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.
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 Moda the way a machine reads it — 5 machine-readable artifacts across 2 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 — Moda scores 37.0/100 (thin), with a separate agent-readiness read of 34/100 (agent aware). 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 Moda
Each block below is one kind of artifact we hold for Moda. 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 2
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.
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 expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.
Model Context Protocol servers that expose these APIs to AI agents.
moda-mcp.yml
MCP SERVERSecurity 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.
Resources
Every other property we hold for Moda — 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.
Get Started 3
Portal, sign-up, and the first successful call
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 3
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 5
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
Operate 3
Status, limits, changes, and where to get help
Commercial 3
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
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