Kumo.AI website screenshot

Kumo.AI

Kumo.AI builds predictive AI for relational data. Its flagship model, KumoRFM, is a pre-trained Relational Foundation Model that generates training-free predictions over multi-table data by interpreting a data warehouse as a temporal heterogeneous graph, queried through a SQL-like Predictive Query Language (PQL). Kumo exposes this through a Python SDK (kumoai), a REST API for automating retraining and batch predictions, an open-source Model Context Protocol server (kumo-rfm-mcp) for agentic workflows, and a published Agent Skills catalog. It ships as SaaS, VPC/BYOC, a Snowflake Native Application, and a Databricks Native Application. Kumo product documentation is now hosted by NVIDIA under Structured Data and Graph Models.

Kumo.AI publishes 2 APIs on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Machine-Learning, Predictive Analytics, and Graph Neural Networks.

Kumo.AI’s developer surface includes documentation, API reference, getting-started guide, support, signup flow, authentication, changelog, and 23 more developer resources.

35.5/100 thin ▬ flat Agent 12/100 agent aware open core · MIT Full breakdown ↓
scored 2026-08-30 · rubric v0.17.2
AccessSelf serve
2 APIs 1 MCP Servers
CompanyArtificial IntelligenceMachine-LearningPredictive AnalyticsGraph Neural NetworksFoundation ModelsData WarehouseMCPAgentsRelational Data

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-08-30 · rubric v0.17.2
Composite quality — 35.5/100 · thin
Contract Quality 0.0 / 23
Developer Ergonomics 12.4 / 18
Access Clarity 4.7 / 18
Operational Transparency 7.1 / 12
Contract Governance 2.0 / 11
Discoverability 6.8 / 9
Open Source Surface 2.5 / 10
Agent readiness — 12/100 · agent aware
Machine-Readable Contract 0 / 18
Agentic Access Contract 0 / 10
Documented Reversibility 0 / 6
MCP Server 0 / 12
Machine-Readable Auth 10 / 10
Idempotency 0 / 9
Stable Error Semantics 8 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 7 / 7
Typed Event Surface 0 / 6
Agent Skills 5 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3
A2A Agent Card 0 / 8
Dry-Run / Simulate Mode 0 / 4
Delegated User Identity 0 / 6
Protected Resource Metadata 0 / 5
Registration Without a Human 0 / 6
Agentic Commerce Surface 0 / 5
Open Source Surface applies to this provider. This product is open source and we read its repository directly, so Open Source Surface carries 10 points of the composite. It is scored from what the repository actually publishes — a security policy, a contribution guide, a release history, a code of conduct — read live from the provider rather than inferred from our own catalog pointers. This facet adds; nothing was taken away to make room for it. An open-source project is not excused from the commercial facets, because exemption would strip it of the points it does earn. If we have the wrong repository, or this product is not open source, say so on your provider repo and we will drop the facet rather than have you publish against it.
The six quality facets above are damped to 90 points between them, because the conditional facet above carries the other 10. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 90% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.
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/kumoai: 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 2

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

Kumo REST API

Automate and schedule predictive query training and batch predictions. Retrain a predictive query (re-ingesting data from the source), trigger recurring batch predictions, monit...

KumoRFM MCP Server

Open-source Model Context Protocol server that empowers AI assistants with KumoRFM. Builds, manages, and visualizes relational graphs directly from CSV or Parquet files, convert...

MCP Servers 1

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

Kumo.AI MCP Server

MCP SERVER

Rate Limits 1

Documented rate limits and quota policies.

Kumoai Rate Limits

4 limits

RATE LIMITS

Security Posture 4

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

Kumoai Authentication

apiKey/snowflakeCredentials/http · 4 schemes

SECURITY

Kumoai Domain Security

TLSv1.2 · DMARC

SECURITY

Kumoai Vulnerability Disclosure

Hackerone · security.txt · contact published

SECURITY

Kumoai Trust Center

SOC 2

SECURITY

Resources

Get Started 3

Portal, sign-up, and the first successful call

Documentation 2

Reference material describing how the API behaves

Agent Surfaces 4

MCP servers, agent skills, and machine-readable catalogs

Design & Contract 4

Pagination, idempotency, versioning, errors, and events

Build 3

SDKs, sample code, and the tooling you integrate with

Access & Security 6

Authentication, authorization, and security posture

Operate 5

Status, limits, changes, and where to get help

Commercial 2

Pricing, plans, and the legal terms of use

Company 1

The organization behind the API

Source (apis.yml)

apis.yml Raw ↑
aid: kumoai
name: Kumo.AI
description: Kumo.AI builds predictive AI for relational data. Its flagship model, KumoRFM, is a pre-trained Relational Foundation
  Model that generates training-free predictions over multi-table data by interpreting a data warehouse as a temporal heterogeneous
  graph, queried through a SQL-like Predictive Query Language (PQL). Kumo exposes this through a Python SDK (kumoai), a REST
  API for automating retraining and batch predictions, an open-source Model Context Protocol server (kumo-rfm-mcp) for agentic
  workflows, and a published Agent Skills catalog. It ships as SaaS, VPC/BYOC, a Snowflake Native Application, and a Databricks
  Native Application. Kumo product documentation is now hosted by NVIDIA under Structured Data and Graph Models.
url: https://raw.githubusercontent.com/api-evangelist/kumoai/refs/heads/main/apis.yml
deliveryModel:
  model: open-core
  license: MIT
  open_source: true
  commercial: true
  callable_host: false
  label: Open core · an OSS project plus a commercial hosted product
  confidence: high
  source:
  - license
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: self-serve
  trial: false
  try_now: false
  public: false
  label: Self-serve signup
  confidence: medium
  source:
  - authentication
  generated: '2026-07-22'
  method: derived
image: https://avatars.githubusercontent.com/u/83320328?v=4
x-type: company
x-source: vc-portfolio
x-backed-by:
- sv-angel
x-tier: enriched
x-tier-reason: enrichment-pipeline
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-19'
tags:
- Company
- Artificial Intelligence
- Machine-Learning
- Predictive Analytics
- Graph Neural Networks
- Foundation Models
- Data Warehouse
- MCP
- Agents
- Relational Data
tags_raw:
- Company
- Ai
- Machine Learning
- Predictive Analytics
- Graph Neural Networks
- Foundation Models
- Data Warehouse
- Model Context Protocol
- Agents
- Relational Data
apis:
- aid: kumoai-rest-api
  name: Kumo REST API
  description: Automate and schedule predictive query training and batch predictions. Retrain a predictive query (re-ingesting
    data from the source), trigger recurring batch predictions, monitor job status and evaluation metrics, and cancel in-progress
    jobs. Authenticates with a tenant-scoped X-API-Key header.
  humanURL: https://docs.nvidia.com/sdgm/rest-api
  baseURL: https://<customer_id>.kumoai.cloud/api
  x-baseURL-note: The public API URL is tenant-scoped; SPCS deployments use https://<kumo_spcs_deployment_url>/api.
  tags:
  - Machine-Learning
  - Batch Prediction
  - Job
  tags_raw:
  - Machine Learning
  - Batch Prediction
  - Jobs
  properties:
  - type: Documentation
    url: https://docs.nvidia.com/sdgm/rest-api
  - type: Authentication
    url: authentication/kumoai-authentication.yml
- aid: kumoai-rfm-mcp
  name: KumoRFM MCP Server
  description: Open-source Model Context Protocol server that empowers AI assistants with KumoRFM. Builds, manages, and visualizes
    relational graphs directly from CSV or Parquet files, converts natural language into PQL queries, and runs training-free
    predictions, evaluations, and explanations. Runs over stdio or streamable-http and is distributed on PyPI and as an MCPB
    bundle.
  humanURL: https://github.com/kumo-ai/kumo-rfm-mcp
  baseURL: https://kumorfm.ai
  tags:
  - MCP
  - Agents
  - Predictions
  tags_raw:
  - Model Context Protocol
  - Agents
  - Predictions
  properties:
  - type: SourceCode
    url: https://github.com/kumo-ai/kumo-rfm-mcp
  - type: MCPServer
    url: mcp/kumoai-mcp.yml
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: IssueTracker
  url: https://github.com/kumo-ai/kumo-rfm-mcp/issues
- type: Releases
  url: https://github.com/kumo-ai/kumo-rfm-mcp/releases
- type: License
  name: MIT
  url: https://github.com/kumo-ai/kumo-rfm-mcp/blob/main/LICENSE
- type: Website
  url: https://kumo.ai
- type: DeveloperPortal
  url: https://docs.nvidia.com/sdgm/rfm/overview
- type: Documentation
  url: https://docs.nvidia.com/sdgm/rfm/introduction
- type: APIReference
  url: https://docs.nvidia.com/sdgm/rest-api
- type: GettingStarted
  url: https://docs.nvidia.com/sdgm/quick-start/rfm
- type: Support
  url: https://join.slack.com/t/kumoaibuilders/shared_invite/zt-2z9uih3lf-fPM1z2ACZg~oS3ObmiQLKQ
- type: GitHubOrganization
  url: https://github.com/kumo-ai
- type: SignUp
  url: https://kumorfm.ai
- type: PrivacyPolicy
  url: https://docs.nvidia.com/sdgm/privacy-policy
- type: Security
  url: https://docs.nvidia.com/sdgm/security-and-governance
- type: Compliance
  url: https://docs.nvidia.com/sdgm/security-and-governance
- type: TrustCenter
  url: security/kumoai-trust-center.yml
- type: VulnerabilityDisclosure
  url: security/kumoai-vulnerability-disclosure.yml
- type: DomainSecurity
  url: security/kumoai-domain-security.yml
- type: Authentication
  url: authentication/kumoai-authentication.yml
- type: Conventions
  url: conventions/kumoai-conventions.yml
- type: ErrorCatalog
  url: errors/kumoai-problem-types.yml
- type: RateLimits
  url: rate-limits/kumoai-rate-limits.yml
- type: Lifecycle
  url: lifecycle/kumoai-lifecycle.yml
- type: ChangeLog
  url: changelog/kumoai-changelog.yml
- type: Packages
  url: packages/kumoai-packages.yml
- type: SDKs
  url: packages/kumoai-packages.yml
- type: MCPServer
  url: mcp/kumoai-mcp.yml
- type: AgentSkill
  url: skills/_index.yml
- type: LLMsTxt
  url: llms/kumoai-llms.txt
- type: WellKnown
  url: well-known/kumoai-well-known.yml
- type: Conformance
  url: conformance/kumoai-conformance.yml
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

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