expect-llm: Test LLM Outputs in Your Existing Runner
expect-llm provides inline expect matchers for LLM outputs in Jest or Vitest, featuring zero dependencies and a tiny 1.41 kB footprint.
Stock photo for illustration only, not from the actual event
- expect-llm introduces expect matchers tailored for common LLM output errors directly in your test runner.
- Features 8 matchers in total, including 7 deterministic checks and 1 opt-in subjective judge.
- Boasts zero runtime dependencies, ESM/CJS and type support, and runs on Node >= 18.
- Weighs approximately 1.41 kB (min+brotli) and integrates smoothly with coerce-json workflows.
Integrating large language models (LLMs) into production software often brings a familiar hurdle: asserting against unstructured or semi-structured model outputs. Responses typically return as JSON objects wrapped in markdown fences, alongside extra phrases that standard unit testing assertions struggle to evaluate cleanly.
While adopting specialized evaluation frameworks like promptfoo, DeepEval, Braintrust, or Evalite is great for comprehensive eval suites, setting up a separate CLI, configuration files, and external mental models can feel heavy for a simple assertion inside an existing Vitest or Jest workflow.
Stock photo for illustration only, not from the actual event
The expect-llm library solves this by providing a set of expect(...) matchers built specifically for the failure modes of LLM responses. Developers can register the matchers once and run inline assertions directly inside the testing framework they already use.
Key technical specifications of expect-llm include:
- Zero runtime dependencies with bundled ESM, CJS, and type definitions.
- Compatible with Node.js version 18 and above.
- Full support for the
.notmodifier across every matcher. - Lightweight design totaling roughly 1.41 kB.
Out of the eight available matchers, seven operate deterministically. Tools like toBeValidJSON and toMatchSchema automatically parse strings and handle code fences, allowing developers to assert on exactly what the model returned without manual pre-cleaning.
Embedding LLM output validation directly into standard test runners like Jest or Vitest significantly streamlines developer workflows. By eliminating the need to context-switch into external evaluation dashboards or CLIs for basic assertions, engineering teams can maintain a unified mental model and enforce AI reliability using familiar testing habits.
For subjective validations—such as determining whether a model output constitutes a polite refusal—expect-llm provides the toSatisfy matcher. Built around a bring-your-model philosophy, it ships with no default SDK, API key handlers, or provider lock-in, letting developers pass custom model calls directly.
Furthermore, expect-llm acts as the assertion layer within a broader ecosystem of zero-dependency LLM development tools. It pairs effectively with utilities like coerce-json, which repairs near-valid model outputs to fit schemas before validation, ensuring seamless structured-output testing pipelines.
Source: Dev.to
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