{"data":{"slug":"evidentlyai-evidently","name":"evidently","tagline":"An open-source ML and LLM observability framework for evaluating, testing, and monitoring AI systems and data pipelines.","github_url":"https://github.com/evidentlyai/evidently","owner":"evidentlyai","repo":"evidently","owner_avatar_url":"https://avatars.githubusercontent.com/u/75031056?v=4","primary_language":"Jupyter Notebook","stars":7924,"forks":919,"topics":["data-drift","data-quality","data-science","data-validation","generative-ai","hacktoberfest","html-report","jupyter-notebook","llm","llmops","machine-learning","mlops","model-monitoring","pandas-dataframe"],"archived":false,"github_pushed_at":"2026-09-11T09:03:28+00:00","maintenance_label":"Very active","stars_delta_30d":134,"url":"https://www.graphcanon.com/tools/evidentlyai-evidently","markdown_url":"https://www.graphcanon.com/tools/evidentlyai-evidently.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/evidentlyai-evidently","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=evidentlyai-evidently","description":"Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.","homepage_url":"https://discord.gg/xZjKRaNp8b","license":"Apache-2.0","open_issues":304,"watchers":56,"ai_summary":"Evidently provides tools for assessing data drift, data quality, and model performance across various AI applications, including generative AI and tabular data.","readme_excerpt":"# :woman_technologist: Install Evidently\n\nTo install from PyPI:\n\n```sh\npip install evidently\n```\nTo install Evidently using the Conda installer, run:\n\n```sh\nconda install -c conda-forge evidently\n```","github_created_at":"2020-11-25T15:20:08+00:00","created_at":"2026-07-07T17:41:49.890839+00:00","updated_at":"2026-09-20T04:30:16.838806+00:00","categories":[{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"}],"tags":[{"slug":"data-drift","name":"data-drift"},{"slug":"data-quality","name":"data-quality"},{"slug":"data-science","name":"data-science"},{"slug":"data-validation","name":"data-validation"},{"slug":"generative-ai","name":"generative-ai"},{"slug":"llm","name":"llm"},{"slug":"llmops","name":"llmops"},{"slug":"machine-learning","name":"machine-learning"}],"trust":{"provenance":{"is_fork":false,"github_id":315977578,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-18T08:22:15.161Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":1,"days_since_push":6,"last_release_at":"2026-09-11T09:01:51Z","stars_delta_30d":134,"open_issues_delta_30d":9},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-09-18T08:22:17.318Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-18T08:22:14.096Z"},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-09-18T08:22:14.096Z"},"languages":{"value":["jupyter notebook","python"],"source":"github.language+pyproject.toml","observed_at":"2026-09-18T08:22:14.096Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-09-18T08:22:14.096Z"}},"decision_facts":{"hosting":null,"pricing":{"model":"freemium","summary":"Evidently is free and open-source, but users may incur costs related to hosting and infrastructure when deploying it."},"requirements":{"notes":["Evidently can be installed via pip or conda, making it accessible to users with different package management preferences."],"min_ram_gb":2,"requires_docker":false},"constraints":{"min_ram_gb":2,"pricing_model":"freemium","requires_docker":false},"when_to_use":["Use Evidently when you need to evaluate and monitor generative AI systems, as it supports a wide range of metrics specifically tailored for these applications.","Consider Evidently if your project involves tabular data, as it provides robust tools for assessing data drift and quality in such datasets."],"when_not_to_use":["Avoid Evidently if your project requires real-time monitoring, as it is primarily designed for batch processing and post-deployment evaluation.","Do not use Evidently if your team is not familiar with Jupyter Notebooks, as this is the primary interface for using the framework."],"source":"enrich:decision_facts","observed_at":"2026-09-18T11:13:19.823Z"},"constraint_facets":{"min_ram_gb":2,"pricing_model":"freemium","requires_docker":false},"decision_summary":[{"label":"Pricing","value":"freemium - Evidently is free and open-source, but users may incur costs related to hosting and infrastructure when deploying it."},{"label":"Requirements","value":"Min 2 GB RAM; Evidently can be installed via pip or conda, making it accessible to users with different package management preferences."},{"label":"Adopt for","value":"Evidently is an open-source framework for evaluating, testing, and monitoring AI systems and data pipelines, offering over 100 metrics for assessing data drift, data quality, and model performance."},{"label":"License detail","value":"Evidently is available under the Apache-2.0 license, allowing for free use, modification, and distribution."}]}}