MLflow
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. observed · 2026-10-19
Health v2 · maintenance only
100/100
- Activity 100
- Release rhythm 99
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.
- gap_med: 12
- age_days: 3059
- days_rel: 7
- days_push: 2
- n_releases_24m: 52
Adoption not part of the score
28441 stars · 6475 forks observed · 2026-10-19
What it is AI-extracted, prompt v1, taxonomy v1, 2026-10-05, confidence 0.97
MLflow is an open‑source AI engineering platform that supports the full lifecycle of machine‑learning, LLM, and agent applications. It provides experiment tracking, model registry, tracing, evaluation, and deployment tools with a unified UI and APIs.
Use cases
- track and compare experiments for scikit‑learn or deep‑learning models
- store and version LLM prompts and evaluate their quality over time
- monitor production LLM agents for latency, cost, and safety issues
- manage model access and enforce rate limits with the AI Gateway
- debug LLM applications by visualizing end‑to‑end traces
- automate prompt optimization using built‑in optimization algorithms
When to choose
- You need a single platform that handles both traditional ML and GenAI workflows.
- Your team requires built‑in observability, evaluation, and prompt management for LLMs and agents.
- You want an open‑source solution with strong community support and Apache‑2.0 licensing.
- You plan to deploy models in containers, Kubernetes, or serverless environments and need a flexible deployment API.
When to avoid
- You only need a lightweight library for a single model without UI or server components.
- Your stack is limited to non‑Python languages and you cannot integrate the Python SDK.
- You require a fully managed SaaS offering with zero operational overhead.
Facets
application · maturity active
deployment monitoring tracing api-gateway artificial-intelligence machine-learning large-language-models data-science python windows cloud cli self-hosted ai-platform llmops agentops model-management prompt-optimization open-source apache-2.0 mlflow experiment-tracking model-registry evaluation prompt-management linux macos docker kubernetes
10 sources
- readme: https://github.com/mlflow/mlflow · fetched 2026-10-19 · ccd1f3f2e9a5
- homepage: https://mlflow.org · fetched 2026-09-21 · eeac466268bf
- site_page: https://mlflow.org/docs/latest/genai · fetched 2026-09-21 · df63a676b581
- site_page: https://mlflow.org/docs/latest/ml · fetched 2026-09-21 · 1ec9d46723ce
- site_page: https://mlflow.org/docs/latest/genai/tracing · fetched 2026-09-21 · c76f8ba36acc
- site_page: https://mlflow.org/docs/latest/genai/eval-monitor · fetched 2026-09-21 · 6eec5b4b801d
- site_page: https://mlflow.org/docs/latest/genai/tracing/prod-tracing · fetched 2026-09-21 · 3c115136a65c
- site_page: https://mlflow.org/docs/latest/genai/tracing/quickstart · fetched 2026-09-21 · cb9ae584a026
- site_page: https://mlflow.org/docs/latest/genai/eval-monitor/ai-insights/detect-issues · fetched 2026-09-21 · 638f78a3e8a6
- registry_pypi: https://pypi.org/pypi/mlflow/json · fetched 2026-09-21 · 6d02f26b1e46
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| mlflow/mlflow | main | 100 |
| mlflow/mlflow-example | examples | 23 |
| mlflow/mlflow-export-import | plugin | 72 |
| mlflow/skills | plugin | 59 |
| mlflow/mlp-regression-template | examples | 10 |
| mlflow/recipes-regression-template | examples | 32 |
For agents
Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem