Laminar
The open-source platform for tracing and evaluating AI applications.
Description
Laminar offers an open-source solution for developers focused on building reliable AI products. It specializes in tracing and evaluating AI applications, providing crucial visibility into every execution step. This observability aids in debugging and improving AI systems by collecting data essential for evaluations and fine-tuning. The platform emphasizes ease of use, allowing developers to integrate tracing capabilities with minimal code.
Furthermore, Laminar introduces advanced observability for browser agents by recording browser sessions and synchronizing them with agent traces. This feature aims to simplify the debugging process for browser-based AI agents. Being open-source, Laminar also offers flexibility for self-hosting, catering to developers who prefer control over their infrastructure.
Key Features
- LLM Observability: Automatically trace all LLM frameworks and SDKs with one line of code.
- Real-time Tracing: Debug AI apps faster with real-time traces.
- Browser Agent Observability: Record browser sessions and sync them with agent traces for enhanced debugging.
- Evaluations (Evals): Helps maintain high accuracy for LLM-based features.
- LLM Playground: Experiment with prompts and models directly from LLM spans.
- Dataset Generation: Build datasets from span data for evaluations, fine-tuning, and prompt engineering.
- Custom Labels: Tag spans with custom labels for better organization.
- Open-Source & Self-Hostable: Provides the flexibility of being open-source and easy to self-host.
Use Cases
- Debugging AI applications
- Improving AI application performance
- Evaluating AI models and features
- Monitoring browser agent behavior
- Fine-tuning AI models
- Prompt engineering
- Building reliable AI products
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