improve agent metrics integration test and cleanup fixtures

- simplified test to use telemetry.query_metrics for verification
- test now validates actual queryable metrics data
- verified by query metrics functionality added in #3074

tests/integration/agents/conftest.py

@@ -0,0 +1,170 @@
+# 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.
+
+from collections.abc import AsyncGenerator, Callable
+from pathlib import Path
+from typing import Any
+from unittest.mock import Mock, patch
+
+import pytest
+
+from llama_stack.apis.inference import ToolDefinition
+from llama_stack.apis.tools import ToolInvocationResult
+from llama_stack.providers.inline.agents.meta_reference.agent_instance import ChatAgent
+from llama_stack.providers.inline.telemetry.meta_reference.config import (
+    TelemetryConfig,
+    TelemetrySink,
+)
+from llama_stack.providers.inline.telemetry.meta_reference.telemetry import (
+    TelemetryAdapter,
+)
+from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
+from llama_stack.providers.utils.kvstore.sqlite.sqlite import SqliteKVStoreImpl
+from llama_stack.providers.utils.telemetry import tracing as telemetry_tracing
+
+
+@pytest.fixture
+def make_agent_fixture():
+    def _make(telemetry, kvstore) -> ChatAgent:
+        agent = ChatAgent(
+            agent_id="test-agent",
+            agent_config=Mock(),
+            inference_api=Mock(),
+            safety_api=Mock(),
+            tool_runtime_api=Mock(),
+            tool_groups_api=Mock(),
+            vector_io_api=Mock(),
+            telemetry_api=telemetry,
+            persistence_store=kvstore,
+            created_at="2025-01-01T00:00:00Z",
+            policy=[],
+        )
+        agent.agent_config.client_tools = []
+        agent.agent_config.max_infer_iters = 5
+        agent.input_shields = []
+        agent.output_shields = []
+        agent.tool_defs = [
+            ToolDefinition(tool_name="web_search", description="", parameters={}),
+            ToolDefinition(tool_name="knowledge_search", description="", parameters={}),
+        ]
+        agent.tool_name_to_args = {}
+
+        # Stub tool runtime invoke_tool
+        async def _mock_invoke_tool(
+            *args: Any,
+            tool_name: str | None = None,
+            kwargs: dict | None = None,
+            **extra: Any,
+        ):
+            return ToolInvocationResult(content="Tool execution result")
+
+        agent.tool_runtime_api.invoke_tool = _mock_invoke_tool
+        return agent
+
+    return _make
+
+
+def _chat_stream(tool_name: str | None, content: str = ""):
+    from llama_stack.apis.common.content_types import (
+        TextDelta,
+        ToolCallDelta,
+        ToolCallParseStatus,
+    )
+    from llama_stack.apis.inference import (
+        ChatCompletionResponseEvent,
+        ChatCompletionResponseEventType,
+        ChatCompletionResponseStreamChunk,
+        StopReason,
+    )
+    from llama_stack.models.llama.datatypes import ToolCall
+
+    async def gen():
+        # Start
+        yield ChatCompletionResponseStreamChunk(
+            event=ChatCompletionResponseEvent(
+                event_type=ChatCompletionResponseEventType.start,
+                delta=TextDelta(text=""),
+            )
+        )
+
+        # Content
+        if content:
+            yield ChatCompletionResponseStreamChunk(
+                event=ChatCompletionResponseEvent(
+                    event_type=ChatCompletionResponseEventType.progress,
+                    delta=TextDelta(text=content),
+                )
+            )
+
+        # Tool call if specified
+        if tool_name:
+            yield ChatCompletionResponseStreamChunk(
+                event=ChatCompletionResponseEvent(
+                    event_type=ChatCompletionResponseEventType.progress,
+                    delta=ToolCallDelta(
+                        tool_call=ToolCall(call_id="call_0", tool_name=tool_name, arguments={}),
+                        parse_status=ToolCallParseStatus.succeeded,
+                    ),
+                )
+            )
+
+        # Complete
+        yield ChatCompletionResponseStreamChunk(
+            event=ChatCompletionResponseEvent(
+                event_type=ChatCompletionResponseEventType.complete,
+                delta=TextDelta(text=""),
+                stop_reason=StopReason.end_of_turn,
+            )
+        )
+
+    return gen()
+
+
+@pytest.fixture
+async def telemetry(tmp_path: Path) -> AsyncGenerator[TelemetryAdapter, None]:
+    db_path = tmp_path / "trace_store.db"
+    cfg = TelemetryConfig(
+        sinks=[TelemetrySink.CONSOLE, TelemetrySink.SQLITE],
+        sqlite_db_path=str(db_path),
+    )
+    telemetry = TelemetryAdapter(cfg, deps={})
+    telemetry_tracing.setup_logger(telemetry)
+    try:
+        yield telemetry
+    finally:
+        await telemetry.shutdown()
+
+
+@pytest.fixture
+async def kvstore(tmp_path: Path) -> SqliteKVStoreImpl:
+    kv_path = tmp_path / "agent_kvstore.db"
+    kv = SqliteKVStoreImpl(SqliteKVStoreConfig(db_path=str(kv_path)))
+    await kv.initialize()
+    return kv
+
+
+@pytest.fixture
+def span_patch():
+    with (
+        patch("llama_stack.providers.inline.agents.meta_reference.agent_instance.get_current_span") as mock_span,
+        patch(
+            "llama_stack.providers.utils.telemetry.tracing.generate_span_id",
+            return_value="0000000000000abc",
+        ),
+    ):
+        mock_span.return_value = Mock(get_span_context=Mock(return_value=Mock(trace_id=0x123, span_id=0xABC)))
+        yield
+
+
+@pytest.fixture
+def make_completion_fn() -> Callable[[str | None, str], Callable]:
+    def _factory(tool_name: str | None = None, content: str = "") -> Callable:
+        async def chat_completion(*args: Any, **kwargs: Any):
+            return _chat_stream(tool_name, content)
+
+        return chat_completion
+
+    return _factory