Merged from main + fixed elasticsearch_url

tests/integration/agents/test_openai_responses.py

@@ -516,3 +516,169 @@ def test_response_with_instructions(openai_client, client_with_models, text_mode
 
     # Verify instructions from previous response was not carried over to the next response
     assert response_with_instructions2.instructions == instructions2
+
+
+@pytest.mark.skip(reason="Tool calling is not reliable.")
+def test_max_tool_calls_with_function_tools(openai_client, client_with_models, text_model_id):
+    """Test handling of max_tool_calls with function tools in responses."""
+    if isinstance(client_with_models, LlamaStackAsLibraryClient):
+        pytest.skip("OpenAI responses are not supported when testing with library client yet.")
+
+    client = openai_client
+    max_tool_calls = 1
+
+    tools = [
+        {
+            "type": "function",
+            "name": "get_weather",
+            "description": "Get weather information for a specified location",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city name (e.g., 'New York', 'London')",
+                    },
+                },
+            },
+        },
+        {
+            "type": "function",
+            "name": "get_time",
+            "description": "Get current time for a specified location",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city name (e.g., 'New York', 'London')",
+                    },
+                },
+            },
+        },
+    ]
+
+    # First create a response that triggers function tools
+    response = client.responses.create(
+        model=text_model_id,
+        input="Can you tell me the weather in Paris and the current time?",
+        tools=tools,
+        stream=False,
+        max_tool_calls=max_tool_calls,
+    )
+
+    # Verify we got two function calls and that the max_tool_calls do not affect function tools
+    assert len(response.output) == 2
+    assert response.output[0].type == "function_call"
+    assert response.output[0].name == "get_weather"
+    assert response.output[0].status == "completed"
+    assert response.output[1].type == "function_call"
+    assert response.output[1].name == "get_time"
+    assert response.output[0].status == "completed"
+
+    # Verify we have a valid max_tool_calls field
+    assert response.max_tool_calls == max_tool_calls
+
+
+def test_max_tool_calls_invalid(openai_client, client_with_models, text_model_id):
+    """Test handling of invalid max_tool_calls in responses."""
+    if isinstance(client_with_models, LlamaStackAsLibraryClient):
+        pytest.skip("OpenAI responses are not supported when testing with library client yet.")
+
+    client = openai_client
+
+    input = "Search for today's top technology news."
+    invalid_max_tool_calls = 0
+    tools = [
+        {"type": "web_search"},
+    ]
+
+    # Create a response with an invalid max_tool_calls value i.e. 0
+    # Handle ValueError from LLS and BadRequestError from OpenAI client
+    with pytest.raises((ValueError, BadRequestError)) as excinfo:
+        client.responses.create(
+            model=text_model_id,
+            input=input,
+            tools=tools,
+            stream=False,
+            max_tool_calls=invalid_max_tool_calls,
+        )
+
+    error_message = str(excinfo.value)
+    assert f"Invalid max_tool_calls={invalid_max_tool_calls}; should be >= 1" in error_message, (
+        f"Expected error message about invalid max_tool_calls, got: {error_message}"
+    )
+
+
+def test_max_tool_calls_with_builtin_tools(openai_client, client_with_models, text_model_id):
+    """Test handling of max_tool_calls with built-in tools in responses."""
+    if isinstance(client_with_models, LlamaStackAsLibraryClient):
+        pytest.skip("OpenAI responses are not supported when testing with library client yet.")
+
+    client = openai_client
+
+    input = "Search for today's top technology and a positive news story. You MUST make exactly two separate web search calls."
+    max_tool_calls = [1, 5]
+    tools = [
+        {"type": "web_search"},
+    ]
+
+    # First create a response that triggers web_search tools without max_tool_calls
+    response = client.responses.create(
+        model=text_model_id,
+        input=input,
+        tools=tools,
+        stream=False,
+    )
+
+    # Verify we got two web search calls followed by a message
+    assert len(response.output) == 3
+    assert response.output[0].type == "web_search_call"
+    assert response.output[0].status == "completed"
+    assert response.output[1].type == "web_search_call"
+    assert response.output[1].status == "completed"
+    assert response.output[2].type == "message"
+    assert response.output[2].status == "completed"
+    assert response.output[2].role == "assistant"
+
+    # Next create a response that triggers web_search tools with max_tool_calls set to 1
+    response_2 = client.responses.create(
+        model=text_model_id,
+        input=input,
+        tools=tools,
+        stream=False,
+        max_tool_calls=max_tool_calls[0],
+    )
+
+    # Verify we got one web search tool call followed by a message
+    assert len(response_2.output) == 2
+    assert response_2.output[0].type == "web_search_call"
+    assert response_2.output[0].status == "completed"
+    assert response_2.output[1].type == "message"
+    assert response_2.output[1].status == "completed"
+    assert response_2.output[1].role == "assistant"
+
+    # Verify we have a valid max_tool_calls field
+    assert response_2.max_tool_calls == max_tool_calls[0]
+
+    # Finally create a response that triggers web_search tools with max_tool_calls set to 5
+    response_3 = client.responses.create(
+        model=text_model_id,
+        input=input,
+        tools=tools,
+        stream=False,
+        max_tool_calls=max_tool_calls[1],
+    )
+
+    # Verify we got two web search calls followed by a message
+    assert len(response_3.output) == 3
+    assert response_3.output[0].type == "web_search_call"
+    assert response_3.output[0].status == "completed"
+    assert response_3.output[1].type == "web_search_call"
+    assert response_3.output[1].status == "completed"
+    assert response_3.output[2].type == "message"
+    assert response_3.output[2].status == "completed"
+    assert response_3.output[2].role == "assistant"
+
+    # Verify we have a valid max_tool_calls field
+    assert response_3.max_tool_calls == max_tool_calls[1]

tests/integration/batches/conftest.py

@@ -14,7 +14,7 @@ from io import BytesIO
 
 import pytest
 
-from llama_stack.apis.files import OpenAIFilePurpose
+from llama_stack_api import OpenAIFilePurpose
 
 
 class BatchHelper:

tests/integration/ci_matrix.json

@@ -1,6 +1,7 @@
 {
   "default": [
     {"suite": "base", "setup": "ollama"},
+    {"suite": "base", "setup": "ollama-postgres", "allowed_clients": ["server"], "stack_config": "server:ci-tests::run-with-postgres-store.yaml"},
     {"suite": "vision", "setup": "ollama-vision"},
     {"suite": "responses", "setup": "gpt"},
     {"suite": "base-vllm-subset", "setup": "vllm"}

tests/integration/files/test_files.py

@@ -10,8 +10,8 @@ from unittest.mock import patch
 import pytest
 import requests
 
-from llama_stack.apis.files import OpenAIFilePurpose
 from llama_stack.core.datatypes import User
+from llama_stack_api import OpenAIFilePurpose
 
 purpose = OpenAIFilePurpose.ASSISTANTS
 
@@ -175,7 +175,7 @@ def test_expires_after_requests(openai_client):
 
 
 @pytest.mark.xfail(message="User isolation broken for current providers, must be fixed.")
-@patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user")
+@patch("llama_stack.core.storage.sqlstore.authorized_sqlstore.get_authenticated_user")
 def test_files_authentication_isolation(mock_get_authenticated_user, llama_stack_client):
     """Test that users can only access their own files."""
     from llama_stack_client import NotFoundError
@@ -275,7 +275,7 @@ def test_files_authentication_isolation(mock_get_authenticated_user, llama_stack
         raise e
 
 
-@patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user")
+@patch("llama_stack.core.storage.sqlstore.authorized_sqlstore.get_authenticated_user")
 def test_files_authentication_shared_attributes(
     mock_get_authenticated_user, llama_stack_client, provider_type_is_openai
 ):
@@ -335,7 +335,7 @@ def test_files_authentication_shared_attributes(
         raise e
 
 
-@patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user")
+@patch("llama_stack.core.storage.sqlstore.authorized_sqlstore.get_authenticated_user")
 def test_files_authentication_anonymous_access(
     mock_get_authenticated_user, llama_stack_client, provider_type_is_openai
 ):

tests/integration/fixtures/common.py

@@ -233,10 +233,21 @@ def instantiate_llama_stack_client(session):
         raise ValueError("You must specify either --stack-config or LLAMA_STACK_CONFIG")
 
     # Handle server:<config_name> format or server:<config_name>:<port>
+    # Also handles server:<distro>::<run_file.yaml> format
     if config.startswith("server:"):
-        parts = config.split(":")
-        config_name = parts[1]
-        port = int(parts[2]) if len(parts) > 2 else int(os.environ.get("LLAMA_STACK_PORT", DEFAULT_PORT))
+        # Strip the "server:" prefix first
+        config_part = config[7:]  # len("server:") == 7
+
+        # Check for :: (distro::runfile format)
+        if "::" in config_part:
+            config_name = config_part
+            port = int(os.environ.get("LLAMA_STACK_PORT", DEFAULT_PORT))
+        else:
+            # Single colon format: either <name> or <name>:<port>
+            parts = config_part.split(":")
+            config_name = parts[0]
+            port = int(parts[1]) if len(parts) > 1 else int(os.environ.get("LLAMA_STACK_PORT", DEFAULT_PORT))
+
         base_url = f"http://localhost:{port}"
 
         force_restart = os.environ.get("LLAMA_STACK_TEST_FORCE_SERVER_RESTART") == "1"
@@ -323,7 +334,13 @@ def require_server(llama_stack_client):
 @pytest.fixture(scope="session")
 def openai_client(llama_stack_client, require_server):
     base_url = f"{llama_stack_client.base_url}/v1"
-    return OpenAI(base_url=base_url, api_key="fake")
+    client = OpenAI(base_url=base_url, api_key="fake", max_retries=0, timeout=30.0)
+    yield client
+    # Cleanup: close HTTP connections
+    try:
+        client.close()
+    except Exception:
+        pass
 
 
 @pytest.fixture(params=["openai_client", "client_with_models"])

tests/integration/inference/test_openai_completion.py

@@ -54,6 +54,7 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id)
         # {"error":{"message":"Unknown request URL: GET /openai/v1/completions. Please check the URL for typos,
         # or see the docs at https://console.groq.com/docs/","type":"invalid_request_error","code":"unknown_url"}}
         "remote::groq",
+        "remote::oci",
         "remote::gemini",  # https://generativelanguage.googleapis.com/v1beta/openai/completions -> 404
         "remote::anthropic",  # at least claude-3-{5,7}-{haiku,sonnet}-* / claude-{sonnet,opus}-4-* are not supported
         "remote::azure",  # {'error': {'code': 'OperationNotSupported', 'message': 'The completion operation

tests/integration/inference/test_openai_embeddings.py

@@ -138,6 +138,7 @@ def skip_if_model_doesnt_support_openai_embeddings(client, model_id):
         "remote::runpod",
         "remote::sambanova",
         "remote::tgi",
+        "remote::oci",
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI embeddings.")
 

tests/integration/inference/test_provider_data_routing.py

@@ -16,15 +16,15 @@ from unittest.mock import AsyncMock, patch
 
 import pytest
 
-from llama_stack.apis.datatypes import Api
-from llama_stack.apis.inference.inference import (
+from llama_stack.core.library_client import LlamaStackAsLibraryClient
+from llama_stack.core.telemetry.telemetry import MetricEvent
+from llama_stack_api import (
+    Api,
     OpenAIAssistantMessageParam,
     OpenAIChatCompletion,
     OpenAIChatCompletionUsage,
     OpenAIChoice,
 )
-from llama_stack.core.library_client import LlamaStackAsLibraryClient
-from llama_stack.core.telemetry.telemetry import MetricEvent
 
 
 class OpenAIChatCompletionWithMetrics(OpenAIChatCompletion):

tests/integration/inference/test_tools_with_schemas.py

@@ -9,8 +9,6 @@ Integration tests for inference/chat completion with JSON Schema-based tools.
 Tests that tools pass through correctly to various LLM providers.
 """
 
-import json
-
 import pytest
 
 from llama_stack.core.library_client import LlamaStackAsLibraryClient
@@ -193,15 +191,11 @@ class TestMCPToolsInChatCompletion:
             mcp_endpoint=dict(uri=uri),
         )
 
-        provider_data = {"mcp_headers": {uri: {"Authorization": f"Bearer {AUTH_TOKEN}"}}}
-        auth_headers = {
-            "X-LlamaStack-Provider-Data": json.dumps(provider_data),
-        }
-
+        # Use the dedicated authorization parameter
         # Get the tools from MCP
         tools_response = llama_stack_client.tool_runtime.list_tools(
             tool_group_id=test_toolgroup_id,
-            extra_headers=auth_headers,
+            authorization=AUTH_TOKEN,
         )
 
         # Convert to OpenAI format for inference

tests/integration/post_training/test_post_training.py

@@ -10,7 +10,8 @@ import uuid
 
 import pytest
 
-from llama_stack.apis.post_training import (
+from llama_stack.log import get_logger
+from llama_stack_api import (
     DataConfig,
     DatasetFormat,
     DPOAlignmentConfig,
@@ -18,7 +19,6 @@ from llama_stack.apis.post_training import (
     LoraFinetuningConfig,
     TrainingConfig,
 )
-from llama_stack.log import get_logger
 
 # Configure logging
 logger = get_logger(name=__name__, category="post_training")

tests/integration/providers/utils/sqlstore/test_authorized_sqlstore.py

@@ -13,14 +13,14 @@ import pytest
 from llama_stack.core.access_control.access_control import default_policy
 from llama_stack.core.datatypes import User
 from llama_stack.core.storage.datatypes import SqlStoreReference
-from llama_stack.providers.utils.sqlstore.api import ColumnType
-from llama_stack.providers.utils.sqlstore.authorized_sqlstore import AuthorizedSqlStore
-from llama_stack.providers.utils.sqlstore.sqlstore import (
+from llama_stack.core.storage.sqlstore.authorized_sqlstore import AuthorizedSqlStore
+from llama_stack.core.storage.sqlstore.sqlstore import (
     PostgresSqlStoreConfig,
     SqliteSqlStoreConfig,
     register_sqlstore_backends,
     sqlstore_impl,
 )
+from llama_stack_api.internal.sqlstore import ColumnType
 
 
 def get_postgres_config():
@@ -96,7 +96,7 @@ async def cleanup_records(sql_store, table_name, record_ids):
 
 
 @pytest.mark.parametrize("backend_config", BACKEND_CONFIGS)
-@patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user")
+@patch("llama_stack.core.storage.sqlstore.authorized_sqlstore.get_authenticated_user")
 async def test_authorized_store_attributes(mock_get_authenticated_user, authorized_store, request):
     """Test that JSON column comparisons work correctly for both PostgreSQL and SQLite"""
     backend_name = request.node.callspec.id
@@ -190,7 +190,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz
 
 
 @pytest.mark.parametrize("backend_config", BACKEND_CONFIGS)
-@patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user")
+@patch("llama_stack.core.storage.sqlstore.authorized_sqlstore.get_authenticated_user")
 async def test_user_ownership_policy(mock_get_authenticated_user, authorized_store, request):
     """Test that 'user is owner' policies work correctly with record ownership"""
     from llama_stack.core.access_control.datatypes import AccessRule, Action, Scope

tests/integration/recordings/README.md

@@ -2,6 +2,10 @@
 
 This directory contains recorded inference API responses used for deterministic testing without requiring live API access.
 
+For more information, see the
+[docs](https://llamastack.github.io/docs/contributing/testing/record-replay).
+This README provides more technical information.
+
 ## Structure
 
 - `responses/` - JSON files containing request/response pairs for inference operations

tests/integration/responses/conftest.py

@@ -0,0 +1,17 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import pytest
+
+from llama_stack.core.library_client import LlamaStackAsLibraryClient
+
+
+@pytest.fixture
+def responses_client(compat_client):
+    """Provide a client for responses tests, skipping library client mode."""
+    if isinstance(compat_client, LlamaStackAsLibraryClient):
+        pytest.skip("Responses API tests are not supported in library client mode")
+    return compat_client

tests/integration/responses/fixtures/fixtures.py

@@ -115,7 +115,15 @@ def openai_client(base_url, api_key, provider):
         client = LlamaStackAsLibraryClient(config, skip_logger_removal=True)
         return client
 
-    return OpenAI(
+    client = OpenAI(
         base_url=base_url,
         api_key=api_key,
+        max_retries=0,
+        timeout=30.0,
     )
+    yield client
+    # Cleanup: close HTTP connections
+    try:
+        client.close()
+    except Exception:
+        pass

tests/integration/responses/recordings/0a4aca0cd075369aaf6133ee82d9d940455cb083c0fd1330c666a12d74df6f89.json

@@ -0,0 +1,549 @@
+{
+  "test_id": "tests/integration/responses/test_tool_responses.py::test_response_streaming_multi_turn_tool_execution[openai_client-txt=openai/gpt-4o-experiment_analysis_streaming]",
+  "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_Q9Gcxub7UbQsxJWVkiy4FETr",
+              "type": "function",
+              "function": {
+                "name": "get_experiment_id",
+                "arguments": "{\"experiment_name\":\"chemical_reaction\"}"
+              }
+            }
+          ]
+        },
+        {
+          "role": "tool",
+          "tool_call_id": "call_Q9Gcxub7UbQsxJWVkiy4FETr",
+          "content": [
+            {
+              "type": "text",
+              "text": "exp_003"
+            }
+          ]
+        }
+      ],
+      "stream": true,
+      "stream_options": {
+        "include_usage": 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": {
+              "properties": {
+                "username": {
+                  "title": "Username",
+                  "type": "string"
+                }
+              },
+              "required": [
+                "username"
+              ],
+              "title": "get_user_idArguments",
+              "type": "object"
+            }
+          }
+        },
+        {
+          "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": {
+              "properties": {
+                "user_id": {
+                  "title": "User Id",
+                  "type": "string"
+                }
+              },
+              "required": [
+                "user_id"
+              ],
+              "title": "get_user_permissionsArguments",
+              "type": "object"
+            }
+          }
+        },
+        {
+          "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": {
+              "properties": {
+                "user_id": {
+                  "title": "User Id",
+                  "type": "string"
+                },
+                "filename": {
+                  "title": "Filename",
+                  "type": "string"
+                }
+              },
+              "required": [
+                "user_id",
+                "filename"
+              ],
+              "title": "check_file_accessArguments",
+              "type": "object"
+            }
+          }
+        },
+        {
+          "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": {
+              "properties": {
+                "experiment_name": {
+                  "title": "Experiment Name",
+                  "type": "string"
+                }
+              },
+              "required": [
+                "experiment_name"
+              ],
+              "title": "get_experiment_idArguments",
+              "type": "object"
+            }
+          }
+        },
+        {
+          "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": {
+              "properties": {
+                "experiment_id": {
+                  "title": "Experiment Id",
+                  "type": "string"
+                }
+              },
+              "required": [
+                "experiment_id"
+              ],
+              "title": "get_experiment_resultsArguments",
+              "type": "object"
+            }
+          }
+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "gpt-4o"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "rec-0a4aca0cd075",
+          "choices": [
+            {
+              "delta": {
+                "content": null,
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": [
+                  {
+                    "index": 0,
+                    "id": "call_yTMuQEKu7x115q8XvhqelRub",
+                    "function": {
+                      "arguments": "",
+                      "name": "get_experiment_results"
+                    },
+                    "type": "function"
+                  }
+                ]
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 0,
+          "model": "gpt-4o-2024-08-06",
+          "object": "chat.completion.chunk",
+          "service_tier": "default",
+          "system_fingerprint": "fp_cbf1785567",
+          "usage": null,
+          "obfuscation": "9CSOZwfG5M7nid"
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
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tests/integration/responses/recordings/2bd4c8dc08b3ee3ffce696864f0bd9f35d82223c7d1cab613ab2e818d79d6f9b.json

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+              "required": [
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+              "title": "check_file_accessArguments",
+              "type": "object"
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tests/integration/responses/recordings/2ed23a4289840f93202f94e7e7027118869d34d768ad87ba072e92e8a43a52f2.json

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+          "content": "Help me with this security check: First, get the user ID for 'charlie', then get the permissions for that user ID, and finally check if that user can access 'secret_file.txt'. Stream your progress as you work through each step. 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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+          "content": [
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+              "text": "no"
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tests/integration/responses/recordings/3177a984c900c2bdc2785b502bded6791b1054ce0f36e967eb3793b5608344f3.json

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tests/integration/responses/recordings/318c5361647df0245c074cd2c7d6f50e862aeddbbeaeb256ef1add34de7c1dc8.json

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+              "required": [
+                "user_id"
+              ],
+              "title": "get_user_permissionsArguments",
+              "type": "object"
+            }
+          }
+        },
+        {
+          "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": {
+              "properties": {
+                "user_id": {
+                  "title": "User Id",
+                  "type": "string"
+                },
+                "filename": {
+                  "title": "Filename",
+                  "type": "string"
+                }
+              },
+              "required": [
+                "user_id",
+                "filename"
+              ],
+              "title": "check_file_accessArguments",
+              "type": "object"
+            }
+          }
+        },
+        {
+          "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": {
+              "properties": {
+                "experiment_name": {
+                  "title": "Experiment Name",
+                  "type": "string"
+                }
+              },
+              "required": [
+                "experiment_name"
+              ],
+              "title": "get_experiment_idArguments",
+              "type": "object"
+            }
+          }
+        },
+        {
+          "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": {
+              "properties": {
+                "experiment_id": {
+                  "title": "Experiment Id",
+                  "type": "string"
+                }
+              },
+              "required": [
+                "experiment_id"
+              ],
+              "title": "get_experiment_resultsArguments",
+              "type": "object"
+            }
+          }
+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "gpt-4o"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "rec-318c5361647d",
+          "choices": [
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+                "role": "assistant",
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+                    "id": "call_skNUKbERbtdoADH834U9OE91",
+                    "function": {
+                      "arguments": "",
+                      "name": "get_experiment_results"
+                    },
+                    "type": "function"
+                  }
+                ]
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 0,
+          "model": "gpt-4o-2024-08-06",
+          "object": "chat.completion.chunk",
+          "service_tier": "default",
+          "system_fingerprint": "fp_cbf1785567",
+          "usage": null,
+          "obfuscation": "5aHvu2xes6Amy8"
+        }
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+          "usage": null,
+          "obfuscation": "ckAh5OXg9JIe"
+        }
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+          ],
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+        }
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+        }
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+          "model": "gpt-4o-2024-08-06",
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+          "service_tier": "default",
+          "system_fingerprint": "fp_cbf1785567",
+          "usage": null,
+          "obfuscation": "rnph"
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "rec-318c5361647d",
+          "choices": [],
+          "created": 0,
+          "model": "gpt-4o-2024-08-06",
+          "object": "chat.completion.chunk",
+          "service_tier": "default",
+          "system_fingerprint": "fp_cbf1785567",
+          "usage": {
+            "completion_tokens": 19,
+            "prompt_tokens": 450,
+            "total_tokens": 469,
+            "completion_tokens_details": {
+              "accepted_prediction_tokens": 0,
+              "audio_tokens": 0,
+              "reasoning_tokens": 0,
+              "rejected_prediction_tokens": 0
+            },
+            "prompt_tokens_details": {
+              "audio_tokens": 0,
+              "cached_tokens": 0
+            }
+          },
+          "obfuscation": "nUptVmnQlQZrH"
+        }
+      }
+    ],
+    "is_streaming": true
+  },
+  "id_normalization_mapping": {}
+}

tests/integration/responses/recordings/42c357284497af596ae6c9341b0c189daa31e88b25d0381a985f24203b7a5a38.json

@@ -10,7 +10,7 @@
   },
   "response": {
     "body": {
-      "__type__": "llama_stack.apis.tools.tools.ToolInvocationResult",
+      "__type__": "llama_stack_api.tools.ToolInvocationResult",
       "__data__": {
         "content": "{\"query\": \"Llama 4 Maverick model experts\", \"top_k\": [{\"url\": \"https://console.groq.com/docs/model/meta-llama/llama-4-maverick-17b-128e-instruct\", \"title\": \"Llama 4 Maverick 17B 128E\", \"content\": \"Llama 4 Maverick is Meta's natively multimodal model that enables text and image understanding. With a 17 billion parameter mixture-of-experts architecture (128 experts), this model offers industry-leading performance for multimodal tasks like natural assistant-like chat, image recognition, and coding tasks. Llama 4 Maverick features an auto-regressive language model that uses a mixture-of-experts (MoE) architecture with 17B activated parameters (400B total) and incorporates early fusion for native multimodality. The model uses 128 experts to efficiently handle both text and image inputs while maintaining high performance across chat, knowledge, and code generation tasks, with a knowledge cutoff of August 2024. * For multimodal applications, this model supports up to 5 image inputs create(  model =\\\"meta-llama/llama-4-maverick-17b-128e-instruct\\\",   messages =[  {  \\\"role\\\":  \\\"user\\\",   \\\"content\\\":  \\\"Explain why fast inference is critical for reasoning models\\\"   }   ]  )  print(completion.\", \"score\": 0.9170729, \"raw_content\": null}, {\"url\": \"https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E\", \"title\": \"meta-llama/Llama-4-Maverick-17B-128E - Hugging Face\", \"content\": \"Model Architecture: The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate\", \"score\": 0.8021998, \"raw_content\": null}, {\"url\": \"https://www.ibm.com/new/announcements/meta-llama-4-maverick-and-llama-4-scout-now-available-in-watsonx-ai\", \"title\": \"Meta Llama 4 Maverick and Llama 4 Scout now available in watsonx ...\", \"content\": \"# Meta Llama 4 Maverick and Llama 4 Scout now available in watsonx.ai **IBM is excited to announce the addition of Meta\\u2019s latest generation of open models, Llama 4, to** **watsonx.ai****.** Llama 4 Scout and Llama 4 Maverick, the first mixture of experts (MoE) models released by Meta, provide frontier multimodal performance, high speeds, low cost, and industry leading context length. With the introduction of these latest offerings from Meta, IBM now supports a total of 13 Meta models in the expansive library of \\u00a0foundation models available in watsonx.ai. Trained on 40 trillion tokens of data, Llama 4 Scout offers performance rivalling or exceeding that of models with significantly larger active parameter counts while keeping costs and latency low. ## Llama 4 models on IBM watsonx\", \"score\": 0.78194773, \"raw_content\": null}, {\"url\": \"https://medium.com/@divyanshbhatiajm19/metas-llama-4-family-the-complete-guide-to-scout-maverick-and-behemoth-ai-models-in-2025-21a90c882e8a\", \"title\": \"Meta's Llama 4 Family: The Complete Guide to Scout, Maverick, and ...\", \"content\": \"# Meta\\u2019s Llama 4 Family: The Complete Guide to Scout, Maverick, and Behemoth AI Models in 2025 Feature Llama 4 Scout Llama 4 Maverick Llama 4 Behemoth **Total Parameters** 109B 400B ~2T **Active Parameters** 17B 17B 288B **Expert Count** 16 128 16 **Context Window** 10M tokens 1M tokens Not specified **Hardware Requirements** Single H100 GPU Single H100 DGX host Multiple GPUs **Inference Cost** Not specified $0.19-$0.49 per 1M tokens Not specified **Release Status** Available now Available now In training **Primary Use Cases** Long-context analysis, code processing High-performance multimodal applications Research, STEM reasoning The Llama 4 family represents Meta\\u2019s most significant AI development to date, with each model offering distinct advantages for different use cases:\", \"score\": 0.69672287, \"raw_content\": null}, {\"url\": \"https://www.llama.com/models/llama-4/\", \"title\": \"Unmatched Performance and Efficiency | Llama 4\", \"content\": \"# Llama 4 # Llama 4 Llama 4 Scout Class-leading natively multimodal model that offers superior text and visual intelligence, single H100 GPU efficiency, and a 10M context window for seamless long document analysis. Llama 4 MaverickIndustry-leading natively multimodal model for image and text understanding with groundbreaking intelligence and fast responses at a low cost. We evaluated model performance on a suite of common benchmarks across a wide range of languages, testing for coding, reasoning, knowledge, vision understanding, multilinguality, and long context. 4. Specialized long context evals are not traditionally reported for generalist models, so we share internal runs to showcase llama's frontier performance. 4. Specialized long context evals are not traditionally reported for generalist models, so we share internal runs to showcase llama's frontier performance.\", \"score\": 0.629889, \"raw_content\": null}]}",
         "error_message": null,

tests/integration/responses/recordings/430a49246c97c29bd958f383627f53ec795fd77ef818827e16691689151bf17c.json

@@ -0,0 +1,413 @@
+{
+  "test_id": "tests/integration/responses/test_tool_responses.py::test_response_non_streaming_multi_turn_tool_execution[openai_client-txt=openai/gpt-4o-user_file_access_check]",
+  "request": {
+    "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."
+        }
+      ],
+      "stream": true,
+      "stream_options": {
+        "include_usage": 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": {
+              "properties": {
+                "username": {
+                  "title": "Username",
+                  "type": "string"
+                }
+              },
+              "required": [
+                "username"
+              ],
+              "title": "get_user_idArguments",
+              "type": "object"
+            }
+          }
+        },
+        {
+          "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": {
+              "properties": {
+                "user_id": {
+                  "title": "User Id",
+                  "type": "string"
+                }
+              },
+              "required": [
+                "user_id"
+              ],
+              "title": "get_user_permissionsArguments",
+              "type": "object"
+            }
+          }
+        },
+        {
+          "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": {
+              "properties": {
+                "user_id": {
+                  "title": "User Id",
+                  "type": "string"
+                },
+                "filename": {
+                  "title": "Filename",
+                  "type": "string"
+                }
+              },
+              "required": [
+                "user_id",
+                "filename"
+              ],
+              "title": "check_file_accessArguments",
+              "type": "object"
+            }
+          }
+        },
+        {
+          "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": {
+              "properties": {
+                "experiment_name": {
+                  "title": "Experiment Name",
+                  "type": "string"
+                }
+              },
+              "required": [
+                "experiment_name"
+              ],
+              "title": "get_experiment_idArguments",
+              "type": "object"
+            }
+          }
+        },
+        {
+          "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": {
+              "properties": {
+                "experiment_id": {
+                  "title": "Experiment Id",
+                  "type": "string"
+                }
+              },
+              "required": [
+                "experiment_id"
+              ],
+              "title": "get_experiment_resultsArguments",
+              "type": "object"
+            }
+          }
+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "gpt-4o"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "rec-430a49246c97",
+          "choices": [
+            {
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+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": [
+                  {
+                    "index": 0,
+                    "id": "call_EsVvmBUqtJb42kNkYnK19QkJ",
+                    "function": {
+                      "arguments": "",
+                      "name": "get_user_id"
+                    },
+                    "type": "function"
+                  }
+                ]
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
+            }
+          ],
+          "created": 0,
+          "model": "gpt-4o-2024-08-06",
+          "object": "chat.completion.chunk",
+          "service_tier": "default",
+          "system_fingerprint": "fp_cbf1785567",
+          "usage": null,
+          "obfuscation": "Ma7aiZxSs"
+        }
+      },
+      {
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tests/integration/responses/recordings/52a2b96781961e252aa3a7b0a5ff77eb5d0989d312e929ed59dda07738487d09.json

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tests/integration/responses/recordings/541b5db7789e61d2400b70bd41c2ff7145784d249c3216c34299c38c28118328.json

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tests/integration/responses/recordings/54aa690e31b5c33a0488a5d7403393e5712917253462292829b37b9320d6df82.json

@@ -10,7 +10,7 @@
   },
   "response": {
     "body": {
-      "__type__": "llama_stack.apis.tools.tools.ToolInvocationResult",
+      "__type__": "llama_stack_api.tools.ToolInvocationResult",
       "__data__": {
         "content": "{\"query\": \"Llama 4 Maverick model number of experts\", \"top_k\": [{\"url\": \"https://console.groq.com/docs/model/meta-llama/llama-4-maverick-17b-128e-instruct\", \"title\": \"Llama 4 Maverick 17B 128E\", \"content\": \"Llama 4 Maverick is Meta's natively multimodal model that enables text and image understanding. With a 17 billion parameter mixture-of-experts architecture (128 experts), this model offers industry-leading performance for multimodal tasks like natural assistant-like chat, image recognition, and coding tasks. Llama 4 Maverick features an auto-regressive language model that uses a mixture-of-experts (MoE) architecture with 17B activated parameters (400B total) and incorporates early fusion for native multimodality. The model uses 128 experts to efficiently handle both text and image inputs while maintaining high performance across chat, knowledge, and code generation tasks, with a knowledge cutoff of August 2024. * For multimodal applications, this model supports up to 5 image inputs create(  model =\\\"meta-llama/llama-4-maverick-17b-128e-instruct\\\",   messages =[  {  \\\"role\\\":  \\\"user\\\",   \\\"content\\\":  \\\"Explain why fast inference is critical for reasoning models\\\"   }   ]  )  print(completion.\", \"score\": 0.9287263, \"raw_content\": null}, {\"url\": \"https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E\", \"title\": \"meta-llama/Llama-4-Maverick-17B-128E\", \"content\": \"... model with 16 experts, and Llama 4 Maverick, a 17 billion parameter model with 128 experts. Model developer: Meta. Model Architecture: The\", \"score\": 0.9183121, \"raw_content\": null}, {\"url\": \"https://build.nvidia.com/meta/llama-4-maverick-17b-128e-instruct/modelcard\", \"title\": \"llama-4-maverick-17b-128e-instruct Model by Meta\", \"content\": \"... model with 16 experts, and Llama 4 Maverick, a 17 billion parameter model with 128 experts. Third-Party Community Consideration. This model\", \"score\": 0.91399205, \"raw_content\": null}, {\"url\": \"https://replicate.com/meta/llama-4-maverick-instruct\", \"title\": \"meta/llama-4-maverick-instruct | Run with an API on ...\", \"content\": \"... model with 16 experts, and Llama 4 Maverick, a 17 billion parameter model with 128 experts. All services are online \\u00b7 Home \\u00b7 About \\u00b7 Changelog\", \"score\": 0.9073207, \"raw_content\": null}, {\"url\": \"https://openrouter.ai/meta-llama/llama-4-maverick\", \"title\": \"Llama 4 Maverick - API, Providers, Stats\", \"content\": \"# Meta: Llama 4 Maverick ### meta-llama/llama-4-maverick Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward pass (400B total). Released on April 5, 2025 under the Llama 4 Community License, Maverick is suited for research and commercial applications requiring advanced multimodal understanding and high model throughput. Llama 4 Maverick - API, Providers, Stats | OpenRouter ## Providers for Llama 4 Maverick ## Performance for Llama 4 Maverick ## Apps using Llama 4 Maverick ## Recent activity on Llama 4 Maverick ## Uptime stats for Llama 4 Maverick ## Sample code and API for Llama 4 Maverick\", \"score\": 0.8958969, \"raw_content\": null}]}",
         "error_message": null,

tests/integration/responses/recordings/56ddb450d81590f461113ec5a55d0532e8f5b9418b22e5f874afff695601da16.json

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+      "stream_options": {
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+                  "type": "string"
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+              },
+              "required": [
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+              "title": "greet_everyoneArguments",
+              "type": "object"
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tests/integration/responses/recordings/6d7f54b7be4845c31ae64498e8018a218bb7f4b8363998abc34ec9bb7ba3a03d.json

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tests/integration/responses/recordings/b6b7282ca0ad5a3c59321d2b045a91ebca1cbaeb4f7aab22c5b9e246b476272f.json

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tests/integration/responses/recordings/c27df465b2996c4d7c909e9ccfac53deb1ac47d064a1b5c70a78b7436438818f.json

@@ -0,0 +1,450 @@
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+              "required": [
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+              "title": "get_user_permissionsArguments",
+              "type": "object"
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+          }
+        },
+        {
+          "type": "function",
+          "function": {
+            "name": "check_file_access",
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+                "experiment_name": {
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+              "required": [
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+              "title": "get_experiment_resultsArguments",
+              "type": "object"
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tests/integration/responses/recordings/d35c1244fbbe9898da3958113c1d054d5f5dd6bdd3c4333db6cef7361fb32feb.json

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+          "usage": {
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+            "prompt_tokens": 195,
+            "total_tokens": 213,
+            "completion_tokens_details": {
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+  },
+  "id_normalization_mapping": {}
+}

tests/integration/responses/test_basic_responses.py

@@ -13,8 +13,8 @@ from .streaming_assertions import StreamingValidator
 
 
 @pytest.mark.parametrize("case", basic_test_cases)
-def test_response_non_streaming_basic(compat_client, text_model_id, case):
-    response = compat_client.responses.create(
+def test_response_non_streaming_basic(responses_client, text_model_id, case):
+    response = responses_client.responses.create(
         model=text_model_id,
         input=case.input,
         stream=False,
@@ -31,10 +31,10 @@ def test_response_non_streaming_basic(compat_client, text_model_id, case):
         "Total tokens should equal input + output tokens"
     )
 
-    retrieved_response = compat_client.responses.retrieve(response_id=response.id)
+    retrieved_response = responses_client.responses.retrieve(response_id=response.id)
     assert retrieved_response.output_text == response.output_text
 
-    next_response = compat_client.responses.create(
+    next_response = responses_client.responses.create(
         model=text_model_id,
         input="Repeat your previous response in all caps.",
         previous_response_id=response.id,
@@ -44,8 +44,8 @@ def test_response_non_streaming_basic(compat_client, text_model_id, case):
 
 
 @pytest.mark.parametrize("case", basic_test_cases)
-def test_response_streaming_basic(compat_client, text_model_id, case):
-    response = compat_client.responses.create(
+def test_response_streaming_basic(responses_client, text_model_id, case):
+    response = responses_client.responses.create(
         model=text_model_id,
         input=case.input,
         stream=True,
@@ -98,15 +98,15 @@ def test_response_streaming_basic(compat_client, text_model_id, case):
     validator.assert_response_consistency()
 
     # Verify stored response matches streamed response
-    retrieved_response = compat_client.responses.retrieve(response_id=response_id)
+    retrieved_response = responses_client.responses.retrieve(response_id=response_id)
     final_event = events[-1]
     assert retrieved_response.output_text == final_event.response.output_text
 
 
 @pytest.mark.parametrize("case", basic_test_cases)
-def test_response_streaming_incremental_content(compat_client, text_model_id, case):
+def test_response_streaming_incremental_content(responses_client, text_model_id, case):
     """Test that streaming actually delivers content incrementally, not just at the end."""
-    response = compat_client.responses.create(
+    response = responses_client.responses.create(
         model=text_model_id,
         input=case.input,
         stream=True,
@@ -170,10 +170,10 @@ def test_response_streaming_incremental_content(compat_client, text_model_id, ca
 
 
 @pytest.mark.parametrize("case", multi_turn_test_cases)
-def test_response_non_streaming_multi_turn(compat_client, text_model_id, case):
+def test_response_non_streaming_multi_turn(responses_client, text_model_id, case):
     previous_response_id = None
     for turn_input, turn_expected in case.turns:
-        response = compat_client.responses.create(
+        response = responses_client.responses.create(
             model=text_model_id,
             input=turn_input,
             previous_response_id=previous_response_id,
@@ -184,8 +184,8 @@ def test_response_non_streaming_multi_turn(compat_client, text_model_id, case):
 
 
 @pytest.mark.parametrize("case", image_test_cases)
-def test_response_non_streaming_image(compat_client, text_model_id, case):
-    response = compat_client.responses.create(
+def test_response_non_streaming_image(responses_client, text_model_id, case):
+    response = responses_client.responses.create(
         model=text_model_id,
         input=case.input,
         stream=False,
@@ -195,10 +195,10 @@ def test_response_non_streaming_image(compat_client, text_model_id, case):
 
 
 @pytest.mark.parametrize("case", multi_turn_image_test_cases)
-def test_response_non_streaming_multi_turn_image(compat_client, text_model_id, case):
+def test_response_non_streaming_multi_turn_image(responses_client, text_model_id, case):
     previous_response_id = None
     for turn_input, turn_expected in case.turns:
-        response = compat_client.responses.create(
+        response = responses_client.responses.create(
             model=text_model_id,
             input=turn_input,
             previous_response_id=previous_response_id,

tests/integration/responses/test_conversation_responses.py

@@ -65,8 +65,14 @@ class TestConversationResponses:
         conversation_items = openai_client.conversations.items.list(conversation.id)
         assert len(conversation_items.data) >= 4  # 2 user + 2 assistant messages
 
+    @pytest.mark.timeout(60, method="thread")
     def test_conversation_context_loading(self, openai_client, text_model_id):
-        """Test that conversation context is properly loaded for responses."""
+        """Test that conversation context is properly loaded for responses.
+
+        Note: 60s timeout added due to CI-specific deadlock in pytest/OpenAI client/httpx
+        after running 25+ tests. Hangs before first HTTP request is made. Works fine locally.
+        Investigation needed: connection pool exhaustion or event loop state issue.
+        """
         conversation = openai_client.conversations.create(
             items=[
                 {"type": "message", "role": "user", "content": "My name is Alice. I like to eat apples."},
@@ -82,6 +88,7 @@ class TestConversationResponses:
 
         assert "apple" in response.output_text.lower()
 
+    @pytest.mark.timeout(60, method="thread")
     def test_conversation_error_handling(self, openai_client, text_model_id):
         """Test error handling for invalid and nonexistent conversations."""
         # Invalid conversation ID format
@@ -125,18 +132,18 @@ class TestConversationResponses:
         assert len(response.output_text.strip()) > 0
 
     # this is not ready yet
-    # def test_conversation_compat_client(self, compat_client, text_model_id):
+    # def test_conversation_compat_client(self, responses_client, text_model_id):
     #     """Test conversation parameter works with compatibility client."""
-    #     if not hasattr(compat_client, "conversations"):
-    #         pytest.skip("compat_client does not support conversations API")
+    #     if not hasattr(responses_client, "conversations"):
+    #         pytest.skip("responses_client does not support conversations API")
     #
-    #     conversation = compat_client.conversations.create()
-    #     response = compat_client.responses.create(
+    #     conversation = responses_client.conversations.create()
+    #     response = responses_client.responses.create(
     #         model=text_model_id, input="Tell me a joke", conversation=conversation.id
     #     )
     #
     #     assert response is not None
     #     assert len(response.output_text.strip()) > 0
     #
-    #     conversation_items = compat_client.conversations.items.list(conversation.id)
+    #     conversation_items = responses_client.conversations.items.list(conversation.id)
     #     assert len(conversation_items.data) >= 2

tests/integration/responses/test_file_search.py

@@ -9,8 +9,6 @@ import time
 
 import pytest
 
-from llama_stack.core.library_client import LlamaStackAsLibraryClient
-
 from .helpers import new_vector_store, upload_file
 
 
@@ -28,12 +26,9 @@ from .helpers import new_vector_store, upload_file
         },
     ],
 )
-def test_response_text_format(compat_client, text_model_id, text_format):
-    if isinstance(compat_client, LlamaStackAsLibraryClient):
-        pytest.skip("Responses API text format is not yet supported in library client.")
-
+def test_response_text_format(responses_client, text_model_id, text_format):
     stream = False
-    response = compat_client.responses.create(
+    response = responses_client.responses.create(
         model=text_model_id,
         input="What is the capital of France?",
         stream=stream,
@@ -47,13 +42,10 @@ def test_response_text_format(compat_client, text_model_id, text_format):
 
 
 @pytest.fixture
-def vector_store_with_filtered_files(compat_client, embedding_model_id, embedding_dimension, tmp_path_factory):
+def vector_store_with_filtered_files(responses_client, embedding_model_id, embedding_dimension, tmp_path_factory):
     # """Create a vector store with multiple files that have different attributes for filtering tests."""
-    if isinstance(compat_client, LlamaStackAsLibraryClient):
-        pytest.skip("upload_file() is not yet supported in library client somehow?")
-
     vector_store = new_vector_store(
-        compat_client, "test_vector_store_with_filters", embedding_model_id, embedding_dimension
+        responses_client, "test_vector_store_with_filters", embedding_model_id, embedding_dimension
     )
     tmp_path = tmp_path_factory.mktemp("filter_test_files")
 
@@ -104,11 +96,11 @@ def vector_store_with_filtered_files(compat_client, embedding_model_id, embeddin
         file_path.write_text(file_data["content"])
 
         # Upload file
-        file_response = upload_file(compat_client, file_data["name"], str(file_path))
+        file_response = upload_file(responses_client, file_data["name"], str(file_path))
         file_ids.append(file_response.id)
 
         # Attach file to vector store with attributes
-        file_attach_response = compat_client.vector_stores.files.create(
+        file_attach_response = responses_client.vector_stores.files.create(
             vector_store_id=vector_store.id,
             file_id=file_response.id,
             attributes=file_data["attributes"],
@@ -117,7 +109,7 @@ def vector_store_with_filtered_files(compat_client, embedding_model_id, embeddin
         # Wait for attachment
         while file_attach_response.status == "in_progress":
             time.sleep(0.1)
-            file_attach_response = compat_client.vector_stores.files.retrieve(
+            file_attach_response = responses_client.vector_stores.files.retrieve(
                 vector_store_id=vector_store.id,
                 file_id=file_response.id,
             )
@@ -127,17 +119,17 @@ def vector_store_with_filtered_files(compat_client, embedding_model_id, embeddin
 
     # Cleanup: delete vector store and files
     try:
-        compat_client.vector_stores.delete(vector_store_id=vector_store.id)
+        responses_client.vector_stores.delete(vector_store_id=vector_store.id)
         for file_id in file_ids:
             try:
-                compat_client.files.delete(file_id=file_id)
+                responses_client.files.delete(file_id=file_id)
             except Exception:
                 pass  # File might already be deleted
     except Exception:
         pass  # Best effort cleanup
 
 
-def test_response_file_search_filter_by_region(compat_client, text_model_id, vector_store_with_filtered_files):
+def test_response_file_search_filter_by_region(responses_client, text_model_id, vector_store_with_filtered_files):
     """Test file search with region equality filter."""
     tools = [
         {
@@ -147,7 +139,7 @@ def test_response_file_search_filter_by_region(compat_client, text_model_id, vec
         }
     ]
 
-    response = compat_client.responses.create(
+    response = responses_client.responses.create(
         model=text_model_id,
         input="What are the updates from the US region?",
         tools=tools,
@@ -168,7 +160,7 @@ def test_response_file_search_filter_by_region(compat_client, text_model_id, vec
         assert "asia" not in result.text.lower()
 
 
-def test_response_file_search_filter_by_category(compat_client, text_model_id, vector_store_with_filtered_files):
+def test_response_file_search_filter_by_category(responses_client, text_model_id, vector_store_with_filtered_files):
     """Test file search with category equality filter."""
     tools = [
         {
@@ -178,7 +170,7 @@ def test_response_file_search_filter_by_category(compat_client, text_model_id, v
         }
     ]
 
-    response = compat_client.responses.create(
+    response = responses_client.responses.create(
         model=text_model_id,
         input="Show me all marketing reports",
         tools=tools,
@@ -198,7 +190,7 @@ def test_response_file_search_filter_by_category(compat_client, text_model_id, v
         assert "revenue figures" not in result.text.lower()
 
 
-def test_response_file_search_filter_by_date_range(compat_client, text_model_id, vector_store_with_filtered_files):
+def test_response_file_search_filter_by_date_range(responses_client, text_model_id, vector_store_with_filtered_files):
     """Test file search with date range filter using compound AND."""
     tools = [
         {
@@ -222,7 +214,7 @@ def test_response_file_search_filter_by_date_range(compat_client, text_model_id,
         }
     ]
 
-    response = compat_client.responses.create(
+    response = responses_client.responses.create(
         model=text_model_id,
         input="What happened in Q1 2023?",
         tools=tools,
@@ -241,7 +233,7 @@ def test_response_file_search_filter_by_date_range(compat_client, text_model_id,
         assert "q3" not in result.text.lower()
 
 
-def test_response_file_search_filter_compound_and(compat_client, text_model_id, vector_store_with_filtered_files):
+def test_response_file_search_filter_compound_and(responses_client, text_model_id, vector_store_with_filtered_files):
     """Test file search with compound AND filter (region AND category)."""
     tools = [
         {
@@ -257,7 +249,7 @@ def test_response_file_search_filter_compound_and(compat_client, text_model_id,
         }
     ]
 
-    response = compat_client.responses.create(
+    response = responses_client.responses.create(
         model=text_model_id,
         input="What are the engineering updates from the US?",
         tools=tools,
@@ -277,7 +269,7 @@ def test_response_file_search_filter_compound_and(compat_client, text_model_id,
         assert "promotional" not in result.text.lower() and "revenue" not in result.text.lower()
 
 
-def test_response_file_search_filter_compound_or(compat_client, text_model_id, vector_store_with_filtered_files):
+def test_response_file_search_filter_compound_or(responses_client, text_model_id, vector_store_with_filtered_files):
     """Test file search with compound OR filter (marketing OR sales)."""
     tools = [
         {
@@ -293,7 +285,7 @@ def test_response_file_search_filter_compound_or(compat_client, text_model_id, v
         }
     ]
 
-    response = compat_client.responses.create(
+    response = responses_client.responses.create(
         model=text_model_id,
         input="Show me marketing and sales documents",
         tools=tools,
@@ -320,7 +312,7 @@ def test_response_file_search_filter_compound_or(compat_client, text_model_id, v
     assert categories_found.issubset({"marketing", "sales"}), f"Found unexpected categories: {categories_found}"
 
 
-def test_response_file_search_streaming_events(compat_client, text_model_id, vector_store_with_filtered_files):
+def test_response_file_search_streaming_events(responses_client, text_model_id, vector_store_with_filtered_files):
     """Test that file search emits proper streaming events (in_progress, searching, completed)."""
     tools = [
         {
@@ -329,7 +321,7 @@ def test_response_file_search_streaming_events(compat_client, text_model_id, vec
         }
     ]
 
-    stream = compat_client.responses.create(
+    stream = responses_client.responses.create(
         model=text_model_id,
         input="What are the marketing updates?",
         tools=tools,

tests/integration/responses/test_mcp_authentication.py

@@ -0,0 +1,105 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+
+import pytest
+
+from tests.common.mcp import make_mcp_server
+
+from .helpers import setup_mcp_tools
+
+# MCP authentication tests with recordings
+# Tests for bearer token authorization support in MCP tool configurations
+
+
+def test_mcp_authorization_bearer(responses_client, text_model_id):
+    """Test that bearer authorization is correctly applied to MCP requests."""
+    test_token = "test-bearer-token-789"
+    with make_mcp_server(required_auth_token=test_token) as mcp_server_info:
+        tools = setup_mcp_tools(
+            [
+                {
+                    "type": "mcp",
+                    "server_label": "auth-mcp",
+                    "server_url": "<FILLED_BY_TEST_RUNNER>",
+                    "authorization": test_token,  # Just the token, not "Bearer <token>"
+                }
+            ],
+            mcp_server_info,
+        )
+
+        # Create response - authorization should be applied
+        response = responses_client.responses.create(
+            model=text_model_id,
+            input="What is the boiling point of myawesomeliquid?",
+            tools=tools,
+            stream=False,
+        )
+
+        # Verify list_tools succeeded (requires auth)
+        assert len(response.output) >= 3
+        assert response.output[0].type == "mcp_list_tools"
+        assert len(response.output[0].tools) == 2
+
+        # Verify tool invocation succeeded (requires auth)
+        assert response.output[1].type == "mcp_call"
+        assert response.output[1].error is None
+
+
+def test_mcp_authorization_error_when_header_provided(responses_client, text_model_id):
+    """Test that providing Authorization in headers raises a security error."""
+    test_token = "test-token-123"
+    with make_mcp_server(required_auth_token=test_token) as mcp_server_info:
+        tools = setup_mcp_tools(
+            [
+                {
+                    "type": "mcp",
+                    "server_label": "header-auth-mcp",
+                    "server_url": "<FILLED_BY_TEST_RUNNER>",
+                    "headers": {"Authorization": f"Bearer {test_token}"},  # Security risk - should be rejected
+                }
+            ],
+            mcp_server_info,
+        )
+
+        # Create response - should raise BadRequestError for security reasons
+        with pytest.raises((ValueError, Exception), match="Authorization header cannot be passed via 'headers'"):
+            responses_client.responses.create(
+                model=text_model_id,
+                input="What is the boiling point of myawesomeliquid?",
+                tools=tools,
+                stream=False,
+            )
+
+
+def test_mcp_authorization_backward_compatibility(responses_client, text_model_id):
+    """Test that MCP tools work without authorization (backward compatibility)."""
+    # No authorization required
+    with make_mcp_server(required_auth_token=None) as mcp_server_info:
+        tools = setup_mcp_tools(
+            [
+                {
+                    "type": "mcp",
+                    "server_label": "noauth-mcp",
+                    "server_url": "<FILLED_BY_TEST_RUNNER>",
+                }
+            ],
+            mcp_server_info,
+        )
+
+        # Create response without authorization
+        response = responses_client.responses.create(
+            model=text_model_id,
+            input="What is the boiling point of myawesomeliquid?",
+            tools=tools,
+            stream=False,
+        )
+
+        # Verify operations succeeded without auth
+        assert len(response.output) >= 3
+        assert response.output[0].type == "mcp_list_tools"
+        assert response.output[1].type == "mcp_call"
+        assert response.output[1].error is None

tests/integration/responses/test_tool_responses.py

@@ -9,6 +9,7 @@ import logging  # allow-direct-logging
 import os
 
 import httpx
+import llama_stack_client
 import openai
 import pytest
 
@@ -29,8 +30,8 @@ from .streaming_assertions import StreamingValidator
 
 
 @pytest.mark.parametrize("case", web_search_test_cases)
-def test_response_non_streaming_web_search(compat_client, text_model_id, case):
-    response = compat_client.responses.create(
+def test_response_non_streaming_web_search(responses_client, text_model_id, case):
+    response = responses_client.responses.create(
         model=text_model_id,
         input=case.input,
         tools=case.tools,
@@ -48,12 +49,9 @@ def test_response_non_streaming_web_search(compat_client, text_model_id, case):
 
 @pytest.mark.parametrize("case", file_search_test_cases)
 def test_response_non_streaming_file_search(
-    compat_client, text_model_id, embedding_model_id, embedding_dimension, tmp_path, case
+    responses_client, text_model_id, embedding_model_id, embedding_dimension, tmp_path, case
 ):
-    if isinstance(compat_client, LlamaStackAsLibraryClient):
-        pytest.skip("Responses API file search is not yet supported in library client.")
-
-    vector_store = new_vector_store(compat_client, "test_vector_store", embedding_model_id, embedding_dimension)
+    vector_store = new_vector_store(responses_client, "test_vector_store", embedding_model_id, embedding_dimension)
 
     if case.file_content:
         file_name = "test_response_non_streaming_file_search.txt"
@@ -65,16 +63,16 @@ def test_response_non_streaming_file_search(
     else:
         raise ValueError("No file content or path provided for case")
 
-    file_response = upload_file(compat_client, file_name, file_path)
+    file_response = upload_file(responses_client, file_name, file_path)
 
     # Attach our file to the vector store
-    compat_client.vector_stores.files.create(
+    responses_client.vector_stores.files.create(
         vector_store_id=vector_store.id,
         file_id=file_response.id,
     )
 
     # Wait for the file to be attached
-    wait_for_file_attachment(compat_client, vector_store.id, file_response.id)
+    wait_for_file_attachment(responses_client, vector_store.id, file_response.id)
 
     # Update our tools with the right vector store id
     tools = case.tools
@@ -83,7 +81,7 @@ def test_response_non_streaming_file_search(
             tool["vector_store_ids"] = [vector_store.id]
 
     # Create the response request, which should query our vector store
-    response = compat_client.responses.create(
+    response = responses_client.responses.create(
         model=text_model_id,
         input=case.input,
         tools=tools,
@@ -105,15 +103,12 @@ def test_response_non_streaming_file_search(
 
 
 def test_response_non_streaming_file_search_empty_vector_store(
-    compat_client, text_model_id, embedding_model_id, embedding_dimension
+    responses_client, text_model_id, embedding_model_id, embedding_dimension
 ):
-    if isinstance(compat_client, LlamaStackAsLibraryClient):
-        pytest.skip("Responses API file search is not yet supported in library client.")
-
-    vector_store = new_vector_store(compat_client, "test_vector_store", embedding_model_id, embedding_dimension)
+    vector_store = new_vector_store(responses_client, "test_vector_store", embedding_model_id, embedding_dimension)
 
     # Create the response request, which should query our vector store
-    response = compat_client.responses.create(
+    response = responses_client.responses.create(
         model=text_model_id,
         input="How many experts does the Llama 4 Maverick model have?",
         tools=[{"type": "file_search", "vector_store_ids": [vector_store.id]}],
@@ -133,13 +128,10 @@ def test_response_non_streaming_file_search_empty_vector_store(
 
 
 def test_response_sequential_file_search(
-    compat_client, text_model_id, embedding_model_id, embedding_dimension, tmp_path
+    responses_client, text_model_id, embedding_model_id, embedding_dimension, tmp_path
 ):
     """Test file search with sequential responses using previous_response_id."""
-    if isinstance(compat_client, LlamaStackAsLibraryClient):
-        pytest.skip("Responses API file search is not yet supported in library client.")
-
-    vector_store = new_vector_store(compat_client, "test_vector_store", embedding_model_id, embedding_dimension)
+    vector_store = new_vector_store(responses_client, "test_vector_store", embedding_model_id, embedding_dimension)
 
     # Create a test file with content
     file_content = "The Llama 4 Maverick model has 128 experts in its mixture of experts architecture."
@@ -147,21 +139,21 @@ def test_response_sequential_file_search(
     file_path = tmp_path / file_name
     file_path.write_text(file_content)
 
-    file_response = upload_file(compat_client, file_name, file_path)
+    file_response = upload_file(responses_client, file_name, file_path)
 
     # Attach the file to the vector store
-    compat_client.vector_stores.files.create(
+    responses_client.vector_stores.files.create(
         vector_store_id=vector_store.id,
         file_id=file_response.id,
     )
 
     # Wait for the file to be attached
-    wait_for_file_attachment(compat_client, vector_store.id, file_response.id)
+    wait_for_file_attachment(responses_client, vector_store.id, file_response.id)
 
     tools = [{"type": "file_search", "vector_store_ids": [vector_store.id]}]
 
     # First response request with file search
-    response = compat_client.responses.create(
+    response = responses_client.responses.create(
         model=text_model_id,
         input="How many experts does the Llama 4 Maverick model have?",
         tools=tools,
@@ -178,7 +170,7 @@ def test_response_sequential_file_search(
     assert "128" in response.output_text or "experts" in response.output_text.lower()
 
     # Second response request using previous_response_id
-    response2 = compat_client.responses.create(
+    response2 = responses_client.responses.create(
         model=text_model_id,
         input="Can you tell me more about the architecture?",
         tools=tools,
@@ -199,14 +191,11 @@ def test_response_sequential_file_search(
 
 
 @pytest.mark.parametrize("case", mcp_tool_test_cases)
-def test_response_non_streaming_mcp_tool(compat_client, text_model_id, case, caplog):
-    if not isinstance(compat_client, LlamaStackAsLibraryClient):
-        pytest.skip("in-process MCP server is only supported in library client")
-
+def test_response_non_streaming_mcp_tool(responses_client, text_model_id, case, caplog):
     with make_mcp_server() as mcp_server_info:
         tools = setup_mcp_tools(case.tools, mcp_server_info)
 
-        response = compat_client.responses.create(
+        response = responses_client.responses.create(
             model=text_model_id,
             input=case.input,
             tools=tools,
@@ -243,15 +232,15 @@ def test_response_non_streaming_mcp_tool(compat_client, text_model_id, case, cap
 
         exc_type = (
             AuthenticationRequiredError
-            if isinstance(compat_client, LlamaStackAsLibraryClient)
-            else (httpx.HTTPStatusError, openai.AuthenticationError)
+            if isinstance(responses_client, LlamaStackAsLibraryClient)
+            else (httpx.HTTPStatusError, openai.AuthenticationError, llama_stack_client.AuthenticationError)
         )
         # Suppress expected auth error logs only for the failing auth attempt
         with caplog.at_level(
             logging.CRITICAL, logger="llama_stack.providers.inline.agents.meta_reference.responses.streaming"
         ):
             with pytest.raises(exc_type):
-                compat_client.responses.create(
+                responses_client.responses.create(
                     model=text_model_id,
                     input=case.input,
                     tools=tools,
@@ -260,9 +249,9 @@ def test_response_non_streaming_mcp_tool(compat_client, text_model_id, case, cap
 
         for tool in tools:
             if tool["type"] == "mcp":
-                tool["headers"] = {"Authorization": "Bearer test-token"}
+                tool["authorization"] = "test-token"
 
-        response = compat_client.responses.create(
+        response = responses_client.responses.create(
             model=text_model_id,
             input=case.input,
             tools=tools,
@@ -272,14 +261,11 @@ def test_response_non_streaming_mcp_tool(compat_client, text_model_id, case, cap
 
 
 @pytest.mark.parametrize("case", mcp_tool_test_cases)
-def test_response_sequential_mcp_tool(compat_client, text_model_id, case):
-    if not isinstance(compat_client, LlamaStackAsLibraryClient):
-        pytest.skip("in-process MCP server is only supported in library client")
-
+def test_response_sequential_mcp_tool(responses_client, text_model_id, case):
     with make_mcp_server() as mcp_server_info:
         tools = setup_mcp_tools(case.tools, mcp_server_info)
 
-        response = compat_client.responses.create(
+        response = responses_client.responses.create(
             model=text_model_id,
             input=case.input,
             tools=tools,
@@ -311,7 +297,7 @@ def test_response_sequential_mcp_tool(compat_client, text_model_id, case):
         text_content = message.content[0].text
         assert "boiling point" in text_content.lower()
 
-        response2 = compat_client.responses.create(
+        response2 = responses_client.responses.create(
             model=text_model_id, input=case.input, tools=tools, stream=False, previous_response_id=response.id
         )
 
@@ -323,16 +309,13 @@ def test_response_sequential_mcp_tool(compat_client, text_model_id, case):
 
 @pytest.mark.parametrize("case", mcp_tool_test_cases)
 @pytest.mark.parametrize("approve", [True, False])
-def test_response_mcp_tool_approval(compat_client, text_model_id, case, approve):
-    if not isinstance(compat_client, LlamaStackAsLibraryClient):
-        pytest.skip("in-process MCP server is only supported in library client")
-
+def test_response_mcp_tool_approval(responses_client, text_model_id, case, approve):
     with make_mcp_server() as mcp_server_info:
         tools = setup_mcp_tools(case.tools, mcp_server_info)
         for tool in tools:
             tool["require_approval"] = "always"
 
-        response = compat_client.responses.create(
+        response = responses_client.responses.create(
             model=text_model_id,
             input=case.input,
             tools=tools,
@@ -352,13 +335,13 @@ def test_response_mcp_tool_approval(compat_client, text_model_id, case, approve)
         approval_request = response.output[1]
         assert approval_request.type == "mcp_approval_request"
         assert approval_request.name == "get_boiling_point"
-        assert json.loads(approval_request.arguments) == {
-            "liquid_name": "myawesomeliquid",
-            "celsius": True,
-        }
+        args = json.loads(approval_request.arguments)
+        assert args["liquid_name"] == "myawesomeliquid"
+        # celsius has a default value of True, so it may be omitted or explicitly set
+        assert args.get("celsius", True) is True
 
         # send approval response
-        response = compat_client.responses.create(
+        response = responses_client.responses.create(
             previous_response_id=response.id,
             model=text_model_id,
             input=[{"type": "mcp_approval_response", "approval_request_id": approval_request.id, "approve": approve}],
@@ -398,8 +381,8 @@ def test_response_mcp_tool_approval(compat_client, text_model_id, case, approve)
 
 
 @pytest.mark.parametrize("case", custom_tool_test_cases)
-def test_response_non_streaming_custom_tool(compat_client, text_model_id, case):
-    response = compat_client.responses.create(
+def test_response_non_streaming_custom_tool(responses_client, text_model_id, case):
+    response = responses_client.responses.create(
         model=text_model_id,
         input=case.input,
         tools=case.tools,
@@ -412,8 +395,8 @@ def test_response_non_streaming_custom_tool(compat_client, text_model_id, case):
 
 
 @pytest.mark.parametrize("case", custom_tool_test_cases)
-def test_response_function_call_ordering_1(compat_client, text_model_id, case):
-    response = compat_client.responses.create(
+def test_response_function_call_ordering_1(responses_client, text_model_id, case):
+    response = responses_client.responses.create(
         model=text_model_id,
         input=case.input,
         tools=case.tools,
@@ -437,13 +420,13 @@ def test_response_function_call_ordering_1(compat_client, text_model_id, case):
             "call_id": response.output[0].call_id,
         }
     )
-    response = compat_client.responses.create(
+    response = responses_client.responses.create(
         model=text_model_id, input=inputs, tools=case.tools, stream=False, previous_response_id=response.id
     )
     assert len(response.output) == 1
 
 
-def test_response_function_call_ordering_2(compat_client, text_model_id):
+def test_response_function_call_ordering_2(responses_client, text_model_id):
     tools = [
         {
             "type": "function",
@@ -468,7 +451,7 @@ def test_response_function_call_ordering_2(compat_client, text_model_id):
             "content": "Is the weather better in San Francisco or Los Angeles?",
         }
     ]
-    response = compat_client.responses.create(
+    response = responses_client.responses.create(
         model=text_model_id,
         input=inputs,
         tools=tools,
@@ -489,7 +472,7 @@ def test_response_function_call_ordering_2(compat_client, text_model_id):
                     "call_id": output.call_id,
                 }
             )
-    response = compat_client.responses.create(
+    response = responses_client.responses.create(
         model=text_model_id,
         input=inputs,
         tools=tools,
@@ -500,15 +483,12 @@ def test_response_function_call_ordering_2(compat_client, text_model_id):
 
 
 @pytest.mark.parametrize("case", multi_turn_tool_execution_test_cases)
-def test_response_non_streaming_multi_turn_tool_execution(compat_client, text_model_id, case):
+def test_response_non_streaming_multi_turn_tool_execution(responses_client, text_model_id, case):
     """Test multi-turn tool execution where multiple MCP tool calls are performed in sequence."""
-    if not isinstance(compat_client, LlamaStackAsLibraryClient):
-        pytest.skip("in-process MCP server is only supported in library client")
-
     with make_mcp_server(tools=dependency_tools()) as mcp_server_info:
         tools = setup_mcp_tools(case.tools, mcp_server_info)
 
-        response = compat_client.responses.create(
+        response = responses_client.responses.create(
             input=case.input,
             model=text_model_id,
             tools=tools,
@@ -550,15 +530,12 @@ def test_response_non_streaming_multi_turn_tool_execution(compat_client, text_mo
 
 
 @pytest.mark.parametrize("case", multi_turn_tool_execution_streaming_test_cases)
-def test_response_streaming_multi_turn_tool_execution(compat_client, text_model_id, case):
+def test_response_streaming_multi_turn_tool_execution(responses_client, text_model_id, case):
     """Test streaming multi-turn tool execution where multiple MCP tool calls are performed in sequence."""
-    if not isinstance(compat_client, LlamaStackAsLibraryClient):
-        pytest.skip("in-process MCP server is only supported in library client")
-
     with make_mcp_server(tools=dependency_tools()) as mcp_server_info:
         tools = setup_mcp_tools(case.tools, mcp_server_info)
 
-        stream = compat_client.responses.create(
+        stream = responses_client.responses.create(
             input=case.input,
             model=text_model_id,
             tools=tools,

tests/integration/safety/test_llama_guard.py

@@ -13,8 +13,8 @@ from collections.abc import Generator
 
 import pytest
 
-from llama_stack.apis.safety import ViolationLevel
 from llama_stack.models.llama.sku_types import CoreModelId
+from llama_stack_api import ViolationLevel
 
 # Llama Guard models available for text and vision shields
 LLAMA_GUARD_TEXT_MODELS = [CoreModelId.llama_guard_4_12b.value]

tests/integration/safety/test_safety.py

@@ -8,7 +8,7 @@ import mimetypes
 
 import pytest
 
-from llama_stack.apis.safety import ViolationLevel
+from llama_stack_api import ViolationLevel
 
 CODE_SCANNER_ENABLED_PROVIDERS = {"ollama", "together", "fireworks"}
 

tests/integration/safety/test_vision_safety.py

@@ -10,7 +10,7 @@ import os
 
 import pytest
 
-from llama_stack.apis.safety import ViolationLevel
+from llama_stack_api import ViolationLevel
 
 VISION_SHIELD_ENABLED_PROVIDERS = {"together"}
 

tests/integration/suites.py

@@ -71,6 +71,26 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
             "embedding_model": "ollama/nomic-embed-text:v1.5",
         },
     ),
+    "ollama-postgres": Setup(
+        name="ollama-postgres",
+        description="Server-mode tests with Postgres-backed persistence",
+        env={
+            "OLLAMA_URL": "http://0.0.0.0:11434",
+            "SAFETY_MODEL": "ollama/llama-guard3:1b",
+            "POSTGRES_HOST": "127.0.0.1",
+            "POSTGRES_PORT": "5432",
+            "POSTGRES_DB": "llamastack",
+            "POSTGRES_USER": "llamastack",
+            "POSTGRES_PASSWORD": "llamastack",
+            "LLAMA_STACK_LOGGING": "openai_responses=info",
+        },
+        defaults={
+            "text_model": "ollama/llama3.2:3b-instruct-fp16",
+            "embedding_model": "sentence-transformers/nomic-embed-text-v1.5",
+            "safety_model": "ollama/llama-guard3:1b",
+            "safety_shield": "llama-guard",
+        },
+    ),
     "vllm": Setup(
         name="vllm",
         description="vLLM provider with a text model",

tests/integration/tool_runtime/test_mcp.py

@@ -4,14 +4,10 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import json
-
 import pytest
 from llama_stack_client.lib.agents.agent import Agent
 from llama_stack_client.lib.agents.turn_events import StepCompleted, StepProgress, ToolCallIssuedDelta
 
-from llama_stack.core.library_client import LlamaStackAsLibraryClient
-
 AUTH_TOKEN = "test-token"
 
 from tests.common.mcp import MCP_TOOLGROUP_ID, make_mcp_server
@@ -24,9 +20,6 @@ def mcp_server():
 
 
 def test_mcp_invocation(llama_stack_client, text_model_id, mcp_server):
-    if not isinstance(llama_stack_client, LlamaStackAsLibraryClient):
-        pytest.skip("The local MCP server only reliably reachable from library client.")
-
     test_toolgroup_id = MCP_TOOLGROUP_ID
     uri = mcp_server["server_url"]
 
@@ -42,31 +35,20 @@ def test_mcp_invocation(llama_stack_client, text_model_id, mcp_server):
         mcp_endpoint=dict(uri=uri),
     )
 
-    provider_data = {
-        "mcp_headers": {
-            uri: {
-                "Authorization": f"Bearer {AUTH_TOKEN}",
-            },
-        },
-    }
-    auth_headers = {
-        "X-LlamaStack-Provider-Data": json.dumps(provider_data),
-    }
-
-    with pytest.raises(Exception, match="Unauthorized"):
-        llama_stack_client.tools.list(toolgroup_id=test_toolgroup_id)
-
-    tools_list = llama_stack_client.tools.list(
-        toolgroup_id=test_toolgroup_id,
-        extra_headers=auth_headers,
+    # Use the dedicated authorization parameter (no more provider_data headers)
+    # This tests direct tool_runtime.invoke_tool API calls
+    tools_list = llama_stack_client.tool_runtime.list_tools(
+        tool_group_id=test_toolgroup_id,
+        authorization=AUTH_TOKEN,  # Use dedicated authorization parameter
     )
     assert len(tools_list) == 2
     assert {t.name for t in tools_list} == {"greet_everyone", "get_boiling_point"}
 
+    # Invoke tool with authorization parameter
     response = llama_stack_client.tool_runtime.invoke_tool(
         tool_name="greet_everyone",
         kwargs=dict(url="https://www.google.com"),
-        extra_headers=auth_headers,
+        authorization=AUTH_TOKEN,  # Use dedicated authorization parameter
     )
     content = response.content
     assert len(content) == 1
@@ -81,9 +63,7 @@ def test_mcp_invocation(llama_stack_client, text_model_id, mcp_server):
             "server_label": test_toolgroup_id,
             "require_approval": "never",
             "allowed_tools": [tool.name for tool in tools_list],
-            "headers": {
-                "Authorization": f"Bearer {AUTH_TOKEN}",
-            },
+            "authorization": AUTH_TOKEN,
         }
     ]
     agent = Agent(
@@ -109,7 +89,6 @@ def test_mcp_invocation(llama_stack_client, text_model_id, mcp_server):
                 }
             ],
             stream=True,
-            extra_headers=auth_headers,
         )
     )
     events = [chunk.event for chunk in chunks]

tests/integration/tool_runtime/test_mcp_json_schema.py

@@ -4,13 +4,10 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-"""
-Integration tests for MCP tools with complex JSON Schema support.
+"""Integration tests for MCP tools with complex JSON Schema support.
 Tests $ref, $defs, and other JSON Schema features through MCP integration.
 """
 
-import json
-
 import pytest
 
 from llama_stack.core.library_client import LlamaStackAsLibraryClient
@@ -123,15 +120,11 @@ class TestMCPSchemaPreservation:
             mcp_endpoint=dict(uri=uri),
         )
 
-        provider_data = {"mcp_headers": {uri: {"Authorization": f"Bearer {AUTH_TOKEN}"}}}
-        auth_headers = {
-            "X-LlamaStack-Provider-Data": json.dumps(provider_data),
-        }
-
+        # Use the dedicated authorization parameter
         # List runtime tools
         response = llama_stack_client.tool_runtime.list_tools(
             tool_group_id=test_toolgroup_id,
-            extra_headers=auth_headers,
+            authorization=AUTH_TOKEN,
         )
 
         tools = response
@@ -166,15 +159,12 @@ class TestMCPSchemaPreservation:
             provider_id="model-context-protocol",
             mcp_endpoint=dict(uri=uri),
         )
-        provider_data = {"mcp_headers": {uri: {"Authorization": f"Bearer {AUTH_TOKEN}"}}}
-        auth_headers = {
-            "X-LlamaStack-Provider-Data": json.dumps(provider_data),
-        }
 
+        # Use the dedicated authorization parameter
         # List tools
         response = llama_stack_client.tool_runtime.list_tools(
             tool_group_id=test_toolgroup_id,
-            extra_headers=auth_headers,
+            authorization=AUTH_TOKEN,
         )
 
         # Find book_flight tool (which should have $ref/$defs)
@@ -216,14 +206,10 @@ class TestMCPSchemaPreservation:
             mcp_endpoint=dict(uri=uri),
         )
 
-        provider_data = {"mcp_headers": {uri: {"Authorization": f"Bearer {AUTH_TOKEN}"}}}
-        auth_headers = {
-            "X-LlamaStack-Provider-Data": json.dumps(provider_data),
-        }
-
+        # Use the dedicated authorization parameter
         response = llama_stack_client.tool_runtime.list_tools(
             tool_group_id=test_toolgroup_id,
-            extra_headers=auth_headers,
+            authorization=AUTH_TOKEN,
         )
 
         # Find get_weather tool
@@ -263,15 +249,10 @@ class TestMCPToolInvocation:
             mcp_endpoint=dict(uri=uri),
         )
 
-        provider_data = {"mcp_headers": {uri: {"Authorization": f"Bearer {AUTH_TOKEN}"}}}
-        auth_headers = {
-            "X-LlamaStack-Provider-Data": json.dumps(provider_data),
-        }
-
-        # List tools to populate the tool index
+        # Use the dedicated authorization parameter
         llama_stack_client.tool_runtime.list_tools(
             tool_group_id=test_toolgroup_id,
-            extra_headers=auth_headers,
+            authorization=AUTH_TOKEN,
         )
 
         # Invoke tool with complex nested data
@@ -283,7 +264,7 @@ class TestMCPToolInvocation:
                     "shipping": {"address": {"street": "123 Main St", "city": "San Francisco", "zipcode": "94102"}},
                 }
             },
-            extra_headers=auth_headers,
+            authorization=AUTH_TOKEN,
         )
 
         # Should succeed without schema validation errors
@@ -309,22 +290,17 @@ class TestMCPToolInvocation:
             mcp_endpoint=dict(uri=uri),
         )
 
-        provider_data = {"mcp_headers": {uri: {"Authorization": f"Bearer {AUTH_TOKEN}"}}}
-        auth_headers = {
-            "X-LlamaStack-Provider-Data": json.dumps(provider_data),
-        }
-
-        # List tools to populate the tool index
+        # Use the dedicated authorization parameter
         llama_stack_client.tool_runtime.list_tools(
             tool_group_id=test_toolgroup_id,
-            extra_headers=auth_headers,
+            authorization=AUTH_TOKEN,
         )
 
         # Test with email format
         result_email = llama_stack_client.tool_runtime.invoke_tool(
             tool_name="flexible_contact",
             kwargs={"contact_info": "user@example.com"},
-            extra_headers=auth_headers,
+            authorization=AUTH_TOKEN,
         )
 
         assert result_email.error_message is None
@@ -333,7 +309,7 @@ class TestMCPToolInvocation:
         result_phone = llama_stack_client.tool_runtime.invoke_tool(
             tool_name="flexible_contact",
             kwargs={"contact_info": "+15551234567"},
-            extra_headers=auth_headers,
+            authorization=AUTH_TOKEN,
         )
 
         assert result_phone.error_message is None
@@ -365,14 +341,10 @@ class TestAgentWithMCPTools:
             mcp_endpoint=dict(uri=uri),
         )
 
-        provider_data = {"mcp_headers": {uri: {"Authorization": f"Bearer {AUTH_TOKEN}"}}}
-        auth_headers = {
-            "X-LlamaStack-Provider-Data": json.dumps(provider_data),
-        }
-
-        tools_list = llama_stack_client.tools.list(
-            toolgroup_id=test_toolgroup_id,
-            extra_headers=auth_headers,
+        # Use the dedicated authorization parameter
+        tools_list = llama_stack_client.tool_runtime.list_tools(
+            tool_group_id=test_toolgroup_id,
+            authorization=AUTH_TOKEN,
         )
         tool_defs = [
             {
@@ -381,6 +353,7 @@ class TestAgentWithMCPTools:
                 "server_label": test_toolgroup_id,
                 "require_approval": "never",
                 "allowed_tools": [tool.name for tool in tools_list],
+                "authorization": AUTH_TOKEN,
             }
         ]
 
@@ -389,7 +362,6 @@ class TestAgentWithMCPTools:
             model=text_model_id,
             instructions="You are a helpful assistant that can process orders and book flights.",
             tools=tool_defs,
-            extra_headers=auth_headers,
         )
 
         session_id = agent.create_session("test-session-complex")
@@ -411,7 +383,6 @@ class TestAgentWithMCPTools:
                     }
                 ],
                 stream=True,
-                extra_headers=auth_headers,
             )
         )
 

tests/integration/tool_runtime/test_registration.py

@@ -8,8 +8,8 @@ import re
 
 import pytest
 
-from llama_stack.apis.common.errors import ToolGroupNotFoundError
 from llama_stack.core.library_client import LlamaStackAsLibraryClient
+from llama_stack_api import ToolGroupNotFoundError
 from tests.common.mcp import MCP_TOOLGROUP_ID, make_mcp_server
 
 

tests/integration/vector_io/test_openai_vector_stores.py

@@ -11,9 +11,9 @@ import pytest
 from llama_stack_client import BadRequestError
 from openai import BadRequestError as OpenAIBadRequestError
 
-from llama_stack.apis.vector_io import Chunk
 from llama_stack.core.library_client import LlamaStackAsLibraryClient
 from llama_stack.log import get_logger
+from llama_stack_api import Chunk, ExpiresAfter
 
 from ..conftest import vector_provider_wrapper
 
@@ -649,7 +649,7 @@ def test_openai_vector_store_attach_file(
 ):
     """Test OpenAI vector store attach file."""
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
-    from llama_stack.apis.files import ExpiresAfter
+    from llama_stack_api import ExpiresAfter
 
     compat_client = compat_client_with_empty_stores
 
@@ -713,7 +713,7 @@ def test_openai_vector_store_attach_files_on_creation(
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
 
     compat_client = compat_client_with_empty_stores
-    from llama_stack.apis.files import ExpiresAfter
+    from llama_stack_api import ExpiresAfter
 
     # Create some files and attach them to the vector store
     valid_file_ids = []
@@ -778,7 +778,7 @@ def test_openai_vector_store_list_files(
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
 
     compat_client = compat_client_with_empty_stores
-    from llama_stack.apis.files import ExpiresAfter
+    from llama_stack_api import ExpiresAfter
 
     # Create a vector store
     vector_store = compat_client.vector_stores.create(
@@ -870,7 +870,7 @@ def test_openai_vector_store_retrieve_file_contents(
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
 
     compat_client = compat_client_with_empty_stores
-    from llama_stack.apis.files import ExpiresAfter
+    from llama_stack_api import ExpiresAfter
 
     # Create a vector store
     vector_store = compat_client.vector_stores.create(
@@ -911,16 +911,16 @@ def test_openai_vector_store_retrieve_file_contents(
     )
 
     assert file_contents is not None
-    assert len(file_contents.content) == 1
-    content = file_contents.content[0]
+    assert file_contents.object == "vector_store.file_content.page"
+    assert len(file_contents.data) == 1
+    content = file_contents.data[0]
 
     # llama-stack-client returns a model, openai-python is a badboy and returns a dict
     if not isinstance(content, dict):
         content = content.model_dump()
     assert content["type"] == "text"
     assert content["text"] == test_content.decode("utf-8")
-    assert file_contents.filename == file_name
-    assert file_contents.attributes == attributes
+    assert file_contents.has_more is False
 
 
 @vector_provider_wrapper
@@ -931,7 +931,7 @@ def test_openai_vector_store_delete_file(
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
 
     compat_client = compat_client_with_empty_stores
-    from llama_stack.apis.files import ExpiresAfter
+    from llama_stack_api import ExpiresAfter
 
     # Create a vector store
     vector_store = compat_client.vector_stores.create(
@@ -997,7 +997,7 @@ def test_openai_vector_store_delete_file_removes_from_vector_store(
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
 
     compat_client = compat_client_with_empty_stores
-    from llama_stack.apis.files import ExpiresAfter
+    from llama_stack_api import ExpiresAfter
 
     # Create a vector store
     vector_store = compat_client.vector_stores.create(
@@ -1049,7 +1049,7 @@ def test_openai_vector_store_update_file(
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
 
     compat_client = compat_client_with_empty_stores
-    from llama_stack.apis.files import ExpiresAfter
+    from llama_stack_api import ExpiresAfter
 
     # Create a vector store
     vector_store = compat_client.vector_stores.create(
@@ -1106,7 +1106,7 @@ def test_create_vector_store_files_duplicate_vector_store_name(
     This test confirms that client.vector_stores.create() creates a unique ID
     """
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
-    from llama_stack.apis.files import ExpiresAfter
+    from llama_stack_api import ExpiresAfter
 
     compat_client = compat_client_with_empty_stores
 
@@ -1487,14 +1487,12 @@ def test_openai_vector_store_file_batch_retrieve_contents(
         )
 
         assert file_contents is not None
-        assert file_contents.filename == file_data[i][0]
-        assert len(file_contents.content) > 0
+        assert file_contents.object == "vector_store.file_content.page"
+        assert len(file_contents.data) > 0
 
         # Verify the content matches what we uploaded
         content_text = (
-            file_contents.content[0].text
-            if hasattr(file_contents.content[0], "text")
-            else file_contents.content[0]["text"]
+            file_contents.data[0].text if hasattr(file_contents.data[0], "text") else file_contents.data[0]["text"]
         )
         assert file_data[i][1].decode("utf-8") in content_text
 
@@ -1610,3 +1608,97 @@ def test_openai_vector_store_embedding_config_from_metadata(
 
     assert "metadata_config_store" in store_names
     assert "consistent_config_store" in store_names
+
+
+@vector_provider_wrapper
+def test_openai_vector_store_file_contents_with_extra_query(
+    compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
+):
+    """Test that vector store file contents endpoint supports extra_query parameter."""
+    skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
+    compat_client = compat_client_with_empty_stores
+
+    # Create a vector store
+    vector_store = compat_client.vector_stores.create(
+        name="test_extra_query_store",
+        extra_body={
+            "embedding_model": embedding_model_id,
+            "provider_id": vector_io_provider_id,
+        },
+    )
+
+    # Create and attach a file
+    test_content = b"This is test content for extra_query validation."
+    with BytesIO(test_content) as file_buffer:
+        file_buffer.name = "test_extra_query.txt"
+        file = compat_client.files.create(
+            file=file_buffer,
+            purpose="assistants",
+            expires_after=ExpiresAfter(anchor="created_at", seconds=86400),
+        )
+
+    file_attach_response = compat_client.vector_stores.files.create(
+        vector_store_id=vector_store.id,
+        file_id=file.id,
+        extra_body={"embedding_model": embedding_model_id},
+    )
+    assert file_attach_response.status == "completed"
+
+    # Wait for processing
+    time.sleep(2)
+
+    # Test that extra_query parameter is accepted and processed
+    content_with_extra_query = compat_client.vector_stores.files.content(
+        vector_store_id=vector_store.id,
+        file_id=file.id,
+        extra_query={"include_embeddings": True, "include_metadata": True},
+    )
+
+    # Test without extra_query for comparison
+    content_without_extra_query = compat_client.vector_stores.files.content(
+        vector_store_id=vector_store.id,
+        file_id=file.id,
+    )
+
+    # Validate that both calls succeed
+    assert content_with_extra_query is not None
+    assert content_without_extra_query is not None
+    assert len(content_with_extra_query.data) > 0
+    assert len(content_without_extra_query.data) > 0
+
+    # Validate that extra_query parameter is processed correctly
+    # Both should have the embedding/metadata fields available (may be None based on flags)
+    first_chunk_with_flags = content_with_extra_query.data[0]
+    first_chunk_without_flags = content_without_extra_query.data[0]
+
+    # The key validation: extra_query fields are present in the response
+    # Handle both dict and object responses (different clients may return different formats)
+    def has_field(obj, field):
+        if isinstance(obj, dict):
+            return field in obj
+        else:
+            return hasattr(obj, field)
+
+    # Validate that all expected fields are present in both responses
+    expected_fields = ["embedding", "chunk_metadata", "metadata", "text"]
+    for field in expected_fields:
+        assert has_field(first_chunk_with_flags, field), f"Field '{field}' missing from response with extra_query"
+        assert has_field(first_chunk_without_flags, field), f"Field '{field}' missing from response without extra_query"
+
+    # Validate content is the same
+    def get_field(obj, field):
+        if isinstance(obj, dict):
+            return obj[field]
+        else:
+            return getattr(obj, field)
+
+    assert get_field(first_chunk_with_flags, "text") == test_content.decode("utf-8")
+    assert get_field(first_chunk_without_flags, "text") == test_content.decode("utf-8")
+
+    with_flags_embedding = get_field(first_chunk_with_flags, "embedding")
+    without_flags_embedding = get_field(first_chunk_without_flags, "embedding")
+
+    # Validate that embeddings are included when requested and excluded when not requested
+    assert with_flags_embedding is not None, "Embeddings should be included when include_embeddings=True"
+    assert len(with_flags_embedding) > 0, "Embedding should be a non-empty list"
+    assert without_flags_embedding is None, "Embeddings should not be included when include_embeddings=False"

tests/integration/vector_io/test_vector_io.py

@@ -6,7 +6,7 @@
 
 import pytest
 
-from llama_stack.apis.vector_io import Chunk
+from llama_stack_api import Chunk
 
 from ..conftest import vector_provider_wrapper