Open-source observability platform for AI agents and LLM applications with tracing, evaluation, and dataset management.
Open-source monitoring for AI agents — trace every step your agent takes and evaluate quality with built-in testing tools.
Laminar (lmnr) is an open-source observability platform purpose-built for AI agents and LLM applications. It provides comprehensive tracing, evaluation, and analytics capabilities that help developers understand, debug, and improve their agent systems in development and production.
The platform captures detailed traces of every agent execution — including LLM calls, tool invocations, retrieval operations, and custom spans — with automatic instrumentation for popular frameworks like LangChain, LlamaIndex, CrewAI, and OpenAI. Each trace includes input/output data, token counts, latency measurements, and cost calculations, giving developers full visibility into what their agents are doing and how much it costs.
Laminar's evaluation system lets developers define custom evaluation functions and run them against traces or datasets. Evaluations can be LLM-as-judge assessments, deterministic checks, or custom Python functions. Results are tracked over time, enabling teams to measure quality trends and catch regressions before they reach users.
The dataset management feature allows teams to curate collections of inputs and expected outputs from production traces, creating golden datasets for testing and evaluation. This production-to-test feedback loop is critical for systematically improving agent quality.
Laminar can be self-hosted via Docker or used as a managed cloud service. The open-source version includes all core features — tracing, evaluation, datasets, and the analytics dashboard. The managed version adds team collaboration, higher retention, and support.
The platform integrates via a lightweight SDK (Python and TypeScript) that adds minimal overhead to agent execution. Auto-instrumentation means most frameworks work out of the box with just an import statement.
For teams building production agent systems, Laminar fills a critical gap between generic observability tools (which don't understand LLM-specific metrics) and framework-specific tools (which lock you into one ecosystem). Its open-source nature, broad framework support, and focus on the development-to-production lifecycle make it a strong choice for teams that want observability without vendor lock-in.
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Auto-instruments LangChain, LlamaIndex, CrewAI, and OpenAI with zero-config tracing of LLM calls, tool use, and retrieval operations.
Use Case:
Getting full visibility into a production agent's behavior by adding two lines of code.
Define evaluation functions (LLM-judge, deterministic, or custom Python) and run them against traces or datasets to measure quality.
Use Case:
Running nightly evaluations against a golden dataset to catch quality regressions in a customer support agent.
Automatic calculation of LLM costs per trace, per user, and per feature based on token usage and model pricing.
Use Case:
Identifying which agent workflows are most expensive and optimizing token usage.
Create golden datasets from production traces for systematic testing and evaluation of agent improvements.
Use Case:
Building a test suite from real customer interactions to validate prompt changes before deployment.
Full platform deployable via Docker with all core features available in the open-source version.
Use Case:
Running observability infrastructure on-premise for compliance with data residency requirements.
Works with LangChain, LlamaIndex, CrewAI, AutoGen, and any OpenAI-compatible setup through standardized instrumentation.
Use Case:
Monitoring a heterogeneous agent system that uses different frameworks for different capabilities.
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View Pricing Options →Agent debugging and development
Production monitoring
Quality evaluation and testing
Cost optimization
We believe in transparent reviews. Here's what Laminar (LMNR) doesn't handle well:
Both are open-source LLM observability tools. Laminar focuses more on integrated evaluation and dataset management, while Langfuse has a larger community and more integrations. Both offer self-hosting.
Laminar auto-instruments LangChain, LlamaIndex, CrewAI, OpenAI, and Anthropic. Custom spans can be added for any framework using the SDK.
The SDK adds minimal overhead — traces are sent asynchronously and don't block agent execution. Typical impact is less than 5ms per span.
Yes, many teams start with Laminar in development for debugging and testing, then expand to production monitoring as they scale.
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