{"data":{"slug":"mlflow-mlflow","name":"mlflow","tagline":"Open source AI engineering platform for agents, LLMs, and ML models","github_url":"https://github.com/mlflow/mlflow","owner":"mlflow","repo":"mlflow","owner_avatar_url":"https://avatars.githubusercontent.com/u/39938107?v=4","primary_language":"Python","stars":28010,"forks":6323,"topics":["agentops","agents","ai","ai-governance","apache-spark","evaluation","langchain","llm-evaluation","llmops","machine-learning","ml","mlflow","mlops","model-management","observability","open-source","openai","prompt-engineering"],"archived":false,"github_pushed_at":"2026-09-18T08:08:32+00:00","maintenance_label":"Very active","stars_delta_30d":895,"url":"https://www.graphcanon.com/tools/mlflow-mlflow","markdown_url":"https://www.graphcanon.com/tools/mlflow-mlflow.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/mlflow-mlflow","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=mlflow-mlflow","description":"The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.","homepage_url":"https://mlflow.org","license":"Apache-2.0","open_issues":2107,"watchers":325,"ai_summary":"MLflow is an open-source platform that supports teams in debugging, evaluating, monitoring, and optimizing production-quality AI applications. It offers vendor-neutral capabilities for managing models and data across various environments.","readme_excerpt":"## Hosting MLflow\n\nMLflow can be used in a variety of environments, including your local environment, on-premises clusters, cloud platforms, and managed services. Being an open-source platform, MLflow is **vendor-neutral** — whether you're building AI agents, LLM applications, or ML models, you have access to MLflow's core capabilities.\n\n<table>\n  <tr>\n    <td align=\"center\" width=\"130\"><a href=\"https://docs.databricks.com/aws/en/mlflow3/genai/\"><img src=\"https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/docs/static/images/logos/databricks-logo.png\" height=\"40\"><br><sub><b>Databricks</b></sub></a></td>\n    <td align=\"center\" width=\"130\"><a href=\"https://aws.amazon.com/sagemaker-ai/experiments/\"><img src=\"https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/docs/static/images/logos/amazon-sagemaker-logo.png\" height=\"40\"><br><sub><b>Amazon SageMaker</b></sub></a></td>\n    <td align=\"center\" width=\"130\"><a href=\"https://learn.microsoft.com/en-us/azure/machine-learning/concept-mlflow?view=azureml-api-2\"><img src=\"https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/docs/static/images/logos/azure-ml-logo.png\" height=\"40\"><br><sub><b>Azure ML</b></sub></a></td>\n    <td align=\"center\" width=\"130\"><a href=\"https://nebius.com/services/managed-mlflow\"><img src=\"https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/docs/static/images/logos/nebius-logo.png\" height=\"40\"><br><sub><b>Nebius</b></sub></a></td>\n    <td align=\"center\" width=\"130\"><a href=\"https://www.redhat.com/en/products/ai/openshift-ai\"><img src=\"https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/docs/static/images/logos/rhoai-logo.png\" height=\"40\"><br><sub><b>Red Hat OpenShift AI</b></sub></a></td>\n    <td align=\"center\" width=\"130\"><a href=\"https://mlflow.org/docs/latest/ml/tracking/\"><img src=\"https://raw.githubusercontent.com/mlflow/mlflow/refs/heads/master/docs/static/images/logos/kubernetes-logo.png\" height=\"40\"><br><sub><b>Self-Hosted</b></sub></a></td>\n  </tr>\n</table>","github_created_at":"2018-06-05T16:05:58+00:00","created_at":"2026-07-07T17:41:29.253491+00:00","updated_at":"2026-09-20T04:30:47.058739+00:00","categories":[{"slug":"developer-tools","name":"Developer Tools","url":"https://www.graphcanon.com/categories/developer-tools","markdown_url":"https://www.graphcanon.com/categories/developer-tools.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/developer-tools"},{"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":"ai-governance","name":"ai-governance"},{"slug":"langchain","name":"langchain"},{"slug":"llm-evaluation","name":"llm-evaluation"},{"slug":"llmops","name":"llmops"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"mlflow","name":"mlflow"}],"trust":{"provenance":{"is_fork":false,"github_id":136202695,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-18T08:26:04.499Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":5,"days_since_push":0,"last_release_at":"2026-09-17T02:40:08Z","stars_delta_30d":895,"open_issues_delta_30d":31},"security_summary":{"status":"findings","scanner":"mcp_manifest@v1","low_count":2,"high_count":0,"last_scan_at":"2026-09-18T08:26:06.105Z","medium_count":0,"scan_profile":"mcp_manifest","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-18T08:26:03.395Z"},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-09-18T08:26:03.395Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-09-18T08:26:03.395Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-09-18T08:26:03.395Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need a vendor-neutral solution for managing the lifecycle of AI and ML models, including debugging, evaluation, monitoring, and optimization.","If your team requires support for a wide range of environments, including local setups, on-premises clusters, cloud platforms, and managed services.","When you are working with AI agents, LLM applications, or ML models and need a comprehensive platform that supports these specific use cases."],"when_not_to_use":["If you are looking for a platform that is tightly integrated with a specific vendor's ecosystem, as MLflow's vendor-neutral approach may not leverage vendor-specific optimizations.","When your project strictly requires proprietary features or services that are not available in open-source platforms like MLflow.","If your team prefers a more specialized tool for a specific aspect of AI/ML lifecycle management, as MLflow is a general-purpose platform and may not offer the depth of features in a niche area."],"source":"enrich:decision_facts","observed_at":"2026-09-18T12:06:58.164Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"MLflow is an open-source platform for managing the lifecycle of AI and ML models, offering vendor-neutral capabilities for debugging, evaluating, monitoring, and optimizing models across different environments."}]}}