Laminar records 0 monthly visits with 0% month-over-month growth and Freemium pricing. PromptLayer records 85,899 monthly visits with -4.6% growth and Free Trial, Contact for Pricing pricing.
Open-source agent observability that catches failures before your users do
The collaboration layer for AI engineering teams
| Dimension / Metric | | |
|---|---|---|
| Official Domain | lmnr.ai | promptlayer.com |
| Pricing Model | Freemium | Free Trial, Contact for Pricing |
| Monthly Visits | 0 (0) | 85.9K (85,899) |
| MoM Traffic Delta | 0 (0%) | -4.18K (-4.6%) |
| Primary Market & Share | 🌐 Global (—) | 🇺🇸 United States (10.9%) |
| Direct Visits / mo | 0 | 14.09K |
| User Rating | 4.7 | 4.5 |
| Supported Platforms | Website | Website |
Open-source agent observability that catches failures before your users do
Automatically records LLM calls, tool calls, sub-agents, tokens and cost, then shows them as a readable transcript with a cost heatmap.
A custom analysis model reviews every run to surface failure modes you never defined, then clusters similar failures into patterns for fast triage.
Convert error clusters into eval datasets and re-run them after each release to catch regressions before they reach users.
Get notified about new failure clusters in Slack and ask the Laminar agent questions about your traces from the same channel.
The collaboration layer for AI engineering teams
Edit, version, diff and roll back prompts from a visual dashboard with no coding, so product, marketing and content teams can iterate on prompts while engineers keep control of the deploy.
Run dataset-backed tests with human and AI graders, trigger regression evals whenever a prompt changes, backtest new versions against historical data and compare prompts across models.
Connect production traces to the exact prompt version that produced them and inspect failures, cost, latency and token usage by feature, model or user.
Build, trace and improve agent workflows with prompts, tool calls, evaluations and logs in one place, so multi-step runs can be tested before they ship.
Unified observability, security and incident response for cloud-scale engineering teams
Unified observability, security and incident response for cloud-scale engineering teams
Free / Open-sourceBuild, deploy, and govern production-ready agentic AI workflows
Free / Open-sourceBuild, deploy, and govern production-ready agentic AI workflows
Track, evaluate, and ship AI models and agents in one connected environment.
Track, evaluate, and ship AI models and agents in one connected environment.
Free / Open-sourceAgent observability, evaluation, and continuous improvement for production AI
Free / Open-sourceAgent observability, evaluation, and continuous improvement for production AI
Free / Open-sourceBuild, simulate, evaluate and monitor self-improving AI agents
Free / Open-sourceBuild, simulate, evaluate and monitor self-improving AI agents
Free / Open-sourceEvaluate, monitor, and govern your AI applications in production
Free / Open-sourceEvaluate, monitor, and govern your AI applications in production
Standardize LLM evaluation, observability, and red teaming across your whole org
Standardize LLM evaluation, observability, and red teaming across your whole org
A multiplayer control plane for AI coding agents
A multiplayer control plane for AI coding agents
AI governance, testing, and observability for regulated enterprises
AI governance, testing, and observability for regulated enterprises
Free / Open-sourceOpen-source observability that finds your AI agent's failures — and fixes them
Free / Open-sourceOpen-source observability that finds your AI agent's failures — and fixes them
Free / Open-sourceOpen-source workspace for building, evaluating and shipping AI agents
Free / Open-sourceOpen-source workspace for building, evaluating and shipping AI agents
Find and fix problems in your AI agents before users do
Find and fix problems in your AI agents before users do
