{"data":{"slug":"mlflow-mlflow","name":"mlflow","tagline":"AI engineering platform for debugging, evaluating, monitoring, and optimizing AI applications","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":27591,"forks":6189,"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":"2026-08-20T00:54:28+00:00","maintenance_label":"Very active","stars_delta_30d":476,"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":2054,"watchers":320,"ai_summary":"MLflow is an open-source platform that supports teams in managing, deploying, and monitoring machine learning models, including LLMs and agents.","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\n \n \n \n \n \n \n \n \n

Databricks
Amazon SageMaker
Azure ML
Nebius
Red Hat OpenShift AI
Self-Hosted
","github_created_at":"2018-06-05T16:05:58+00:00","created_at":"2026-07-07T17:41:29.253491+00:00","updated_at":"2026-08-20T06:01:21.726312+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":"ai-governance","name":"ai-governance"},{"slug":"evaluation","name":"evaluation"},{"slug":"llm-evaluation","name":"llm-evaluation"},{"slug":"mlflow","name":"mlflow"},{"slug":"model-management","name":"model-management"},{"slug":"prompt-engineering","name":"prompt-engineering"}],"trust":{"provenance":{"is_fork":false,"github_id":136202695,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-20T06:01:20.870Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":5,"days_since_push":0,"last_release_at":"2026-08-03T11:09:58Z","stars_delta_30d":476,"open_issues_delta_30d":-22},"security_summary":{"status":"findings","scanner":"mcp_manifest@v1","low_count":2,"high_count":0,"last_scan_at":"2026-07-11T11:19:10.122Z","medium_count":0,"scan_profile":"mcp_manifest","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-20T06:01:21.371Z"},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-08-20T06:01:21.371Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-20T06:01:21.371Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-20T06:01:21.371Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["- Use when you're working with a diverse range of environments like local or cloud platforms because MLflow is **vendor-neutral**.","- Ideal for teams that want to ensure their workflows are compatible across different providers without being locked into one vendor's technology stack.","- Suitable if the project entails evaluating, monitoring, and optimizing production-quality AI applications where cost control and access management of models and data are critical."],"when_not_to_use":["- Avoid if your organization has strong preferences for proprietary solutions with advanced features not available in the open-source domain.","- Not recommended for users who prefer a fully managed service without self-hosting options, as competitors like Databricks or Azure ML offer integrated services tailored for their cloud environments."],"source":"enrich:decision_facts","observed_at":"2026-07-11T13:29:29.821Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"MLflow is an open-source platform that offers comprehensive capabilities for managing, deploying, and monitoring machine learning models as well as large language models (LLMs) and AI agents. MLflow supports various use,"}]}}