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Laminar – Understand why your agent failed. Iterate fast to fix it.

Open-source observability platform for long-running agents — trace in one line, replay-debug from any step, detect patterns at scale.

Details

External ID
98606
Source
YC
Company
Laminar
Product
Laminar – Understand why your agent failed. Iterate fast to fix it.
Website domain
laminar.sh
Launched
March 9, 2026
Cohort
Summer 2024
Upvotes
5
Upvotes percentile
0.059782608695652176
Tags
AIOps, Developer Tools, SaaS, B2B, AI
Fetched at
Oct. 1, 2026, 1 a.m.
Updated at
Oct. 1, 2026, 1 a.m.

Enrichment

Theme
AI agent frameworks and developer tools
Vertical
Horizontal
Function
Observability & eval
Audience
Developer
AI stance
AI feature
Project type
Commercial product
Normalized one-liner
debugging platform for ai agents
Manually corrected
False

Could you build this?

Partial Creating a basic LLM tracing dashboard is straightforward, but Laminar provides high-throughput real-time tracing, replay-debugging from arbitrary execution steps, and automatic unsupervised clustering of failure modes at scale.

What it would actually take: Stack typically uses Rust or Go for high-throughput OpenTelemetry trace ingestion, ClickHouse or PostgreSQL with pgvector for storing millions of spans, and Next.js for the UI. The difficult engineering challenges are building zero-overhead SDK instrumentation, state serialization for step-level replay debugging, and scalable clustering algorithms (e.g., HDBSCAN over embeddings) to identify failure signals across millions of agent runs.

Competitors

Other products that read as similar to this one — 118 launches clear the similarity bar, closest 8 shown.

Attention rank: #116 of 119 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 125 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a observability & eval tool for Media & entertainment yet.