connects to a backend service (RagaAI platform). Commercial usage likely requires a paid service agreement with RagaAI Inc. beyond the open-source license. Review RagaAI's commercial terms separately.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

SDK transmits traces and evaluation results to RagaAI backend; review data residency and encryption in transit. Credentials (access_key, secret_key) should never be hardcoded; use environment variables. Auto-instrumentation via decorators may capture sensitive data (prompts, responses, tool outputs); sanitize PII before tracing. Self-hosted dashboard security posture is Unknown—requires review of network isolation, authentication, and audit logs. No CVE or security audit data provided.

Alternatives to consider

Langsmith (LangChain)

Similar tracing and evaluation for LLM/agent apps; stronger LangChain ecosystem integration; more mature (2023+); comparable pricing model but requires LangChain architecture.

Arize / Galileo

Enterprise-grade ML/LLM observability with stronger enterprise SLAs, compliance certifications, and custom integrations; higher cost but broader feature parity (synthetic data, evaluation, monitoring).

OpenTelemetry + Jaeger / Datadog

Open-source, infrastructure-agnostic tracing. Requires more in-house integration work but offers vendor neutrality and fine-grained control; no built-in LLM-specific metrics.

Software development agency

Build on RagaAI-Catalyst with DEV.co software developers

Explore RagaAI Catalyst for comprehensive LLM tracing, multi-agent debugging, and evaluation. Check the documentation and GitHub repository to get started.

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RagaAI-Catalyst FAQ

Do I need RagaAI's hosted backend to use RagaAI Catalyst, or is it fully self-hosted?

The SDK connects to a RagaAI backend service (credentials required). A self-hosted dashboard is offered, but backend dependency exists. Full air-gapped deployment is not available without a self-hosted RagaAI server (requires commercial arrangement).

What LLM providers are supported for metric evaluation?

OpenAI and LiteLLM-supported providers (e.g., Anthropic, Cohere, Azure). Evaluation config specifies model and provider; you must supply valid API keys for those providers.

Can I use this with non-Python agents or LLMs?

The SDK is Python-only. External systems can be traced via HTTP calls or custom wrapper code, but native instrumentation requires Python.

How is pricing calculated for RagaAI Catalyst?

Unknown from README. Likely based on trace volumes, evaluation metric runs, or dashboard users. Contact RagaAI sales for commercial terms.

Software developers & web developers for hire

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Ready to Monitor and Debug Your AI Agents?

Explore RagaAI Catalyst for comprehensive LLM tracing, multi-agent debugging, and evaluation. Check the documentation and GitHub repository to get started.

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