Bentolabs AI

Bentolabs AI builds Bento, a self-learning production infrastructure platform for AI agents. Bento ingests OpenTelemetry traces from agent applications and turns them into readable trajectories, plain-English failure-mode detectors (signals), alerts, clusters, evaluations, and versioned improvements — a closed loop that monitors what runs, surfaces silent regressions and behavioral drift, and compounds learnings back into the agent. It ships a Python SDK, a command-line client, direct OTLP export from any OpenTelemetry-speaking framework (LangChain, LlamaIndex, Mastra, Vercel AI SDK, Google ADK), a hosted MCP server, and packaged Agent Skills for AI coding tools. Y Combinator-backed.

Bentolabs AI is profiled on the APIs.io network. Tagged areas include Company, AI Agents, Observability, OpenTelemetry, and Tracing.

Bentolabs AI’s developer surface includes documentation, getting-started guide, engineering blog, CLI, authentication, and 13 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

25.4/100 emerging ▬ flat Agent 10/100 agent aware Front door AI 5/6 prominent Heartbeat 32/100 going silent saas Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

0 APIs 1 MCP Servers 3 Agent Skills

Company AI Agents Observability OpenTelemetry Tracing LLM Monitoring Evaluation Agent Infrastructure Developer Tools

Kin Score

Kin Score How this is scored →
scored 2026-11-06 · rubric v0.27.0

Composite quality — 25.4/100 · emerging

Contract Quality 0.0 / 21

Developer Ergonomics 11.3 / 17

Access Clarity 4.7 / 17

Operational Transparency 0.3 / 11

Contract Governance 0.0 / 10

Discoverability 4.7 / 9

Regulatory Posture 2.9 / 15

Agent readiness — 10/100 · agent aware

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 12 / 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

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) 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)
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0
Rate-Limit Signaling +7 pts — OpenAPI responses document X-RateLimit-* / RateLimit headers (verified), else a rate_limits artifact or RateLimits link (documented)

10 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Contract Quality scores 0.0 — up to +25.0 composite points
Access Clarity scores 27.6 — up to +14.5 composite points
Operational Transparency scores 2.6 — up to +12.7 composite points

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/bentolabs-ai/apis.yml: open an issue on github.com/api-evangelist/bentolabs-ai 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.

Market	Position	Depth	Registered MCP server	Declares compatibility
Agent Skills (SKILL.md packages)	undeclared	-	-	2

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.

Standards implemented 2

Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.

Model Context Protocol (MCP)

measured — observed by APIs.io (documented)

Agent Skills

measured — observed by APIs.io (published)

MCP Servers 1

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

Bentolabs AI MCP Server

Hosted MCP server for the Bento documentation surface, advertised in the Bento quickstart for per-tool MCP setup across AI coding tools (Claude, Cursor, and others). Serves the ...

MCP SERVER

Agent Skills 3

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

bentolabs-cli

AGENT SKILL

bentolabs-integrate

AGENT SKILL

bentolabs-migrate

AGENT SKILL

Security Posture 2

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

Bentolabs Ai Authentication

apiKey · 2 schemes

SECURITY

Bentolabs Ai Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Get Started 3

Portal, sign-up, and the first successful call

DeveloperPortal

DeveloperPortal

GettingStarted

GettingStarted

Login

Documentation 1

Reference material describing how the API behaves

Documentation

Documentation

Agent Surfaces 3

MCP servers, agent skills, and machine-readable catalogs

MCPServer

MCPServer

LLMsTxt

LLMsTxt

AgentSkill

AgentSkill

Design & Contract 1

Pagination, idempotency, versioning, errors, and events

Conventions

Conventions

Build 4

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

Packages

SDKs

CLI

Access & Security 2

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Authentication

Authentication

Commercial 2

Pricing, plans, and the legal terms of use

TermsOfService

TermsOfService

PrivacyPolicy

PrivacyPolicy

Company 2

The organization behind the API

Website

Website

Blog

Blog

Source (apis.yml)

aid: bentolabs-ai
name: Bentolabs AI
description: Bentolabs AI builds Bento, a self-learning production infrastructure platform for AI agents. Bento ingests OpenTelemetry
  traces from agent applications and turns them into readable trajectories, plain-English failure-mode detectors (signals),
  alerts, clusters, evaluations, and versioned improvements — a closed loop that monitors what runs, surfaces silent regressions
  and behavioral drift, and compounds learnings back into the agent. It ships a Python SDK, a command-line client, direct
  OTLP export from any OpenTelemetry-speaking framework (LangChain, LlamaIndex, Mastra, Vercel AI SDK, Google ADK), a hosted
  MCP server, and packaged Agent Skills for AI coding tools. Y Combinator-backed.
url: https://raw.githubusercontent.com/api-evangelist/bentolabs-ai/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- y-combinator
x-tier: stub
x-tier-reason: portfolio-lead
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
  generated: '2026-11-06'
  method: derived
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-18'
image: https://bentolabs.ai/og/default.png
x-docs-platform: mintlify
x-docs-platform-confidence: medium
x-docs-platform-checked: 2026-09-20
tags:
- Company
- AI Agents
- Observability
- OpenTelemetry
- Tracing
- LLM Monitoring
- Evaluation
- Agent Infrastructure
- Developer Tools
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: DomainSecurity
  url: security/bentolabs-ai-domain-security.yml
- type: Website
  url: https://bentolabs.ai
- type: DeveloperPortal
  url: https://docs.bentolabs.ai
- type: Documentation
  url: https://docs.bentolabs.ai
- type: GettingStarted
  url: https://docs.bentolabs.ai/quickstart
- type: Blog
  url: https://bentolabs.ai/blog
- type: GitHubOrganization
  url: https://github.com/BentoLabs-ai
- type: Login
  url: https://platform.bentolabs.ai
- type: TermsOfService
  url: https://bentolabs.ai/terms
- type: PrivacyPolicy
  url: https://bentolabs.ai/privacy
- type: Packages
  url: packages/bentolabs-ai-packages.yml
- type: SDKs
  url: packages/bentolabs-ai-packages.yml
- type: CLI
  url: cli/bentolabs-ai-cli.yml
- type: MCPServer
  url: mcp/bentolabs-ai-mcp.yml
- type: LLMsTxt
  url: llms/bentolabs-ai-llms.txt
- type: Authentication
  url: authentication/bentolabs-ai-authentication.yml
- type: Conventions
  url: conventions/bentolabs-ai-conventions.yml
- type: AgentSkill
  url: skills/_index.yml
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

Bentolabs AI API — frequently asked questions

How do I authenticate with the Bentolabs AI API?

Bentolabs AI uses bearer-token authentication (apiKey · 2 schemes). The details are profiled on APIs.io as Bentolabs Ai Authentication.

Does Bentolabs AI have an MCP server?

Yes. APIs.io lists 1 MCP server for Bentolabs AI: Hosted MCP server for the Bento documentation surface, advertised in the Bento quickstart for per-tool MCP setup across AI coding tools (Claude, Cursor, and others). Serves the documentation/knowledge

Is the Bentolabs AI API ready for AI agents?

Bentolabs AI has a Kin Score of 25.4 out of 100 (emerging) and an Agent Readiness score of 9.6 (agent-aware), scored by API Evangelist on 2026-11-06.

Work with this as data

Every provider here is available over the APIs.io API and to AI agents over MCP.

MCP server

One button, every client — Claude, Cursor, VS Code and the rest.

https://apis.io/mcp

Tools for providers

9 MCP tools reach this

find_providersBrowse and filter every provider in the catalog.
get_provider_artifactsEvery artifact this provider publishes, grouped by type.
get_provider_operationsEvery operation across all of their OpenAPIs — one call instead of parsing every spec.
get_provider_toolsEvery MCP tool they ship, with the operation each wraps.
get_provider_evidenceHow each part of their score was established. Free — the basis for a claim should not sit behind it.
get_provider_ratingPRO — composite, band, trend and facet scores.
apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
resolveTurn a domain, URL or GitHub org into the provider it belongs to.
find_cohortsEvery scored population of providers in the catalog.

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