Confident AI
DeepEval-powered LLM evaluation platform to test, benchmark, and safeguard apps
Laminar
Trace, evaluate, and scale AI applications with minimal code.
Laminar provides automatic OpenTelemetry tracing, cost and token metrics, parallel evaluation, and dataset export for LLM apps, all via a Rust backend and SDKs for Python and TypeScript.
Overview
Overview
Laminar is a unified platform that brings observability, evaluation, and data management to AI applications. By leveraging OpenTelemetry, it automatically instruments popular LLM frameworks such as LangChain, OpenAI, and Anthropic, capturing inputs, outputs, latency, cost, and token counts with just a few lines of code.
Capabilities & Deployment
The platform offers a Rust‑based backend that streams traces over gRPC for low overhead, stores metadata in Postgres, performs analytics in ClickHouse, and orchestrates processing via RabbitMQ. Users can run evaluations in parallel, export production traces to datasets, and visualize everything through built‑in dashboards. Laminar can be self‑hosted using Docker Compose for quick starts or a full‑stack deployment for production, and a managed SaaS version is also available.
Who Benefits
Developers and teams building production LLM services gain deep performance insights, cost visibility, and a feedback loop for continuous improvement, all while retaining the flexibility of an open‑source, self‑hosted solution.
Highlights
OpenTelemetry‑based automatic tracing for major LLM frameworks
Built‑in observability of latency, cost, and token usage
Parallel evaluation SDK with dataset integration
High‑performance stack (Rust, gRPC, ClickHouse, RabbitMQ)
Pros
- Low‑overhead tracing via gRPC and Rust
- Supports Python and TypeScript SDKs across popular AI libraries
- Unified platform for tracing, evaluation, and dataset management
- Scalable architecture with ClickHouse analytics
Considerations
- Self‑hosting requires managing multiple services (Postgres, ClickHouse, RabbitMQ)
- Full feature set needs the heavier docker‑compose configuration
- Observability limited to supported SDKs out‑of‑the‑box
- Learning curve for configuring the Rust backend and gRPC endpoints
Managed products teams compare with
When teams consider Laminar, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Teams building production LLM services that need detailed performance metrics
- Developers wanting automated tracing with minimal code changes
- Organizations that want to run large‑scale evals on custom datasets
- Companies preferring self‑hosted, open‑source observability solutions
Not ideal when
- Small scripts where adding a full observability stack is overkill
- Projects that rely on unsupported AI frameworks
- Teams without capacity to maintain multi‑service infrastructure
- Users seeking a turnkey SaaS without any self‑hosting effort
How teams use it
Real‑time latency monitoring for a chatbot
Detect and alert on response slowdowns, reducing user‑perceived latency.
Cost analysis of multi‑model LLM pipelines
Track token usage and API spend per model to optimize budgeting.
Parallel benchmark evaluation of new prompts
Run thousands of prompt tests simultaneously, aggregating results in the dashboard.
Export production traces to a training dataset
Create labeled datasets from live interactions for continuous model improvement.
Tech snapshot
TypeScript74%
Rust23%
Python1%
MDX1%
CSS1%
Dockerfile1%
Frequently asked questions
How do I add tracing to my existing code?
Install the Laminar SDK for your language, initialize with your project API key, and the OpenTelemetry instrumentation automatically captures calls; you can also wrap functions with the observe decorator.
What databases does Laminar use and can I replace them?
The default stack includes Postgres for metadata, ClickHouse for analytics, and RabbitMQ for message queuing; you can customize the deployment but the components are tightly integrated.
Is there a hosted version?
Yes, Laminar offers a managed platform at lmnr.ai for quick onboarding without self‑hosting.
Can I run evaluations on my own datasets?
Yes, you can export traces to datasets and run evals on hosted or self‑uploaded datasets via the SDK.
What licensing applies to Laminar?
Laminar is released under the Apache‑2.0 license.
Project at a glance
Active- Stars
- 2,543
- Watchers
- 2,543
- Forks
- 161
LicenseApache-2.0
Repo age1 year old
Last commit3 hours ago
Self-hostingSupported
Primary languageTypeScript
Last synced 3 hours ago