{"data":{"slug":"mlrun-mlrun","name":"mlrun","tagline":"MLOps Platform for Building and Managing Continuous ML Applications","github_url":"https://github.com/mlrun/mlrun","owner":"mlrun","repo":"mlrun","owner_avatar_url":"https://avatars.githubusercontent.com/u/54775696?v=4","primary_language":"Python","stars":1690,"forks":315,"topics":["data-engineering","data-science","experiment-tracking","kubernetes","machine-learning","mlops","mlops-workflow","model-serving","python","workflow"],"archived":false,"github_pushed_at":"2026-08-02T20:20:38+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/mlrun-mlrun","markdown_url":"https://www.graphcanon.com/tools/mlrun-mlrun.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/mlrun-mlrun","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=mlrun-mlrun","description":"MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.","homepage_url":"https://mlrun.org","license":"Apache-2.0","open_issues":110,"watchers":24,"ai_summary":"MLRun is an open-source MLOps platform aiding in the rapid development and management of continuous machine learning applications. It automates pipelines and integrates seamlessly into development environments, supporting workflow creation from event handling to model inference.","readme_excerpt":"### Deployment\nMLRun serving can productize the newly trained LLM as a serverless function using real-time auto-scaling Nuclio serverless functions. \nThe application pipeline includes all the steps from accepting events or data, contextualizing it with a state  preparing the required model features, \ninferring results using one or more models, and driving actions. \n\n\n**Docs:**\n[Serving gen AI models](https://docs.mlrun.org/en/stable/genai/deployment/genai_serving.html), [GPU utilization](https://docs.mlrun.org/en/stable/genai/deployment/gpu_utilization.html), [Gen AI realtime serving graph](https://docs.mlrun.org/en/stable/genai/deployment/genai_serving_graph.html)\n**Tutorial:**\n[Deploy LLM using MLRun](https://docs.mlrun.org/en/stable/tutorials/genai-01-basic-tutorial.html)\n**Demos:**\n[Call center demo](https://github.com/mlrun/demo-call-center),\n[Banking agent demo](https://github.com/mlrun/demo-banking-agent)\n**Video:**\n[Call center](https://youtu.be/YycMbxRgLBA)","github_created_at":"2019-09-01T16:59:19+00:00","created_at":"2026-07-11T23:25:43.454793+00:00","updated_at":"2026-08-03T06:02:05.192724+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":"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":"ci-cd","name":"ci-cd"},{"slug":"machine-learning-pipelines","name":"machine learning pipelines"},{"slug":"mlops","name":"mlops"},{"slug":"serverless-functions","name":"serverless functions"}],"trust":{"provenance":{"is_fork":false,"github_id":205706595,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-03T06:02:04.463Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":25,"days_since_push":0,"last_release_at":"2026-08-02T03:43:10Z"},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":8,"high_count":0,"last_scan_at":"2026-07-11T23:25:48.090Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-03T06:02:04.913Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-03T06:02:04.913Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-03T06:02:04.913Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["Requires seamless integration of ML workflows into existing CI/CD environments","Need for automating production data, ML pipelines, and online applications delivery"],"when_not_to_use":["Lacks requirement for serverless function deployment with auto-scaling capabilities","CI/CD integration is not a priority or already fully catered to by alternative tools"],"source":"enrich:decision_facts","observed_at":"2026-07-17T03:11:13.201Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"MLRun: Open-source MLOps platform for rapid ML application development integrated into CI/CD pipelines using Python."}]}}