diff --git a/cognee/tests/test_permissions_example.py b/cognee/tests/test_permissions_example.py
new file mode 100644
index 000000000..ae833f8da
--- /dev/null
+++ b/cognee/tests/test_permissions_example.py
@@ -0,0 +1,122 @@
+import asyncio
+import os
+import pathlib
+
+import pytest
+import pytest_asyncio
+from unittest.mock import AsyncMock, patch
+
+import cognee
+from cognee.context_global_variables import backend_access_control_enabled
+from cognee.modules.engine.operations.setup import setup as engine_setup
+from cognee.modules.search.types import SearchType
+from cognee.modules.users.exceptions import PermissionDeniedError
+from cognee.modules.users.methods import create_user, get_user
+from cognee.modules.users.permissions.methods import authorized_give_permission_on_datasets
+from cognee.modules.users.roles.methods import add_user_to_role, create_role
+from cognee.modules.users.tenants.methods import (
+    add_user_to_tenant,
+    create_tenant,
+    select_tenant,
+)
+
+pytestmark = pytest.mark.asyncio
+
+
+def _extract_dataset_id_from_cognify(cognify_result: dict):
+    """Extract dataset_id from cognify output dictionary."""
+    for dataset_id, _pipeline_result in cognify_result.items():
+        return dataset_id
+    return None
+
+
+async def _reset_engines_and_prune() -> None:
+    """Reset db engine caches and prune data/system."""
+    try:
+        from cognee.infrastructure.databases.vector import get_vector_engine
+
+        vector_engine = get_vector_engine()
+        if hasattr(vector_engine, "engine") and hasattr(vector_engine.engine, "dispose"):
+            await vector_engine.engine.dispose(close=True)
+    except Exception:
+        pass
+
+    from cognee.infrastructure.databases.graph.get_graph_engine import create_graph_engine
+    from cognee.infrastructure.databases.relational.create_relational_engine import (
+        create_relational_engine,
+    )
+    from cognee.infrastructure.databases.vector.create_vector_engine import create_vector_engine
+
+    create_graph_engine.cache_clear()
+    create_vector_engine.cache_clear()
+    create_relational_engine.cache_clear()
+
+    await cognee.prune.prune_data()
+    await cognee.prune.prune_system(metadata=True)
+
+
+@pytest.fixture(scope="module")
+def event_loop():
+    """Single event loop for this module (avoids cross-loop futures)."""
+    loop = asyncio.new_event_loop()
+    try:
+        yield loop
+    finally:
+        loop.close()
+
+
+@pytest_asyncio.fixture(scope="module")
+async def permissions_example_env(tmp_path_factory):
+    """One-time environment setup for the permissions example test."""
+    # Ensure permissions feature is enabled (example requires it), but don't override if caller set it already.
+    os.environ.setdefault("ENABLE_BACKEND_ACCESS_CONTROL", "True")
+
+    root = tmp_path_factory.mktemp("permissions_example")
+    cognee.config.data_root_directory(str(root / "data"))
+    cognee.config.system_root_directory(str(root / "system"))
+
+    await _reset_engines_and_prune()
+    await engine_setup()
+
+    assert backend_access_control_enabled(), (
+        "Expected permissions to be enabled via ENABLE_BACKEND_ACCESS_CONTROL=True"
+    )
+
+    yield
+
+    await _reset_engines_and_prune()
+
+
+async def test_permissions_example_flow(permissions_example_env):
+    """Pytest version of `examples/python/permissions_example.py` (same scenarios, asserts instead of prints)."""
+    # Patch LLM calls so GRAPH_COMPLETION can run without external API keys.
+    llm_patch = patch(
+        "cognee.infrastructure.llm.LLMGateway.LLMGateway.acreate_structured_output",
+        new_callable=AsyncMock,
+        return_value="MOCK_ANSWER",
+    )
+
+    # Resolve example data file path (repo-shipped PDF).
+    repo_root = pathlib.Path(__file__).resolve().parent
+    explanation_file_path = str(repo_root / "test_data" / "artificial-intelligence.pdf")
+    assert pathlib.Path(explanation_file_path).exists(), (
+        f"Expected example PDF to exist at {explanation_file_path}"
+    )
+
+    # Same QUANTUM text as in the example.
+    text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena.
+    At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages
+    this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the
+    preparation and manipulation of quantum states.
+    """
+
+    # Create user_1, add AI dataset.
+    user_1 = await create_user("user_1@example.com", "example")
+    await cognee.add([explanation_file_path], dataset_name="AI", user=user_1)
+
+    # Create user_2, add QUANTUM dataset.
+    user_2 = await create_user("user_2@example.com", "example")
+    await cognee.add([text], dataset_name="QUANTUM", user=user_2)
+
+    ai_cognify_result = await cognee.cognify(["AI"], user=user_1)
+    quantum_cognify_result = await cognee.cognify(["QUANTUM"], user=user_2)
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