{"data":{"slug":"zenml-io-zenml","name":"zenml","tagline":"One AI Platform from Pipelines to Agents","github_url":"https://github.com/zenml-io/zenml","owner":"zenml-io","repo":"zenml","owner_avatar_url":"https://avatars.githubusercontent.com/u/88676955?v=4","primary_language":"Python","stars":5552,"forks":653,"topics":["agentops","agents","ai","automl","data-science","deep-learning","devops-tools","genai","llm","llmops","machine-learning","metadata-tracking","ml","mlops","pipelines","production-ready","pytorch","tensorflow","workflow","zenml"],"archived":false,"github_pushed_at":"2026-08-20T10:37:52+00:00","maintenance_label":"Very active","stars_delta_30d":58,"url":"https://www.graphcanon.com/tools/zenml-io-zenml","markdown_url":"https://www.graphcanon.com/tools/zenml-io-zenml.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/zenml-io-zenml","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=zenml-io-zenml","description":"ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.","homepage_url":"https://zenml.io","license":"Apache-2.0","open_issues":149,"watchers":42,"ai_summary":"ZenML is an AI platform for pipelines and agents that supports metadata tracking and production-ready machine learning workflows with frameworks like PyTorch and TensorFlow.","readme_excerpt":"# Install ZenML with server capabilities\npip install \"zenml[server]\"  # pip install zenml will install a slimmer client\n\n---\n\n## 📜 License\n\nZenML is distributed under the terms of the Apache License Version 2.0. See\n[LICENSE](LICENSE) for details.\n\n---\n\n<div align=\"center\">\n  <a href=\"https://www.linuxfoundation.org/\"><img src=\"docs/book/.gitbook/assets/lf-member-silver.svg\" alt=\"Linux Foundation Silver Member\" height=\"100\"/></a>\n  &nbsp;&nbsp;&nbsp;&nbsp;\n  <a href=\"https://www.cncf.io/\"><img src=\"docs/book/.gitbook/assets/cncf-member-silver.svg\" alt=\"CNCF Silver Member\" height=\"100\"/></a>\n</div>\n\n<img referrerpolicy=\"no-referrer-when-downgrade\" src=\"https://static.scarf.sh/a.png?x-pxid=0fcbab94-8fbe-4a38-93e8-c2348450a42e\" />","github_created_at":"2020-11-19T09:25:46+00:00","created_at":"2026-07-07T17:42:06.635186+00:00","updated_at":"2026-08-20T12:01:45.031007+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"},{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"agentops","name":"agentops"},{"slug":"agents","name":"agents"},{"slug":"automl","name":"automl"},{"slug":"data-science","name":"data-science"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"devops-tools","name":"devops tools"},{"slug":"genai","name":"genai"},{"slug":"llm","name":"llm"}],"trust":{"provenance":{"is_fork":false,"github_id":314197645,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-20T12:01:43.989Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":8,"days_since_push":0,"last_release_at":"2026-08-07T13:24:25Z","stars_delta_30d":58,"open_issues_delta_30d":2},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:20:09.125Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-20T12:01:44.677Z"},"deploy":{"source":"dockerfile:docker-compose.yml","self_host":true,"observed_at":"2026-08-20T12:01:44.677Z","managed_saas":false},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-08-20T12:01:44.677Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-20T12:01:44.677Z"},"has_docker":{"value":true,"source":"dockerfile:docker-compose.yml","observed_at":"2026-08-20T12:01:44.677Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-20T12:01:44.677Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you require an AI platform that extends from pipelines to agents for comprehensive flow management","If your workflow includes deep-learning models in need of detailed metadata tracking","For projects leveraging both TensorFlow and PyTorch, where a unified interface is beneficial"],"when_not_to_use":["If the project strictly limits itself to a single machine learning framework without requiring pipeline or agent support","In scenarios prioritizing bare-metal performance over managed services, as ZenML's abstraction layer might introduce overhead"],"source":"enrich:decision_facts","observed_at":"2026-07-14T18:47:55.736Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"ZenML caters to those building production-ready machine learning workflows with support for Pipelines and Agents, ensuring metadata tracking across frameworks like TensorFlow and PyTorch."}]}}