{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Enforces content moderation on LLM prompts and responses using configurable guard rules, blocking or modifying text according to a YAML-defined policy before and after LLM inference.","skillfed_tags":["llm-safety","content-moderation","guardrails"],"use_cases":["Block adversarial or injection-attack prompts before they reach a production LLM deployment.","Enforce output safety policies on LLM responses (e.g., block factually inconsistent or off-topic completions).","Monitor and log moderation metrics (token counts, ROUGE scores, cost) for compliance and audit trails.","Integrate model-backed evaluators for task-specific guards like faithfulness or guideline adherence.","Ship traces and metrics to DataRobot's telemetry backend for centralized monitoring of LLM safety."],"what_it_does":"DataRobot Moderations is a library that wraps LLM inference pipelines to enforce content moderation policies. It reads guard rules from YAML configuration and evaluates prompts before they reach the LLM (prescore guards) and responses after generation (postscore guards), returning blocked/modified text and metric values. The library supports multiple guard types including token-count, ROUGE-1, cost, and optional evaluators for tasks like faithfulness and task adherence.\n\nThe core workflow is: load a pipeline from YAML, dict, or Pydantic config, call evaluate_prompt_async or evaluate_response_async for individual checks, or use evaluate_full_pipeline_async to run prescore \u2192 LLM \u2192 postscore in sequence. It integrates with OpenTelemetry for tracing and metrics export. The library depends on aiohttp, requests, pydantic, and data libraries (numpy, pandas, pillow, tiktoken, rouge-score) for core functionality; optional extras pull in heavier ML runtimes and cloud SDK dependencies.","worth_installing":"Yes, if you are already in the DataRobot ecosystem and need LLM moderation with centralized telemetry. The library is actively maintained, has low install friction, and covers common guard types out of the box. However, the proprietary license and unclear license treatment require legal review before use outside DataRobot deployments. Consider alternatives if you need a permissively licensed or vendor-neutral moderation framework."},"id":"datarobot-moderations","links":{"html":"https://skillfed.io/packages/datarobot-moderations","md":"https://skillfed.io/packages/datarobot-moderations.md","pypi":"https://pypi.org/project/datarobot-moderations/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":null,"license_treatment":"unclear","name":"datarobot-moderations","python_support":"capped_below_current","summary":"DataRobot Monitoring and Moderation framework"},"popularity":{"monthly_downloads":89547,"position":13652,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"11.3.1"}