RagaAI Catalyst
A Python SDK for evaluating, tracing, debugging, and safety-testing LLM and multi-agent applications.
Per-dimension scores and reasoning
Evidence shows: requires user API keys (e.g., OpenAI, Anthropic) and RagaAI credentials, but no explicit least-privilege principle; user confirmation exists (explicit initialization, manual triggers) but not enforced; data flow transparency: tracing features exist but data transmission/storage not explained; sensitive data handling: not specified; dependency security: versions pinned but no vulnerability scanning mentioned; external effects: network calls exist but impact scope not described; rollback: not mentioned; source attribution: author info present but publisher unverified. Deductions: lack of explicit least-privilege, data flow transparency, sensitive data handling, dependency security, external effects, and rollback details.
Evidence shows: README and code examples consistent, but inconsistencies exist (e.g., pyproject.toml version 2.1.7.4 vs README not mentioning); dependency availability: dependency list complete but version compatibility not explained; failure messages: error handling examples exist but not systematic. Deductions: minor self-consistency issues, dependency availability not fully explained, failure messages not comprehensive.
Evidence shows: targeted at developers, multiple use cases (project, dataset, evaluation, tracing, etc.); capability boundaries: modules defined but boundaries not explicit; trigger precision: API call examples exist but trigger conditions not specified; environment fit: supports Python 3.10-3.13 but other environments not mentioned. Deductions: capability boundaries and trigger precision not explicit, environment fit info limited.
Evidence shows: clear information architecture with TOC and doc links; install notes: pip install command; naming stability: version number present but naming rules not explained; examples and FAQ: extensive examples but no FAQ; known limitations: not explicitly listed; license: Apache-2.0; versioning changelog: not provided; maintenance responsibility: author info present but maintenance plan not stated. Deductions: missing known limitations, versioning changelog, and maintenance responsibility.
Evidence shows: output usability: example outputs but format not specified; marginal value: feature-rich but no comparison with other tools; cost-benefit: not explained. Deductions: output format not explicit, cost-benefit not addressed.
Evidence shows: claims supported by docs but no specific evidence; cross-source: multiple files but not cross-validated; fact-inference separation: not clearly distinguished. Deductions: claims lack traceability, cross-validation insufficient, facts and inferences not separated.
- Publisher identity unverified; proceed with caution.
- Dependency security not explicitly addressed; check for vulnerabilities.
- Data flow and sensitive data handling opaque; verify data transmission and storage.
- Missing known limitations and changelog; may affect upgrade decisions.
What does this agent do, and when should you use it?
RagaAI Catalyst is a Python SDK for LLM projects that covers project, dataset, evaluation, trace, prompt, synthetic-data, guardrail, and red-teaming management. Its primary interfaces include RagaAICatalyst, Dataset, Evaluation, and Tracer, and authentication with RagaAI Catalyst credentials is required. Agentic Tracing records LLM interactions, tool use, network activity, user feedback, and decision-making; Tracer can run as a context manager or through explicit start/stop calls. Evaluations return experiment status and results, while red-team runs return a dataframe and save path and can optionally upload results to the Catalyst dashboard. The README documents pip installation and SDK use, but not local or self-hosted dashboard deployment steps.
An application authenticates with RagaAICatalyst(access_key, secret_key, base_url), then creates projects with create_project. Dataset lists datasets, creates one from a CSV file, and retrieves schema mappings; Evaluation lists metrics, configures metrics such as Faithfulness and Hallucination with add_metrics, retrieves get_status and get_results, and uses append_metrics for new rows. Tracer runs application code inside with tracer(): or through start(), stop(), and get_upload_status(); an Agentic tracer with init_tracing(catalyst=catalyst, tracer=tracer) enables auto-instrumentation. PromptManager retrieves and compiles prompts, SyntheticDataGeneration processes a file path and generates Q&A or examples, GuardExecutor evaluates messages against deployed guardrails, and RedTeaming.run invokes a user-supplied response_model with generated or supplied test cases to produce a dataframe and save path.
- A RAG application team needs to create an evaluation dataset from CSV files and assess experiments with configured Faithfulness and Hallucination metrics.
- A multi-agent developer needs Agentic Tracing to inspect LLM interactions, tool executions, network activity, and decision-making behavior.
- A prompt engineer needs PromptManager to retrieve a named or versioned prompt, compile its variables, and send it to an OpenAI or LiteLLM call.
- A safety team needs to red-team a recruiting chatbot with harmful_content, stereotypes, or custom detectors.
- A release owner needs to configure guardrails for an existing deployment and evaluate messages and model parameters through GuardExecutor.
What are this agent's strengths and limitations?
- One SDK exposes dataset, evaluation, tracing, prompt, synthetic-data, guardrail, and red-teaming workflows, which can consolidate several LLM quality processes in one project.
- Agentic Tracing explicitly covers LLM token usage, tool execution, network activity, user feedback, and decision-making, with init_tracing for auto-instrumentation.
- The evaluation API can add multiple metrics to an experiment, configure thresholds, retrieve status and results, and calculate only newly added rows.
- RedTeaming supports built-in and custom detectors, generated or user-supplied cases, and optional dashboard uploads.
- The README says authentication is necessary for all listed operations, so adoption requires an access key, secret key, and base URL for RagaAI Catalyst.
- Although environment-variable configuration is mentioned, variable names are not provided; the README also lacks local or self-hosted dashboard deployment instructions.
- OpenAI, LiteLLM, and XAI appear as examples or supported module providers, but the exact integration scope for other model providers and runtimes is not documented.
- Users must supply inputs such as CSVs, document paths, response_model functions, and deployment IDs; the README does not document input validation or failure recovery behavior.
How do you install or deploy this agent?
Install the package:
pip install ragaai-catalystThen authenticate by passing credentials directly:
from ragaai_catalyst import RagaAICatalyst
catalyst = RagaAICatalyst(access_key="YOUR_ACCESS_KEY", secret_key="YOUR_SECRET_KEY", base_url="BASE_URL")The README also permits environment-variable configuration, but does not name those variables. Generate the access and secret keys from Authenticate in profile settings.
How do you use this agent?
After authentication, create a project:
project = catalyst.create_project(project_name="Test-RAG-App-1", usecase="Chatbot")To record execution, create a Tracer and run:
with tracer():
# Your code hereAlternatively, call tracer.start(), run the code, then call tracer.stop() and tracer.get_upload_status(). The README states that authentication is necessary for the listed operations.