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Decision brief
MLRun: Open-source MLOps platform for rapid ML application development integrated into CI/CD pipelines using Python.
Good fit when
- Requires seamless integration of ML workflows into existing CI/CD environments
- Need for automating production data, ML pipelines, and online applications delivery
Avoid when
- 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
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- 8 low (8 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install mlrunSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
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.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Aug 3, 2026
Categories
Tags
README
Deployment
MLRun serving can productize the newly trained LLM as a serverless function using real-time auto-scaling Nuclio serverless functions. The application pipeline includes all the steps from accepting events or data, contextualizing it with a state preparing the required model features, inferring results using one or more models, and driving actions.
Docs: Serving gen AI models, GPU utilization, Gen AI realtime serving graph Tutorial: Deploy LLM using MLRun Demos: Call center demo, Banking agent demo Video: Call center
For agents
This page has a .md twin and JSON over the API.