diff --git a/python/examples/mlir/compile_and_run.py b/python/examples/mlir/compile_and_run.py
index 345251a..d0529f6 100644
--- a/python/examples/mlir/compile_and_run.py
+++ b/python/examples/mlir/compile_and_run.py
@@ -1,5 +1,5 @@
 import torch
-import os
+import argparse
 
 from mlir import ir
 from mlir.dialects import transform
@@ -65,7 +65,8 @@ def create_schedule(ctx: ir.Context) -> ir.Module:
             func = structured.MatchOp.match_op_names(
                 named_seq.bodyTarget, ["func.func"]
             )
-            # Use C interface wrappers - required to make function executable after jitting.
+            # Use C interface wrappers - required to make function executable
+            # after jitting.
             func = transform.apply_registered_pass(
                 anytype, func, "llvm-request-c-wrappers"
             )
@@ -126,7 +127,7 @@ def create_pass_pipeline(ctx: ir.Context) -> PassManager:
 
 
 # The example's entry point.
-def main():
+def main(args):
     ### Baseline computation ###
     # Create inputs.
     a = torch.randn(16, 32, dtype=torch.float32)
@@ -149,26 +150,23 @@ def main():
     pm.run(kernel.operation)
 
     ### Compilation ###
-    # External shared libraries, containing MLIR runner utilities, are generally
-    # required to execute the compiled module.
-    # In this case, MLIR runner utils libraries are expected:
-    #   - libmlir_runner_utils.so
-    #   - libmlir_c_runner_utils.so
+    # Parse additional libraries if present.
     #
-    # Get paths to MLIR runner shared libraries through an environment variable.
+    # External shared libraries, runtime utilities, might be needed to execute
+    # the compiled module.
     # The execution engine requires full paths to the libraries.
-    # For example, the env variable can be set as:
-    #   LIGHTHOUSE_SHARED_LIBS=$PATH_TO_LLVM/build/lib/lib1.so:$PATH_TO_LLVM/build/lib/lib2.so
-    mlir_libs = os.environ.get("LIGHTHOUSE_SHARED_LIBS", default="").split(":")
+    mlir_libs = []
+    if args.shared_libs:
+        mlir_libs += args.shared_libs.split(",")
 
     # JIT the kernel.
     eng = ExecutionEngine(kernel, opt_level=2, shared_libs=mlir_libs)
 
     # Initialize the JIT engine.
     #
-    # The deferred initialization executes global constructors that might have been
-    # created by the module during engine creation (for example, when `gpu.module`
-    # is present) or registered afterwards.
+    # The deferred initialization executes global constructors that might
+    # have been created by the module during engine creation (for example,
+    # when `gpu.module` is present) or registered afterwards.
     #
     # Initialization is not strictly necessary in this case.
     # However, it is a good practice to perform it regardless.
@@ -194,4 +192,21 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    parser = argparse.ArgumentParser()
+
+    # External shared libraries, runtime utilities, might be needed to
+    # execute the compiled module.
+    # For example, MLIR runner utils libraries such as:
+    #   - libmlir_runner_utils.so
+    #   - libmlir_c_runner_utils.so
+    #
+    # Full paths to the libraries should be provided.
+    # For example:
+    #   --shared-libs=$LLVM_BUILD/lib/lib1.so,$LLVM_BUILD/lib/lib2.so
+    parser.add_argument(
+        "--shared-libs",
+        type=str,
+        help="Comma-separated list of libraries to link dynamically",
+    )
+    args = parser.parse_args()
+    main(args)