> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/1.-introduction/what-is-layerlens.md).

# What is LayerLens?

LayerLens is the AI evaluation platform — private workspace and SDK.

LayerLens is a complete platform for evaluating AI — from the moment you pick a model, through the build, into production, and across an organization. The platform is structured as **two product lines** (Model Evaluations and Agentic Evaluations) delivered through **two customer-facing experiences** (the LayerLens workspace and the Python SDK) that share one catalog, one grader engine, and one trace pipeline.

If you came in via the marketing site, you've seen the two product lines. If you're starting from this documentation, you'll mostly hear about the two experiences. They reconcile: the products are *what* you can do; the experiences are *where* you do it.

## High Level Overview

Most AI teams ship by intuition. They pick a model because it's popular. They iterate prompts because the last few examples felt better. They watch CSAT in production because that's the only signal that survives. By the time they know something's wrong, customers have already noticed.

LayerLens gives you a real signal. Two of them, actually:

1. **Private workspace** — run evaluations on your own data, build LLM graders that match your team's quality bar, and score live traces. Stop guessing whether the new model is better; measure it.
2. **CI/CD gates** — wire evaluations into your pipeline. Block regressions. Promote winners. Ship with the same confidence you ship traditional code.

## The two product lines (what you can do)

* **Model Evaluations** — model + benchmark + score. Use it to pick the right model and to track how the frontier is moving.
* **Agentic Evaluations** — pre- and post-deployment quality gates for multi-step AI agents: assertions, deterministic rules, and LLM graders combined into a verdict + root-cause report.

Both share the same trace pipeline, the same graders. You don't pick "one product" — you pick the workflow.

## What ships today

* **Models:** 200+ in the catalog, including GPT-5.3, Claude Opus 4.6, Gemini 3.1 Pro/Flash.
* **Benchmarks:** 52+ public benchmarks (MMLU, HumanEval, GSM8K, AGIEval, MATH, etc.).
* **Compare models:** head-to-head model comparisons across any benchmark.
* **Graders:** code graders for objective dimensions, and LLM graders for subjective dimensions with the GEPA optimizer.
* **Traces:** upload trace JSON; run trace-evaluations against graders.
* **ECU credits:** pay-as-you-go billing for compute-intensive runs.
* **BYOK custom models:** point at your own OpenAI-compatible endpoint.
* **Multi-org:** users belong to multiple orgs; switch from the picker.
* **Auth:** GitHub OAuth, Google OAuth, email/password.
* **Search, notifications, onboarding wizard.**

## Where to next

* [Workspace and SDK](/1.-introduction/three-experiences.md) — side-by-side
* [Agentic evaluations](/4.1-general-use-cases/agentic-evals-overview.md) — the pre- and post-deployment workflow
* [Getting started](/2.-get-started/02-get-started.md) — pick a path and ship