diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py
index 11e5bc8..efb9936 100644
--- a/llama_cpp/llama_cpp.py
+++ b/llama_cpp/llama_cpp.py
@@ -1157,6 +1157,24 @@ _lib.llama_sample_temperature.argtypes = [
 _lib.llama_sample_temperature.restype = None
 
 
+# /// @details Apply constraints from grammar
+# LLAMA_API void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar);
+def llama_sample_grammar(
+    ctx: llama_context_p,
+    candidates,  # type: _Pointer[llama_token_data_array]
+    grammar: llama_grammar_p,
+):
+    return _lib.llama_sample_grammar(ctx, candidates, grammar)
+
+
+_lib.llama_sample_grammar.argtypes = [
+    llama_context_p,
+    llama_token_data_array_p,
+    llama_grammar_p,
+]
+_lib.llama_sample_grammar.restype = None
+
+
 # @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
 # @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
 # @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.