# mlflow/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.

Repository: https://github.com/mlflow/mlflow
Canonical: https://ross.abutalabs.com/products/mlflow
Homepage: https://mlflow.org
Language: Python
License: Apache-2.0
License Family: permissive
Topics: machine-learning, ai, ml, mlflow, apache-spark, model-management, agentops, agents, evaluation, langchain, llm-evaluation, llmops, observability, open-source, openai, prompt-engineering, ai-governance, mlops
Last push: 2026-08-27T00:12:02+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 99, longevity 100
- inputs: {"age_days": 3011, "days_push": 7, "days_rel": 7, "gap_med": 12.5, "n_releases_24m": 51}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 27688, forks 6227 (observed 2026-08-28T04:11:47.968895+00:00)

## What it is
MLflow is an open source AI engineering platform for agents, LLMs, and traditional ML models, providing experiment tracking, model registry, OpenTelemetry-compatible tracing, evaluation with LLM judges, prompt management, and an AI Gateway. It supports the full lifecycle from prototype to production with 100+ framework integrations and a FastAPI-based agent server for deployment.

## Use cases
- track machine learning experiments and compare model runs
- trace and debug LLM applications and AI agents
- evaluate LLM output quality with LLM-as-a-judge metrics
- version and manage prompts with lineage tracking
- deploy AI agents to production endpoints
- monitor production LLM app quality, cost, and safety
- manage model versions and deploy models to production
- route LLM requests through a unified OpenAI-compatible gateway

## When to choose
- you need end-to-end MLOps or LLMOps tooling in one platform
- you want vendor-neutral, OpenTelemetry-compatible LLM observability
- you need systematic evaluation and regression detection for AI apps
- you want experiment tracking and a model registry for ML teams
- you use multiple LLM providers and need unified access control and cost management

## When to avoid
- you only need lightweight logging without a tracking server or UI
- you need a fully managed SaaS with zero self-hosting effort
- your use case is simple LLM API calls with no evaluation or tracing needs
- you require a non-Python-first SDK as your primary interface

## Facets
- artifact type: framework
- maturity: stable
- function: machine-learning, llm-training, agent-framework, monitoring, tracing, benchmarking, api-gateway, prompt-engineering, rag, data-science, developer-tools
- domain: machine-learning, large-language-models, data-science, developer-tools, monitoring
- platform: python, cross-platform, self-hosted, cloud, cli
- tags: mlops, experiment-tracking, model-registry, llm-evaluation, llm-tracing, opentelemetry, prompt-management, ai-gateway, model-deployment, llm-judges, agent-observability, apache-2.0, ai-agents, docker

## Member repositories
- mlflow/mlflow (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:47.968895+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:54:30.046023+00:00, confidence not recorded.
  - readme: https://github.com/mlflow/mlflow (fetched 2026-08-28T04:11:47.968895+00:00, sha 5c5069c14487)
  - homepage: https://mlflow.org (fetched 2026-08-29T07:51:35.518231+00:00, sha eeac466268bf)
  - site_page: https://mlflow.org/docs/latest/genai (fetched 2026-08-29T07:51:35.524644+00:00, sha df63a676b581)
  - site_page: https://mlflow.org/docs/latest/ml (fetched 2026-08-29T07:51:35.526668+00:00, sha 1ec9d46723ce)
  - site_page: https://mlflow.org/docs/latest/genai/tracing (fetched 2026-08-29T07:51:35.528682+00:00, sha c76f8ba36acc)
  - site_page: https://mlflow.org/docs/latest/genai/eval-monitor (fetched 2026-08-29T07:51:35.530795+00:00, sha 6eec5b4b801d)
  - site_page: https://mlflow.org/docs/latest/genai/tracing/prod-tracing (fetched 2026-08-29T07:51:35.532730+00:00, sha 3c115136a65c)
  - site_page: https://mlflow.org/docs/latest/genai/tracing/quickstart (fetched 2026-08-29T07:51:35.534679+00:00, sha cb9ae584a026)
  - site_page: https://mlflow.org/docs/latest/genai/eval-monitor/ai-insights/detect-issues (fetched 2026-08-29T07:51:35.536388+00:00, sha f71c155ad62b)
  - registry_pypi: https://pypi.org/pypi/mlflow/json (fetched 2026-08-29T07:51:35.538412+00:00, sha b318ec70c87d)
- Data as of 2026-08-30T08:39:29.467469+00:00.