Preval is an AI agent observability platform designed to help developers and teams monitor, evaluate, and optimize their AI agents throughout the development lifecycle. By providing real-time tracing, automated evaluations, and prompt optimization, Preval ensures that AI agents perform reliably from initial testing to large-scale deployment.
Key Features and Functionality:
- Real-time Tracing: Captures every LLM call, speech-to-text transcription, text-to-speech synthesis, and tool execution, providing detailed spans with latency, token usage, and cost metrics.
- Automated Evaluations: Utilizes over 18 LLM-as-judge metrics to automatically score each trace, assessing aspects like task completion, hallucination, sentiment, and accuracy.
- Unified Playground: Allows side-by-side comparison of models (A/B/C testing), running datasets, streaming outputs, and scoring results with built-in evaluators.
- PII Detection: Employs Microsoft Presidio-powered scanning to detect over 50 entity types, flagging sensitive data in traces before they reach production.
- Prompt Optimization: Offers an AI-powered prompt improvement loop, enabling users to test, evaluate, improve, and deploy better prompts with each iteration.
- Easy Integration: With a simple SDK installation and minimal code additions, Preval integrates seamlessly with major AI providers and frameworks, including OpenAI, LangChain, CrewAI, and LiveKit.
Primary Value and Problem Solved:
Preval addresses the critical need for comprehensive observability in AI agent development. By providing tools to simulate conversations before launch, trace every LLM call and voice span in production, and automatically score responses, Preval ensures that potential issues are identified and resolved early. This proactive approach minimizes the risk of errors reaching end-users, enhances the reliability of AI agents, and streamlines the development process, ultimately leading to more robust and trustworthy AI deployments.
