⚡Stack Radar

AI Observability · Reviewed 19 Oct 2026

Laminar

Open-source observability platform for AI agents with tracing, evals, and alerts.

from $30 · free tier · laminar.sh ↗

Paid against free › AI Observability › Laminar

SEO analysis

Collected Oct 19, 2026

laminar.sh: an estimated 14,467 total monthly visits and about 1,489 monthly organic visits across 85 ranking keywords. Scout crawled 15 pages and measured a technical health score of 93/100.

14.5KEstimated total trafficModeled monthly visits85Ranking keywordsPublic search sample93/100Technical health15 pages checked1.2KReferring domainsLive backlink summary

Overview

01

Summary

The useful answer before the supporting detail.

Laminar is an open-source observability platform for AI agents that proactively analyzes every trace and alerts you when a run goes wrong S1. It is used by developers and AI engineers to trace, debug, and evaluate AI agent runs, solving the problem of understanding and fixing failures in complex agent behavior S1. Its main practical value is realtime tracing, SQL querying, and the ability to replay runs and prove fixes with evals S1.

02

Product identity and positioning

Name, category, positioning, audience, and first-party claims.

  • Identity: Laminar, an open-source observability platform for AI agents S1.
  • Core purpose: Proactively analyzes every trace and alerts when a run goes wrong S1.
  • Intended users: Developers and AI engineers building and debugging AI agents S1.
  • Product scope: A platform with UI tools, CLI, SDK, and MCP server for tracing and debugging S2 S3 S30.
  • Positioning: Open-source, OpenTelemetry-native observability for AI agents S26.
  • Claimed distinction: First-class support for AI SDK, AI modelAgent SDK, LangChain, and Browser Use S1.
03

Use cases and audiences

Who the product serves and the jobs it is designed to handle.

AudiencePractical use case
AI engineersTrace and debug agent runs to identify failure modes S1.
DevelopersBuild evaluation datasets from production traces S1.
Teams using SlackGet signal alerts and query project data via Laminar Agent in Slack S18.
Coding agentsUse the CLI to onboard projects and query data without leaving the terminal S3.

Features

09

Features and capabilities

Core functions and evidence-backed product capabilities.

CapabilityWhat it does
Realtime tracingWatch a trace update as spans arrive S2.
SQL queryingQuery traces, spans, and events with raw SQL from the UI, CLI, or MCP S1.
Custom dashboardsBuild and share custom dashboards with custom SQL queries S2 S138.
PII redactionRedact or mark PII in traces per project S32.
Laminar AgentAI agent that answers questions about your data by running SQL and reading traces S22.
Slack integrationConnect Slack for signal alerts, reports, and Laminar Agent S2.
CLIManage datasets, Signals, LLM profiles, and debug sessions from the terminal S3.
MCP serverConnect MCP clients to query traces with SQL and summarize trace context S30.
10

Integrations and ecosystem

Connected products, exports, APIs, and workflow compatibility.

Integration or interfaceTypeWhat it enables
AI SDK, AI modelAgent SDK, LangChain, Browser UseIntegrationFirst-class tracing support for these agent frameworks S1.
SlackIntegrationReceive signal alerts and interact with Laminar Agent in channels S18.
MCP serverMCP serverLets MCP clients query traces with SQL and summarize trace context S30.
CLI (lmnr-cli)CLIOnboard projects, query data, manage datasets, Signals, and LLM profiles from the terminal S3.
SQL EditorAPIProvides raw SQL access from the UI, CLI, or MCP S1.
LLM ProfilesAPIConfigure custom LLM providers and API keys for use in Signals and playground S26.

Pricing

11

Pricing and plans

Published plans, free access, trials, credits, and pricing limitations.

Plan or offerVerified price and billingIncludedLimits
Free$0 per month S11 seat, community support S1Limited research allowance S1
Mid tier$30 per month S1Unlimited seats, $5 Signals credit S1Then $2 per GB S1
Upper tier$150 per month S1Unlimited seats, $5 Signals credit S1Then $1.50 per GB S1

Competition

12

Competitive landscape

Domains competing for the same organic search demand.

Sampled competitors

CompetitorFirst-page appearancesSampled queriesAverage positionQueries observed
github.com222.5laminar, lmnr
runlaminar.com111laminar
investor.limoneira.com112lmnr
en.wikipedia.org113laminar
finance.yahoo.com113lmnr
laminar.dev115laminar
nasdaq.com115lmnr
merriam-webster.com116laminar
cnn.com116lmnr
linkedin.com117laminar

Website intelligence — Collected evidence

Customers, testimonials, and first-party review claims

No supporting public-page evidence was collected for this category.

Independent review sources discovered

No links to supported independent review platforms were discovered on the collected pages.

14

AI visibility

Measured ChatGPT and Google AI mentions with cited sources.

AI-assistant referral traffic

  • Estimated current generative-AI referral share: 0.1%

Scout traffic model did not return a separate AI-referral trend series for this domain.

Website intelligence — Collected evidence

Site-stated AI capabilities

Page typeEvidenceSource
otherOpen-source platform to trace, evaluate, and debug AI agents. Laminar surfaces agent failures, and helps you fix them.https://laminar.sh/
otherLaminar is an open-source agent observability platform.https://laminar.sh/
otherThe agent ran web\_search three times for the same question, carried on past a `404` from fetch\_page without retrying, then ai.llm answered from a snippet without linking the page it read.https://laminar.sh/
featuresThe `lmnr-cli` is a command-line tool for onboarding a project, querying Laminar data, managing datasets, Signals, and LLM profiles, and annotating debug sessions from your terminal. It is the fastest way to wire up a new project, pull traces into a shell pipeline, script dataset maintenance, or connect an AI coding agent that can shell out, all without leaving the command line.https://laminar.sh/docs/platform/cli
featuresFor a self-hosted deployment, point the CLI at your instance. `login` talks to your frontend (the auth issuer); the data commands (`sql`, `dataset`, `signal`, `llm-profile`, `project`, `debug`) talk to your API. The cleanest way is to set the URLs once via environment variables. The CLI reads them from your environment and from a `.env` or `.env.local` file in the current directory.https://laminar.sh/docs/platform/cli
featuresLaminar prices every LLM span from a built-in table covering the major providers. Custom model costs let you define your own per-project pricing for models that table doesn’t know about: fine-tuned deployments, self-hosted open-weight models, private inference endpoints, or a negotiated rate that differs from list price. Set a price once in project settings and every matching span ingested from then on is costed with it.https://laminar.sh/docs/platform/custom-model-costs
featuresModel is matched against `gen_ai.response.model`, falling back to `gen_ai.request.model` when there is no response model. A date or version suffix is part of the name: an entry for `acme-llm-v1` does not match a span reporting `acme-llm-v1-2026-01-01`. Add a separate entry per deployed variant.https://laminar.sh/docs/platform/custom-model-costs
featuresFor each LLM span, in order:https://laminar.sh/docs/platform/custom-model-costs
featuresThe `lmnr-cli` is a command-line tool for onboarding a project, querying Laminar data, managing datasets, Signals, and LLM profiles, and annotating debug sessions from your terminal. It is the fastest way to wire up a new project, pull traces into a shell pipeline, script dataset maintenance, or connect an AI coding agent that can shell out, all without leaving the command line.https://laminar.sh/docs/platform/cli
featuresThe CLI is a standalone npm package (`lmnr-cli`) and ships independently from the `@lmnr-ai/lmnr` SDK. You do not need the SDK installed to onboard a project, query data, or manage datasets.https://laminar.sh/docs/platform/cli
featuresLaminar prices every LLM span from a built-in table covering the major providers. Custom model costs let you define your own per-project pricing for models that table doesn’t know about: fine-tuned deployments, self-hosted open-weight models, private inference endpoints, or a negotiated rate that differs from list price. Set a price once in project settings and every matching span ingested from then on is costed with it.https://laminar.sh/docs/platform/custom-model-costs

These are first-party product claims, not independent measurements of AI-search visibility.

15

Marketing opportunities

Actions calculated directly from the collected evidence.

OpportunityRecommended actionEvidence
Striking-distance keywordMove “arize phoenix vs langfuse” from position 5 toward the top three.Volume 50 · https://laminar.sh/article/arize-phoenix-alternatives-2026
Important pageStrengthen the page already earning search visibility before creating an unrelated page.https://laminar.sh/article/langsmith-alternatives-2026 · 4 sampled keywords · best position 9
Competitor gapReview the sampled queries where github.com appears.2 first-page appearances across laminar, lmnr

Website intelligence — Collected evidence

Website evidence gaps

  • integrations: no usable public page was collected; verify whether the site publishes this evidence.
  • customers: no usable public page was collected; verify whether the site publishes this evidence.
  • reviews: no usable public page was collected; verify whether the site publishes this evidence.
  • changes: no usable public page was collected; verify whether the site publishes this evidence.
  • identity: no usable public page was collected; verify whether the site publishes this evidence.
  • technical: no usable public page was collected; verify whether the site publishes this evidence.

Trust and proof

16

Customers, proof, and reviews

Published proof, customer signals, and review evidence.

Independent reviews

No citation-grounded independent review links were found.

Customer proof

No named customers, testimonials, or customer case studies were found.

Independent mentions

  • lmnr-ai/lmnr — Laminar is an open-source observability platform purpose-built for AI agents. ... Describe any behavior of your agent that you want to track in plain English (e. ... Let your coding agent investigate and debug issues based on your traces -
  • Laminar - Open-source observability for AI agents — Laminar is an open-source agent observability platform. It automatically catches agent failures at scale and helps you fix them. ... Laminar automatically captures LLM calls, tool calls, sub-agents, costs, and tokens, and shows it in a readable transcript view. ... Laminar Signals analyze every agent run to surface failure modes you didn't anticipate, powered by our custom model optimized for trace analysis and cost. Similar failures are clustered into distinct patterns
  • Laminar - Laminar documentation — Laminar is an open-source observability platform for AI agents that proactively analyzes every trace and alerts you when a run goes wrong. Our fine-tuned model flow-1 delivers frontier-level trace analysis at a cost that makes it practical to monitor every run without sampling. These findings help you turn real failures into eval cases, discover unknown failure modes, and prioritize improvements based on where users struggle. Get started with Laminar Cloud or self-host Laminar on your own infras
  • What Is Agent Observability? Tracing and Debugging AI Agents / Laminar — practice of recording every step an AI agent takes (each LLM call, tool call, ... and subagent) as structured trace data you can inspect, query, and evaluate. It exists because agents are non-deterministic: the same input can produce a different path, so
  • Tracing - Laminar documentation — Tracing records what your agent does (LLM calls, tool use, and control flow) so you can debug individual runs and understand behavior over time. Tracing is also the entry point into Laminar: it’s how production runs become data in the platform. Once you’re tracing, you can read each run as a transcript in the trace view, define Signals to extract outcomes and failures across traces, and replay runs in the Debugger to reproduce and fix what went wrong. From there, turn real failures into Datasets
  • Platform - Laminar documentation — Laminar includes a set of UI tools for exploring traces and debugging ... ## Laminar Agent Ask about your traces, costs, and signals in plain language from MCP, the CLI, or Slack. ## MCP Server Connect AI modelCode, Cursor, or any MCP client to Laminar.
  • Agent and LLM Observability Integrations - Laminar documentation — Laminar is an open-source, OpenTelemetry-native observability platform for AI agents. It auto-instruments LLM provider SDKs, agent frameworks, and browser agents, so you trace, debug, and monitor every call without changing your code. Initialize Laminar once and calls to OpenAI, Anthropic, LangChain, the Vercel AI SDK, and the rest of the integrations below are captured with full detail: prompts, responses, tool calls, tokens, latency, and cost. Each integration page below has setup steps for Ty
  • AI Agent Debugger: Record, Replay, Evaluate - Laminar documentation — Laminar to understand what worked and what didn’t, changes your code, and runs again, with each rerun served from cache up to the point it’s testing so the turn is fast and cheap. Every run, evaluation

External links were grounded in one live web search. Search snippets are leads, not independently scraped page content; rejected links remain available in Review evidence.

17

Company, security, and trust

Ownership, policies, security signals, and public trust evidence.

SignalFindingSource
Company and ownershipModel is matched against `gen_ai.response.model`, falling back to `gen_ai.request.model` when there is no response model. A date or version suffix is part of the name: an entry for `acme-llm-v1` does not match asource
Contact and supportLaminar is an open-source, OpenTelemetry-native observability platform for AI agents. PII redaction is a project-level setting that runs every span through a PII detection model before storage and replaces names, emasource
Legal and privacylmnr-cli signal create "Refund requests" \source
Security and complianceHIPAA & SOC 2 Type II Compliantsource
Uptime and status pageNot published on the collected pages.—

Not found on the collected pages: uptime and status page. This only means Scout did not find it on the pages it read; it says nothing about the company itself.

18

Documentation and product updates

Help resources, product guidance, and visible changes over time.

Resource or updateTypeWhat is availableDate or freshness
Laminar - Laminar documentationDocumentationDocumentation is publicly available. (source)Not dated
LLM Observability Pricing (2026): 6 Platforms Compared / LaminarGuideGuides, comparisons, pricing breakdowns, use cases, and how-to articles. (source)Not dated
Platform - Laminar documentationDocumentationDocumentation is publicly available. (source)Not dated
CLI - Laminar documentationDocumentationDocumentation is publicly available. (source)Not dated
Custom Model Costs for Self-Hosted and Fine-Tuned Models - Laminar documentationDocumentationDocumentation is publicly available. (source)Not dated
Slack integration - Laminar documentationDocumentationDocumentation is publicly available. (source)Not dated
Laminar Agent - Laminar documentationDocumentationDocumentation is publicly available. (source)Not dated
Live tracing - Laminar documentationDocumentationDocumentation is publicly available. (source)Not dated

Not found in the collected public sources: public roadmap, changelog or release notes, status page, separate help center or knowledge base.

Technical

19

Technical and delivery signals

Delivery model, platforms, API, data ownership, reliability claims and release activity.

SignalFindingSource
Delivery model (cloud, self-hosted, desktop, browser extension)Laminar Cloud is available, and self-hosted deployments are supported S26 S3.S26 S3
Platforms and appsWeb app and CLI are available S2 S3.S2 S3
API and extensibility (API, SDK, webhooks, MCP, plugins)Provides an SDK, CLI, MCP server, and SQL API S3 S30 S1.S3 S30 S1
Data ownership and exportNot published on the collected pages.—
Performance and reliability claimsNot published on the collected pages.—
Release activityNot published on the collected pages.—
20

Measured infrastructure

Domain, certificate, and hosting signals, measured directly.

SignalFinding
TLS certificateLet's Encrypt, valid, expires 2027-01-12
Security headersHSTS, CSP, X-Content-Type-Options, X-Frame-Options, Referrer-Policy
Server headercloudflare
Mail handled byeforward3.registrar-servers.com, eforward1.registrar-servers.com
Nameserverssantino.ns.cloudflare.com, liv.ns.cloudflare.com

Not measurable: wayback: rate limited by archive.org, ladder stopped after 1 probe(s); rdap: exceeded the overall budget; wayback: exceeded the overall budget; tls: exceeded the overall budget; dns: exceeded the overall budget; headers: exceeded the overall budget. A gap here means the lookup was unavailable, not that the finding is negative.

21

Technical site audit

A bounded multi-page crawl with page-level issues and URLs.

Scout — Bounded technical crawl

  • Pages crawled: 15 of 15 maximum
  • Technical health: 93/100
  • Critical issues: 0
  • Warnings: 20
  • Notices: 4

Issues

SeverityIssueValueAffected URL
warningMissing canonical URL—https://laminar.sh/
warningSlow HTML response4592https://laminar.sh/
warningMissing meta description—https://laminar.sh/docs/overview
warningPage should have one primary H10https://laminar.sh/blog
warningMissing canonical URL—https://laminar.sh/blog
warningPage should have one primary H10https://laminar.sh/pricing
warningMissing canonical URL—https://laminar.sh/pricing
noticeMeta description length should be reviewed32https://laminar.sh/sign-in
warningPage should have one primary H10https://laminar.sh/sign-in
warningMissing canonical URL—https://laminar.sh/sign-in
noticeLow visible word count32https://laminar.sh/sign-in
warningPage should have one primary H10https://laminar.sh/sign-up
warningMissing canonical URL—https://laminar.sh/sign-up
noticeLow visible word count33https://laminar.sh/sign-up
warningMissing meta description—https://laminar.sh/docs/platform/viewing-traces
warningMissing meta description—https://laminar.sh/docs/signals/introduction
warningMissing meta description—https://laminar.sh/docs/signals/clusters
warningTitle length should be reviewed67https://laminar.sh/blog/flow-1
noticeImages are missing alt text2https://laminar.sh/blog/flow-1
warningMissing meta description—https://laminar.sh/docs/platform/laminar-agent
warningMissing meta description—https://laminar.sh/docs/platform/cli
warningTitle length should be reviewed67https://laminar.sh/docs/debugger/introduction
warningMissing meta description—https://laminar.sh/docs/debugger/introduction
warningMissing meta description—https://laminar.sh/docs/evaluations/introduction

Crawled pages

StatusTitleH1sWordsResponseURL
200Laminar - Open-source observability for AI agents115634,592 mshttps://laminar.sh/
200Laminar - Laminar documentation1404268 mshttps://laminar.sh/docs/overview
200Blog \Laminar02281,256 ms
200Pricing \Laminar05491,461 ms
200Sign In - Laminar \Laminar0321,060 ms
200Sign Up - Laminar \Laminar033389 ms
200Viewing Traces - Laminar documentation11647652 mshttps://laminar.sh/docs/platform/viewing-traces
200Signals - Laminar documentation11196391 mshttps://laminar.sh/docs/signals/introduction
200Clusters - Laminar documentation11228560 mshttps://laminar.sh/docs/signals/clusters
200flow-1: Frontier trace intelligence at 1/20th of the cost \Laminar11266298 ms
200Laminar Agent - Laminar documentation1741201 mshttps://laminar.sh/docs/platform/laminar-agent
200CLI - Laminar documentation12844208 mshttps://laminar.sh/docs/platform/cli
200MCP Server - Laminar documentation1720234 mshttps://laminar.sh/docs/platform/mcp
200AI Agent Debugger: Record, Replay, Evaluate - Laminar documentation1927152 mshttps://laminar.sh/docs/debugger/introduction
200Evaluations - Laminar documentation11035228 mshttps://laminar.sh/docs/evaluations/introduction

This is a bounded Scout crawl. Core Web Vitals and Lighthouse scores are not claimed unless a dedicated performance audit is connected.

22

Search Console and Analytics

First-party search and behavioral data connection state.

Modeled acquisition channels

ChannelEstimated share
direct40.1%
search43.6%
search organic43.5%
search paid0.1%
social7.0%
social organic6.9%
social paid0.0%
referrals8.7%
paid referrals0.2%
mail0.2%
gen ai0.1%
affiliate0.1%
other0.0%

First-party connections

IntegrationStatusData available after connection
Google Search ConsoleNot connectedClicks, impressions, CTR, average position, queries, pages, countries, and devices
Google AnalyticsNot connectedOrganic sessions, engagement, landing pages, conversions, geography, and devices

No first-party Search Console or Analytics numbers are shown until the site owner authorizes the corresponding Google account. Scout traffic model values above are modeled estimates, not private analytics totals.

Limits and method

23

Limitations and open questions

Known constraints and missing evidence from the collected pages.

The collected pages do not mention any competitors. Evidence gaps include lack of customer names, reviews, uptime, and release dates. Pricing details are limited to the authoritative table; no additional offers are documented.

24

Sources, method, and limitations

Evidence inventory, collection method, provider use, and constraints.

This report is based on 15 collected pages from laminar.sh, including documentation and the homepage. Evidence was gathered via static and browser rendering rendering. External proof search was enabled but only one request was made; no external reviews or customer evidence were collected. Pricing was verified from the pricing page on 2026-10-19. Limitations: no customer testimonials, no uptime data, and no release history were found in the collected pages.

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