Moda

Moda is a continual-learning and observability layer for AI agents and LLM-native software, built by ModaLabs (YC W26, San Francisco). It turns production agent traces into validated improvements to the agent harness (prompts, tools, workflows, retrieval, memory, evals) rather than the model weights. The platform ingests conversations via a lightweight SDK plus OpenTelemetry/OTLP intake, then provides intent discovery, behavioral failure detection, tool-call failure taxonomies, frustration root-cause attribution, and prompt management. Moda exposes an HTTP ingestion API, a read-only Data API for analytics, first-party Python and TypeScript SDKs, a CLI, a production MCP server, and Claude Code skills so teams and agents can query and act on their agent analytics.

Moda is listed with 2 APIs on the APIs.io network. Tagged areas include Company, AI Agents, LLM Observability, Agent Analytics, and Continual Learning.

Moda’s developer surface includes documentation, API reference, getting-started guide, engineering blog, changelog, pricing, support, and 20 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

35.7/100 thin ▬ flat Agent 12/100 agent aware Front door AI 5/6 prominent Heartbeat 96/100 alive saas Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

2 APIs 1 MCP Servers 2 Agent Skills

Company AI Agents LLM Observability Agent Analytics Continual Learning Monitoring Developer Tools MCP OpenTelemetry Prompt Management Y Combinator

Kin Score

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

Composite quality — 35.7/100 · thin

Contract Quality 0.0 / 21

Developer Ergonomics 13.4 / 17

Access Clarity 6.5 / 17

Operational Transparency 2.0 / 11

Contract Governance 1.9 / 10

Discoverability 5.4 / 9

Regulatory Posture 3.7 / 15

Agent readiness — 12/100 · 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 8 / 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)
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)
Request/Response Examples +7 pts — share of operations carrying request/response examples; else an Examples / Code Samples link

9 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 38.2 — up to +12.4 composite points
Operational Transparency scores 18.4 — up to +10.6 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/moda/apis.yml: open an issue on github.com/api-evangelist/moda 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 →

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)

APIs 2

Individual APIs this provider publishes, each with its own machine-readable definition.

Moda Ingestion API

HTTP ingestion API for sending LLM/agent conversation events to Moda. Accepts batched events (conversation_id, role, message, plus token/model/trace metadata) over a single POST...

Moda Data API

Read-only analytics API for programmatic access to Moda conversation data: overview/KPIs, conversations, world state, topic clusters, frustrations, and tool failures. Authentica...

MCP Servers 1

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

Moda MCP Server

Remote MCP server over HTTP requiring an API key; 9 tools listed.

MCP SERVER

Agent Skills 2

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

moda-instrument-and-ingest

AGENT SKILL

moda-setup

AGENT SKILL

Security Posture 2

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

Moda Authentication

apiKey · 2 schemes

SECURITY

Moda 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 2

Reference material describing how the API behaves

Documentation

Documentation

APIReference

APIReference

Agent Surfaces 3

MCP servers, agent skills, and machine-readable catalogs

MCPServer

MCPServer

AgentSkill

AgentSkill

LLMsTxt

LLMsTxt

Design & Contract 5

Pagination, idempotency, versioning, errors, and events

Conventions

ErrorCatalog

Lifecycle

Conformance

DataModel

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

Operate 3

Status, limits, changes, and where to get help

ChangeLog

ChangeLog

Support

Support

ChangeLog

ChangeLog

Commercial 3

Pricing, plans, and the legal terms of use

Pricing

Pricing

TermsOfService

TermsOfService

PrivacyPolicy

PrivacyPolicy

Company 2

The organization behind the API

Website

Website

Blog

Blog

Source (apis.yml)

aid: moda
name: Moda
description: Moda is a continual-learning and observability layer for AI agents and LLM-native software, built by ModaLabs
  (YC W26, San Francisco). It turns production agent traces into validated improvements to the agent harness (prompts, tools,
  workflows, retrieval, memory, evals) rather than the model weights. The platform ingests conversations via a lightweight
  SDK plus OpenTelemetry/OTLP intake, then provides intent discovery, behavioral failure detection, tool-call failure taxonomies,
  frustration root-cause attribution, and prompt management. Moda exposes an HTTP ingestion API, a read-only Data API for
  analytics, first-party Python and TypeScript SDKs, a CLI, a production MCP server, and Claude Code skills so teams and agents
  can query and act on their agent analytics.
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
image: https://avatars.githubusercontent.com/u/242691421?v=4
url: https://raw.githubusercontent.com/api-evangelist/moda/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- general-catalyst
- y-combinator
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-20'
x-docs-platform: mintlify
x-docs-platform-confidence: medium
x-docs-platform-checked: 2026-10-04
x-docs-hosting: vercel
tags:
- Company
- AI Agents
- LLM Observability
- Agent Analytics
- Continual Learning
- Monitoring
- Developer Tools
- MCP
- OpenTelemetry
- Prompt Management
- Y Combinator
apis:
- name: Moda Ingestion API
  description: HTTP ingestion API for sending LLM/agent conversation events to Moda. Accepts batched events (conversation_id,
    role, message, plus token/model/trace metadata) over a single POST endpoint, authenticated with a Bearer API key.
  humanURL: https://docs.moda.dev/ingestion/direct-api
  baseURL: https://moda-ingest.modas.workers.dev
  tags:
  - Ingestion
  - Event
  - Observability
  tags_raw:
  - Ingestion
  - Events
  - Observability
  properties:
  - type: Documentation
    url: https://docs.moda.dev/ingestion/direct-api
  - type: Authentication
    url: authentication/moda-authentication.yml
  - type: ErrorCatalog
    url: errors/moda-problem-types.yml
- name: Moda Data API
  description: 'Read-only analytics API for programmatic access to Moda conversation data: overview/KPIs, conversations, world
    state, topic clusters, frustrations, and tool failures. Authenticated with an API key via the x-api-key header or the
    MODA_API_KEY environment variable; wrapped by the Moda CLI and MCP server.'
  humanURL: https://docs.moda.dev/data-api/overview
  tags:
  - Analytics
  - Data API
  - Read Only
  tags_raw:
  - Analytics
  - Data API
  - Read-only
  properties:
  - type: Documentation
    url: https://docs.moda.dev/data-api/overview
  - type: CLI
    url: cli/moda-cli.yml
  - type: MCPServer
    url: mcp/moda-mcp.yml
  - type: DataModel
    url: data-model/moda-data-model.yml
common:
- type: DomainSecurity
  url: security/moda-domain-security.yml
- type: Website
  url: https://moda.dev
- type: DeveloperPortal
  url: https://docs.moda.dev
- type: Documentation
  url: https://docs.moda.dev
- type: APIReference
  url: https://docs.moda.dev/ingestion/direct-api
- type: GettingStarted
  url: https://docs.moda.dev/quickstart
- type: Blog
  url: https://moda.dev/blog
- type: ChangeLog
  url: https://moda.dev/changelog
- type: Pricing
  url: https://moda.dev/pricing
- type: TermsOfService
  url: https://moda.dev/terms
- type: PrivacyPolicy
  url: https://moda.dev/privacy-policy
- type: Login
  url: https://moda.dev/login
- type: Support
  url: https://moda.dev/support
- type: GitHubOrganization
  url: https://github.com/ModaLabs
- type: Packages
  url: packages/moda-packages.yml
- type: SDKs
  url: packages/moda-packages.yml
- type: CLI
  url: cli/moda-cli.yml
- type: MCPServer
  url: mcp/moda-mcp.yml
- type: AgentSkill
  url: skills/_index.yml
- type: LLMsTxt
  url: llms/moda-llms.txt
- type: Authentication
  url: authentication/moda-authentication.yml
- type: Conventions
  url: conventions/moda-conventions.yml
- type: ErrorCatalog
  url: errors/moda-problem-types.yml
- type: Lifecycle
  url: lifecycle/moda-lifecycle.yml
- type: Conformance
  url: conformance/moda-conformance.yml
- type: DataModel
  url: data-model/moda-data-model.yml
- type: ChangeLog
  url: changelog/moda-changelog.yml
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-07-20'
  status: enriched
  artifacts_added: 16
  pass: local-v1

Moda API — frequently asked questions

Does Moda have an API?

Yes. APIs.io lists 2 APIs from Moda, including Moda Ingestion API and Moda Data API.

How do I authenticate with the Moda API?

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

Does Moda have an MCP server?

Yes. APIs.io lists 1 MCP server for Moda: Remote MCP server over HTTP requiring an API key; 9 tools listed.

Is the Moda API ready for AI agents?

Moda has a Kin Score of 35.7 out of 100 (thin) and an Agent Readiness score of 12.4 (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.

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