No, language-agnostic NER is NER_CRF that is to train custom entity. previously the ner_crf was dependent on spacY’s tokens to be able to build a model for entity extraction. that means you will have to provide a spacy’s vectors in order to extract entities making it dependent on the languages provided by spaCy. However now, language-agnostic NER CRF actually uses whitespace tokens( tokenised by whitespace) in order to build the entity extraction model. so it does not depend on spacY anymore so you can build a NER on Klingnon if you like
Duckling is a rule based entity extractor so very system like entity extraction like Date, time, duration, distance, currency. You can also add custom dimensions in duckling for countries, cities etc etc since they all can be predefined by a set of rules unlike CRF where entities are extracted from patterns.