feat(tests): make inference_recorder into api_recorder (include tool_invoke)

docs/source/contributing/testing/record-replay.md

@@ -58,7 +58,9 @@ recordings/
 Direct API calls with no recording or replay:
 
 ```python
-with inference_recording(mode=InferenceMode.LIVE):
+from llama_stack.testing.api_recorder import api_recording, APIRecordingMode
+
+with api_recording(mode=APIRecordingMode.LIVE):
     response = await client.chat.completions.create(...)
 ```
 
@@ -69,7 +71,7 @@ Use for initial development and debugging against real APIs.
 Captures API interactions while passing through real responses:
 
 ```python
-with inference_recording(mode=InferenceMode.RECORD, storage_dir="./recordings"):
+with api_recording(mode=APIRecordingMode.RECORD, storage_dir="./recordings"):
     response = await client.chat.completions.create(...)
     # Real API call made, response captured AND returned
 ```
@@ -86,7 +88,7 @@ The recording process:
 Returns stored responses instead of making API calls:
 
 ```python
-with inference_recording(mode=InferenceMode.REPLAY, storage_dir="./recordings"):
+with api_recording(mode=APIRecordingMode.REPLAY, storage_dir="./recordings"):
     response = await client.chat.completions.create(...)
     # No API call made, cached response returned instantly
 ```

llama_stack/core/stack.py

@@ -321,10 +321,10 @@ async def construct_stack(
     run_config: StackRunConfig, provider_registry: ProviderRegistry | None = None
 ) -> dict[Api, Any]:
     if "LLAMA_STACK_TEST_INFERENCE_MODE" in os.environ:
-        from llama_stack.testing.inference_recorder import setup_inference_recording
+        from llama_stack.testing.api_recorder import setup_api_recording
 
         global TEST_RECORDING_CONTEXT
-        TEST_RECORDING_CONTEXT = setup_inference_recording()
+        TEST_RECORDING_CONTEXT = setup_api_recording()
         if TEST_RECORDING_CONTEXT:
             TEST_RECORDING_CONTEXT.__enter__()
             logger.info(f"Inference recording enabled: mode={os.environ.get('LLAMA_STACK_TEST_INFERENCE_MODE')}")

llama_stack/testing/api_recorder.py

@@ -19,7 +19,7 @@ from llama_stack.log import get_logger
 
 logger = get_logger(__name__, category="testing")
 
-# Global state for the recording system
+# Global state for the API recording system
 _current_mode: str | None = None
 _current_storage: ResponseStorage | None = None
 _original_methods: dict[str, Any] = {}
@@ -34,7 +34,7 @@ REPO_ROOT = Path(__file__).parent.parent.parent
 DEFAULT_STORAGE_DIR = REPO_ROOT / "tests/integration/recordings"
 
 
-class InferenceMode(StrEnum):
+class APIRecordingMode(StrEnum):
     LIVE = "live"
     RECORD = "record"
     REPLAY = "replay"
@@ -53,16 +53,27 @@ def normalize_request(method: str, url: str, headers: dict[str, Any], body: dict
     return hashlib.sha256(normalized_json.encode()).hexdigest()
 
 
-def get_inference_mode() -> InferenceMode:
+def normalize_tool_request(provider_name: str, tool_name: str, kwargs: dict[str, Any]) -> str:
-    return InferenceMode(os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE", "replay").lower())
+    """Create a normalized hash of the tool request for consistent matching."""
+    normalized = {"provider": provider_name, "tool_name": tool_name, "kwargs": kwargs}
+
+    # Create hash - sort_keys=True ensures deterministic ordering
+    normalized_json = json.dumps(normalized, sort_keys=True)
+    return hashlib.sha256(normalized_json.encode()).hexdigest()
 
 
-def setup_inference_recording():
+def get_api_recording_mode() -> APIRecordingMode:
+    return APIRecordingMode(os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE", "replay").lower())
+
+
+def setup_api_recording():
     """
-    Returns a context manager that can be used to record or replay inference requests. This is to be used in tests
+    Returns a context manager that can be used to record or replay API requests (inference and tools).
-    to increase their reliability and reduce reliance on expensive, external services.
+    This is to be used in tests to increase their reliability and reduce reliance on expensive, external services.
 
-    Currently, this is only supported for OpenAI and Ollama clients. These should cover the vast majority of use cases.
+    Currently supports:
+    - Inference: OpenAI, Ollama, and LiteLLM clients
+    - Tools: Search providers (Tavily for now)
 
     Two environment variables are supported:
     - LLAMA_STACK_TEST_INFERENCE_MODE: The mode to run in. Must be 'live', 'record', or 'replay'. Default is 'replay'.
@@ -70,12 +81,12 @@ def setup_inference_recording():
 
     The recordings are stored as JSON files.
     """
-    mode = get_inference_mode()
+    mode = get_api_recording_mode()
-    if mode == InferenceMode.LIVE:
+    if mode == APIRecordingMode.LIVE:
         return None
 
     storage_dir = os.environ.get("LLAMA_STACK_TEST_RECORDING_DIR", DEFAULT_STORAGE_DIR)
-    return inference_recording(mode=mode, storage_dir=storage_dir)
+    return api_recording(mode=mode, storage_dir=storage_dir)
 
 
 def _serialize_response(response: Any) -> Any:
@@ -112,7 +123,7 @@ def _deserialize_response(data: dict[str, Any]) -> Any:
 
 
 class ResponseStorage:
-    """Handles SQLite index + JSON file storage/retrieval for inference recordings."""
+    """Handles storage/retrieval for API recordings (inference and tools)."""
 
     def __init__(self, test_dir: Path):
         self.test_dir = test_dir
@@ -243,12 +254,55 @@ def _combine_model_list_responses(endpoint: str, records: list[dict[str, Any]])
     return {"request": canonical_req, "response": {"body": body, "is_streaming": False}}
 
 
+async def _patched_tool_invoke_method(
+    original_method, provider_name: str, self, tool_name: str, kwargs: dict[str, Any]
+):
+    """Patched version of tool runtime invoke_tool method for recording/replay."""
+    global _current_mode, _current_storage
+
+    if _current_mode == APIRecordingMode.LIVE or _current_storage is None:
+        # Normal operation
+        return await original_method(self, tool_name, kwargs)
+
+    request_hash = normalize_tool_request(provider_name, tool_name, kwargs)
+
+    if _current_mode == APIRecordingMode.REPLAY:
+        recording = _current_storage.find_recording(request_hash)
+        if recording:
+            return recording["response"]["body"]
+        else:
+            raise RuntimeError(
+                f"No recorded tool result found for {provider_name}.{tool_name}\n"
+                f"Request: {kwargs}\n"
+                f"To record this response, run with LLAMA_STACK_TEST_INFERENCE_MODE=record"
+            )
+
+    elif _current_mode == APIRecordingMode.RECORD:
+        result = await original_method(self, tool_name, kwargs)
+
+        request_data = {
+            "provider": provider_name,
+            "tool_name": tool_name,
+            "kwargs": kwargs,
+        }
+        response_data = {"body": result, "is_streaming": False}
+
+        _current_storage.store_recording(request_hash, request_data, response_data)
+        return result
+
+    else:
+        raise AssertionError(f"Invalid mode: {_current_mode}")
+
+
 async def _patched_inference_method(original_method, self, client_type, endpoint, *args, **kwargs):
     global _current_mode, _current_storage
 
-    if _current_mode == InferenceMode.LIVE or _current_storage is None:
+    if _current_mode == APIRecordingMode.LIVE or _current_storage is None:
         # Normal operation
-        return await original_method(self, *args, **kwargs)
+        if client_type == "litellm":
+            return await original_method(*args, **kwargs)
+        else:
+            return await original_method(self, *args, **kwargs)
 
     # Get base URL based on client type
     if client_type == "openai":
@@ -258,6 +312,9 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
         base_url = getattr(self, "host", "http://localhost:11434")
         if not base_url.startswith("http"):
             base_url = f"http://{base_url}"
+    elif client_type == "litellm":
+        # For LiteLLM, extract base URL from kwargs if available
+        base_url = kwargs.get("api_base", "https://api.openai.com")
     else:
         raise ValueError(f"Unknown client type: {client_type}")
 
@@ -268,7 +325,7 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
 
     request_hash = normalize_request(method, url, headers, body)
 
-    if _current_mode == InferenceMode.REPLAY:
+    if _current_mode == APIRecordingMode.REPLAY:
         # Special handling for model-list endpoints: return union of all responses
         if endpoint in ("/api/tags", "/v1/models"):
             records = _current_storage._model_list_responses(request_hash[:12])
@@ -295,8 +352,11 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
                 f"To record this response, run with LLAMA_STACK_TEST_INFERENCE_MODE=record"
             )
 
-    elif _current_mode == InferenceMode.RECORD:
+    elif _current_mode == APIRecordingMode.RECORD:
-        response = await original_method(self, *args, **kwargs)
+        if client_type == "litellm":
+            response = await original_method(*args, **kwargs)
+        else:
+            response = await original_method(self, *args, **kwargs)
 
         request_data = {
             "method": method,
@@ -336,17 +396,21 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
         raise AssertionError(f"Invalid mode: {_current_mode}")
 
 
-def patch_inference_clients():
+def patch_api_clients():
-    """Install monkey patches for OpenAI client methods and Ollama AsyncClient methods."""
+    """Install monkey patches for inference clients and tool runtime methods."""
     global _original_methods
 
+    import litellm
     from ollama import AsyncClient as OllamaAsyncClient
     from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions
     from openai.resources.completions import AsyncCompletions
     from openai.resources.embeddings import AsyncEmbeddings
     from openai.resources.models import AsyncModels
 
-    # Store original methods for both OpenAI and Ollama clients
+    from llama_stack.providers.remote.tool_runtime.tavily_search.tavily_search import TavilySearchToolRuntimeImpl
+    from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
+
+    # Store original methods for OpenAI, Ollama, LiteLLM clients, and tool runtimes
     _original_methods = {
         "chat_completions_create": AsyncChatCompletions.create,
         "completions_create": AsyncCompletions.create,
@@ -358,6 +422,10 @@ def patch_inference_clients():
         "ollama_ps": OllamaAsyncClient.ps,
         "ollama_pull": OllamaAsyncClient.pull,
         "ollama_list": OllamaAsyncClient.list,
+        "litellm_acompletion": litellm.acompletion,
+        "litellm_atext_completion": litellm.atext_completion,
+        "litellm_openai_mixin_get_api_key": LiteLLMOpenAIMixin.get_api_key,
+        "tavily_invoke_tool": TavilySearchToolRuntimeImpl.invoke_tool,
     }
 
     # Create patched methods for OpenAI client
@@ -426,21 +494,61 @@ def patch_inference_clients():
     OllamaAsyncClient.pull = patched_ollama_pull
     OllamaAsyncClient.list = patched_ollama_list
 
+    # Create patched methods for LiteLLM
+    async def patched_litellm_acompletion(*args, **kwargs):
+        return await _patched_inference_method(
+            _original_methods["litellm_acompletion"], None, "litellm", "/chat/completions", *args, **kwargs
+        )
 
-def unpatch_inference_clients():
+    async def patched_litellm_atext_completion(*args, **kwargs):
-    """Remove monkey patches and restore original OpenAI and Ollama client methods."""
+        return await _patched_inference_method(
+            _original_methods["litellm_atext_completion"], None, "litellm", "/completions", *args, **kwargs
+        )
+
+    # Apply LiteLLM patches
+    litellm.acompletion = patched_litellm_acompletion
+    litellm.atext_completion = patched_litellm_atext_completion
+
+    # Create patched method for LiteLLMOpenAIMixin.get_api_key
+    def patched_litellm_get_api_key(self):
+        global _current_mode
+        if _current_mode != APIRecordingMode.REPLAY:
+            return _original_methods["litellm_openai_mixin_get_api_key"](self)
+        else:
+            # For record/replay modes, return a fake API key to avoid exposing real credentials
+            return "fake-api-key-for-testing"
+
+    # Apply LiteLLMOpenAIMixin patch
+    LiteLLMOpenAIMixin.get_api_key = patched_litellm_get_api_key
+
+    # Create patched methods for tool runtimes
+    async def patched_tavily_invoke_tool(self, tool_name: str, kwargs: dict[str, Any]):
+        return await _patched_tool_invoke_method(
+            _original_methods["tavily_invoke_tool"], "tavily", self, tool_name, kwargs
+        )
+
+    # Apply tool runtime patches
+    TavilySearchToolRuntimeImpl.invoke_tool = patched_tavily_invoke_tool
+
+
+def unpatch_api_clients():
+    """Remove monkey patches and restore original client methods and tool runtimes."""
     global _original_methods
 
     if not _original_methods:
         return
 
     # Import here to avoid circular imports
+    import litellm
     from ollama import AsyncClient as OllamaAsyncClient
     from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions
     from openai.resources.completions import AsyncCompletions
     from openai.resources.embeddings import AsyncEmbeddings
     from openai.resources.models import AsyncModels
 
+    from llama_stack.providers.remote.tool_runtime.tavily_search.tavily_search import TavilySearchToolRuntimeImpl
+    from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
+
     # Restore OpenAI client methods
     AsyncChatCompletions.create = _original_methods["chat_completions_create"]
     AsyncCompletions.create = _original_methods["completions_create"]
@@ -455,12 +563,20 @@ def unpatch_inference_clients():
     OllamaAsyncClient.pull = _original_methods["ollama_pull"]
     OllamaAsyncClient.list = _original_methods["ollama_list"]
 
+    # Restore LiteLLM methods
+    litellm.acompletion = _original_methods["litellm_acompletion"]
+    litellm.atext_completion = _original_methods["litellm_atext_completion"]
+    LiteLLMOpenAIMixin.get_api_key = _original_methods["litellm_openai_mixin_get_api_key"]
+
+    # Restore tool runtime methods
+    TavilySearchToolRuntimeImpl.invoke_tool = _original_methods["tavily_invoke_tool"]
+
     _original_methods.clear()
 
 
 @contextmanager
-def inference_recording(mode: str, storage_dir: str | Path | None = None) -> Generator[None, None, None]:
+def api_recording(mode: str, storage_dir: str | Path | None = None) -> Generator[None, None, None]:
-    """Context manager for inference recording/replaying."""
+    """Context manager for API recording/replaying (inference and tools)."""
     global _current_mode, _current_storage
 
     # Store previous state
@@ -474,14 +590,14 @@ def inference_recording(mode: str, storage_dir: str | Path | None = None) -> Gen
             if storage_dir is None:
                 raise ValueError("storage_dir is required for record and replay modes")
             _current_storage = ResponseStorage(Path(storage_dir))
-            patch_inference_clients()
+            patch_api_clients()
 
         yield
 
     finally:
         # Restore previous state
         if mode in ["record", "replay"]:
-            unpatch_inference_clients()
+            unpatch_api_clients()
 
         _current_mode = prev_mode
         _current_storage = prev_storage

tests/integration/recordings/responses/010508a236f2.json

@@ -0,0 +1,740 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.openai.com/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "gpt-4o",
+      "messages": [
+        {
+          "role": "user",
+          "content": "I need a complete analysis: First, get the experiment ID for 'chemical_reaction', then get the results for that experiment, and tell me if the yield was above 80%. Return only one tool call per step.  Please stream your analysis process."
+        },
+        {
+          "role": "assistant",
+          "content": "",
+          "tool_calls": [
+            {
+              "index": 0,
+              "id": "call_wZBYO2S9hJYjPA0o5EPKyMDz",
+              "type": "function",
+              "function": {
+                "name": "get_experiment_id",
+                "arguments": "{\"experiment_name\":\"chemical_reaction\"}"
+              }
+            }
+          ]
+        },
+        {
+          "role": "tool",
+          "tool_call_id": "call_wZBYO2S9hJYjPA0o5EPKyMDz",
+          "content": [
+            {
+              "type": "text",
+              "text": "exp_003"
+            }
+          ]
+        },
+        {
+          "role": "assistant",
+          "content": "",
+          "tool_calls": [
+            {
+              "index": 0,
+              "id": "call_vkTAElUtms5rFGEgx3ShB7sC",
+              "type": "function",
+              "function": {
+                "name": "get_experiment_results",
+                "arguments": "{\"experiment_id\":\"exp_003\"}"
+              }
+            }
+          ]
+        },
+        {
+          "role": "tool",
+          "tool_call_id": "call_vkTAElUtms5rFGEgx3ShB7sC",
+          "content": [
+            {
+              "type": "text",
+              "text": "Yield: 85%, Status: Complete"
+            }
+          ]
+        }
+      ],
+      "response_format": {
+        "type": "text"
+      },
+      "stream": true,
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "get_user_id",
+            "description": "\n        Get the user ID for a given username. This ID is needed for other operations.\n\n        :param username: The username to look up\n        :return: The user ID for the username\n        ",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "username": {
+                  "type": "string"
+                }
+              },
+              "required": [
+                "username"
+              ]
+            }
+          }
+        },
+        {
+          "type": "function",
+          "function": {
+            "name": "get_user_permissions",
+            "description": "\n        Get the permissions for a user ID. Requires a valid user ID from get_user_id.\n\n        :param user_id: The user ID to check permissions for\n        :return: The permissions for the user\n        ",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "user_id": {
+                  "type": "string"
+                }
+              },
+              "required": [
+                "user_id"
+              ]
+            }
+          }
+        },
+        {
+          "type": "function",
+          "function": {
+            "name": "check_file_access",
+            "description": "\n        Check if a user can access a specific file. Requires a valid user ID.\n\n        :param user_id: The user ID to check access for\n        :param filename: The filename to check access to\n        :return: Whether the user can access the file (yes/no)\n        ",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "user_id": {
+                  "type": "string"
+                },
+                "filename": {
+                  "type": "string"
+                }
+              },
+              "required": [
+                "user_id",
+                "filename"
+              ]
+            }
+          }
+        },
+        {
+          "type": "function",
+          "function": {
+            "name": "get_experiment_id",
+            "description": "\n        Get the experiment ID for a given experiment name. This ID is needed to get results.\n\n        :param experiment_name: The name of the experiment\n        :return: The experiment ID\n        ",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "experiment_name": {
+                  "type": "string"
+                }
+              },
+              "required": [
+                "experiment_name"
+              ]
+            }
+          }
+        },
+        {
+          "type": "function",
+          "function": {
+            "name": "get_experiment_results",
+            "description": "\n        Get the results for an experiment ID. Requires a valid experiment ID from get_experiment_id.\n\n        :param experiment_id: The experiment ID to get results for\n        :return: The experiment results\n        ",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "experiment_id": {
+                  "type": "string"
+                }
+              },
+              "required": [
+                "experiment_id"
+              ]
+            }
+          }
+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "gpt-4o"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-CEJ4TrgD3BIScDl2XbaNgc3Ea5oeI",
+          "choices": [
+            {
+              "delta": {
+                "content": "",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1757525937,
+          "model": "gpt-4o-2024-08-06",
+          "object": "chat.completion.chunk",
+          "service_tier": "default",
+          "system_fingerprint": "fp_cbf1785567",
+          "usage": null,
+          "obfuscation": "V6qbyeuaPmU"
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-CEJ4TrgD3BIScDl2XbaNgc3Ea5oeI",
+          "choices": [
+            {
+              "delta": {
+                "content": "The",
+                "function_call": null,
+                "refusal": null,
+                "role": null,
+                "tool_calls": null
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1757525937,
+          "model": "gpt-4o-2024-08-06",
+          "object": "chat.completion.chunk",
+          "service_tier": "default",
+          "system_fingerprint": "fp_cbf1785567",
+          "usage": null,
+          "obfuscation": "ZQKXNmax4A"
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-CEJ4TrgD3BIScDl2XbaNgc3Ea5oeI",
+          "choices": [
+            {
+              "delta": {
+                "content": " experiment",
+                "function_call": null,
+                "refusal": null,
+                "role": null,
+                "tool_calls": null
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1757525937,
+          "model": "gpt-4o-2024-08-06",
+          "object": "chat.completion.chunk",
+          "service_tier": "default",
+          "system_fingerprint": "fp_cbf1785567",
+          "usage": null,
+          "obfuscation": "Mp"
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-CEJ4TrgD3BIScDl2XbaNgc3Ea5oeI",
+          "choices": [
+            {
+              "delta": {
+                "content": " results",
+                "function_call": null,
+                "refusal": null,
+                "role": null,
+                "tool_calls": null
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 1757525937,
+          "model": "gpt-4o-2024-08-06",
+          "object": "chat.completion.chunk",
+          "service_tier": "default",
+          "system_fingerprint": "fp_cbf1785567",
+          "usage": null,
+          "obfuscation": "4LIBE"
+        }
+      },
+      {
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+}

tests/integration/recordings/responses/1212bee16156.json

@@ -0,0 +1,730 @@
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+    "headers": {},
+    "body": {
+      "model": "gpt-4o",
+      "messages": [
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+          "role": "user",
+          "content": "I need to check if user 'alice' can access the file 'document.txt'. First, get alice's user ID, then check if that user ID can access the file 'document.txt'. Do this as a series of steps, where each step is a separate message. Return only one tool call per step. Summarize the final result with a single 'yes' or 'no' response."
+        },
+        {
+          "role": "assistant",
+          "content": "",
+          "tool_calls": [
+            {
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+              "type": "function",
+              "function": {
+                "name": "get_user_id",
+                "arguments": "{\"username\":\"alice\"}"
+              }
+            }
+          ]
+        },
+        {
+          "role": "tool",
+          "tool_call_id": "call_fVDVzVtVnZdhj9dN7WuG8lHp",
+          "content": [
+            {
+              "type": "text",
+              "text": "user_12345"
+            }
+          ]
+        }
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+      "response_format": {
+        "type": "text"
+      },
+      "stream": true,
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "get_user_id",
+            "description": "\n        Get the user ID for a given username. This ID is needed for other operations.\n\n        :param username: The username to look up\n        :return: The user ID for the username\n        ",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "username": {
+                  "type": "string"
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+              },
+              "required": [
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+        },
+        {
+          "type": "function",
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+            "description": "\n        Get the permissions for a user ID. Requires a valid user ID from get_user_id.\n\n        :param user_id: The user ID to check permissions for\n        :return: The permissions for the user\n        ",
+            "parameters": {
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tests/integration/recordings/responses/257e85e44508.json

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tests/integration/recordings/responses/3dea1bc17d40.json

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tests/integration/recordings/responses/3e2eb2f91354.json

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tests/integration/recordings/responses/50f013d13436.json

@@ -0,0 +1,724 @@
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tests/integration/recordings/responses/54aa690e31b5.json

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+    "tool_name": "web_search",
+    "kwargs": {
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tests/integration/recordings/responses/586972869156.json

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+          "content": "",
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+            }
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+              "properties": {
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tests/integration/recordings/responses/5ab326cbc496.json

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tests/integration/recordings/responses/5c39c8fd2a2d.json

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tests/integration/recordings/responses/5d7902c7a058.json

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tests/integration/recordings/responses/5e4b68e1056a.json

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tests/integration/recordings/responses/60c9dd312fdc.json

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tests/integration/recordings/responses/7542def0bde4.json

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tests/integration/recordings/responses/79ecb13fe698.json

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tests/integration/recordings/responses/7afcb562daec.json

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+          "service_tier": "default",
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+}

tests/integration/recordings/responses/7bbddaa0bca8.json

@@ -0,0 +1,372 @@
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+    "method": "POST",
+    "url": "https://api.openai.com/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "gpt-4o",
+      "messages": [
+        {
+          "role": "user",
+          "content": "I need to check if user 'alice' can access the file 'document.txt'. First, get alice's user ID, then check if that user ID can access the file 'document.txt'. Do this as a series of steps, where each step is a separate message. Return only one tool call per step. Summarize the final result with a single 'yes' or 'no' response."
+        }
+      ],
+      "response_format": {
+        "type": "text"
+      },
+      "stream": true,
+      "tools": [
+        {
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+          "function": {
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+            "parameters": {
+              "type": "object",
+              "properties": {
+                "username": {
+                  "type": "string"
+                }
+              },
+              "required": [
+                "username"
+              ]
+            }
+          }
+        },
+        {
+          "type": "function",
+          "function": {
+            "name": "get_user_permissions",
+            "description": "\n        Get the permissions for a user ID. Requires a valid user ID from get_user_id.\n\n        :param user_id: The user ID to check permissions for\n        :return: The permissions for the user\n        ",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "user_id": {
+                  "type": "string"
+                }
+              },
+              "required": [
+                "user_id"
+              ]
+            }
+          }
+        },
+        {
+          "type": "function",
+          "function": {
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+            "parameters": {
+              "type": "object",
+              "properties": {
+                "user_id": {
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+                "filename": {
+                  "type": "string"
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+              },
+              "required": [
+                "user_id",
+                "filename"
+              ]
+            }
+          }
+        },
+        {
+          "type": "function",
+          "function": {
+            "name": "get_experiment_id",
+            "description": "\n        Get the experiment ID for a given experiment name. This ID is needed to get results.\n\n        :param experiment_name: The name of the experiment\n        :return: The experiment ID\n        ",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "experiment_name": {
+                  "type": "string"
+                }
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+              "required": [
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+          }
+        },
+        {
+          "type": "function",
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+            "description": "\n        Get the results for an experiment ID. Requires a valid experiment ID from get_experiment_id.\n\n        :param experiment_id: The experiment ID to get results for\n        :return: The experiment results\n        ",
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+              "type": "object",
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+}

tests/integration/recordings/responses/8bf2a56d90c7.json

@@ -0,0 +1,630 @@
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+    "body": {
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+      "messages": [
+        {
+          "role": "user",
+          "content": "How many experts does the Llama 4 Maverick model have?"
+        }
+      ],
+      "response_format": {
+        "type": "text"
+      },
+      "stream": true,
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "web_search",
+            "description": "Search the web for information",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "query": {
+                  "type": "string",
+                  "description": "The query to search for"
+                }
+              },
+              "required": [
+                "query"
+              ]
+            }
+          }
+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "gpt-4o"
+  },
+  "response": {
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tests/integration/recordings/responses/9ecfae0efac2.json

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tests/integration/recordings/responses/a355096141ec.json

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tests/integration/recordings/responses/acc7ee4234d9.json

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+}

tests/integration/recordings/responses/b82e02325e1a.json

@@ -0,0 +1,508 @@
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+          "content": "I need a complete analysis: First, get the experiment ID for 'chemical_reaction', then get the results for that experiment, and tell me if the yield was above 80%. Return only one tool call per step.  Please stream your analysis process."
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tests/integration/recordings/responses/b8870267241e.json

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+}

tests/integration/recordings/responses/c636d1760cc8.json

@@ -0,0 +1,254 @@
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+    "body": {
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+        {
+          "role": "user",
+          "content": "I need to check if user 'alice' can access the file 'document.txt'. First, get alice's user ID, then check if that user ID can access the file 'document.txt'. Do this as a series of steps, where each step is a separate message. Return only one tool call per step. Summarize the final result with a single 'yes' or 'no' response."
+        },
+        {
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+          "content": "",
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+              "text": "user_12345"
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+                "name": "check_file_access",
+                "arguments": "{\"user_id\":\"user_12345\",\"filename\":\"document.txt\"}"
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+        {
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+          "tool_call_id": "call_CBax0ct4ujremPv3wjGFCX7O",
+          "content": [
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+            "name": "get_user_id",
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+              "type": "object",
+              "properties": {
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+              "properties": {
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+}

tests/integration/recordings/responses/e8ac11c788dd.json

@@ -0,0 +1,613 @@
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+    "headers": {},
+    "body": {
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+      "messages": [
+        {
+          "role": "user",
+          "content": "How many experts does the Llama 4 Maverick model have?"
+        },
+        {
+          "role": "assistant",
+          "content": "",
+          "tool_calls": [
+            {
+              "index": 0,
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+              "type": "function",
+              "function": {
+                "name": "web_search",
+                "arguments": "{\"query\":\"Llama 4 Maverick model number of experts\"}"
+              }
+            }
+          ]
+        },
+        {
+          "role": "tool",
+          "tool_call_id": "call_319QZhydsGa9KKOaVafYDMO2",
+          "content": "Pass Search provider's API Key in the header X-LlamaStack-Provider-Data as { \"tavily_search_api_key\": <your api key>}"
+        }
+      ],
+      "response_format": {
+        "type": "text"
+      },
+      "stream": true,
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "web_search",
+            "description": "Search the web for information",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "query": {
+                  "type": "string",
+                  "description": "The query to search for"
+                }
+              },
+              "required": [
+                "query"
+              ]
+            }
+          }
+        }
+      ]
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+    "endpoint": "/v1/chat/completions",
+    "model": "gpt-4o"
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tests/integration/recordings/responses/ef88c2d492d7.json

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+          "service_tier": "default",
+          "system_fingerprint": "fp_cbf1785567",
+          "usage": null,
+          "obfuscation": "KchtKrHvUMRMtOv"
+        }
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+          ],
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+          "model": "gpt-4o-2024-08-06",
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+          "usage": null,
+          "obfuscation": "2"
+        }
+      }
+    ],
+    "is_streaming": true
+  }
+}

tests/integration/recordings/responses/fe2b30bf875d.json

@@ -0,0 +1,579 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.openai.com/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "gpt-4o",
+      "messages": [
+        {
+          "role": "user",
+          "content": "What is the boiling point of myawesomeliquid in Celsius?"
+        },
+        {
+          "role": "assistant",
+          "content": "",
+          "tool_calls": [
+            {
+              "index": 0,
+              "id": "call_rDWnMcQ5w1C1O5hSCUtTIelJ",
+              "type": "function",
+              "function": {
+                "name": "get_boiling_point",
+                "arguments": "{\"liquid_name\":\"myawesomeliquid\",\"celsius\":true}"
+              }
+            }
+          ]
+        },
+        {
+          "role": "tool",
+          "tool_call_id": "call_rDWnMcQ5w1C1O5hSCUtTIelJ",
+          "content": [
+            {
+              "type": "text",
+              "text": "-100"
+            }
+          ]
+        }
+      ],
+      "response_format": {
+        "type": "text"
+      },
+      "stream": true,
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "greet_everyone",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "url": {
+                  "type": "string"
+                }
+              },
+              "required": [
+                "url"
+              ]
+            }
+          }
+        },
+        {
+          "type": "function",
+          "function": {
+            "name": "get_boiling_point",
+            "description": "\n        Returns the boiling point of a liquid in Celsius or Fahrenheit.\n\n        :param liquid_name: The name of the liquid\n        :param celsius: Whether to return the boiling point in Celsius\n        :return: The boiling point of the liquid in Celcius or Fahrenheit\n        ",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "liquid_name": {
+                  "type": "string"
+                },
+                "celsius": {
+                  "type": "boolean"
+                }
+              },
+              "required": [
+                "liquid_name",
+                "celsius"
+              ]
+            }
+          }
+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "gpt-4o"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-CEJ3DKY5J0Uge5WyEuYu14fNYflIA",
+          "choices": [
+            {
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+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
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+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
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+          "created": 1757525859,
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+          "object": "chat.completion.chunk",
+          "service_tier": "default",
+          "system_fingerprint": "fp_1827dd0c55",
+          "usage": null,
+          "obfuscation": "yJL6aNlSHvM"
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "chatcmpl-CEJ3DKY5J0Uge5WyEuYu14fNYflIA",
+          "choices": [
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+        }
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+        }
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+        }
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+        }
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+        }
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+          "model": "gpt-4o-2024-08-06",
+          "object": "chat.completion.chunk",
+          "service_tier": "default",
+          "system_fingerprint": "fp_1827dd0c55",
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+      },
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+        "__data__": {
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+              "finish_reason": "stop",
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+          ],
+          "created": 1757525859,
+          "model": "gpt-4o-2024-08-06",
+          "object": "chat.completion.chunk",
+          "service_tier": "default",
+          "system_fingerprint": "fp_1827dd0c55",
+          "usage": null,
+          "obfuscation": "h0Yaot1"
+        }
+      }
+    ],
+    "is_streaming": true
+  }
+}

tests/unit/distribution/test_api_recordings.py

@@ -20,10 +20,10 @@ from llama_stack.apis.inference import (
     OpenAIEmbeddingsResponse,
     OpenAIEmbeddingUsage,
 )
-from llama_stack.testing.inference_recorder import (
+from llama_stack.testing.api_recorder import (
-    InferenceMode,
+    APIRecordingMode,
     ResponseStorage,
-    inference_recording,
+    api_recording,
     normalize_request,
 )
 
@@ -159,7 +159,7 @@ class TestInferenceRecording:
 
         temp_storage_dir = temp_storage_dir / "test_recording_mode"
         with patch("openai.resources.chat.completions.AsyncCompletions.create", side_effect=mock_create):
-            with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)):
+            with api_recording(mode=APIRecordingMode.RECORD, storage_dir=str(temp_storage_dir)):
                 client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
 
                 response = await client.chat.completions.create(
@@ -185,7 +185,7 @@ class TestInferenceRecording:
         temp_storage_dir = temp_storage_dir / "test_replay_mode"
         # First, record a response
         with patch("openai.resources.chat.completions.AsyncCompletions.create", side_effect=mock_create):
-            with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)):
+            with api_recording(mode=APIRecordingMode.RECORD, storage_dir=str(temp_storage_dir)):
                 client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
 
                 response = await client.chat.completions.create(
@@ -197,7 +197,7 @@ class TestInferenceRecording:
 
         # Now test replay mode - should not call the original method
         with patch("openai.resources.chat.completions.AsyncCompletions.create") as mock_create_patch:
-            with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)):
+            with api_recording(mode=APIRecordingMode.REPLAY, storage_dir=str(temp_storage_dir)):
                 client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
 
                 response = await client.chat.completions.create(
@@ -217,7 +217,7 @@ class TestInferenceRecording:
         """Test that replay mode fails when no recording is found."""
         temp_storage_dir = temp_storage_dir / "test_replay_missing_recording"
         with patch("openai.resources.chat.completions.AsyncCompletions.create"):
-            with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)):
+            with api_recording(mode=APIRecordingMode.REPLAY, storage_dir=str(temp_storage_dir)):
                 client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
 
                 with pytest.raises(RuntimeError, match="No recorded response found"):
@@ -234,7 +234,7 @@ class TestInferenceRecording:
         temp_storage_dir = temp_storage_dir / "test_embeddings_recording"
         # Record
         with patch("openai.resources.embeddings.AsyncEmbeddings.create", side_effect=mock_create):
-            with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)):
+            with api_recording(mode=APIRecordingMode.RECORD, storage_dir=str(temp_storage_dir)):
                 client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
 
                 response = await client.embeddings.create(
@@ -245,7 +245,7 @@ class TestInferenceRecording:
 
         # Replay
         with patch("openai.resources.embeddings.AsyncEmbeddings.create") as mock_create_patch:
-            with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)):
+            with api_recording(mode=APIRecordingMode.REPLAY, storage_dir=str(temp_storage_dir)):
                 client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
 
                 response = await client.embeddings.create(
@@ -266,7 +266,7 @@ class TestInferenceRecording:
             return real_openai_chat_response
 
         with patch("openai.resources.chat.completions.AsyncCompletions.create", side_effect=mock_create):
-            with inference_recording(mode=InferenceMode.LIVE, storage_dir="foo"):
+            with api_recording(mode=APIRecordingMode.LIVE, storage_dir="foo"):
                 client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
 
                 response = await client.chat.completions.create(