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RagaAI Catalyst

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Basic Information

RagaAI Catalyst is a developer-focused platform and Python SDK for managing, monitoring, evaluating and safeguarding large language model projects and agentic systems. It provides programmatic interfaces to create and organize projects, ingest and manage datasets, define evaluation experiments and metrics, record and analyze execution traces, and manage prompts and guardrails. The README documents how to install the package with pip and how to configure authentication keys for the RagaAI Catalyst service. Typical usage shown in the README covers creating projects, uploading or mapping CSV data, running metric evaluations, recording LLM and agent traces, and integrating generated or compiled prompts into LLM calls. The project is intended to centralize observability and operational tooling for RAG and other LLM applications so teams can track behavior, evaluate performance, reproduce runs, and apply safety checks across models and deployments.

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App URL
https://github.com/raga-ai-hub/RagaAI-Catalyst
Github Repository
https://github.com/raga-ai-hub/RagaAI-Catalyst/blob/main/README.md

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App Details

Features
The repository exposes modules and examples for key capabilities: project management, dataset management with CSV schema mapping, evaluation management for metric experiments and thresholds, and trace management including a Tracer class with context-manager and start/stop APIs. Agentic tracing utilities capture LLM interactions, tool usage, network calls, token usage, and cost/performance signals. Prompt management supports versions, variables and compilation for downstream LLM calls. Synthetic data generation produces Q&A and example data and supports provider configuration. Guardrail management allows listing, configuring and applying guardrails and running a GuardExecutor against model responses. A Red-teaming module provides detectors, auto-generated test cases, custom test inputs and upload of results. The README also demonstrates integrations with multiple LLM providers and client libraries through code examples.
Use Cases
RagaAI Catalyst helps engineering and ML teams operationalize LLM applications by providing a single SDK and set of workflows to manage lifecycle tasks. It simplifies dataset preparation and schema mapping so evaluations and metrics can be run consistently. Trace and agentic tracing features enable collecting execution traces for debugging, cost tracking and behavioral analysis across agents, tools and API calls. Prompt management centralizes templates and versioning for reproducible prompts. Synthetic data generation and red-teaming help create test cases to probe model robustness and safety. Guardrail management supports defining and enforcing response checks and alternate behaviors at deployment time. The combined features make it easier to evaluate model faithfulness, detect hallucinations and unsafe outputs, iterate on prompts and guardrails, and upload results to a dashboard for team visibility.

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