Open-source observability platform purpose-built for AI agents, with OpenTelemetry-native tracing, plain-English signals, an evals SDK and CLI, SQL dashboards, dataset annotation, and MCP/CLI access, self-hostable with Apache-2.0 SDKs for Python and TypeScript.
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## Editorial notes
Laminar is useful when Claude-adjacent teams need to see what their AI agents actually did — the traces, tool calls, and outcomes across a run — and to evaluate and debug that behavior over time. It is an open-source observability platform purpose-built for AI agents, with OpenTelemetry-native tracing, evaluations, and dashboards, and it is self-hostable with Apache-2.0 SDKs for Python and TypeScript.
This is distinct from the agent frameworks, structured-output libraries, and gateway in the directory: rather than building or routing agents, Laminar is the observability and evaluation layer those workflows report into. It also exposes MCP and CLI access so a coding agent can query traces directly.
## Key capabilities
- **OpenTelemetry-native tracing** — an OTel-based SDK that can auto-trace popular frameworks (Vercel AI SDK, LangChain, OpenAI, Anthropic, Gemini, Browser Use, Stagehand, and more) with minimal setup.
- **Signals** — describe agent behavior you want to watch in plain English (for example, an agent stuck in a loop); Laminar reads runs and can alert you (for example in Slack) when it happens.
- **Evals** — an unopinionated, extensible SDK and CLI for running evaluations locally or in CI/CD, with a UI to visualize and compare results.
- **SQL dashboards** — a dashboard builder for traces, metrics, and events, including custom SQL queries.
- **Datasets and annotation** — a data-annotation UI to build datasets from traces for evaluations.
- **MCP and CLI access** — query traces, spans, metrics, and events with SQL, so a coding agent can investigate and debug issues from your trace data.
- **Performance** — trace compression, a realtime engine for viewing traces as they happen, full-text search over span data, and a gRPC exporter for tracing data.
- **Self-hostable** — run the platform yourself, or use Laminar Cloud.
## How teams use it
- **Agent debugging** — inspect the trace of a failed or surprising agent run to see the exact tool calls and outputs.
- **Regression evals** — run evaluations in CI/CD to catch quality regressions before shipping agent changes.
- **Behavior monitoring** — use signals to get alerted when an agent exhibits a defined failure mode in production.
- **Operational dashboards** — build SQL-backed dashboards over traces, metrics, and events for ongoing visibility.
- **Dataset curation** — annotate real traces to build datasets that feed evaluations and improvements.
## Getting started
Laminar is open source and works with a self-hosted instance or Laminar Cloud. Add the SDK to your
project — `lmnr` for Python or `@lmnr-ai/lmnr` for TypeScript — and initialize it so the
OpenTelemetry-native tracer sends spans to your Laminar destination. From there you can auto-instrument
supported frameworks, run evals with the SDK or CLI, define signals, and build SQL dashboards. For
self-hosting, run the platform with Docker per the documentation and point the SDK at your instance.
## Source notes
- The official repository describes Laminar as an open-source observability platform purpose-built for AI agents.
- Documented capabilities include OpenTelemetry-native tracing with one-line auto-instrumentation for frameworks such as Vercel AI SDK, Browser Use, Stagehand, LangChain, OpenAI, Anthropic, and Gemini; plain-English signals with alerting; an evals SDK and CLI with a comparison UI; SQL dashboards; dataset annotation; and MCP/CLI access for querying traces with SQL.
- The repository also notes performance features including trace compression, a realtime trace-viewing engine, full-text span search, and a gRPC exporter.
- The SDKs are published as `lmnr` on PyPI and `@lmnr-ai/lmnr` on npm, both Apache-2.0 licensed, with documentation at laminar.sh.
- The GitHub repository is `lmnr-ai/lmnr`, and a managed Laminar Cloud is available separately from the self-hosted open-source platform.
## Duplicate check
Checked current `content/tools/`, `content/mcp/`, agents, skills, hooks, rules, commands, guides, open pull requests, and repository-wide content for `Laminar`, `lmnr`, `lmnr-ai`, `laminar.sh`, `github.com/lmnr-ai/lmnr`, `@lmnr-ai/lmnr`, `agent observability`, and `llm tracing`. Existing entries cover agent frameworks, structured-output libraries, and gateways, and reference observability in passing, but no dedicated Laminar tools entry, Laminar source URL duplicate, or open duplicate PR was found.
## Disclosure
Editorial listing. No paid placement or affiliate link is used.Laminar is useful when Claude-adjacent teams need to see what their AI agents actually did — the traces, tool calls, and outcomes across a run — and to evaluate and debug that behavior over time. It is an open-source observability platform purpose-built for AI agents, with OpenTelemetry-native tracing, evaluations, and dashboards, and it is self-hostable with Apache-2.0 SDKs for Python and TypeScript.
This is distinct from the agent frameworks, structured-output libraries, and gateway in the directory: rather than building or routing agents, Laminar is the observability and evaluation layer those workflows report into. It also exposes MCP and CLI access so a coding agent can query traces directly.
Laminar is open source and works with a self-hosted instance or Laminar Cloud. Add the SDK to your
project — lmnr for Python or @lmnr-ai/lmnr for TypeScript — and initialize it so the
OpenTelemetry-native tracer sends spans to your Laminar destination. From there you can auto-instrument
supported frameworks, run evals with the SDK or CLI, define signals, and build SQL dashboards. For
self-hosting, run the platform with Docker per the documentation and point the SDK at your instance.
lmnr on PyPI and @lmnr-ai/lmnr on npm, both Apache-2.0 licensed, with documentation at laminar.sh.lmnr-ai/lmnr, and a managed Laminar Cloud is available separately from the self-hosted open-source platform.Checked current content/tools/, content/mcp/, agents, skills, hooks, rules, commands, guides, open pull requests, and repository-wide content for Laminar, lmnr, lmnr-ai, laminar.sh, github.com/lmnr-ai/lmnr, @lmnr-ai/lmnr, agent observability, and llm tracing. Existing entries cover agent frameworks, structured-output libraries, and gateways, and reference observability in passing, but no dedicated Laminar tools entry, Laminar source URL duplicate, or open duplicate PR was found.
Editorial listing. No paid placement or affiliate link is used.
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[](https://heyclau.de/entry/tools/laminar)Laminar side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.
1 trust signal differ across this comparison (Submitter).
| Field | Open-source observability platform purpose-built for AI agents, with OpenTelemetry-native tracing, plain-English signals, an evals SDK and CLI, SQL dashboards, dataset annotation, and MCP/CLI access, self-hostable with Apache-2.0 SDKs for Python and TypeScript. Open dossier | Open-source observability platform and SDK for tracing, debugging, replaying, and cost-monitoring AI agent and LLM application runs. Open dossier | Open-source observability and evaluation tooling for LLM applications, traces, datasets, and experiments. Open dossier | Open-source LLM engineering platform for tracing, prompt management, evaluation, metrics, and observability. Open dossier |
|---|---|---|---|---|
| Next steps | ||||
| Trust | ||||
| Review status | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed |
| Package trust | Package not verified | Package not verified | Package not verified | Package not verified |
| Source provenance | Source-backed | Source-backed | Source-backed | Source-backed |
| SubmitterDiffers | davion-knight | oktofeesh1 | — | — |
| Install risk | Review first | Review first | Review first | Review first |
| Notes | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety · Privacy · | Safety · Privacy ✓ |
| Brand | ||||
| Category | tools | tools | tools | tools |
| Source | source-backed | source-backed | source-backed | source-backed |
| Author | lmnr-ai | AgentOps | Arize AI | Langfuse |
| Added | 2026-07-09 | 2026-06-03 | 2026-04-27 | 2026-04-27 |
| Platforms | CLI | CLI | CLI | CLI |
| Source repo | — | — | — | — |
| Safety notes | ✓Laminar captures traces of your AI application, including prompts, tool calls, and outputs, so decide what to instrument and keep sensitive fields out of traces where they are not needed. Self-hosting exposes a platform endpoint and console; run it on a trusted network or behind authentication, and do not expose an unauthenticated instance publicly. SQL dashboards and MCP/CLI access let agents and users query trace and event data; scope who can run those queries and connect a coding agent only to data it should see. Signals evaluate agent runs and can send alerts (for example to Slack), so confirm alert destinations and the data included in them before enabling. Treat trace data and eval results as inputs for review, not proof that an agent is correct or safe, and keep production access narrower than local examples. | ✓AgentOps instruments LLM calls, tools, operations, and agent workflows, so enable it intentionally in environments where captured traces are allowed. Cost and latency dashboards are useful for operations, but alerting and budget decisions still need human-reviewed thresholds. Self-hosted deployments require normal backend hardening for database access, secrets, authentication, and retained trace data. | — missing | — missing |
| Privacy notes | ✓Traces can contain prompts, inputs, tool arguments, outputs, and metadata from your AI application; this data is sent to the Laminar instance or cloud you configure. Datasets and data-annotation features store selected trace data for evals, so apply retention and access-control policies to those datasets. If you use Laminar Cloud rather than self-hosting, trace and eval data is processed under its terms; self-hosting keeps that data in your own environment. Project keys, dashboard queries, and exported eval reports should be kept out of version control and access-controlled like other operational data. | ✓Traces can include prompts, completions, tool inputs, tool outputs, errors, costs, tokens, tags, and application metadata. The docs say AgentOps automatically collects basic host environment details such as OS, Python version, anonymized hostname, and SDK version. Hosted dashboard use sends telemetry to AgentOps infrastructure; self-hosted use still requires retention, access-control, and log-review policies. | — missing | ✓Langfuse receives traces of your LLM/agent runs — prompts, outputs, and metadata — sent to Langfuse Cloud or your self-hosted instance; review what trace data leaves your environment and keep secrets out of logged inputs. |
| Prerequisites |
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| — none listed | — none listed |
| Install | — | — | — | — |
| Config | — | — | — | — |
| Citations | ||||
| Claim | Unclaimed | Unclaimed | Unclaimed | Unclaimed |
Open-source observability for AI agent traces, replays, and costs.
Open-source LLM observability and evaluation tooling.
Source-backed guides for putting this to work.
Instrument LLM and agent apps with traces, metrics, logs, and redaction.
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