# Laminar

According to OpenAITools.app telemetry verified on 2026-11-11, Laminar (lmnr.ai) is an AI platform indexed under AI Monitor generating 0 monthly visits with a month-over-month traffic delta of +0 (0%). Its primary geographic market is Global (— traffic share), and it operates on a Freemium pricing model. Top competing alternatives by monthly volume include Arize AI (249.03K visits/mo), Raindrop (42.81K visits/mo), Maxim AI (121.35K visits/mo).

Open-source agent observability that catches failures before your users do

## Key Telemetry & Specifications

| Metric | Value |
| :--- | :--- |
| Tool Name | Laminar |
| Handle | laminar |
| Status | active |
| Origin | open_source |
| Maintenance | active |
| Official Domain | lmnr.ai |
| Website URL | https://www.lmnr.ai |
| Categories | AI Monitor, AI Developer Tools, Large Language Models (LLMs), AI Agent |
| Pricing Model | Freemium |
| Monthly Visits | 0 |
| MoM Traffic Change | 0 |
| MoM Growth Rate | 0% |
| Primary Market | Global |
| Market Share | — |
| Direct Visits | 0 |
| Organic Search Visits | 0 |
| Social Visits | 0 |
| Referral Visits | 0 |
| Email Visits | 0 |
| Review Score | 4.7 |
| Saved Count | 1 |
| Supported Platforms | Website |
| Billing Platforms |  |
| Attributes | Features: Website; Pricing: Freemium |
| Tags | LLM Observability, AI Observability, Tracing, Evaluation, LLM Ops, Open Source, Browser Agent, AI Monitoring, Debugging, Fine-tuning |

## Overview

Laminar is built for teams shipping autonomous agents, where traditional application monitoring misses the interesting part: the reasoning chain. It instruments your stack to capture LLM calls, tool calls, sub-agents, token usage and cost, then renders everything as a readable transcript so you can see exactly what an agent did and why. Integrations cover the common agent frameworks and SDKs, so setup is typically a few lines of code rather than a custom telemetry project.

The differentiating feature is Signals: instead of requiring you to define every error condition in advance, Laminar analyzes every run with a custom model optimized for trace analysis and clusters similar failures into patterns. You get a distribution of what your agent actually does wrong, which makes triage far faster than reading logs one by one. Alerts land in Slack, and an agent can answer questions about your traces in-channel.

Once you fix a failure cluster, Laminar can turn it into an eval dataset. Run those evals after each change to catch regressions, and use the CLI or MCP integration to give a coding agent SQL access to platform data so it can investigate traces and verify fixes itself. Laminar is open source, offers a free starting point with GitHub sign-in, and provides paid tiers for production scale on its pricing page.

## Pricing & Plans

Laminar is freemium: you can sign up and get started for free (GitHub sign-in is supported), and the site links to a pricing page for paid production tiers and a book-a-demo option. Check lmnr.ai/pricing for current limits and rates.

## Key Features & Capabilities

- **Full agent tracing:** Automatically records LLM calls, tool calls, sub-agents, tokens and cost, then shows them as a readable transcript with a cost heatmap.
- **Signals failure detection:** A custom analysis model reviews every run to surface failure modes you never defined, then clusters similar failures into patterns for fast triage.
- **Evals from real traces:** Convert error clusters into eval datasets and re-run them after each release to catch regressions before they reach users.
- **Slack alerts and agent:** Get notified about new failure clusters in Slack and ask the Laminar agent questions about your traces from the same channel.
- **SQL access, CLI and MCP:** Query all platform data with raw SQL, build custom dashboards, and let coding agents investigate traces and verify fixes through the CLI or MCP.

## Primary Use Cases

- **Debug a misbehaving agent (AI engineer):** Read the full transcript of a failed run to see which tool call went wrong, whether a 404 was retried, and what the model assumed.
- **Catch failures at scale (ML platform team):** Let Signals cluster thousands of runs into distinct failure patterns so you can prioritise the highest-impact fix instead of reading logs manually.
- **Prevent regressions (QA / release engineer):** Turn each fixed failure into an eval case and run the suite after every model or prompt change to confirm nothing broke.
- **Control cost and reliability (Engineering manager):** Track token spend and cost per span per model, and route alerts into Slack so the team sees reliability problems as they appear.

## Strengths

- Open-source core with public GitHub repository and SDKs
- Signals finds failure modes without requiring predefined rules
- Direct path from production failure to regression eval dataset
- Broad framework and SDK coverage, plus CLI/MCP for coding agents

## Limitations

- Requires instrumentation work before you get value
- Trace-analysis quality still depends on your agent emitting good spans
- Advanced production tiers require contacting sales or checking the pricing page

## Frequently Asked Questions

### Is Laminar open source?
Yes. Laminar describes itself as an open-source agent observability platform and links to its GitHub repository and SDK documentation from the site.

### How is it different from generic APM?
Generic APM sees HTTP calls and latency. Laminar understands the agent layer: LLM calls, tool calls, sub-agents, prompts, tokens and cost, plus agent-specific failure detection.

### Do I have to define every failure type?
No. Signals analyzes each run with a purpose-built model to surface failure modes you did not anticipate, then clusters similar ones together.

### Can it integrate with my coding assistant?
Laminar provides a CLI and an MCP integration that give coding agents SQL access to Laminar data so they can investigate traces, build evals and verify fixes.

### What is Laminar's monthly traffic and growth trend?
Laminar currently receives 0 monthly visits, with a month-over-month traffic change of +0 (0% growth rate). Its primary source market is Global (— share), including 0 direct visits/mo and 0 organic search visits/mo.

### Is Laminar free? What is its pricing model?
Laminar operates on a Freemium pricing model. Laminar is freemium: you can sign up and get started for free (GitHub sign-in is supported), and the site links to a pricing page for paid production tiers and a book-a-demo option. Check lmnr.ai/pricing for current limits and rates. Supported billing and checkout platforms include: not listed.

### What are the best Laminar alternatives in 2026?
Based on OpenAITools.app category traffic and feature fit, the top five alternatives to Laminar are: 1. Arize AI (249.03K visits/mo); 2. Raindrop (42.81K visits/mo); 3. Maxim AI (121.35K visits/mo); 4. honeyhive.ai (8.84K visits/mo); 5. Future AGI (121.74K visits/mo).

### Which platforms and core AI capabilities does Laminar support?
Laminar is classified under AI Monitor, AI Developer Tools, Large Language Models (LLMs), AI Agent, supports the following platforms: Website, and its core capability tags include: LLM Observability, AI Observability, Tracing, Evaluation, LLM Ops, Open Source.

## Alternatives

| Tool Name | Monthly Visits | Pricing Model | Compare |
| :--- | :--- | :--- | :--- |
| [Arize AI](https://openaitools.app/tool/arize-com) | 249,028 | Paid, Contact for Pricing | [Compare](https://openaitools.app/compare/arize-com-vs-laminar) |
| [Raindrop](https://openaitools.app/tool/raindrop) | 42,807 | Free Trial, Paid, Contact for Pricing | [Compare](https://openaitools.app/compare/laminar-vs-raindrop) |
| [Maxim AI](https://openaitools.app/tool/maxim-ai) | 121,351 | Contact for Pricing, Free Open Source | [Compare](https://openaitools.app/compare/laminar-vs-maxim-ai) |
| [honeyhive.ai](https://openaitools.app/tool/honeyhive-ai) | 8,835 | Freemium, Contact for Pricing | [Compare](https://openaitools.app/compare/honeyhive-ai-vs-laminar) |
| [Future AGI](https://openaitools.app/tool/future-agi) | 121,741 | Freemium, Usage-based | [Compare](https://openaitools.app/compare/future-agi-vs-laminar) |
| [Datadog](https://openaitools.app/tool/datadog) | 5,175,760 | Freemium, Free Trial, Paid, Contact for Pricing, Free | [Compare](https://openaitools.app/compare/datadog-vs-laminar) |
| [PromptLayer](https://openaitools.app/tool/promptlayer) | 85,899 | Free Trial, Contact for Pricing | [Compare](https://openaitools.app/compare/laminar-vs-promptlayer) |
| [Confident AI](https://openaitools.app/tool/confident-ai) | 83,400 | Freemium, Paid, Contact for Pricing | [Compare](https://openaitools.app/compare/confident-ai-vs-laminar) |
| [Middleware](https://openaitools.app/tool/middleware) | 35,967 | Freemium, Contact for Pricing | [Compare](https://openaitools.app/compare/laminar-vs-middleware) |
| [Weights & Biases (CoreWeave Forge)](https://openaitools.app/tool/prompts) | 315,624 | Freemium, Contact for Pricing, Free | [Compare](https://openaitools.app/compare/laminar-vs-prompts) |
| [TraceRoot](https://openaitools.app/tool/traceroot-ai) | 2,070 | Free + Paid | [Compare](https://openaitools.app/compare/laminar-vs-traceroot-ai) |
| [Latitude](https://openaitools.app/tool/latitude) | 48,210 | Freemium, Open Source, Enterprise | [Compare](https://openaitools.app/compare/laminar-vs-latitude) |

[laminar](https://discord.gg/nNFUUDAKub)

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Source: https://openaitools.app/tool/laminar