Pass parallel_tool_calls directly and document intended usage in integration test

Signed-off-by: Anastas Stoyanovsky <[email protected]>

Author: anastasds

docs/docs/providers/agents/index.mdx

@@ -2,7 +2,7 @@
 description: |
   Agents
 
-      APIs for creating and interacting with agentic systems.
+  APIs for creating and interacting with agentic systems.
 sidebar_label: Agents
 title: Agents
 ---
@@ -13,6 +13,6 @@ title: Agents
 
 Agents
 
-    APIs for creating and interacting with agentic systems.
+APIs for creating and interacting with agentic systems.
 
 This section contains documentation for all available providers for the **agents** API.

docs/docs/providers/batches/index.mdx

@@ -1,15 +1,15 @@
 ---
 description: |
   The Batches API enables efficient processing of multiple requests in a single operation,
-      particularly useful for processing large datasets, batch evaluation workflows, and
-      cost-effective inference at scale.
+  particularly useful for processing large datasets, batch evaluation workflows, and
+  cost-effective inference at scale.
 
-      The API is designed to allow use of openai client libraries for seamless integration.
+  The API is designed to allow use of openai client libraries for seamless integration.
 
-      This API provides the following extensions:
-       - idempotent batch creation
+  This API provides the following extensions:
+   - idempotent batch creation
 
-      Note: This API is currently under active development and may undergo changes.
+  Note: This API is currently under active development and may undergo changes.
 sidebar_label: Batches
 title: Batches
 ---
@@ -19,14 +19,14 @@ title: Batches
 ## Overview
 
 The Batches API enables efficient processing of multiple requests in a single operation,
-    particularly useful for processing large datasets, batch evaluation workflows, and
-    cost-effective inference at scale.
+particularly useful for processing large datasets, batch evaluation workflows, and
+cost-effective inference at scale.
 
-    The API is designed to allow use of openai client libraries for seamless integration.
+The API is designed to allow use of openai client libraries for seamless integration.
 
-    This API provides the following extensions:
-     - idempotent batch creation
+This API provides the following extensions:
+ - idempotent batch creation
 
-    Note: This API is currently under active development and may undergo changes.
+Note: This API is currently under active development and may undergo changes.
 
 This section contains documentation for all available providers for the **batches** API.

docs/docs/providers/eval/index.mdx

@@ -2,7 +2,7 @@
 description: |
   Evaluations
 
-      Llama Stack Evaluation API for running evaluations on model and agent candidates.
+  Llama Stack Evaluation API for running evaluations on model and agent candidates.
 sidebar_label: Eval
 title: Eval
 ---
@@ -13,6 +13,6 @@ title: Eval
 
 Evaluations
 
-    Llama Stack Evaluation API for running evaluations on model and agent candidates.
+Llama Stack Evaluation API for running evaluations on model and agent candidates.
 
 This section contains documentation for all available providers for the **eval** API.

docs/docs/providers/files/index.mdx

@@ -2,7 +2,7 @@
 description: |
   Files
 
-      This API is used to upload documents that can be used with other Llama Stack APIs.
+  This API is used to upload documents that can be used with other Llama Stack APIs.
 sidebar_label: Files
 title: Files
 ---
@@ -13,6 +13,6 @@ title: Files
 
 Files
 
-    This API is used to upload documents that can be used with other Llama Stack APIs.
+This API is used to upload documents that can be used with other Llama Stack APIs.
 
 This section contains documentation for all available providers for the **files** API.

docs/docs/providers/inference/index.mdx

@@ -2,12 +2,12 @@
 description: |
   Inference
 
-      Llama Stack Inference API for generating completions, chat completions, and embeddings.
+  Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
-      This API provides the raw interface to the underlying models. Three kinds of models are supported:
-      - LLM models: these models generate "raw" and "chat" (conversational) completions.
-      - Embedding models: these models generate embeddings to be used for semantic search.
-      - Rerank models: these models reorder the documents based on their relevance to a query.
+  This API provides the raw interface to the underlying models. Three kinds of models are supported:
+  - LLM models: these models generate "raw" and "chat" (conversational) completions.
+  - Embedding models: these models generate embeddings to be used for semantic search.
+  - Rerank models: these models reorder the documents based on their relevance to a query.
 sidebar_label: Inference
 title: Inference
 ---
@@ -18,11 +18,11 @@ title: Inference
 
 Inference
 
-    Llama Stack Inference API for generating completions, chat completions, and embeddings.
+Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
-    This API provides the raw interface to the underlying models. Three kinds of models are supported:
-    - LLM models: these models generate "raw" and "chat" (conversational) completions.
-    - Embedding models: these models generate embeddings to be used for semantic search.
-    - Rerank models: these models reorder the documents based on their relevance to a query.
+This API provides the raw interface to the underlying models. Three kinds of models are supported:
+- LLM models: these models generate "raw" and "chat" (conversational) completions.
+- Embedding models: these models generate embeddings to be used for semantic search.
+- Rerank models: these models reorder the documents based on their relevance to a query.
 
 This section contains documentation for all available providers for the **inference** API.

docs/docs/providers/safety/index.mdx

@@ -2,7 +2,7 @@
 description: |
   Safety
 
-      OpenAI-compatible Moderations API.
+  OpenAI-compatible Moderations API.
 sidebar_label: Safety
 title: Safety
 ---
@@ -13,6 +13,6 @@ title: Safety
 
 Safety
 
-    OpenAI-compatible Moderations API.
+OpenAI-compatible Moderations API.
 
 This section contains documentation for all available providers for the **safety** API.

src/llama_stack/providers/inline/agents/meta_reference/responses/streaming.py

@@ -242,6 +242,7 @@ async def create_response(self) -> AsyncIterator[OpenAIResponseObjectStream]:
                     messages=messages,
                     # Pydantic models are dict-compatible but mypy treats them as distinct types
                     tools=self.ctx.chat_tools,  # type: ignore[arg-type]
+                    parallel_tool_calls=self.parallel_tool_calls,
                     stream=True,
                     temperature=self.ctx.temperature,
                     response_format=response_format,

tests/integration/agents/test_openai_responses.py

@@ -516,169 +516,3 @@ 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]