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 is listed with 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.

40.3/100 developing ▬ flat Agent 15/100 agent aware Front door AI 4/6 moderate open core · MIT Full breakdown ↓
scored 2026-10-31 · rubric v0.25.0 · Show this score on your site →
2 APIs 1 MCP Servers 2 Agent Skills
CompanyArtificial IntelligenceMachine LearningPredictive AnalyticsGraph Neural NetworksFoundation ModelsData WarehouseMCPAgentsRelational Data

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-10-31 · rubric v0.25.0
Regulatory Posture applies to this provider. Its tags matched the AI & Biometrics regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.
Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.
The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.
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Agent readiness — checks not met, largest first (exact, from this profile):
  • Machine-Readable Contract +18 pts — api_specs includes an OpenAPI document
  • Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author
  • Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented)
  • A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0
  • Request/Response Examples +7 pts — share of operations carrying request/response examples; else an Examples / Code Samples link
  • Delegated User Identity +6 pts — a served discovery document listing authorization_code in grant_types_supported (served), else an oauth2 authorizationCode flow or openIdConnect declared in an OpenAPI (documented)
8 more unmet checks in the Agent Readiness table above.
Kin Score — facets with the most composite points still available (weight × headroom):
Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from the provider profile at artifacts.apis.io/kumoai/apis.yml: open an issue on github.com/api-evangelist/kumoai to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Markets this provider is in provisional

The Kin Score Market layer reads a provider inside its market: its position, and how deep its integrations go. Depth is only comparable within a position. Provisional and unweighted: not part of the Kin Score or agent readiness.

MarketPositionDepthRegistered MCP serverDeclares compatibility
Agent Skills (SKILL.md packages) standard 36.4 - 1

Registered MCP server and declares compatibility are report only — not weighted. Undeclared means we found nothing we can read, so there is no number: we never infer integrations.

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

Remote MCP server at kumo-sdk-public.s3.us-west-2.amazonaws.com.

MCP SERVER

Agent Skills 2

Packaged agent skills for driving this provider's APIs from an AI assistant.

kumo-rfm-agent

AGENT SKILL

kumo-tuning-agent

AGENT SKILL

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: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source:
  - authentication
  - rate-limits
  - security
  - url: https://kumo.ai
    status: 301
    note: declared website redirects to https://docs.nvidia.com/sdgm/rfm/overview — a different registrable domain (kumo.ai
      -> nvidia.com), possible rename or acquisition (probed 2026-09-03, roadmap#169)
  generated: '2026-09-03'
  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'
x-docs-platform: swagger-ui
x-docs-platform-confidence: medium
x-docs-platform-checked: 2026-10-03
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
- Machine-Learning
apis:
- aid: kumoai: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
  - Machine-Learning
  properties:
  - type: Documentation
    url: https://docs.nvidia.com/sdgm/rest-api
  - type: Authentication
    url: authentication/kumoai-authentication.yml
- aid: kumoai: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

Kumo.AI API — frequently asked questions

Does Kumo.AI have an API?

Yes. APIs.io lists 2 APIs from Kumo.AI, including Kumo REST API and KumoRFM MCP Server.

How do I authenticate with the Kumo.AI API?

Kumo.AI uses bearer-token authentication (apiKey/snowflakeCredentials/http · 4 schemes). The details are profiled on APIs.io as Kumoai Authentication.

Does Kumo.AI have an MCP server?

Yes. APIs.io lists 1 MCP server for Kumo.AI: Remote MCP server at kumo-sdk-public.s3.us-west-2.amazonaws.com.

Is the Kumo.AI API ready for AI agents?

Kumo.AI has a Kin Score of 40.3 out of 100 (developing) and an Agent Readiness score of 15.0 (agent-aware), scored by API Evangelist on 2026-10-31.

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