MLflow website screenshot

MLflow

MLflow is an Apache 2.0 open-source platform for the end-to-end ML and GenAI lifecycle: tracking, model registry, deployment, evaluation, traces, prompts, and GenAI gateway. The tracking server exposes a REST API under `/api/2.0/mlflow`.

MLflow publishes 6 APIs on the APIs.io network, including Artifacts API, Experiments API, Metrics API, and 3 more. Tagged areas include ML, MLOps, GenAI, Experiment Tracking, and Open Source.

MLflow’s developer surface includes authentication, developer portal, engineering blog, and 11 more developer resources.

33.1/100 thin ▬ flat Agent 31/100 agent aware Full breakdown ↓
scored 2026-08-05 · rubric v0.9.1
AccessSelf serve
8 APIs
MLMLOpsGenAIExperiment TrackingOpen Source

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-08-05 · rubric v0.9.1
Composite quality — 33.1/100 · thin
Contract Quality 12.8 / 25
Developer Ergonomics 4.3 / 20
Commercial Clarity 5.8 / 20
Operational Transparency 2.7 / 13
Governance 0.0 / 12
Discoverability 7.4 / 10
Agent readiness — 31/100 · agent aware
Machine-Readable Contract 18 / 18
Agentic Access Contract 10 / 10
MCP Server 0 / 12
Machine-Readable Auth 10 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 7 / 7
Typed Event Surface 0 / 6
Agent Skills 0 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3
A2A Agent Card 0 / 8
Dry-Run / Simulate Mode 0 / 4
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. This rating is computed from github.com/api-evangelist/mlflow: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

APIs 8

Individual APIs this provider publishes, each with its own machine-readable definition.

MLflow REST API

The MLflow tracking server exposes a REST API for experiments, runs, parameters, metrics, tags, registered models, model versions, model aliases, and artifacts, plus newer endpo...

MLflow AI Gateway API

The MLflow AI Gateway provides a unified HTTP interface to LLM providers with routing, rate-limiting, and secret management features.

MLflow Artifacts API

List and obtain presigned URLs for run artifacts.

MLflow Experiments API

Manage MLflow experiments.

MLflow Metrics API

Log and retrieve metric history for runs.

MLflow Model Versions API

Manage versions of registered models.

MLflow Registered Models API

Manage registered models in the MLflow Model Registry.

MLflow Runs API

Create and manage runs within an experiment.

Scroll for all 8

Open Collections 1

Open, tool-agnostic API collections (OpenAPI-derived and Bruno).

MLflow Tracking REST API

OPEN COLLECTION

Pricing Plans 1

Published pricing tiers and plan structures.

Mlflow Plans Pricing

1 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Mlflow Rate Limits

1 limits

RATE LIMITS

FinOps 1

Cost, billing, and metering signals for API financial operations.

Security Posture 2

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Mlflow Authentication

http · 1 scheme

SECURITY

Mlflow Domain Security

TLSv1.3

SECURITY

Agentic Access 1

Recommended x-agentic-access execution contracts for AI agents.

Mlflow Agentic Access

23 operations · 17 acting

23 operations · 17 acting

AGENTIC

Resources

Get Started 1

Portal, sign-up, and the first successful call

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

Build 1

SDKs, sample code, and the tooling you integrate with

Access & Security 2

Authentication, authorization, and security posture

Operate 1

Status, limits, changes, and where to get help

Commercial 3

Pricing, plans, and the legal terms of use

Company 3

The organization behind the API

Other 1

Properties that don't map to a standard resource type

Source (apis.yml)

apis.yml Raw ↑
aid: mlflow
url: https://raw.githubusercontent.com/api-evangelist/mlflow/refs/heads/main/apis.yml
name: MLflow
kind: opensource
description: 'MLflow is an Apache 2.0 open-source platform for the end-to-end ML and GenAI lifecycle: tracking, model registry,
  deployment, evaluation, traces, prompts, and GenAI gateway. The tracking server exposes a REST API under `/api/2.0/mlflow`.'
accessModel:
  pricing: unknown
  onboarding: self-serve
  trial: false
  try_now: false
  public: false
  label: Self-serve signup
  confidence: high
  source:
  - plans
  - authentication
  generated: '2026-07-22'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/mlflow.png
tags:
- ML
- MLOps
- GenAI
- Experiment Tracking
- Open Source
created: '2026-05-08'
modified: '2026-05-08'
specificationVersion: '0.19'
apis:
- aid: mlflow:mlflow-tracking-rest-api
  name: MLflow REST API
  humanURL: https://mlflow.org/docs/latest/rest-api.html
  baseURL: http://{tracking_server}/api/2.0/mlflow
  tags:
  - REST
  - Experiments
  - Runs
  - Model Registry
  - Artifacts
  description: The MLflow tracking server exposes a REST API for experiments, runs, parameters, metrics, tags, registered
    models, model versions, model aliases, and artifacts, plus newer endpoints for traces, prompts, datasets, and the AI gateway.
    POST requests use `application/json`. Pagination via `max_results` / `page_token`. Authentication is deployment-specific
    (basic auth, OIDC, or none).
  properties:
  - type: Documentation
    url: https://mlflow.org/docs/latest/rest-api.html
  - type: SDKs
    title: Python (mlflow)
    url: https://mlflow.org/docs/latest/python_api/index.html
  - type: SDKs
    title: Java
    url: https://mlflow.org/docs/latest/java_api/index.html
  - type: SDKs
    title: R
    url: https://mlflow.org/docs/latest/R-api.html
- aid: mlflow:mlflow-gateway-api
  name: MLflow AI Gateway API
  humanURL: https://mlflow.org/docs/latest/llms/gateway/index.html
  baseURL: http://{gateway}/gateway
  tags:
  - GenAI
  - LLM Gateway
  - REST
  description: The MLflow AI Gateway provides a unified HTTP interface to LLM providers with routing, rate-limiting, and secret
    management features.
  properties:
  - type: Documentation
    url: https://mlflow.org/docs/latest/llms/gateway/index.html
- aid: mlflow:mlflow-artifacts-api
  name: MLflow Artifacts API
  description: List and obtain presigned URLs for run artifacts.
  humanURL: https://mlflow.org/docs/latest/rest-api.html
  baseURL: http://{tracking_server}/api/2.0/mlflow
  tags:
  - Artifacts
  properties:
  - type: OpenAPI
    url: openapi/mlflow-artifacts-api-openapi.yml
- aid: mlflow:mlflow-experiments-api
  name: MLflow Experiments API
  description: Manage MLflow experiments.
  humanURL: https://mlflow.org/docs/latest/rest-api.html
  baseURL: http://{tracking_server}/api/2.0/mlflow
  tags:
  - Experiments
  properties:
  - type: OpenAPI
    url: openapi/mlflow-experiments-api-openapi.yml
- aid: mlflow:mlflow-metrics-api
  name: MLflow Metrics API
  description: Log and retrieve metric history for runs.
  humanURL: https://mlflow.org/docs/latest/rest-api.html
  baseURL: http://{tracking_server}/api/2.0/mlflow
  tags:
  - Metrics
  properties:
  - type: OpenAPI
    url: openapi/mlflow-metrics-api-openapi.yml
- aid: mlflow:mlflow-model-versions-api
  name: MLflow Model Versions API
  description: Manage versions of registered models.
  humanURL: https://mlflow.org/docs/latest/rest-api.html
  baseURL: http://{tracking_server}/api/2.0/mlflow
  tags:
  - Model Versions
  properties:
  - type: OpenAPI
    url: openapi/mlflow-model-versions-api-openapi.yml
- aid: mlflow:mlflow-registered-models-api
  name: MLflow Registered Models API
  description: Manage registered models in the MLflow Model Registry.
  humanURL: https://mlflow.org/docs/latest/rest-api.html
  baseURL: http://{tracking_server}/api/2.0/mlflow
  tags:
  - Registered Models
  properties:
  - type: OpenAPI
    url: openapi/mlflow-registered-models-api-openapi.yml
- aid: mlflow:mlflow-runs-api
  name: MLflow Runs API
  description: Create and manage runs within an experiment.
  humanURL: https://mlflow.org/docs/latest/rest-api.html
  baseURL: http://{tracking_server}/api/2.0/mlflow
  tags:
  - Runs
  properties:
  - type: OpenAPI
    url: openapi/mlflow-runs-api-openapi.yml
common:
- type: AgenticAccess
  url: agentic-access/mlflow-agentic-access.yml
- type: DomainSecurity
  url: security/mlflow-domain-security.yml
- type: Authentication
  url: authentication/mlflow-authentication.yml
- type: LinkedIn
  url: https://www.linkedin.com/company/mlflow-org
- type: Website
  url: https://mlflow.org/
- type: Portal
  url: https://mlflow.org/docs/latest/
- type: SourceCode
  url: https://github.com/mlflow/mlflow
- type: License
  url: https://github.com/mlflow/mlflow/blob/master/LICENSE.txt
- type: CommercialOffering
  title: Databricks Managed MLflow
  url: https://www.databricks.com/product/managed-mlflow
- type: Plans
  url: plans/mlflow-plans-pricing.yml
- type: RateLimits
  url: rate-limits/mlflow-rate-limits.yml
- type: FinOps
  url: finops/mlflow-finops.yml
- type: LlmsText
  url: https://mlflow.org/llms.txt
- url: https://mlflow.org/blog/rss.xml
  type: Blog
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com