lighteval vs weave
Language model evaluation and application tracing compared
lighteval evaluates language models across tasks and supported inference backends, with sample-level results and options for custom tasks and metrics. weave focuses on tracing generative AI application calls and organizing evaluations across experimentation and production.
lighteval: Evaluate Language Models Across Backends
Lighteval is a Python toolkit for running LLM evaluations across local models and remote inference backends. It combines a broad task catalog with custom metrics and detailed sample-level results for teams comparing or debugging model performance.
weave: Trace and Evaluate Generative AI Applications
Weave helps developers trace language model inputs, outputs, and function calls, then organize and evaluate application runs. It suits teams building AI features that need debugging and repeatable comparisons across experiments and production.
| lighteval | weave | |
|---|---|---|
| Language | Python | Python |
| License | MIT | Apache-2.0 |
| Stars | 2.5k | 1.1k |
| Forks | 566 | 171 |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- lighteval provides a catalog of more than 1,000 evaluation tasks across domains and languages, while weave emphasizes tracing application execution and structured evaluations.
- lighteval supports evaluation through backends including Accelerate, Nanotron, vLLM, SGLang, and inference endpoints; weave integrates tracing into Python code with the `weave.op` decorator.
- lighteval reports an MIT license, while weave reports Apache-2.0.
- lighteval lists Windows as untested and unsupported; weave requires Python 3.10 or higher and a Weights & Biases account.
- lighteval lists 2.5k stars and 566 forks, while weave lists 1.1k stars and 171 forks.
- weave says work on its engine and boards is paused during a focus on tracing and evaluations; lighteval notes 429 open issues.
Choose lighteval if you…
- need to compare models across established tasks and supported inference backends.
- want to inspect individual evaluation samples or define custom tasks and metrics.
- work with multilingual or domain-specific evaluations represented in its task catalog.
Choose weave if you…
- need to trace inputs, outputs, and nested function calls in an AI application.
- want to compare model or prompt changes through structured evaluations.
- track workflows that combine model calls with validation and data transformations.
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This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.