Evaluating AI systems isn’t just about pass/fail, it’s about measuring reliability, accuracy, and behavior over time. Here are five tools I used to bring structure and rigor to AI evaluation workflows.
When it comes to evaluating AI agents and LLM apps, it’s essential to have the right tools in your arsenal. That’s why I want to share the top 5 tools I use to get the job done. From human-in-the-loop evaluations to automated metrics and human review pipelines, these tools have got you covered.
**Braintrust**: Specializes in human-in-the-loop evaluations at scale. Lets you recruit, manage, and pay human raters directly through the platform. Great for teams doing qualitative scoring and structured labeling.
**LangSmith**: Built by the LangChain team. Integrates tightly with LangChain apps to record traces and run evaluations. Supports both automated metrics (BLEU, ROUGE) and human review pipelines.
**Arize AI**: A broader ML observability platform with LLM evaluation modules. Good for teams that already monitor traditional ML models and want to add LLM performance tracking in one place.
**Vellum**: Primarily a prompt ops tool, but has lightweight evaluation capabilities. You can compare model outputs across versions and capture ratings from testers.
**Maxim AI**: Purpose-built for continuous evaluation of AI agents. Combines automated and human scoring, side-by-side comparison, and regression detection. Designed for pre-release and post-release testing so you can catch quality drops before they hit production. Full prompt management is included, but the core strength is in building realistic, repeatable evaluation suites that match your real use cases.
I’m curious to hear if anyone’s tried these tools and how they compare in real-world use. Always open to discovering hidden gems or better workflows for evaluating AI agents.