Oumi

Oumi (Open Universal Machine Intelligence) is an open-source, Apache-2.0 platform for ML engineers and researchers to train, fine-tune, evaluate, and deploy foundation models (LLMs and VLMs) through a single unified interface. It ships as a Python library and a first-party command-line tool (train, evaluate, infer, launch, deploy, analyze, synth, tune) covering the full model development lifecycle — data synthesis, supervised fine-tuning, DPO/preference learning, evaluation judges, quantization, hyperparameter tuning, and inference across local, cloud, and HPC targets. A hosted managed platform (platform.oumi.ai) adds a Free/Pro/Enterprise product layer, and an oumi-mcp Model Context Protocol server exposes Oumi to MCP-capable assistants such as Claude and Cursor. Backed by Obvious Ventures.

Oumi is profiled on the APIs.io network. Tagged areas include Company, Economic Health, Artificial Intelligence, Machine Learning, and LLM.

Oumi’s developer surface includes developer portal, documentation, API reference, getting-started guide, support, engineering blog, pricing, and 13 more developer resources.

🌐 Visit website 📡 Source on GitHub

26.8/100 emerging ▬ flat Agent 15/100 agent aware Full breakdown ↓
scored 2026-07-27 · rubric v0.5

0 APIs 1 MCP Servers

CompanyEconomic HealthArtificial IntelligenceMachine LearningLLMFoundation ModelsFine-TuningModel TrainingOpen SourceMLOpsDeveloper ToolsInference

Kin Score

Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5

Composite quality — 26.8/100 · emerging

Contract Quality 0.0 / 25

Developer Ergonomics 12.6 / 20

Commercial Clarity 4.7 / 20

Operational Transparency 2.7 / 13

Governance 0.0 / 12

Discoverability 6.8 / 10

Agent readiness — 15/100 · agent aware

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 15

MCP Server 12 / 12

Machine-Readable Auth 0 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 4 / 4

Consent & Bot Identity 0 / 3

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/oumi: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

MCP Servers 1

Model Context Protocol servers that expose these APIs to AI agents.

Get Started 4

Portal, sign-up, and the first successful call

DeveloperPortal

Portal

GettingStarted

SignUp

Documentation 2

Reference material describing how the API behaves

Documentation

Documentation

APIReference

APIReference

Agent Surfaces 3

MCP servers, agent skills, and machine-readable catalogs

MCPServer

MCPServer

LLMsTxt

LLMsTxt

WellKnown

WellKnown

Design & Contract 1

Pagination, idempotency, versioning, errors, and events

Lifecycle

Lifecycle

Build 4

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

CLI

Packages

SDKs

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Operate 2

Status, limits, changes, and where to get help

Support

Support

ChangeLog

ChangeLog

Commercial 1

Pricing, plans, and the legal terms of use

Pricing

Pricing

Company 2

The organization behind the API

Website

Website

Blog

Blog

Source (apis.yml)

aid: oumi
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/oumi.png
name: Oumi
description: Oumi (Open Universal Machine Intelligence) is an open-source, Apache-2.0 platform for ML engineers and researchers
  to train, fine-tune, evaluate, and deploy foundation models (LLMs and VLMs) through a single unified interface. It ships
  as a Python library and a first-party command-line tool (train, evaluate, infer, launch, deploy, analyze, synth, tune) covering
  the full model development lifecycle — data synthesis, supervised fine-tuning, DPO/preference learning, evaluation judges,
  quantization, hyperparameter tuning, and inference across local, cloud, and HPC targets. A hosted managed platform (platform.oumi.ai)
  adds a Free/Pro/Enterprise product layer, and an oumi-mcp Model Context Protocol server exposes Oumi to MCP-capable assistants
  such as Claude and Cursor. Backed by Obvious Ventures.
url: https://raw.githubusercontent.com/api-evangelist/oumi/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- obvious-ventures
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.20'
created: '2026-07-17'
modified: '2026-07-20'
tags:
- Company
- Economic Health
- Artificial Intelligence
- Machine Learning
- LLM
- Foundation Models
- Fine-Tuning
- Model Training
- Open Source
- MLOps
- Developer Tools
- Inference
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
apis: []
common:
- type: DomainSecurity
  url: security/oumi-domain-security.yml
- type: Website
  url: https://oumi.ai
- type: DeveloperPortal
  url: https://platform.oumi.ai
- type: Portal
  url: https://platform.oumi.ai
- type: Documentation
  url: https://oumi.ai/docs
- type: APIReference
  url: https://oumi.ai/docs/en/latest/api/oumi.html
- type: GettingStarted
  url: https://oumi.ai/docs/en/latest/get_started/quickstart.html
- type: Support
  url: https://discord.gg/oumi
- type: Blog
  url: https://oumiai.substack.com
- type: GitHubOrganization
  url: https://github.com/oumi-ai
- type: Pricing
  url: https://oumi.ai/pricing
- type: SignUp
  url: https://platform.oumi.ai/signin
- type: MCPServer
  url: mcp/oumi-mcp.yml
- type: CLI
  url: cli/oumi-cli.yml
- type: Packages
  url: packages/oumi-packages.yml
- type: SDKs
  url: packages/oumi-packages.yml
- type: ChangeLog
  url: changelog/oumi-changelog.yml
- type: Lifecycle
  url: lifecycle/oumi-lifecycle.yml
- type: LLMsTxt
  url: llms/oumi-llms.txt
- type: WellKnown
  url: well-known/oumi-well-known.yml
x-enrichment:
  date: '2026-07-20'
  status: enriched
  artifacts_added: 8
  pass: local-v1