Flowise
Flowise is an open-source, low-code visual builder for LangChain-based LLM workflows and AI agents. Built on Node.js and TypeScript as a pnpm/Turbo monorepo, Flowise lets developers and non-developers compose chatflows, multi-agent agentflows, RAG pipelines, tools, and assistants on a drag-and-drop canvas, then expose them as REST APIs, embeddable chat widgets, or programmatic SDK calls. The project ships a self-hostable server, a React admin UI, a third-party node component library, an auto-generated Swagger UI, official TypeScript and Python SDKs, an embed widget, and a managed Flowise Cloud offering with metered prediction quotas. Flowise was acquired by Workday in 2025.
Real signal across most facets with visible, nameable gaps — the contract exists but is thin, or the portal is good while governance and commercial terms are absent.
API Evangelist profiles Flowise the way a machine reads it — 36 machine-readable artifacts across 13 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Flowise scores 55.7/100 (developing), with a separate agent-readiness read of 48/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
How we profile Flowise
Each block below is one kind of artifact we hold for Flowise. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 13
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Flowise assistants API
The assistants API from Flowise — 2 operation(s) for assistants.
Flowise attachments API
The attachments API from Flowise — 1 operation(s) for attachments.
Flowise chatflows API
The chatflows API from Flowise — 3 operation(s) for chatflows.
Flowise chatmessage API
The chatmessage API from Flowise — 1 operation(s) for chatmessage.
Flowise document-store API
The document-store API from Flowise — 9 operation(s) for document-store.
Flowise feedback API
The feedback API from Flowise — 2 operation(s) for feedback.
Flowise leads API
The leads API from Flowise — 2 operation(s) for leads.
Flowise ping API
The ping API from Flowise — 1 operation(s) for ping.
Flowise prediction API
The prediction API from Flowise — 1 operation(s) for prediction.
Flowise tools API
The tools API from Flowise — 2 operation(s) for tools.
Flowise upsert-history API
The upsert-history API from Flowise — 1 operation(s) for upsert-history.
Flowise variables API
The variables API from Flowise — 2 operation(s) for variables.
Flowise vector API
The vector API from Flowise — 1 operation(s) for vector.
Scroll within the panel for all 13 ·
Open Collections 1
Open, tool-agnostic collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
Flowise APIs
OPEN COLLECTIONPricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Flowise Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Flowise Finops
FINOPSSemantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Flowise Context
JSON-LDSpectral Rules 2
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Flowise API Rules
SPECTRALFlowise API Rules
SPECTRALJSON Schema 7
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
Assistant
JSON SCHEMAChatMessage
JSON SCHEMAChatflow
JSON SCHEMADocumentStore
JSON SCHEMAPrediction
JSON SCHEMATool
JSON SCHEMAVariable
JSON SCHEMAScroll within the panel for all 7 ·
JSON Structure 2
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Flowise Chatflow Structure
JSON STRUCTUREFlowise Prediction Structure
JSON STRUCTUREExamples 4
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Security Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Resources
Every other property we hold for Flowise — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 1
Portal, sign-up, and the first successful call
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Build 7
SDKs, sample code, and the tooling you integrate with
Scroll within the panel for all 7 ·
Access & Security 2
Authentication, authorization, and security posture
Learn 1
Tutorials, courses, talks, and written guidance
Operate 2
Status, limits, changes, and where to get help
Commercial 4
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
Other 2
Properties that don't map to a standard resource type
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