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Laminar

Open-source observability platform purpose-built for AI agents.

Laminar (YC S24) is an open-source observability platform for AI agents. Its OpenTelemetry-native SDK auto-traces Vercel AI SDK, Browser Use, Stagehand, LangChain, OpenAI, Anthropic and Gemini. Signals let teams describe behaviors in plain English, such as an agent stuck in a loop, and get Slack alerts when they occur. It also offers an evals SDK and CLI for local or CI runs, SQL-queryable traces via MCP and CLI so coding agents can debug from traces, custom dashboards, and dataset annotation. The backend is written in Rust and can be self-hosted with Docker Compose.

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In the news

Hi HN, we’re Robert and Din from Laminar ( https://laminar.sh ). We’ve been building open-source observability for AI agents since 2024. Recently, we released flow-1, a model trained with RL to find errors in agent traces. On our benchmark ( https://laminar.sh/blog/flow-1 ), i...

Featured

The de-facto standard toolkit for building LLM applications and agents.

LangChain makes the LangChain framework and LangGraph, the low-level orchestration standard for stateful, controllable agents, plus the LangSmith platform for tracing, evaluating and monitoring them in production.

~$75M raised

Enterprise-grade evals, data and AI gateway.

Braintrust turns prompts and agent traces into testable datasets and CI, plus an inference gateway — helping teams measure whether harness changes actually improve agent quality.

Observability and evaluation for AI — makers of OSS Phoenix.

Arize traces and evaluates LLM and agent systems in production; their open-source Phoenix tracer is a common instrumentation choice for agent harnesses that need to be debuggable.

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Sources

  1. GitHub repository and README
  2. Y Combinator company profile