The best Pydantic Evals alternatives

Vellum suits individuals looking for an AI assistant to handle personal tasks across files, calendars, email, code, and computer controls.

It suits AI developers working on model training, fine-tuning, AI application development, or production monitoring.

Evidently AI suits teams evaluating or monitoring language models, RAG applications, agents, or predictive ML workflows.

Opik suits teams developing applications or agents that call an LLM and need to inspect, evaluate, and monitor their behavior.

DeepEval suits developers building repeatable evaluations for AI systems, especially those running tests in Python or CI/CD.

Langfuse suits teams building AI agent applications that need tracing, prompt management, and evaluation tools.

LangWatch suits AI teams working on agents, including developers, product owners, QA, and domain experts involved in evaluation workflows.

LangSmith suits teams developing AI agents that need evaluation, prompt tools, tracing, and production monitoring.

It is designed for cross-functional AI teams, including product managers who want to define and analyze evaluations without code and developers who…

Promptfoo is for developers and enterprises building AI applications who need prompt evaluation or red-team testing.

It suits teams assessing conversational AI agents for safety and quality issues before production.

Braintrust is intended for teams running AI agents in production, from early-stage projects to enterprise deployments.

It is designed for engineers, QA teams, product managers, subject-matter experts, and annotators working on AI quality, evaluation, or production…

Rhesis is intended for domain experts, engineers, and product managers working on AI agents.

Galileo is designed for engineers, product managers, and subject matter experts building AI applications.

HELM suits teams and researchers who want an open-source framework for evaluating foundation models and can run it themselves.

NeMo Evaluator suits developers and research, development, or testing teams assessing LLMs, RAG systems, and agents.

Phoenix suits teams that need to inspect AI application traces and compare evaluation results across versions.

It suits AI platform teams, AI engineers, domain experts, and risk and governance teams that need production agent visibility.

It suits researchers, organizations, and developers evaluating language models with shared benchmarks or custom metrics.