BenchLLM

BenchLLM is an open source framework for testing LLM applications: define YAML or JSON tests, run suites via CLI, evaluate outputs with automated or custom strategies, and catch regressions in CI/CD.

BenchLLM is an open and flexible testing framework for LLM-powered applications. Built by the engineering team at V7, it gives developers a structured way to write test suites for models, run evaluations on the fly, and produce quality reports that reveal regressions. The tool is designed for people who already build with language models and want predictable results without giving up the flexibility of their own code. The workflow starts with YAML or JSON test files, which can be grouped into suites and versioned alongside the codebase. A Tester object wraps your application function and generates predictions, while an Evaluator scores those predictions. BenchLLM supports automated evaluation strategies (including semantic scoring) alongside interactive and custom approaches, so teams can match the method to the task. A command line interface runs suites with simple commands, prints readable failure tables for mismatches, and is built to plug into continuous integration pipelines for regression detection in production. Because it works with OpenAI, Langchain, and any other API out of the box, BenchLLM sits comfortably in existing stacks. It is less a hosted platform than a developer toolkit: you keep code organized the way you like, define your own agent or chat function, and let BenchLLM handle test organization, prediction generation, evaluation, and reporting. That places it in the same conversation as prompt testing and evaluation frameworks, with an emphasis on CLI automation and shareable reports rather than a visual dashboard. BenchLLM is aimed at AI engineers, ML platform teams, and technical leads who need a repeatable evaluation loop before shipping model-driven features. It is available through its GitHub repository and invites feedback and contributions through its maintainers.