test: recordable mocks use json only (#1443)

# Summary:
removes the use of pickle

# Test Plan:
Run the following with `--record-responses` first, then another time
without.

LLAMA_STACK_CONFIG=fireworks pytest -s -v
tests/integration/agents/test_agents.py --safety-shield
meta-llama/Llama-Guard-3-8B --text-model
meta-llama/Llama-3.1-8B-Instruct

tests/integration/fixtures/common.py

@@ -59,7 +59,7 @@ def llama_stack_client_with_mocked_inference(llama_stack_client, request):
         return llama_stack_client
 
     record_responses = request.config.getoption("--record-responses")
-    cache_dir = Path(__file__).parent / "fixtures" / "recorded_responses"
+    cache_dir = Path(__file__).parent / "recorded_responses"
 
     # Create a shallow copy of the client to avoid modifying the original
     client = copy.copy(llama_stack_client)

tests/integration/fixtures/recordable_mock.py

@@ -3,10 +3,12 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+import importlib
 import json
 import os
-import pickle
 import re
+from datetime import datetime
+from enum import Enum
 from pathlib import Path
 
 
@@ -15,18 +17,18 @@ class RecordableMock:
 
     def __init__(self, real_func, cache_dir, func_name, record=False):
         self.real_func = real_func
-        self.pickle_path = Path(cache_dir) / f"{func_name}.pickle"
         self.json_path = Path(cache_dir) / f"{func_name}.json"
         self.record = record
         self.cache = {}
 
         # Load existing cache if available and not recording
-        if self.pickle_path.exists():
+        if self.json_path.exists():
             try:
-                with open(self.pickle_path, "rb") as f:
+                with open(self.json_path, "r") as f:
-                    self.cache = pickle.load(f)
+                    self.cache = json.load(f)
             except Exception as e:
-                print(f"Error loading cache from {self.pickle_path}: {e}")
+                print(f"Error loading cache from {self.json_path}: {e}")
+                raise
 
     async def __call__(self, *args, **kwargs):
         """
@@ -98,23 +100,19 @@ class RecordableMock:
             # Check if it's a value or chunks
             if cached_data.get("type") == "value":
                 # It's a regular value
-                return cached_data["value"]
+                return self._reconstruct_object(cached_data["value"])
             else:
                 # It's chunks from an async generator
                 async def replay_generator():
                     for chunk in cached_data["chunks"]:
-                        yield chunk
+                        yield self._reconstruct_object(chunk)
 
                 return replay_generator()
 
     def _create_cache_key(self, args, kwargs):
         """Create a hashable key from the function arguments, ignoring auto-generated IDs."""
-        # Convert args and kwargs to a string representation directly
+        # Convert to JSON strings with sorted keys
-        args_str = str(args)
+        key = json.dumps((args, kwargs), sort_keys=True, default=self._json_default)
-        kwargs_str = str(sorted([(k, kwargs[k]) for k in kwargs]))
-
-        # Combine into a single key
-        key = f"{args_str}_{kwargs_str}"
 
         # Post-process the key with regex to replace IDs with placeholders
         # Replace UUIDs and similar patterns
@@ -126,83 +124,95 @@ class RecordableMock:
         return key
 
     def _save_cache(self):
-        """Save the cache to disk in both pickle and JSON formats."""
+        """Save the cache to disk in JSON format."""
-        os.makedirs(self.pickle_path.parent, exist_ok=True)
+        os.makedirs(self.json_path.parent, exist_ok=True)
 
-        # Save as pickle for exact object preservation
+        # Write the JSON file with pretty formatting
-        with open(self.pickle_path, "wb") as f:
-            pickle.dump(self.cache, f)
-
-        # Also save as JSON for human readability and diffing
         try:
-            # Create a simplified version of the cache for JSON
-            json_cache = {}
-            for key, value in self.cache.items():
-                if value.get("type") == "generator":
-                    # For generators, create a simplified representation of each chunk
-                    chunks = []
-                    for chunk in value["chunks"]:
-                        chunk_dict = self._object_to_json_safe_dict(chunk)
-                        chunks.append(chunk_dict)
-                    json_cache[key] = {"type": "generator", "chunks": chunks}
-                else:
-                    # For values, create a simplified representation
-                    val = value["value"]
-                    val_dict = self._object_to_json_safe_dict(val)
-                    json_cache[key] = {"type": "value", "value": val_dict}
-
-            # Write the JSON file with pretty formatting
             with open(self.json_path, "w") as f:
-                json.dump(json_cache, f, indent=2, sort_keys=True)
+                json.dump(self.cache, f, indent=2, sort_keys=True, default=self._json_default)
+                # write another empty line at the end of the file to make pre-commit happy
+                f.write("\n")
         except Exception as e:
             print(f"Error saving JSON cache: {e}")
 
-    def _object_to_json_safe_dict(self, obj):
+    def _json_default(self, obj):
-        """Convert an object to a JSON-safe dictionary."""
+        """Default function for JSON serialization of objects."""
-        # Handle enum types
+
-        if hasattr(obj, "value") and hasattr(obj.__class__, "__members__"):
+        if isinstance(obj, datetime):
-            return {"__enum__": obj.__class__.__name__, "value": obj.value}
+            return {
+                "__datetime__": obj.isoformat(),
+                "__module__": obj.__class__.__module__,
+                "__class__": obj.__class__.__name__,
+            }
+
+        if isinstance(obj, Enum):
+            return {
+                "__enum__": obj.__class__.__name__,
+                "value": obj.value,
+                "__module__": obj.__class__.__module__,
+            }
 
         # Handle Pydantic models
         if hasattr(obj, "model_dump"):
-            return self._process_dict(obj.model_dump())
+            model_data = obj.model_dump()
-        elif hasattr(obj, "dict"):
+            return {
-            return self._process_dict(obj.dict())
+                "__pydantic__": obj.__class__.__name__,
+                "__module__": obj.__class__.__module__,
+                "data": model_data,
+            }
 
-        # Handle regular objects with __dict__
+    def _reconstruct_object(self, data):
-        try:
+        """Reconstruct an object from its JSON representation."""
-            return self._process_dict(vars(obj))
+        if isinstance(data, dict):
-        except Exception as e:
+            # Check if this is a serialized datetime
-            print(f"Error converting object to JSON-safe dict: {e}")
+            if "__datetime__" in data:
-            # If we can't get a dict, convert to string
+                try:
-            return str(obj)
+                    module_name = data.get("__module__", "datetime")
+                    class_name = data.get("__class__", "datetime")
 
-    def _process_dict(self, d):
+                    # Try to import the specific datetime class
-        """Process a dictionary to make all values JSON-safe."""
+                    module = importlib.import_module(module_name)
-        if not isinstance(d, dict):
+                    dt_class = getattr(module, class_name)
-            return d
 
-        result = {}
+                    # Parse the ISO format string
-        for k, v in d.items():
+                    dt = dt_class.fromisoformat(data["__datetime__"])
-            if isinstance(v, dict):
+                    return dt
-                result[k] = self._process_dict(v)
+                except (ImportError, AttributeError, ValueError) as e:
-            elif isinstance(v, list):
+                    print(f"Error reconstructing datetime: {e}")
-                result[k] = [
+                    return data
-                    self._process_dict(item)
-                    if isinstance(item, dict)
-                    else self._object_to_json_safe_dict(item)
-                    if hasattr(item, "__dict__")
-                    else item
-                    for item in v
-                ]
-            elif hasattr(v, "value") and hasattr(v.__class__, "__members__"):
-                # Handle enum
-                result[k] = {"__enum__": v.__class__.__name__, "value": v.value}
-            elif hasattr(v, "__dict__"):
-                # Handle nested objects
-                result[k] = self._object_to_json_safe_dict(v)
-            else:
-                # Basic types
-                result[k] = v
 
-        return result
+            # Check if this is a serialized enum
+            elif "__enum__" in data:
+                try:
+                    module_name = data.get("__module__", "builtins")
+                    enum_class = self._import_class(module_name, data["__enum__"])
+                    return enum_class(data["value"])
+                except (ImportError, AttributeError) as e:
+                    print(f"Error reconstructing enum: {e}")
+                    return data
+
+            # Check if this is a serialized Pydantic model
+            elif "__pydantic__" in data:
+                try:
+                    module_name = data.get("__module__", "builtins")
+                    model_class = self._import_class(module_name, data["__pydantic__"])
+                    return model_class(**self._reconstruct_object(data["data"]))
+                except (ImportError, AttributeError) as e:
+                    print(f"Error reconstructing Pydantic model: {e}")
+                    return data
+
+            # Regular dictionary
+            return {k: self._reconstruct_object(v) for k, v in data.items()}
+
+        # Handle lists
+        elif isinstance(data, list):
+            return [self._reconstruct_object(item) for item in data]
+
+        # Return primitive types as is
+        return data
+
+    def _import_class(self, module_name, class_name):
+        """Import a class from a module."""
+        module = __import__(module_name, fromlist=[class_name])
+        return getattr(module, class_name)