feat(agents)!: changing agents API signatures to use OpenAI types

Replace legacy Message/SamplingParams usage with OpenAI chat message structures across agents: schemas, meta-reference implementation, and tests now rely on OpenAI message/tool payloads and generation knobs.

tests/integration/agents/test_agents.py

@@ -62,14 +62,9 @@ def agent_config(llama_stack_client, text_model_id):
     agent_config = dict(
         model=text_model_id,
         instructions="You are a helpful assistant",
-        sampling_params={
-            "strategy": {
-                "type": "top_p",
-                "temperature": 0.0001,
-                "top_p": 0.9,
-            },
-            "max_tokens": 512,
-        },
+        temperature=0.0001,
+        top_p=0.9,
+        max_output_tokens=512,
         tools=[],
         input_shields=available_shields,
         output_shields=available_shields,
@@ -83,14 +78,9 @@ def agent_config_without_safety(text_model_id):
     agent_config = dict(
         model=text_model_id,
         instructions="You are a helpful assistant",
-        sampling_params={
-            "strategy": {
-                "type": "top_p",
-                "temperature": 0.0001,
-                "top_p": 0.9,
-            },
-            "max_tokens": 512,
-        },
+        temperature=0.0001,
+        top_p=0.9,
+        max_output_tokens=512,
         tools=[],
         enable_session_persistence=False,
     )
@@ -194,14 +184,9 @@ def test_tool_config(agent_config):
     common_params = dict(
         model="meta-llama/Llama-3.2-3B-Instruct",
         instructions="You are a helpful assistant",
-        sampling_params={
-            "strategy": {
-                "type": "top_p",
-                "temperature": 1.0,
-                "top_p": 0.9,
-            },
-            "max_tokens": 512,
-        },
+        temperature=1.0,
+        top_p=0.9,
+        max_output_tokens=512,
         toolgroups=[],
         enable_session_persistence=False,
     )
@@ -212,40 +197,25 @@ def test_tool_config(agent_config):
 
     agent_config = AgentConfig(
         **common_params,
-        tool_choice="auto",
+        tool_config=ToolConfig(tool_choice="auto"),
     )
     server_config = Server__AgentConfig(**agent_config)
     assert server_config.tool_config.tool_choice == ToolChoice.auto
 
     agent_config = AgentConfig(
         **common_params,
-        tool_choice="auto",
-        tool_config=ToolConfig(
-            tool_choice="auto",
-        ),
+        tool_config=ToolConfig(tool_choice="auto"),
     )
     server_config = Server__AgentConfig(**agent_config)
     assert server_config.tool_config.tool_choice == ToolChoice.auto
 
     agent_config = AgentConfig(
         **common_params,
-        tool_config=ToolConfig(
-            tool_choice="required",
-        ),
+        tool_config=ToolConfig(tool_choice="required"),
     )
     server_config = Server__AgentConfig(**agent_config)
     assert server_config.tool_config.tool_choice == ToolChoice.required
 
-    agent_config = AgentConfig(
-        **common_params,
-        tool_choice="required",
-        tool_config=ToolConfig(
-            tool_choice="auto",
-        ),
-    )
-    with pytest.raises(ValueError, match="tool_choice is deprecated"):
-        Server__AgentConfig(**agent_config)
-
 
 def test_builtin_tool_web_search(llama_stack_client, agent_config):
     agent_config = {

tests/integration/agents/test_persistence.py

@@ -7,7 +7,7 @@
 import pytest
 
 from llama_stack.apis.agents import AgentConfig, Turn
-from llama_stack.apis.inference import SamplingParams, UserMessage
+from llama_stack.apis.inference import OpenAIUserMessageParam
 from llama_stack.providers.datatypes import Api
 from llama_stack.providers.utils.kvstore import kvstore_impl
 from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
@@ -16,7 +16,7 @@ from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
 @pytest.fixture
 def sample_messages():
     return [
-        UserMessage(content="What's the weather like today?"),
+        OpenAIUserMessageParam(content="What's the weather like today?"),
     ]
 
 
@@ -36,7 +36,9 @@ def common_params(inference_model):
         model=inference_model,
         instructions="You are a helpful assistant.",
         enable_session_persistence=True,
-        sampling_params=SamplingParams(temperature=0.7, top_p=0.95),
+        temperature=0.7,
+        top_p=0.95,
+        max_output_tokens=256,
         input_shields=[],
         output_shields=[],
         tools=[],

tests/unit/providers/agent/test_meta_reference_agent.py

@@ -69,30 +69,26 @@ async def agents_impl(config, mock_apis):
 @pytest.fixture
 def sample_agent_config():
     return AgentConfig(
-        sampling_params={
-            "strategy": {"type": "greedy"},
-            "max_tokens": 0,
-            "repetition_penalty": 1.0,
-        },
+        temperature=0.0,
+        top_p=1.0,
+        max_output_tokens=0,
         input_shields=["string"],
         output_shields=["string"],
         toolgroups=["mcp::my_mcp_server"],
         client_tools=[
             {
+                "type": "function",
                 "name": "client_tool",
                 "description": "Client Tool",
-                "parameters": [
-                    {
-                        "name": "string",
-                        "parameter_type": "string",
-                        "description": "string",
-                        "required": True,
-                        "default": None,
-                    }
-                ],
-                "metadata": {
-                    "property1": None,
-                    "property2": None,
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "string": {
+                            "type": "string",
+                            "description": "string",
+                        }
+                    },
+                    "required": ["string"],
                 },
             }
         ],