Humanloop
Humanloop was an LLM development platform for managing prompts, datasets, evaluations, and production observability of LLM applications, used by AI product teams at companies like Duolingo and Gusto. The platform offered a collaborative prompt editor, versioned prompts and tools, evaluation runs (LLM-as-judge and code-based), dataset management, online evaluators, and full request logging across OpenAI, Anthropic, Google, and other providers, with Python and TypeScript SDKs and a REST API. In 2025 the Humanloop team was acquired by Anthropic and the company has announced it is sunsetting the standalone platform; an official migration guide is published for existing customers. This record captures the historical API surface for archival and discovery purposes.
Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.
API Evangelist profiles Humanloop the way a machine reads it — 14 machine-readable artifacts across 7 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 — Humanloop scores 40.6/100 (thin), 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.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Humanloop
Each block below is one kind of artifact we hold for Humanloop. 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 7
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.
Humanloop LLM Platform API
The Humanloop REST API and SDKs covered prompts, tools, datasets, evaluations, evaluators, and logs for LLM applications. Developers called prompt endpoints from production to c...
Humanloop Datasets API
The Datasets API from Humanloop — 2 operation(s) for datasets.
Humanloop Evaluators API
The Evaluators API from Humanloop — 2 operation(s) for evaluators.
Humanloop Logs API
The Logs API from Humanloop — 2 operation(s) for logs.
Humanloop Prompts API
The Prompts API from Humanloop — 4 operation(s) for prompts.
Humanloop Sessions API
The Sessions API from Humanloop — 2 operation(s) for sessions.
Humanloop Tools API
The Tools API from Humanloop — 2 operation(s) for tools.
Scroll within the panel for all 7 ·
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).
Humanloop API
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.
Humanloop 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.
Humanloop Finops
FINOPSSecurity 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 Humanloop — 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.
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 2
MCP servers, agent skills, and machine-readable catalogs
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
Commercial 2
Pricing, plans, and the legal terms of use
Company 4
The organization behind the API
Other 1
Properties that don't map to a standard resource type
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