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 📡 Source on GitHub

28.2/100 emerging ▬ flat Agent 26/100 agent aware Full breakdown ↓
scored 2026-07-27 · rubric v0.5

AccessSelf serve

0 APIs 1 MCP Servers

CompanyAI AgentsObservabilityOpenTelemetryTracingLLM MonitoringEvaluationAgent InfrastructureDeveloper Tools

Kin Score

Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5

Composite quality — 28.2/100 · emerging

Contract Quality 0.0 / 25

Developer Ergonomics 13.9 / 20

Commercial Clarity 6.8 / 20

Operational Transparency 0.7 / 13

Governance 0.0 / 12

Discoverability 6.8 / 10

Agent readiness — 26/100 · agent aware

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 15

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

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. This rating is computed from github.com/api-evangelist/bentolabs-ai: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

MCP Servers 1

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

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
accessModel:
  pricing: unknown
  onboarding: self-serve
  trial: false
  try_now: false
  public: false
  label: Self-serve signup
  confidence: medium
  source:
  - authentication
  generated: '2026-07-22'
  method: derived
specificationVersion: '0.20'
created: '2026-07-17'
modified: '2026-07-18'
image: https://bentolabs.ai/og/default.png
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