Add Natural Memory architecture and tooling

natural_memory_app.py

@@ -0,0 +1,54 @@
+"""Unified entry point for Natural Memory v2 local production workflows.
+
+Examples:
+  python -m dynamic_memory_lab.natural_memory_app chat --model-path ...
+  python -m dynamic_memory_lab.natural_memory_app serve --port 8765
+  python -m dynamic_memory_lab.natural_memory_app build-dataset
+  python -m dynamic_memory_lab.natural_memory_app train-policy --steps 240
+  python -m dynamic_memory_lab.natural_memory_app stress --rounds 40
+  python -m dynamic_memory_lab.natural_memory_app make-mega-validation
+  python -m dynamic_memory_lab.natural_memory_app benchmark-kv --limit 32
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "command",
+        choices=(
+            "chat",
+            "serve",
+            "build-dataset",
+            "train-policy",
+            "stress",
+            "make-mega-validation",
+            "benchmark-kv",
+        ),
+        help="workflow to run; remaining arguments are passed to that workflow",
+    )
+    args, remaining = parser.parse_known_args()
+    sys.argv = [sys.argv[0], *remaining]
+    if args.command == "chat":
+        from .stream_chat_qwen_memory import main as run
+    elif args.command == "serve":
+        from .natural_memory_service import main as run
+    elif args.command == "build-dataset":
+        from .build_production_memory_dataset import main as run
+    elif args.command == "train-policy":
+        from .train_production_memory_policy import main as run
+    elif args.command == "stress":
+        from .stress_test_natural_memory import main as run
+    elif args.command == "make-mega-validation":
+        from .make_mega_memory_validation import main as run
+    else:
+        from .benchmark_memory_vs_full_kv import main as run
+    run()
+
+
+if __name__ == "__main__":
+    main()