Bentolabs AI website screenshot

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.

24.9/100 emerging ▬ flat Agent 10/100 agent aware saas Full breakdown ↓
scored 2026-09-08 · rubric v0.20.0
0 APIs 1 MCP Servers
CompanyAI AgentsObservabilityOpenTelemetryTracingLLM MonitoringEvaluationAgent InfrastructureDeveloper Tools

Kin Score

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

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

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

Documentation 1

Reference material describing how the API behaves

Agent Surfaces 3

MCP servers, agent skills, and machine-readable catalogs

Design & Contract 1

Pagination, idempotency, versioning, errors, and events

Build 4

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 2

The organization behind the API

Source (apis.yml)

apis.yml Raw ↑
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: '2026-09-03'
  method: derived
specificationVersion: '0.23'
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

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