Agent Eval frameworks

Several open-source and commercial frameworks can help teams implement agent evaluations without building infrastructure from scratch. The right choice depends on your agent type, existing stack, and whether you need offline evaluation, production observability, or both.

Harbor (Harbor)is designed for running agents in containerized environments, with infrastructure for running trials at scale across cloud providers and a standardized format for defining tasks and graders. Popular benchmarks like Terminal-Bench 2.0 ship through the Harbor registry, making it easy to run established benchmarks along with custom eval suites.

Promptfoo (Promptfoo) is a lightweight, flexible, and open-source framework that focuses on declarative YAML configuration for prompt testing, with assertion types ranging from string matching to LLM-as-judge rubrics. We use a version of Promptfoo for many of our product evals.

Braintrust (Braintrust) is a platform that combines offline evaluation with production observability and experiment tracking–useful for teams that need to both iterate during development and monitor quality in production. Its autoevals library includes pre-built scorers for factuality, relevance, and other common dimensions.

LangSmith offers tracing, offline and online evaluations, and dataset management with tight integration into the LangChain ecosystem.  Langfuse provides similar capabilities as a self-hosted open-source alternative for teams with data residency requirements.

Many teams combine multiple tools, roll their own eval framework, or just use simple evaluation scripts as a starting point. We find that while frameworks can be a valuable way to accelerate progress and standardize, they’re only as good as the eval tasks you run through them. It’s often best to quickly pick a framework that fits your workflow, then invest your energy in the evals themselves by iterating on high-quality test cases and graders.