Cuey
Freemium
Power users comparing multiple LLMs daily
You ship your agent in English, run your evals, everything passes — then a French-speaking user triggers a completely different tool-calling pattern and you find out in production. Langdrift is a locale-aware eval harness built to catch that class of failure before it reaches users.
Langdrift runs your agent prompts across multiple locales and compares behavior — checking whether tool calls, response structure, and decision paths stay consistent when the input language changes. The core problem it addresses is language-induced behavior drift: the same logical request, rephrased in German or Japanese, producing a different agent output than the English baseline. It fits cleanly into CI pipelines where you need deterministic, repeatable checks across locale variants. The project is built and maintained by a single developer, Rubén González, which means the feature surface reflects a focused scope — not a product roadmap backed by a team.
Bottom line: Langdrift earns its place in a CI pipeline for any team shipping agents to multilingual users; it does not replace a full eval framework when your team needs cross-model benchmarking, dataset management, or hosted result dashboards.
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Freemium
Power users comparing multiple LLMs daily
Free · Open source
Researchers tracking verifiable AI capability trends
Locale consistency is one of the least-tested dimensions of agent behavior — most eval harnesses treat language as a cosmetic variable, not a behavioral one. Langdrift treats locale as a first-class test axis: you define expected agent behaviors and tool-call patterns once, then the harness runs those expectations across a set of locale-specific prompt variants, flagging wherever the agent diverges from baseline. The workflow follows the pattern of writing locale-annotated test cases, executing them against your agent, and collecting structured diff output that identifies where language choice changed the outcome.
The differentiating design decision is that Langdrift is built specifically around locale-induced drift rather than general agent correctness. Generic eval frameworks will let you write multilingual tests manually, but they give you no tooling for the comparison logic — you end up maintaining your own diffing layer. Langdrift bakes that comparison in, so the eval output directly answers the question: did this locale produce a different behavior, and where.
For teams running CI regression checks, Langdrift’s self-hosted, open-source structure means it installs into an existing pipeline without a hosted dependency or API key requirement. The tradeoff is scope: there is no result dashboard, no dataset versioning, no model-routing layer. Teams that outgrow the harness — because they need cross-model comparisons, large-scale benchmark datasets, or collaborative result review — will find themselves building wrapper tooling or migrating to a broader eval platform. The project is maintained by one developer, so feature velocity and long-term support carry the risks that come with solo-maintained open-source software.
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