Laminar
Laminar is an open-source, OpenTelemetry-native observability and debugging platform built for AI agents and LLM applications. It traces every LLM call, tool call, and sub-agent a run produces, renders each trace as a readable transcript rather than a raw span tree, and turns that data into answers: a record-and-replay Debugger that serves everything before your change from cache so each iteration takes seconds, and Signals that let you describe outcomes and failures in plain language and extract structured events across all traces for clustering and alerting. The platform adds evaluations, datasets and labeling queues, a playground, full-text search, custom dashboards, and read-only ClickHouse SQL over trace data from the UI, the lmnr-cli, or a hosted MCP server. Laminar ships TypeScript and Python SDKs with auto-instrumentation for the Vercel AI SDK, Claude Agent SDK, OpenAI Agents SDK, LangChain/LangGraph, Pydantic AI, Browser Use, Playwright and more, and can run as managed Laminar Cloud or fully self-hosted via Docker Compose or Kubernetes/Helm.
Real signal across most facets with visible, nameable gaps — the contract exists but is thin, or the portal is good while governance and commercial terms are absent.
API Evangelist profiles Laminar the way a machine reads it — 7 machine-readable artifacts across 3 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 — Laminar scores 46.1/100 (developing), with a separate agent-readiness read of 30/100 (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 Laminar
Each block below is one kind of artifact we hold for Laminar. 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 3
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.
Laminar SQL Query API
Run SELECT-only ClickHouse SQL over a project's observability data via POST /v1/sql/query. Authenticates with a project API key as a bearer token, accepts a query plus typed {na...
Laminar OpenTelemetry Trace Ingest API
The standard OpenTelemetry trace ingest endpoint at /v1/traces. Laminar accepts OTLP over gRPC, HTTP+protobuf, and (since May 2026) HTTP+JSON, so any OpenTelemetry-capable runti...
Laminar Evaluations API
The lower-level LaminarClient.evals surface for wiring evaluations into an existing pipeline: create an evaluation, pre-register each datapoint so a row is visible in the UI bef...
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.
laminar-mcp.yml
MCP SERVERPricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Laminar Plans
PLANSSecurity 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 Laminar — 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 4
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 3
Pagination, idempotency, versioning, errors, and events
Build 5
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 4
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
Other 1
Properties that don't map to a standard resource type
← All providers · Data indexed from github.com/api-evangelist/laminar · machine-readable index on apis.io