Flow AI

Flow AI builds infrastructure for embedding schema-aware, deterministic AI agents directly inside analytical SaaS products. Its flagship flowai-harness is a Rust-native runtime with a Python SDK for building production-grade data agents on top of your own data product, organized around five primitives - a data catalog agents use to resolve intent, typed plans and actions executed as auditable state machines with human approval gates, an embedded runtime, Studio (a local UI to run, debug, and evaluate agents), and self-improvement. It runs inside your infrastructure with your own model keys (OpenAI, Anthropic, Gemini, Llama, Mistral, Qwen) and warehouse (Postgres, Snowflake, BigQuery, Databricks, DuckDB). The team also publishes the open Flow Judge LLM-as-a-judge evaluation model and the flow-eval evaluation engine.

Flow AI is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, AI Agents, Agent Infrastructure, and LLM Evaluation.

Flow AI’s developer surface includes documentation, API reference, getting-started guide, pricing, engineering blog, support, YouTube channel, and 19 more developer resources.

🌐 Visit website 📡 Source on GitHub

32.2/100 thin ▬ flat Agent 3/100 human only saas Full breakdown ↓
scored 2026-09-08 · rubric v0.20.0

0 APIs 1 MCP Servers

Company Artificial Intelligence AI Agents Agent Infrastructure LLM Evaluation Data Runtime SDK MCP Analytics

Kin Score

Kin Score How this is scored →
scored 2026-09-08 · rubric v0.20.0

Composite quality — 32.2/100 · thin

Contract Quality 0.0 / 25

Developer Ergonomics 16.1 / 20

Access Clarity 5.8 / 20

Operational Transparency 2.4 / 13

Contract Governance 2.2 / 12

Discoverability 5.7 / 10

Agent readiness — 3/100 · human only

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 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 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 Well-Known Document 0 / 5

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.

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 github.com/api-evangelist/flow-ai: open an issue to ask a question, or submit a pull request to add artifacts. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

MCP Servers 1

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

Flow AI MCP Server

MCP SERVER

Security Posture 2

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

Flow Ai Authentication

provider-api-key/sso · 2 schemes

SECURITY

Flow Ai Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Get Started 3

Portal, sign-up, and the first successful call

DeveloperPortal

DeveloperPortal

GettingStarted

GettingStarted

Sandbox

Sandbox

Documentation 2

Reference material describing how the API behaves

Documentation

Documentation

APIReference

APIReference

Agent Surfaces 3

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

MCPServer

MCPServer

AgentSkill

AgentSkill

Design & Contract 2

Pagination, idempotency, versioning, errors, and events

Conventions

Conventions

Conformance

Conformance

Build 4

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

Packages

SDKs

CLI

Access & Security 3

Authentication, authorization, and security posture

Authentication

Authentication

Compliance

Compliance

DomainSecurity

DomainSecurity

Learn 1

Tutorials, courses, talks, and written guidance

YouTube

YouTube

Operate 2

Status, limits, changes, and where to get help

Support

Support

ChangeLog

ChangeLog

Commercial 2

Pricing, plans, and the legal terms of use

Pricing

Pricing

PrivacyPolicy

PrivacyPolicy

Company 4

The organization behind the API

Website

Website

Blog

Blog

LinkedIn

Twitter

Twitter

Source (apis.yml)

aid: flow-ai
deliveryModel:
  model: saas
  open_source: false
  commercial: true
  callable_host: false
  label: Hosted service · you call their endpoint
  confidence: medium
  source:
  - 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
  - security
  - sandbox
  generated: '2026-09-03'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/flow-ai.png
name: Flow AI
description: Flow AI builds infrastructure for embedding schema-aware, deterministic AI agents directly inside analytical
  SaaS products. Its flagship flowai-harness is a Rust-native runtime with a Python SDK for building production-grade data
  agents on top of your own data product, organized around five primitives - a data catalog agents use to resolve intent,
  typed plans and actions executed as auditable state machines with human approval gates, an embedded runtime, Studio (a local
  UI to run, debug, and evaluate agents), and self-improvement. It runs inside your infrastructure with your own model keys
  (OpenAI, Anthropic, Gemini, Llama, Mistral, Qwen) and warehouse (Postgres, Snowflake, BigQuery, Databricks, DuckDB). The
  team also publishes the open Flow Judge LLM-as-a-judge evaluation model and the flow-eval evaluation engine.
url: https://raw.githubusercontent.com/api-evangelist/flow-ai/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- project-a
- seedcamp
- lifeline
- moonfire
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-19'
tags:
- Company
- Artificial Intelligence
- AI Agents
- Agent Infrastructure
- LLM Evaluation
- Data
- Runtime
- SDK
- MCP
- Analytics
tags_raw:
- Company
- Artificial Intelligence
- AI Agents
- Agent Infrastructure
- LLM Evaluation
- Data
- Runtime
- SDK
- Model Context Protocol
- Analytics
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Website
  url: https://flow-ai.com
- type: DeveloperPortal
  url: https://flow-ai.com/docs
- type: Documentation
  url: https://flow-ai.com/docs
- type: APIReference
  url: https://flow-ai.com/docs/reference
- type: GettingStarted
  url: https://flow-ai.com/docs/quickstart
- type: Pricing
  url: https://flow-ai.com/pricing
- type: Blog
  url: https://flow-ai.com/blog
- type: GitHubOrganization
  url: https://github.com/flowaicom
- type: PrivacyPolicy
  url: https://flow-ai.com/legal
- type: Support
  url: mailto:hello@flow-ai.com
- type: LinkedIn
  url: https://linkedin.com/company/flowaicom
- type: Twitter
  url: https://x.com/flowaicom
- type: YouTube
  url: https://youtube.com/@flowaicom
- type: LLMsTxt
  url: llms/flow-ai-llms.txt
- type: Packages
  url: packages/flow-ai-packages.yml
- type: SDKs
  url: packages/flow-ai-packages.yml
- type: MCPServer
  url: mcp/flow-ai-mcp.yml
- type: AgentSkill
  url: skills/_index.yml
- type: ChangeLog
  url: changelog/flow-ai-changelog.yml
- type: CLI
  url: cli/flow-ai-cli.yml
- type: Authentication
  url: authentication/flow-ai-authentication.yml
- type: Conventions
  url: conventions/flow-ai-conventions.yml
- type: Sandbox
  url: sandbox/flow-ai-sandbox.yml
- type: Conformance
  url: conformance/flow-ai-conformance.yml
- type: Compliance
  url: https://flow-ai.com/legal
- type: DomainSecurity
  url: security/flow-ai-domain-security.yml
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

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