Cltk

CLTK's core strength is breadth with a single interface. Instead of stitching together one model per language, you install one library and annotate Latin, Ancient Greek, Old Norse, Sanskrit, Classical Chinese, Coptic, and ancient Arabic through the same pipeline. The 2.0 rewrite matters because it swaps per-language trained models for generative LLM backends — OpenAI, Mistral, or local Ollama — so POS tagging and dependency parsing work on under-resourced texts without a training run. The pluggable backend design is the smartest part: route to a cloud API when you want accuracy and convenience, or point at Ollama when confidentiality or cost makes cloud calls a non-starter. Install is genuinely one command with optional extras, and the CLI means you don't have to live inside a Python session for every job. Documentation lives at docs.cltk.org and the source is on GitHub. The honest weaknesses: it is a library, not an app — there is no GUI or web interface, so non-programmers are shut out. You also supply the compute or the key; nothing runs without either an API credential or a local model, and local inference brings its own hardware demands. Scope is deliberately pre-modern Eurasia, so languages of the Americas or sub-Saharan Africa are out. Legacy 1.x and 0.x versions are unsupported, though preserved for reproducibility. Where it fits: research groups, digital humanities labs, and graduate students comparing annotation accuracy across generative models, and anyone building digital editions at scale. Where it doesn't: production real-time NLP services, teams without Python skills, or anyone who needs a point-and-click tool.