Update bug_report.md

Merge branch 'main' into ymodak-patch-1
2026-07-09 18:19:28 -07:00 · 2026-01-22 12:40:37 -08:00 · 2026-01-21 19:32:46 -08:00 · 2026-01-21 14:05:11 -08:00 · 2026-01-20 13:27:59 -08:00 · 2026-01-20 13:27:45 -08:00
22 changed files with 66 additions and 1984 deletions
@@ -7,34 +7,49 @@ assignees: ''

 ---

-** Please make sure you read the contribution guide and file the issues in the right place. **
-[Contribution guide.](https://google.github.io/adk-docs/contributing-guide/)
+## 🔴 Required Information
+*Please ensure all items in this section are completed to allow for efficient triaging. Requests without complete information may be rejected / deprioritized. If an item is not applicable to you - please mark it as N/A*

-**Describe the bug**
+**Describe the Bug**
 A clear and concise description of what the bug is.

-**To Reproduce**
-Please share a minimal code and data to reproduce your problem.
-Steps to reproduce the behavior:
+**Steps to Reproduce**
+Please provide a numbered list of steps to reproduce the behavior:
 1. Install '...'
 2. Run '....'
 3. Open '....'
 4. Provide error or stacktrace

-**Expected behavior**
+**Expected Behavior**
 A clear and concise description of what you expected to happen.

-**Screenshots**
-If applicable, add screenshots to help explain your problem.
+**Observed Behavior**
+What actually happened? Include error messages or crash stack traces here.

-**Desktop (please complete the following information):**
- - OS: [e.g. macOS, Linux, Windows]
- - Python version(python -V):
- - ADK version(pip show google-adk):
+**Environment Details**
+* **ADK Library Version:** (e.g., 2.0.1)
+* **Desktop OS:** (e.g., macOS, Linux, Windows)
+* **Python Version:**

- **Model Information:**
- - Are you using LiteLLM: Yes/No
- - Which model is being used(e.g. gemini-2.5-pro)
+**Model Information**
+* **Are you using LiteLLM:** Yes/No
+* **Which model is being used:** (e.g., gemini-2.5-pro)

-**Additional context**
+---
+
+## 🟡 Optional Information
+*Providing this information greatly speeds up the resolution process.*
+
+**Regression**
+Did this work in a previous version of ADK? If so, which one?
+
+**Logs**
+Please attach relevant logs. Wrap them in code blocks (```) or attach a text file.
+```text
+// Paste logs here
+```
+**Screenshots / Video**
+If applicable, add screenshots or screen recordings to help explain your problem.
+
+**Additional Context**
 Add any other context about the problem here.
@@ -1,88 +1,5 @@
 # Changelog

-## [1.23.0](https://github.com/google/adk-python/compare/v1.22.1...v1.23.0) (2026-01-22)
-
-### ⚠ BREAKING CHANGES
-
-* Breaking: Use OpenTelemetry for BigQuery plugin tracing, replacing custom `ContextVar` implementation ([ab89d12](https://github.com/google/adk-python/commit/ab89d1283430041afb303834749869e9ee331721))
-* Add support to automatically create a session if one does not exist ([8e69a58](https://github.com/google/adk-python/commit/8e69a58df4eadeccbb100b7264bb518a46b61fd7))
-
-### Features
-
-* **[Core]**
-  * Remove `@experimental` decorator from `AgentEngineSandboxCodeExecutor` ([135f763](https://github.com/google/adk-python/commit/135f7633253f6a415302142abc3579b664601d5b))
-  * Add `--disable_features` CLI option to override default feature enable state ([53b67ce](https://github.com/google/adk-python/commit/53b67ce6340f3f3f8c3d732f9f7811e445c76359))
-  * Add `otel_to_cloud` flag to `adk deploy agent_engine` command ([21f63f6](https://github.com/google/adk-python/commit/21f63f66ee424501d9a70806277463ef718ae843))
-  * Add `is_computer_use` field to agent information in `adk-web` server ([5923da7](https://github.com/google/adk-python/commit/5923da786eb1aaef6f0bcbc6adc906cbc8bf9b36))
-  * Allow `thinking_config` in `generate_content_config` ([e162bb8](https://github.com/google/adk-python/commit/e162bb8832a806e2380048e39165bf837455f88c))
-  * Convert A2UI messages between A2A `DataPart` metadata and ADK events ([1133ce2](https://github.com/google/adk-python/commit/1133ce219c5a7a9a85222b03e348ba6b13830c8f))
-  * Add `--enable_features` CLI option to override default feature enable state ([79fcddb](https://github.com/google/adk-python/commit/79fcddb39f71a4c1342e63b4d67832b3eccb2652))
-
-* **[Tools]**
-  * Add flush mechanism to `BigQueryAgentAnalyticsPlugin` to ensure pending log events are written to BigQuery ([9579bea](https://github.com/google/adk-python/commit/9579bea05d946b3d8b4bfec35e510725dd371224))
-  * Allow Google Search tool to set a different model ([b57a3d4](https://github.com/google/adk-python/commit/b57a3d43e4656f5a3c5db53addff02b67d1fde26))
-  * Support authentication for MCP tool listing ([e3d542a](https://github.com/google/adk-python/commit/e3d542a5ba3d357407f8cd29cfdd722f583c8564) [19315fe](https://github.com/google/adk-python/commit/19315fe557039fa8bf446525a4830b1c9f40cba9))
-  * Use JSON schema for `base_retrieval_tool`, `load_artifacts_tool`, and `load_memory_tool` declarations when the feature is enabled ([69ad605](https://github.com/google/adk-python/commit/69ad605bc4bbe9a4f018127fd3625169ee70488e))
-  * Use JSON schema for `IntegrationConnectorTool` declaration when the feature is enabled ([2ed6865](https://github.com/google/adk-python/commit/2ed686527ac75ff64128ce7d9b1a3befc2b37c64))
-  * Start and close `ClientSession` in a single task in `McpSessionManager` ([cce430d](https://github.com/google/adk-python/commit/cce430da799766686e65f6cae02ba64e916d5c8a))
-  * Use JSON schema for `RestApiTool` declaration when the feature is enabled ([a5f0d33](https://github.com/google/adk-python/commit/a5f0d333d7f26f2966ed511d5d9def7a1933f0c2))
-
-* **[Evals]**
-  * Update `adk eval` CLI to consume custom metrics by adding `CustomMetricEvaluator` ([ea0934b](https://github.com/google/adk-python/commit/ea0934b9934c1fefd129a1026d6af369f126870e))
-  * Update `EvalConfig` and `EvalMetric` data models to support custom metrics ([6d2f33a](https://github.com/google/adk-python/commit/6d2f33a59cfba358dd758378290125fc2701c411))
-
-* **[Observability]**
-  * Add minimal `generate_content {model.name}` spans and logs for non-Gemini inference and when `opentelemetry-inference-google-genai` dependency is missing ([935c279](https://github.com/google/adk-python/commit/935c279f8281bde99224f03d936b8abe51cbabfc))
-
-* **[Integrations]**
-  * Enhance `TraceManager` asynchronous safety, enrich BigQuery plugin logging, and fix serialization ([a4116a6](https://github.com/google/adk-python/commit/a4116a6cbfadc161982af5dabd55a711d79348b7))
-
-* **[Live]**
-  * Persist user input content to session in live mode ([a04828d](https://github.com/google/adk-python/commit/a04828dd8a848482acbd48acc7da432d0d2cb0aa))
-
-### Bug Fixes
-
-* Recursively extract input/output schema for AgentTool ([bf2b56d](https://github.com/google/adk-python/commit/bf2b56de6d0052e40b6d871b2d22c56e9225e145))
-* Yield buffered `function_call` and `function_response` events during live streaming ([7b25b8f](https://github.com/google/adk-python/commit/7b25b8fb1daf54d7694bf405d545d46d2c012d2b))
-* Update `authlib` and `mcp` dependency versions ([7955177](https://github.com/google/adk-python/commit/7955177fb28b8e5dc19aae8be94015a7b5d9882a))
-* Set `LITELLM_MODE` to `PRODUCTION` before importing LiteLLM to prevent implicit `.env` file loading ([215c2f5](https://github.com/google/adk-python/commit/215c2f506e21a3d8c39551b80f6356943ecae320))
-* Redact sensitive information from URIs in logs ([5257869](https://github.com/google/adk-python/commit/5257869d91a77ebd1381538a85e7fdc3a600da90))
-* Handle asynchronous driver URLs in the migration tool ([4b29d15](https://github.com/google/adk-python/commit/4b29d15b3e5df65f3503daffa6bc7af85159507b))
-* Remove custom metadata from A2A response events ([81eaeb5](https://github.com/google/adk-python/commit/81eaeb5eba6d40cde0cf6147d96921ed1bf7bb31))
-* Handle `None` inferences in eval results ([7d4326c](https://github.com/google/adk-python/commit/7d4326c3606a7ff2ba3c0fdef08d4f6af52ee71e))
-* Mark all parts of a thought event as thought ([f92d4e3](https://github.com/google/adk-python/commit/f92d4e397f37445fe9032a95ce26646a3a69300b))
-* Use `json.dumps` for error messages in SSE events ([6ad18cc](https://github.com/google/adk-python/commit/6ad18cc2fc3a3315a0fc240cb51b3283b53116b4))
-* Use the correct path for config-based agents when deploying to AgentEngine ([83d7bb6](https://github.com/google/adk-python/commit/83d7bb6ef0d952ad04c5d9a61aaf202672c7e17d))
-* Support Generator and Async Generator tool declarations in JSON schema ([19555e7](https://github.com/google/adk-python/commit/19555e7dce6d60c3b960ca0bc2f928c138ac3cc0) [7c28297](https://github.com/google/adk-python/commit/7c282973ea193841fee79f90b8a91c5e02627ccc))
-* Prevent stopping event processing on events with `None` content ([ed2c3eb](https://github.com/google/adk-python/commit/ed2c3ebde9cafbb5e2bf375f44db1e77cee9fb24))
-* Fix `'NoneType'` object is not iterable error ([7db3ce9](https://github.com/google/adk-python/commit/7db3ce9613b1c2c97e6ca3cd8115736516dc1556))
-* Use canonical tools to find streaming tools and register them by `tool.name` ([ec6abf4](https://github.com/google/adk-python/commit/ec6abf401019c39e8e1a8d1b2c7d5cf5e8c7ac56))
-* Initialize `self._auth_config` inside `BaseAuthenticatedTool` to access authentication headers in `McpTool` ([d4da1bb](https://github.com/google/adk-python/commit/d4da1bb7330cdb87c1dcbe0b9023148357a6bd07))
-* Only filter out audio content when sending history ([712b5a3](https://github.com/google/adk-python/commit/712b5a393d44e7b5ce35fc459da98361bae4bb16))
-* Add finish reason mapping and remove custom file URI handling in LiteLLM ([89bed43](https://github.com/google/adk-python/commit/89bed43f5e0c5ad12dd31c716d372145b7e33e78))
-* Convert unsupported inline artifact MIME types to text in `LoadArtifactsTool` ([fdc98d5](https://github.com/google/adk-python/commit/fdc98d5c927bfef021e87cf72103892e4c2ac12a))
-* Pass `log_level` to `uvicorn` in `web` and `api_server` commands ([38d52b2](https://github.com/google/adk-python/commit/38d52b247600fb45a2beeb041c4698e90c00d705))
-* Use the agent name as the author of the audio event ([ab62b1b](https://github.com/google/adk-python/commit/ab62b1bffd7ad2df5809d430ad1823872b8bd67a))
-* Handle `NOT_FOUND` error when fetching Vertex AI sessions ([75231a3](https://github.com/google/adk-python/commit/75231a30f1857d930804769caf88bcc20839dd08))
-* Fix `httpx` client closure during event pagination ([b725045](https://github.com/google/adk-python/commit/b725045e5a1192bc9fd5190cbd2758ab6ff02590))
-
-### Improvements
-
-* Add new conversational analytics API toolset ([82fa10b](https://github.com/google/adk-python/commit/82fa10b71e037b565cb407c82e9e908432dab0ff))
-* Filter out `adk_request_input` event from content list ([295b345](https://github.com/google/adk-python/commit/295b34558774d1f64022009980e3edd8eb79527b))
-* Always skip executing partial function calls ([d62f9c8](https://github.com/google/adk-python/commit/d62f9c896c301aba3a781e868735e16f946a8862))
-* Update comments of request confirmation preprocessor ([1699b09](https://github.com/google/adk-python/commit/1699b090edc9e5b13c34f461c8e664187157c5c0))
-* Fix various typos ([a8f2ddd](https://github.com/google/adk-python/commit/a8f2ddd943301bbf53f49b3a23300ece45803cc0))
-* Update sample live streaming tools agent to use latest live models ([3dd7e3f](https://github.com/google/adk-python/commit/3dd7e3f1b9be05c28adb061864d84c4202a2d922))
-* Make the regex to catch CLI reference strict by adding word boundary anchor ([c222a45](https://github.com/google/adk-python/commit/c222a45ef74f7b55c48dc151ba98cd8c30a15c57))
-* Migrate `ToolboxToolset` to use `toolbox-adk` and align validation ([7dc6adf](https://github.com/google/adk-python/commit/7dc6adf4e563330a09e4cf28d2b1994c24b007d1) [277084e](https://github.com/google/adk-python/commit/277084e31368302e6338b69d456affd35d5fedfe))
-* Always log API backend when connecting to live model ([7b035aa](https://github.com/google/adk-python/commit/7b035aa9fc43a43489aeffea8f877cd7eaa09f35))
-* Add a sample BigQuery agent using BigQuery MCP tools ([672b57f](https://github.com/google/adk-python/commit/672b57f1b76580023d1f348de76227291a9c1012))
-* Add a `DebugLoggingPlugin` to record human-readable debugging logs ([8973618](https://github.com/google/adk-python/commit/8973618b0b0e90c513873e22af272c147efb4904))
-* Upgrade the sample BigQuery agent model version to `gemini-2.5-flash` ([fd2c0f5](https://github.com/google/adk-python/commit/fd2c0f556b786417a9f6add744827b07e7a06b7d))
-* Import `migration_runner` lazily within the migrate command ([905604f](https://github.com/google/adk-python/commit/905604faac82aca8ae0935eebea288f82985e9c5))
-
-

 ## [1.22.1](https://github.com/google/adk-python/compare/v1.22.0...v1.22.1) (2026-01-09)

@@ -1,61 +0,0 @@
-# Data Agent Sample
-
-This sample agent demonstrates ADK's first-party tools for interacting with
-Data Agents powered by [Conversational Analytics API](https://docs.cloud.google.com/gemini/docs/conversational-analytics-api/overview).
-These tools are distributed via
-the `google.adk.tools.data_agent` module and allow you to list,
-inspect, and
-chat with Data Agents using natural language.
-
-These tools leverage stateful conversations, meaning you can ask follow-up
-questions in the same session, and the agent will maintain context.
-
-## Prerequisites
-
-1.  An active Google Cloud project with BigQuery and Gemini APIs enabled.
-2.  Google Cloud authentication configured for Application Default Credentials:
-    ```bash
-    gcloud auth application-default login
-    ```
-3.  At least one Data Agent created. You could create data agents via
-    [Conversational API](https://docs.cloud.google.com/gemini/docs/conversational-analytics-api/overview),
-    its
-    [Python SDK](https://docs.cloud.google.com/gemini/docs/conversational-analytics-api/build-agent-sdk),
-    or for BigQuery data
-    [BigQuery Studio](https://docs.cloud.google.com/bigquery/docs/create-data-agents#create_a_data_agent).
-    These agents are created and configured in the Google Cloud console and
-    point to your BigQuery tables or other data sources.
-4.  Follow the official
-    [Setup and prerequisites](https://docs.cloud.google.com/gemini/docs/conversational-analytics-api/overview#setup)
-    guide to enable the API and configure IAM permissions and authentication for
-    your data sources.
-
-## Tools Used
-
-*   `list_accessible_data_agents`: Lists Data Agents you have permission to
-    access in the configured GCP project.
-*   `get_data_agent_info`: Retrieves details about a specific Data Agent given
-    its full resource name.
-*   `ask_data_agent`: Chats with a specific Data Agent using natural language.
-    This tool maintains conversation state: if you ask multiple
-    questions to the same agent in one session, it will use the same
-    conversation, allowing for follow-ups. If you switch agents, a new
-    conversation will be started for the new agent.
-
-## How to Run
-
-1.  Navigate to the root of the ADK repository.
-2.  Run the agent using the ADK CLI:
-    ```bash
-    adk run --agent-path contributing/samples/data_agent
-    ```
-3.  The CLI will prompt you for input. You can ask questions like the examples
-    below.
-
-## Sample prompts
-
-*   "List accessible data agents."
-*   "Using agent
-    `projects/my-project/locations/global/dataAgents/sales-agent-123`, who were
-    my top 3 customers last quarter?"
-*   "How does that compare to the quarter before?"
@@ -1,15 +0,0 @@
-# Copyright 2026 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from . import agent
@@ -1,84 +0,0 @@
-# Copyright 2026 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-
-from google.adk.agents import Agent
-from google.adk.auth.auth_credential import AuthCredentialTypes
-from google.adk.tools.data_agent.config import DataAgentToolConfig
-from google.adk.tools.data_agent.credentials import DataAgentCredentialsConfig
-from google.adk.tools.data_agent.data_agent_toolset import DataAgentToolset
-import google.auth
-import google.auth.transport.requests
-
-# Define the desired credential type.
-# By default use Application Default Credentials (ADC) from the local
-# environment, which can be set up by following
-# https://cloud.google.com/docs/authentication/provide-credentials-adc.
-CREDENTIALS_TYPE = None
-
-if CREDENTIALS_TYPE == AuthCredentialTypes.OAUTH2:
-  # Initiaze the tools to do interactive OAuth
-  # The environment variables OAUTH_CLIENT_ID and OAUTH_CLIENT_SECRET
-  # must be set
-  credentials_config = DataAgentCredentialsConfig(
-      client_id=os.getenv("OAUTH_CLIENT_ID"),
-      client_secret=os.getenv("OAUTH_CLIENT_SECRET"),
-  )
-elif CREDENTIALS_TYPE == AuthCredentialTypes.SERVICE_ACCOUNT:
-  # Initialize the tools to use the credentials in the service account key.
-  # If this flow is enabled, make sure to replace the file path with your own
-  # service account key file
-  # https://cloud.google.com/iam/docs/service-account-creds#user-managed-keys
-  creds, _ = google.auth.load_credentials_from_file(
-      "service_account_key.json",
-      scopes=["https://www.googleapis.com/auth/cloud-platform"],
-  )
-  creds.refresh(google.auth.transport.requests.Request())
-  credentials_config = DataAgentCredentialsConfig(credentials=creds)
-else:
-  # Initialize the tools to use the application default credentials.
-  # https://cloud.google.com/docs/authentication/provide-credentials-adc
-  application_default_credentials, _ = google.auth.default()
-  credentials_config = DataAgentCredentialsConfig(
-      credentials=application_default_credentials
-  )
-
-tool_config = DataAgentToolConfig(
-    max_query_result_rows=100,
-)
-da_toolset = DataAgentToolset(
-    credentials_config=credentials_config,
-    data_agent_tool_config=tool_config,
-    tool_filter=[
-        "list_accessible_data_agents",
-        "get_data_agent_info",
-        "ask_data_agent",
-    ],
-)
-
-root_agent = Agent(
-    name="data_agent",
-    model="gemini-2.0-flash",
-    description="Agent to answer user questions using Data Agents.",
-    instruction=(
-        "## Persona\nYou are a helpful assistant that uses Data Agents"
-        " to answer user questions about their data.\n\n## Tools\n- You can"
-        " list available data agents using `list_accessible_data_agents`.\n-"
-        " You can get information about a specific data agent using"
-        " `get_data_agent_info`.\n- You can chat with a specific data"
-        " agent using `ask_data_agent`.\n"
-    ),
-    tools=[da_toolset],
-)
@@ -126,7 +126,6 @@ test = [
  "litellm>=1.75.5, <1.80.17",                    # For LiteLLM tests
  "llama-index-readers-file>=0.4.0",              # For retrieval tests
  "openai>=1.100.2",                              # For LiteLLM
-  "opentelemetry-instrumentation-google-genai>=0.3b0, <1.0.0",
  "pytest-asyncio>=0.25.0",
  "pytest-mock>=3.14.0",
  "pytest-xdist>=3.6.1",
@@ -33,8 +33,6 @@ class FeatureName(str, Enum):
  BIGTABLE_TOOL_SETTINGS = "BIGTABLE_TOOL_SETTINGS"
  BIGTABLE_TOOLSET = "BIGTABLE_TOOLSET"
  COMPUTER_USE = "COMPUTER_USE"
-  DATA_AGENT_TOOL_CONFIG = "DATA_AGENT_TOOL_CONFIG"
-  DATA_AGENT_TOOLSET = "DATA_AGENT_TOOLSET"
  GOOGLE_CREDENTIALS_CONFIG = "GOOGLE_CREDENTIALS_CONFIG"
  GOOGLE_TOOL = "GOOGLE_TOOL"
  JSON_SCHEMA_FOR_FUNC_DECL = "JSON_SCHEMA_FOR_FUNC_DECL"
@@ -99,12 +97,6 @@ _FEATURE_REGISTRY: dict[FeatureName, FeatureConfig] = {
    FeatureName.COMPUTER_USE: FeatureConfig(
        FeatureStage.EXPERIMENTAL, default_on=True
    ),
-    FeatureName.DATA_AGENT_TOOL_CONFIG: FeatureConfig(
-        FeatureStage.EXPERIMENTAL, default_on=True
-    ),
-    FeatureName.DATA_AGENT_TOOLSET: FeatureConfig(
-        FeatureStage.EXPERIMENTAL, default_on=True
-    ),
    FeatureName.GOOGLE_CREDENTIALS_CONFIG: FeatureConfig(
        FeatureStage.EXPERIMENTAL, default_on=True
    ),
@@ -41,7 +41,6 @@ from ...events.event import Event
 from ...models.base_llm_connection import BaseLlmConnection
 from ...models.llm_request import LlmRequest
 from ...models.llm_response import LlmResponse
-from ...telemetry import tracing
 from ...telemetry.tracing import trace_call_llm
 from ...telemetry.tracing import trace_send_data
 from ...telemetry.tracing import tracer
@@ -772,7 +771,7 @@ class BaseLlmFlow(ABC):
    llm = self.__get_llm(invocation_context)

    async def _call_llm_with_tracing() -> AsyncGenerator[LlmResponse, None]:
-      with tracer.start_as_current_span('call_llm') as span:
+      with tracer.start_as_current_span('call_llm'):
        if invocation_context.run_config.support_cfc:
          invocation_context.live_request_queue = LiveRequestQueue()
          responses_generator = self.run_live(invocation_context)
@@ -823,7 +822,6 @@ class BaseLlmFlow(ABC):
                  model_response_event.id,
                  llm_request,
                  llm_response,
-                  span,
              )
              # Runs after_model_callback if it exists.
              if altered_llm_response := await self._handle_after_model_callback(
@@ -1052,12 +1050,8 @@ class BaseLlmFlow(ABC):

    try:
      async with Aclosing(response_generator) as agen:
-        with tracing.use_generate_content_span(
-            llm_request, invocation_context, model_response_event
-        ) as span:
-          async for llm_response in agen:
-            tracing.trace_generate_content_result(span, llm_response)
-            yield llm_response
+        async for response in agen:
+          yield response
    except Exception as model_error:
      callback_context = CallbackContext(
          invocation_context, event_actions=model_response_event.actions
@@ -82,7 +82,7 @@ class DebugLoggingPlugin(BasePlugin):
  Example:
      >>> debug_plugin = DebugLoggingPlugin(output_path="/tmp/adk_debug.yaml")
      >>> runner = Runner(
-      ...     agent=my_agent,
+      ...     agents=[my_agent],
      ...     plugins=[debug_plugin],
      ... )

@@ -23,59 +23,35 @@

 from __future__ import annotations

-from collections.abc import Iterator
-from collections.abc import Mapping
-from contextlib import contextmanager
 import json
-import logging
 import os
 from typing import Any
+from typing import Optional
 from typing import TYPE_CHECKING

 from google.genai import types
-from google.genai.models import Models
-from opentelemetry import _logs
 from opentelemetry import trace
-from opentelemetry._logs import LogRecord
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_AGENT_DESCRIPTION
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_AGENT_NAME
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_CONVERSATION_ID
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_OPERATION_NAME
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_REQUEST_MODEL
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_RESPONSE_FINISH_REASONS
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_SYSTEM
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_TOOL_CALL_ID
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_TOOL_DESCRIPTION
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_TOOL_NAME
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_TOOL_TYPE
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_USAGE_INPUT_TOKENS
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_USAGE_OUTPUT_TOKENS
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GenAiSystemValues
-from opentelemetry.semconv.schemas import Schemas
-from opentelemetry.trace import Span
-from opentelemetry.util.types import AnyValue
-from opentelemetry.util.types import AttributeValue
-from pydantic import BaseModel

 from .. import version
-from ..utils.model_name_utils import is_gemini_model
+from ..events.event import Event

 # By default some ADK spans include attributes with potential PII data.
 # This env, when set to false, allows to disable populating those attributes.
 ADK_CAPTURE_MESSAGE_CONTENT_IN_SPANS = 'ADK_CAPTURE_MESSAGE_CONTENT_IN_SPANS'
-
-# Standard OTEL env variable to enable logging of prompt/response content.
-OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT = (
-    'OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT'
-)
-
-USER_CONTENT_ELIDED = '<elided>'
+# TODO: Replace with constant from opentelemetry.semconv when it reaches version 1.37 in g3.
+GEN_AI_AGENT_DESCRIPTION = 'gen_ai.agent.description'
+GEN_AI_AGENT_NAME = 'gen_ai.agent.name'
+GEN_AI_CONVERSATION_ID = 'gen_ai.conversation.id'
+GEN_AI_OPERATION_NAME = 'gen_ai.operation.name'
+GEN_AI_TOOL_CALL_ID = 'gen_ai.tool.call.id'
+GEN_AI_TOOL_DESCRIPTION = 'gen_ai.tool.description'
+GEN_AI_TOOL_NAME = 'gen_ai.tool.name'
+GEN_AI_TOOL_TYPE = 'gen_ai.tool.type'

 # Needed to avoid circular imports
 if TYPE_CHECKING:
  from ..agents.base_agent import BaseAgent
  from ..agents.invocation_context import InvocationContext
-  from ..events.event import Event
  from ..models.llm_request import LlmRequest
  from ..models.llm_response import LlmResponse
  from ..tools.base_tool import BaseTool
@@ -83,17 +59,10 @@ if TYPE_CHECKING:
 tracer = trace.get_tracer(
    instrumenting_module_name='gcp.vertex.agent',
    instrumenting_library_version=version.__version__,
-    schema_url=Schemas.V1_36_0.value,
+    # TODO: Replace with constant from opentelemetry.semconv when it reaches version 1.37 in g3.
+    schema_url='https://opentelemetry.io/schemas/1.37.0',
 )

-otel_logger = _logs.get_logger(
-    instrumenting_module_name='gcp.vertex.agent',
-    instrumenting_library_version=version.__version__,
-    schema_url=Schemas.V1_36_0.value,
-)
-
-logger = logging.getLogger('google_adk.' + __name__)
-

 def _safe_json_serialize(obj) -> str:
  """Convert any Python object to a JSON-serializable type or string.
@@ -150,7 +119,7 @@ def trace_agent_invocation(
 def trace_tool_call(
    tool: BaseTool,
    args: dict[str, Any],
-    function_response_event: Event | None,
+    function_response_event: Optional[Event],
 ):
  """Traces tool call.

@@ -265,7 +234,6 @@ def trace_call_llm(
    event_id: str,
    llm_request: LlmRequest,
    llm_response: LlmResponse,
-    span: Span | None = None,
 ):
  """Traces a call to the LLM.

@@ -278,7 +246,7 @@ def trace_call_llm(
    llm_request: The LLM request object.
    llm_response: The LLM response object.
  """
-  span = span or trace.get_current_span()
+  span = trace.get_current_span()
  # Special standard Open Telemetry GenaI attributes that indicate
  # that this is a span related to a Generative AI system.
  span.set_attribute('gen_ai.system', 'gcp.vertex.agent')
@@ -422,167 +390,3 @@ def _should_add_request_response_to_spans() -> bool:
      ADK_CAPTURE_MESSAGE_CONTENT_IN_SPANS, 'true'
  ).lower() in ('false', '0')
  return not disabled_via_env_var
-
-
-@contextmanager
-def use_generate_content_span(
-    llm_request: LlmRequest,
-    invocation_context: InvocationContext,
-    model_response_event: Event,
-) -> Iterator[Span | None]:
-  """Context manager encompassing `generate_content {model.name}` span.
-
-  When an external library for inference instrumentation is installed (e.g. opentelemetry-instrumentation-google-genai),
-  span creation is delegated to said library.
-  """
-
-  common_attributes = {
-      GEN_AI_CONVERSATION_ID: invocation_context.session.id,
-      'gcp.vertex.agent.event_id': model_response_event.id,
-  }
-  if (
-      _is_gemini_agent(invocation_context.agent)
-      and _instrumented_with_opentelemetry_instrumentation_google_genai()
-  ):
-    yield None
-  else:
-    with _use_native_generate_content_span(
-        llm_request=llm_request,
-        common_attributes=common_attributes,
-    ) as span:
-      yield span
-
-
-def _should_log_prompt_response_content() -> bool:
-  return os.getenv(
-      OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT, ''
-  ).lower() in ('1', 'true')
-
-
-def _serialize_content(content: types.ContentUnion) -> AnyValue:
-  if isinstance(content, BaseModel):
-    return content.model_dump()
-  if isinstance(content, str):
-    return content
-  if isinstance(content, list):
-    return [_serialize_content(part) for part in content]
-  return _safe_json_serialize(content)
-
-
-def _serialize_content_with_elision(
-    content: types.ContentUnion | None,
-) -> AnyValue:
-  if not _should_log_prompt_response_content():
-    return USER_CONTENT_ELIDED
-  if content is None:
-    return None
-  return _serialize_content(content)
-
-
-def _instrumented_with_opentelemetry_instrumentation_google_genai() -> bool:
-  maybe_wrapped_function = Models.generate_content
-  print(f'{Models.generate_content.__code__.co_filename=}')
-  while wrapped := getattr(maybe_wrapped_function, '__wrapped__', None):
-    if (
-        'opentelemetry/instrumentation/google_genai'
-        in maybe_wrapped_function.__code__.co_filename
-    ):
-      return True
-    maybe_wrapped_function = wrapped  # pyright: ignore[reportAny]
-
-  return False
-
-
-def _is_gemini_agent(agent: BaseAgent) -> bool:
-  from ..agents.llm_agent import LlmAgent
-
-  if not isinstance(agent, LlmAgent):
-    return False
-
-  if isinstance(agent.model, str):
-    return is_gemini_model(agent.model)
-
-  from ..models.google_llm import Gemini
-
-  return isinstance(agent.model, Gemini)
-
-
-@contextmanager
-def _use_native_generate_content_span(
-    llm_request: LlmRequest,
-    common_attributes: Mapping[str, AttributeValue],
-) -> Iterator[Span]:
-  with tracer.start_as_current_span(
-      f"generate_content {llm_request.model or ''}"
-  ) as span:
-    span.set_attribute(GEN_AI_SYSTEM, _guess_gemini_system_name())
-    span.set_attribute(GEN_AI_OPERATION_NAME, 'generate_content')
-    span.set_attribute(GEN_AI_REQUEST_MODEL, llm_request.model or '')
-    span.set_attributes(common_attributes)
-
-    otel_logger.emit(
-        LogRecord(
-            event_name='gen_ai.system.message',
-            body={
-                'content': _serialize_content_with_elision(
-                    llm_request.config.system_instruction
-                )
-            },
-            attributes={GEN_AI_SYSTEM: _guess_gemini_system_name()},
-        )
-    )
-
-    for content in llm_request.contents:
-      otel_logger.emit(
-          LogRecord(
-              event_name='gen_ai.user.message',
-              body={'content': _serialize_content_with_elision(content)},
-              attributes={GEN_AI_SYSTEM: _guess_gemini_system_name()},
-          )
-      )
-
-    yield span
-
-
-def trace_generate_content_result(span: Span | None, llm_response: LlmResponse):
-  """Trace result of the inference in generate_content span."""
-
-  if span is None:
-    return
-
-  if llm_response.partial:
-    return
-
-  if finish_reason := llm_response.finish_reason:
-    span.set_attribute(GEN_AI_RESPONSE_FINISH_REASONS, [finish_reason.lower()])
-  if usage_metadata := llm_response.usage_metadata:
-    if usage_metadata.prompt_token_count is not None:
-      span.set_attribute(
-          GEN_AI_USAGE_INPUT_TOKENS, usage_metadata.prompt_token_count
-      )
-    if usage_metadata.candidates_token_count is not None:
-      span.set_attribute(
-          GEN_AI_USAGE_OUTPUT_TOKENS, usage_metadata.candidates_token_count
-      )
-
-  otel_logger.emit(
-      LogRecord(
-          event_name='gen_ai.choice',
-          body={
-              'content': _serialize_content_with_elision(llm_response.content),
-              'index': 0,  # ADK always returns a single candidate
-          }
-          | {'finish_reason': llm_response.finish_reason.value}
-          if llm_response.finish_reason is not None
-          else {},
-          attributes={GEN_AI_SYSTEM: _guess_gemini_system_name()},
-      )
-  )
-
-
-def _guess_gemini_system_name() -> str:
-  return (
-      GenAiSystemValues.VERTEX_AI.name.lower()
-      if os.getenv('GOOGLE_GENAI_USE_VERTEXAI', '').lower() in ('true', '1')
-      else GenAiSystemValues.GEMINI.name.lower()
-  )
@@ -15,11 +15,9 @@
 from __future__ import annotations

 from typing import Any
-from typing import Optional
 from typing import TYPE_CHECKING

 from google.genai import types
-from pydantic import BaseModel
 from pydantic import model_validator
 from typing_extensions import override

@@ -39,56 +37,6 @@ if TYPE_CHECKING:
  from ..agents.base_agent import BaseAgent


-def _get_input_schema(agent: BaseAgent) -> Optional[type[BaseModel]]:
-  """Extracts the input_schema from an agent.
-
-  For LlmAgent, returns its input_schema directly.
-  For agents with sub_agents, recursively searches the first sub-agent for an
-  input_schema.
-
-  Args:
-    agent: The agent to extract input_schema from.
-
-  Returns:
-    The input_schema if found, None otherwise.
-  """
-  from ..agents.llm_agent import LlmAgent
-
-  if isinstance(agent, LlmAgent):
-    return agent.input_schema
-
-  # For composite agents, check the first sub-agent
-  if agent.sub_agents:
-    return _get_input_schema(agent.sub_agents[0])
-
-  return None
-
-
-def _get_output_schema(agent: BaseAgent) -> Optional[type[BaseModel]]:
-  """Extracts the output_schema from an agent.
-
-  For LlmAgent, returns its output_schema directly.
-  For agents with sub_agents, recursively searches the last sub-agent for an
-  output_schema.
-
-  Args:
-    agent: The agent to extract output_schema from.
-
-  Returns:
-    The output_schema if found, None otherwise.
-  """
-  from ..agents.llm_agent import LlmAgent
-
-  if isinstance(agent, LlmAgent):
-    return agent.output_schema
-
-  # For composite agents, check the last sub-agent
-  if agent.sub_agents:
-    return _get_output_schema(agent.sub_agents[-1])
-
-  return None
-
-
 class AgentTool(BaseTool):
  """A tool that wraps an agent.

@@ -126,14 +74,12 @@ class AgentTool(BaseTool):

  @override
  def _get_declaration(self) -> types.FunctionDeclaration:
+    from ..agents.llm_agent import LlmAgent
    from ..utils.variant_utils import GoogleLLMVariant

-    input_schema = _get_input_schema(self.agent)
-    output_schema = _get_output_schema(self.agent)
-
-    if input_schema:
+    if isinstance(self.agent, LlmAgent) and self.agent.input_schema:
      result = _automatic_function_calling_util.build_function_declaration(
-          func=input_schema, variant=self._api_variant
+          func=self.agent.input_schema, variant=self._api_variant
      )
      # Override the description with the agent's description
      result.description = self.agent.description
@@ -168,7 +114,7 @@ class AgentTool(BaseTool):
    # Set response schema for non-GEMINI_API variants
    if self._api_variant != GoogleLLMVariant.GEMINI_API:
      # Determine response type based on agent's output schema
-      if output_schema:
+      if isinstance(self.agent, LlmAgent) and self.agent.output_schema:
        # Agent has structured output schema - response is an object
        if is_feature_enabled(FeatureName.JSON_SCHEMA_FOR_FUNC_DECL):
          result.response_json_schema = {'type': 'object'}
@@ -191,15 +137,15 @@ class AgentTool(BaseTool):
      args: dict[str, Any],
      tool_context: ToolContext,
  ) -> Any:
+    from ..agents.llm_agent import LlmAgent
    from ..runners import Runner
    from ..sessions.in_memory_session_service import InMemorySessionService

    if self.skip_summarization:
      tool_context.actions.skip_summarization = True

-    input_schema = _get_input_schema(self.agent)
-    if input_schema:
-      input_value = input_schema.model_validate(args)
+    if isinstance(self.agent, LlmAgent) and self.agent.input_schema:
+      input_value = self.agent.input_schema.model_validate(args)
      content = types.Content(
          role='user',
          parts=[
@@ -266,11 +212,10 @@ class AgentTool(BaseTool):
    merged_text = '\n'.join(
        p.text for p in last_content.parts if p.text and not p.thought
    )
-    output_schema = _get_output_schema(self.agent)
-    if output_schema:
-      tool_result = output_schema.model_validate_json(merged_text).model_dump(
-          exclude_none=True
-      )
+    if isinstance(self.agent, LlmAgent) and self.agent.output_schema:
+      tool_result = self.agent.output_schema.model_validate_json(
+          merged_text
+      ).model_dump(exclude_none=True)
    else:
      tool_result = merged_text
    return tool_result
@@ -1,25 +0,0 @@
-# Copyright 2026 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Data Agent Tools."""
-
-from __future__ import annotations
-
-from .credentials import DataAgentCredentialsConfig
-from .data_agent_toolset import DataAgentToolset
-
-__all__ = [
-    "DataAgentCredentialsConfig",
-    "DataAgentToolset",
-]
@@ -1,35 +0,0 @@
-# Copyright 2026 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import annotations
-
-from pydantic import BaseModel
-from pydantic import ConfigDict
-
-from ...features import experimental
-from ...features import FeatureName
-
-
-@experimental(FeatureName.DATA_AGENT_TOOL_CONFIG)
-class DataAgentToolConfig(BaseModel):
-  """Configuration for Data Agent tools."""
-
-  # Forbid any fields not defined in the model
-  model_config = ConfigDict(extra='forbid')
-
-  max_query_result_rows: int = 50
-  """Maximum number of rows to return from a query.
-
-  By default, the query result will be limited to 50 rows.
-  """
@@ -1,36 +0,0 @@
-# Copyright 2026 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import annotations
-
-from .._google_credentials import BaseGoogleCredentialsConfig
-
-DATA_AGENT_TOKEN_CACHE_KEY = "data_agent_token_cache"
-DATA_AGENT_DEFAULT_SCOPE = ["https://www.googleapis.com/auth/bigquery"]
-
-
-class DataAgentCredentialsConfig(BaseGoogleCredentialsConfig):
-  """Data Agent Credentials Configuration for Google API tools."""
-
-  def __post_init__(self) -> DataAgentCredentialsConfig:
-    """Populate default scope if scopes is None."""
-    super().__post_init__()
-
-    if not self.scopes:
-      self.scopes = DATA_AGENT_DEFAULT_SCOPE
-
-    # Set the token cache key
-    self._token_cache_key = DATA_AGENT_TOKEN_CACHE_KEY
-
-    return self
@@ -1,491 +0,0 @@
-# Copyright 2026 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from __future__ import annotations
-
-import json
-from typing import Any
-
-from google.auth.credentials import Credentials
-import requests
-
-from ..tool_context import ToolContext
-from .config import DataAgentToolConfig
-
-BASE_URL = "https://geminidataanalytics.googleapis.com/v1beta"
-
-
-def _get_http_headers(
-    credentials: Credentials,
-) -> dict[str, str]:
-  """Prepares headers for HTTP requests."""
-  if not credentials.token:
-    error_details = (
-        "The provided credentials object does not have a valid access"
-        " token.\n\nThis is often because the credentials need to be"
-        " refreshed or require specific API scopes. Please ensure the"
-        " credentials are prepared correctly before calling this"
-        " function.\n\nThere may be other underlying causes as well."
-    )
-    raise ValueError(error_details)
-  return {
-      "Authorization": f"Bearer {credentials.token}",
-      "Content-Type": "application/json",
-  }
-
-
-def list_accessible_data_agents(
-    project_id: str,
-    credentials: Credentials,
-) -> dict[str, Any]:
-  """Lists accessible data agents in a project.
-
-  Args:
-      project_id: The project to list agents in.
-      credentials: The credentials to use for the request.
-
-  Returns:
-      A dictionary containing the status and a list of data agents with their
-      detailed information, including name, display_name, description (if
-      available), create_time, update_time, and data_analytics_agent context,
-      or error details if the request fails.
-
-  Examples:
-      >>> list_accessible_data_agents(
-      ...     project_id="my-gcp-project",
-      ...     credentials=credentials,
-      ... )
-      {
-        "status": "SUCCESS",
-        "response": [
-          {
-            "name": "projects/my-project/locations/global/dataAgents/agent1",
-            "displayName": "My Test Agent",
-            "createTime": "2025-10-01T22:44:22.473927629Z",
-            "updateTime": "2025-10-01T22:44:23.094541325Z",
-            "dataAnalyticsAgent": {
-              "publishedContext": {
-                "datasourceReferences": [{
-                  "bq": {
-                    "tableReferences": [{
-                      "projectId": "my-project",
-                      "datasetId": "dataset1",
-                      "tableId": "table1"
-                    }]
-                  }
-                }]
-              }
-            }
-          },
-          {
-            "name": "projects/my-project/locations/global/dataAgents/agent2",
-            "displayName": "",
-            "description": "Description for Agent 2.",
-            "createTime": "2025-06-23T20:23:48.650597312Z",
-            "updateTime": "2025-06-23T20:23:49.437095391Z",
-            "dataAnalyticsAgent": {
-              "publishedContext": {
-                "datasourceReferences": [{
-                  "bq": {
-                    "tableReferences": [{
-                      "projectId": "another-project",
-                      "datasetId": "dataset2",
-                      "tableId": "table2"
-                    }]
-                  }
-                }],
-                "systemInstruction": "You are a helpful assistant.",
-                "options": {"analysis": {"python": {"enabled": True}}}
-              }
-            }
-          }
-        ]
-      }
-  """
-  try:
-    headers = _get_http_headers(credentials)
-    list_url = f"{BASE_URL}/projects/{project_id}/locations/global/dataAgents:listAccessible"
-    resp = requests.get(
-        list_url,
-        headers=headers,
-    )
-    resp.raise_for_status()
-    return {
-        "status": "SUCCESS",
-        "response": resp.json().get("dataAgents", []),
-    }
-  except Exception as ex:  # pylint: disable=broad-except
-    return {
-        "status": "ERROR",
-        "error_details": repr(ex),
-    }
-
-
-def get_data_agent_info(
-    data_agent_name: str,
-    credentials: Credentials,
-) -> dict[str, Any]:
-  """Gets a data agent by name.
-
-  Args:
-      data_agent_name: The name of the agent to get, in format
-        projects/{project}/locations/{location}/dataAgents/{agent}.
-      credentials: The credentials to use for the request.
-
-  Returns:
-      A dictionary containing the status and details of a data agent,
-      including name, display_name, description (if available),
-      create_time, update_time, and data_analytics_agent context,
-      or error details if the request fails.
-
-  Examples:
-      >>> get_data_agent_info(
-      ...
-      data_agent_name="projects/my-project/locations/global/dataAgents/agent-1",
-      ...     credentials=credentials,
-      ... )
-      {
-          "status": "SUCCESS",
-          "response": {
-              "name": "projects/my-project/locations/global/dataAgents/agent-1",
-              "description": "Description for Agent 1.",
-              "createTime": "2025-06-23T20:23:48.650597312Z",
-              "updateTime": "2025-06-23T20:23:49.437095391Z",
-              "dataAnalyticsAgent": {
-                  "publishedContext": {
-                      "systemInstruction": "You are a helpful assistant.",
-                      "options": {"analysis": {"python": {"enabled": True}}},
-                      "datasourceReferences": {
-                          "bq": {
-                              "tableReferences": [{
-                                  "projectId": "my-gcp-project",
-                                  "datasetId": "dataset1",
-                                  "tableId": "table1"
-                              }]
-                          }
-                      },
-                  }
-              }
-          }
-      }
-  """
-  try:
-    headers = _get_http_headers(credentials)
-    get_url = f"{BASE_URL}/{data_agent_name}"
-    resp = requests.get(
-        get_url,
-        headers=headers,
-    )
-    resp.raise_for_status()
-    return {
-        "status": "SUCCESS",
-        "response": resp.json(),
-    }
-  except Exception as ex:  # pylint: disable=broad-except
-    return {
-        "status": "ERROR",
-        "error_details": repr(ex),
-    }
-
-
-def ask_data_agent(
-    data_agent_name: str,
-    query: str,
-    *,
-    credentials: Credentials,
-    settings: DataAgentToolConfig,
-    tool_context: ToolContext,
-) -> dict[str, Any]:
-  """Asks a question to a data agent.
-
-  Args:
-      data_agent_name: The resource name of an existing data agent to ask, in
-        format projects/{project}/locations/{location}/dataAgents/{agent}.
-      query: The question to ask the agent.
-      credentials: The credentials to use for the request.
-      tool_context: The context for the tool.
-
-  Returns:
-      A dictionary with two keys:
-      - 'status': A string indicating the final status (e.g., "SUCCESS").
-      - 'response': A list of dictionaries, where each dictionary
-        represents a step in the agent's execution process (e.g., SQL
-        generation, data retrieval, final answer). Note that the 'Answer'
-        step contains a text response which may summarize findings or refer
-        to previous steps of agent execution, such as 'Data Retrieved', in
-        which cases, the 'Answer' step does not include the result data.
-
-  Examples:
-      A query to a data agent, showing the full return structure.
-      The original question: "Which customer from New York spent the most last
-      month?"
-
-      >>> ask_data_agent(
-      ...
-      data_agent_name="projects/my-project/locations/global/dataAgents/sales-agent",
-      ...     query="Which customer from New York spent the most last month?",
-      ...     credentials=credentials,
-      ...     tool_context=tool_context,
-      ... )
-      {
-        "status": "SUCCESS",
-        "response": [
-          {
-            "Question": "Which customer from New York spent the most last
-            month?"
-          },
-          {
-            "Schema Resolved": [
-              {
-                "source_name": "my-gcp-project.sales_data.customers",
-                "schema": {
-                  "headers": ["Column", "Type", "Description", "Mode"],
-                  "rows": [
-                    ["customer_id", "INT64", "Customer ID", "REQUIRED"],
-                    ["customer_name", "STRING", "Customer Name", "NULLABLE"],
-                  ]
-                }
-              }
-            ]
-          },
-          {
-            "Retrieval Query": {
-              "Query Name": "top_spender",
-              "Question": "Find top spending customer from New York in the last
-              month."
-            }
-          },
-          {
-            "SQL Generated": "SELECT t1.customer_name, SUM(t2.order_total) ... "
-          },
-          {
-            "Data Retrieved": {
-              "headers": ["customer_name", "total_spent"],
-              "rows": [["Jane Doe", 1234.56]],
-              "summary": "Showing all 1 rows."
-            }
-          },
-          {
-            "Answer": "The customer who spent the most last month was Jane Doe."
-          }
-        ]
-      }
-  """
-  try:
-    headers = _get_http_headers(credentials)
-
-    agent_info = get_data_agent_info(data_agent_name, credentials)
-    if agent_info.get("status") == "ERROR":
-      return agent_info
-    parent = data_agent_name.rsplit("/", 2)[0]
-    chat_url = f"{BASE_URL}/{parent}:chat"
-    chat_payload = {
-        "messages": [{"userMessage": {"text": query}}],
-        "dataAgentContext": {
-            "dataAgent": data_agent_name,
-        },
-        "clientIdEnum": "GOOGLE_ADK",
-    }
-    resp = _get_stream(
-        chat_url,
-        chat_payload,
-        headers=headers,
-        max_query_result_rows=settings.max_query_result_rows,
-    )
-    return {"status": "SUCCESS", "response": resp}
-  except Exception as ex:  # pylint: disable=broad-except
-    return {
-        "status": "ERROR",
-        "error_details": repr(ex),
-    }
-
-
-def _get_stream(
-    url: str,
-    ca_payload: dict[str, Any],
-    *,
-    headers: dict[str, str],
-    max_query_result_rows: int,
-) -> list[dict[str, Any]]:
-  """Sends a JSON request to a streaming API and returns a list of messages."""
-  s = requests.Session()
-
-  accumulator = ""
-  messages = []
-
-  with s.post(url, json=ca_payload, headers=headers, stream=True) as resp:
-    for line in resp.iter_lines():
-      if not line:
-        continue
-
-      decoded_line = str(line, encoding="utf-8")
-
-      if decoded_line == "[{":
-        accumulator = "{"
-      elif decoded_line == "}]":
-        accumulator += "}"
-      elif decoded_line == ",":
-        continue
-      else:
-        accumulator += decoded_line
-
-      try:
-        data_json = json.loads(accumulator)
-      except ValueError:
-        continue
-      if "systemMessage" not in data_json:
-        if "error" in data_json:
-          _append_message(
-              messages,
-              _handle_error(data_json["error"]),
-          )
-        continue
-
-      system_message = data_json["systemMessage"]
-      if "text" in system_message:
-        _append_message(
-            messages,
-            _handle_text_response(system_message["text"]),
-        )
-      elif "schema" in system_message:
-        _append_message(
-            messages,
-            _handle_schema_response(system_message["schema"]),
-        )
-      elif "data" in system_message:
-        _append_message(
-            messages,
-            _handle_data_response(
-                system_message["data"], max_query_result_rows
-            ),
-        )
-      accumulator = ""
-  return messages
-
-
-def _format_bq_table_ref(table_ref: dict[str, str]) -> str:
-  """Formats a BigQuery table reference dictionary into a string."""
-  return f"{table_ref.get('projectId')}.{table_ref.get('datasetId')}.{table_ref.get('tableId')}"
-
-
-def _format_schema_as_dict(
-    data: dict[str, Any],
-) -> dict[str, list[Any]]:
-  """Extracts schema fields into a dictionary."""
-  fields = data.get("fields", [])
-  if not fields:
-    return {"columns": []}
-
-  column_details = []
-  headers = ["Column", "Type", "Description", "Mode"]
-  rows: list[list[str, str, str, str]] = []
-  for field in fields:
-    row_list = [
-        field.get("name", ""),
-        field.get("type", ""),
-        field.get("description", ""),
-        field.get("mode", ""),
-    ]
-    rows.append(row_list)
-
-  return {"headers": headers, "rows": rows}
-
-
-def _format_datasource_as_dict(datasource: dict[str, Any]) -> dict[str, Any]:
-  """Formats a full datasource object into a dictionary with its name and schema."""
-  source_name = _format_bq_table_ref(datasource["bigqueryTableReference"])
-
-  schema = _format_schema_as_dict(datasource["schema"])
-  return {"source_name": source_name, "schema": schema}
-
-
-def _handle_text_response(resp: dict[str, Any]) -> dict[str, str]:
-  """Formats a text response into a dictionary."""
-  parts = resp.get("parts", [])
-  return {"Answer": "".join(parts)}
-
-
-def _handle_schema_response(resp: dict[str, Any]) -> dict[str, Any]:
-  """Formats a schema response into a dictionary."""
-  if "query" in resp:
-    return {"Question": resp["query"].get("question", "")}
-  elif "result" in resp:
-    datasources = resp["result"].get("datasources", [])
-    # Format each datasource and join them with newlines
-    formatted_sources = [_format_datasource_as_dict(ds) for ds in datasources]
-    return {"Schema Resolved": formatted_sources}
-  return {}
-
-
-def _handle_data_response(
-    resp: dict[str, Any], max_query_result_rows: int
-) -> dict[str, Any]:
-  """Formats a data response into a dictionary."""
-  if "query" in resp:
-    query = resp["query"]
-    return {
-        "Retrieval Query": {
-            "Query Name": query.get("name", "N/A"),
-            "Question": query.get("question", "N/A"),
-        }
-    }
-  elif "generatedSql" in resp:
-    return {"SQL Generated": resp["generatedSql"]}
-  elif "result" in resp:
-    schema = resp["result"]["schema"]
-    headers = [field.get("name") for field in schema.get("fields", [])]
-
-    all_rows = resp["result"].get("data", [])
-    total_rows = len(all_rows)
-
-    compact_rows = []
-    for row_dict in all_rows[:max_query_result_rows]:
-      row_values = [row_dict.get(header) for header in headers]
-      compact_rows.append(row_values)
-
-    summary_string = f"Showing all {total_rows} rows."
-    if total_rows > max_query_result_rows:
-      summary_string = (
-          f"Showing the first {len(compact_rows)} of {total_rows} total rows."
-      )
-
-    return {
-        "Data Retrieved": {
-            "headers": headers,
-            "rows": compact_rows,
-            "summary": summary_string,
-        }
-    }
-
-  return {}
-
-
-def _handle_error(resp: dict[str, Any]) -> dict[str, dict[str, Any]]:
-  """Formats an error response into a dictionary."""
-  return {
-      "Error": {
-          "Code": resp.get("code", "N/A"),
-          "Message": resp.get("message", "No message provided."),
-      }
-  }
-
-
-def _append_message(
-    messages: list[dict[str, Any]],
-    new_message: dict[str, Any],
-):
-  """Appends a message to the list."""
-  if not new_message:
-    return
-
-  messages.append(new_message)
@@ -1,93 +0,0 @@
-# Copyright 2026 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import annotations
-
-from typing import List
-from typing import Optional
-from typing import Union
-
-from google.adk.agents.readonly_context import ReadonlyContext
-from typing_extensions import override
-
-from . import data_agent_tool
-from ...features import experimental
-from ...features import FeatureName
-from ...tools.base_tool import BaseTool
-from ...tools.base_toolset import BaseToolset
-from ...tools.base_toolset import ToolPredicate
-from ...tools.google_tool import GoogleTool
-from .config import DataAgentToolConfig
-from .credentials import DataAgentCredentialsConfig
-
-
-@experimental(FeatureName.DATA_AGENT_TOOLSET)
-class DataAgentToolset(BaseToolset):
-  """Data Agent Toolset contains tools for interacting with data agents."""
-
-  def __init__(
-      self,
-      *,
-      tool_filter: Optional[Union[ToolPredicate, List[str]]] = None,
-      credentials_config: Optional[DataAgentCredentialsConfig] = None,
-      data_agent_tool_config: Optional[DataAgentToolConfig] = None,
-  ):
-    super().__init__(tool_filter=tool_filter)
-    self._credentials_config = credentials_config
-    self._tool_settings = (
-        data_agent_tool_config
-        if data_agent_tool_config
-        else DataAgentToolConfig()
-    )
-
-  def _is_tool_selected(
-      self, tool: BaseTool, readonly_context: ReadonlyContext
-  ) -> bool:
-    if self.tool_filter is None:
-      return True
-
-    if isinstance(self.tool_filter, ToolPredicate):
-      return self.tool_filter(tool, readonly_context)
-
-    if isinstance(self.tool_filter, list):
-      return tool.name in self.tool_filter
-
-    return False
-
-  @override
-  async def get_tools(
-      self, readonly_context: Optional[ReadonlyContext] = None
-  ) -> List[BaseTool]:
-    all_tools = [
-        GoogleTool(
-            func=func,
-            credentials_config=self._credentials_config,
-            tool_settings=self._tool_settings,
-        )
-        for func in [
-            data_agent_tool.list_accessible_data_agents,
-            data_agent_tool.get_data_agent_info,
-            data_agent_tool.ask_data_agent,
-        ]
-    ]
-
-    return [
-        tool
-        for tool in all_tools
-        if self._is_tool_selected(tool, readonly_context)
-    ]
-
-  @override
-  async def close(self):
-    pass
@@ -13,4 +13,4 @@
 # limitations under the License.

 # version: major.minor.patch
-__version__ = "1.23.0"
+__version__ = "1.22.1"
@@ -12,17 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

+import asyncio
 import gc
 import sys

 from google.adk.agents import base_agent
 from google.adk.agents.llm_agent import Agent
 from google.adk.models.base_llm import BaseLlm
+from google.adk.models.llm_response import LlmResponse
 from google.adk.telemetry import tracing
 from google.adk.tools import FunctionTool
 from google.adk.utils.context_utils import Aclosing
+from google.genai.types import Content
 from google.genai.types import Part
-from opentelemetry.instrumentation.google_genai import GoogleGenAiSdkInstrumentor
 from opentelemetry.sdk.trace import TracerProvider
 from opentelemetry.sdk.trace.export import SimpleSpanProcessor
 from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter
@@ -119,8 +121,6 @@ async def test_tracer_start_as_current_span(
      'call_llm',
      'call_llm',
      'execute_tool some_tool',
-      'generate_content mock',
-      'generate_content mock',
      'invocation',
      'invoke_agent some_root_agent',
  ]
@@ -162,50 +162,3 @@ async def test_exception_preserves_attributes(
      for span in spans
      if span.name != 'invocation'  # not expected to have attributes
  )
-
-
-@pytest.mark.asyncio
-async def test_no_generate_content_for_gemini_model_when_already_instrumented(
-    test_runner: TestInMemoryRunner,
-    span_exporter: InMemorySpanExporter,
-    monkeypatch: pytest.MonkeyPatch,
-):
-  """Tests"""
-  # Arrange
-  monkeypatch.setattr(
-      tracing,
-      '_instrumented_with_opentelemetry_instrumentation_google_genai',
-      lambda: True,
-  )
-  monkeypatch.setattr(
-      tracing,
-      '_is_gemini_agent',
-      lambda _: True,
-  )
-
-  # Act
-  async with Aclosing(test_runner.run_async_with_new_session_agen('')) as agen:
-    async for _ in agen:
-      pass
-
-  # Assert
-  spans = span_exporter.get_finished_spans()
-  assert not any(span.name.startswith('generate_content') for span in spans)
-
-
-def test_instrumented_with_opentelemetry_instrumentation_google_genai():
-  instrumentor = GoogleGenAiSdkInstrumentor()
-
-  assert (
-      not tracing._instrumented_with_opentelemetry_instrumentation_google_genai()
-  )
-  try:
-    instrumentor.instrument()
-    assert (
-        tracing._instrumented_with_opentelemetry_instrumentation_google_genai()
-    )
-  finally:
-    instrumentor.uninstrument()
-  assert (
-      not tracing._instrumented_with_opentelemetry_instrumentation_google_genai()
-  )
@@ -26,21 +26,11 @@ from google.adk.sessions.in_memory_session_service import InMemorySessionService
 from google.adk.telemetry.tracing import ADK_CAPTURE_MESSAGE_CONTENT_IN_SPANS
 from google.adk.telemetry.tracing import trace_agent_invocation
 from google.adk.telemetry.tracing import trace_call_llm
-from google.adk.telemetry.tracing import trace_generate_content_result
 from google.adk.telemetry.tracing import trace_merged_tool_calls
 from google.adk.telemetry.tracing import trace_send_data
 from google.adk.telemetry.tracing import trace_tool_call
-from google.adk.telemetry.tracing import use_generate_content_span
 from google.adk.tools.base_tool import BaseTool
 from google.genai import types
-from opentelemetry._logs import LogRecord
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_CONVERSATION_ID
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_OPERATION_NAME
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_REQUEST_MODEL
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_RESPONSE_FINISH_REASONS
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_SYSTEM
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_USAGE_INPUT_TOKENS
-from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GEN_AI_USAGE_OUTPUT_TOKENS
 import pytest


@@ -622,142 +612,3 @@ async def test_trace_send_data_disabling_request_response_content(
      call_obj.args
      for call_obj in mock_span_fixture.set_attribute.call_args_list
  )
-
-
-@pytest.mark.asyncio
-@mock.patch('google.adk.telemetry.tracing.otel_logger')
-@mock.patch('google.adk.telemetry.tracing.tracer')
-@mock.patch(
-    'google.adk.telemetry.tracing._guess_gemini_system_name',
-    return_value='test_system',
-)
-@pytest.mark.parametrize('capture_content', [True, False])
-async def test_generate_content_span(
-    mock_guess_system_name,
-    mock_tracer,
-    mock_otel_logger,
-    monkeypatch,
-    capture_content,
-):
-  """Test native generate_content span creation with attributes and logs."""
-  # Arrange
-  monkeypatch.setenv(
-      'OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT',
-      str(capture_content).lower(),
-  )
-  monkeypatch.setattr(
-      'google.adk.telemetry.tracing._instrumented_with_opentelemetry_instrumentation_google_genai',
-      lambda: False,
-  )
-
-  agent = LlmAgent(name='test_agent', model='not-a-gemini-model')
-  invocation_context = await _create_invocation_context(agent)
-
-  system_instruction = types.Content(
-      parts=[types.Part.from_text(text='You are a helpful assistant.')],
-  )
-  user_content1 = types.Content(role='user', parts=[types.Part(text='Hello')])
-  user_content2 = types.Content(role='user', parts=[types.Part(text='World')])
-
-  model_content = types.Content(
-      role='model', parts=[types.Part(text='Response')]
-  )
-
-  llm_request = LlmRequest(
-      model='some-model',
-      contents=[user_content1, user_content2],
-      config=types.GenerateContentConfig(system_instruction=system_instruction),
-  )
-  llm_response = LlmResponse(
-      content=model_content,
-      finish_reason=types.FinishReason.STOP,
-      usage_metadata=types.GenerateContentResponseUsageMetadata(
-          prompt_token_count=10,
-          candidates_token_count=20,
-      ),
-  )
-
-  model_response_event = mock.MagicMock()
-  model_response_event.id = 'event-123'
-
-  mock_span = (
-      mock_tracer.start_as_current_span.return_value.__enter__.return_value
-  )
-
-  # Act
-  with use_generate_content_span(
-      llm_request, invocation_context, model_response_event
-  ) as span:
-    assert span is mock_span
-
-    trace_generate_content_result(span, llm_response)
-
-  # Assert Span
-  mock_tracer.start_as_current_span.assert_called_once_with(
-      'generate_content some-model'
-  )
-
-  mock_span.set_attribute.assert_any_call(GEN_AI_SYSTEM, 'test_system')
-  mock_span.set_attribute.assert_any_call(
-      GEN_AI_OPERATION_NAME, 'generate_content'
-  )
-  mock_span.set_attribute.assert_any_call(GEN_AI_REQUEST_MODEL, 'some-model')
-  mock_span.set_attribute.assert_any_call(
-      GEN_AI_RESPONSE_FINISH_REASONS, ['stop']
-  )
-  mock_span.set_attribute.assert_any_call(GEN_AI_USAGE_INPUT_TOKENS, 10)
-  mock_span.set_attribute.assert_any_call(GEN_AI_USAGE_OUTPUT_TOKENS, 20)
-
-  mock_span.set_attributes.assert_called_once_with({
-      GEN_AI_CONVERSATION_ID: invocation_context.session.id,
-      'gcp.vertex.agent.event_id': 'event-123',
-  })
-
-  # Assert Logs
-  assert mock_otel_logger.emit.call_count == 4
-
-  expected_system_body = {
-      'content': (
-          system_instruction.model_dump() if capture_content else '<elided>'
-      )
-  }
-  expected_user1_body = {
-      'content': user_content1.model_dump() if capture_content else '<elided>'
-  }
-  expected_user2_body = {
-      'content': user_content2.model_dump() if capture_content else '<elided>'
-  }
-  expected_choice_body = {
-      'content': model_content.model_dump() if capture_content else '<elided>',
-      'index': 0,
-      'finish_reason': 'STOP',
-  }
-
-  log_records: list[LogRecord] = [
-      call.args[0] for call in mock_otel_logger.emit.call_args_list
-  ]
-
-  system_log = next(
-      (lr for lr in log_records if lr.event_name == 'gen_ai.system.message'),
-      None,
-  )
-  assert system_log is not None
-  assert system_log.body == expected_system_body
-  assert system_log.attributes == {GEN_AI_SYSTEM: 'test_system'}
-
-  user_logs = [
-      lr for lr in log_records if lr.event_name == 'gen_ai.user.message'
-  ]
-  assert len(user_logs) == 2
-  assert expected_user1_body == user_logs[0].body
-  assert expected_user2_body == user_logs[1].body
-  for log in user_logs:
-    assert log.attributes == {GEN_AI_SYSTEM: 'test_system'}
-
-  choice_log = next(
-      (lr for lr in log_records if lr.event_name == 'gen_ai.choice'),
-      None,
-  )
-  assert choice_log is not None
-  assert choice_log.body == expected_choice_body
-  assert choice_log.attributes == {GEN_AI_SYSTEM: 'test_system'}
@@ -1,198 +0,0 @@
-# Copyright 2026 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import pathlib
-from unittest import mock
-
-from google.adk.tools.data_agent import data_agent_tool
-from google.adk.tools.tool_context import ToolContext
-import pytest
-import requests
-import yaml
-
-
-@mock.patch.object(data_agent_tool, "requests", autospec=True)
-def test_list_accessible_data_agents_success(mock_requests):
-  """Tests list_accessible_data_agents success path."""
-  mock_creds = mock.Mock()
-  mock_creds.token = "fake-token"
-  mock_response = mock.Mock()
-  mock_response.json.return_value = {"dataAgents": ["agent1", "agent2"]}
-  mock_response.raise_for_status.return_value = None
-  mock_requests.get.return_value = mock_response
-  result = data_agent_tool.list_accessible_data_agents(
-      "test-project", mock_creds
-  )
-  assert result["status"] == "SUCCESS"
-  assert result["response"] == ["agent1", "agent2"]
-  mock_requests.get.assert_called_once()
-
-
-@mock.patch.object(data_agent_tool, "requests", autospec=True)
-def test_list_accessible_data_agents_exception(mock_requests):
-  """Tests list_accessible_data_agents exception path."""
-  mock_creds = mock.Mock()
-  mock_creds.token = "fake-token"
-  mock_requests.get.side_effect = Exception("List failed!")
-  result = data_agent_tool.list_accessible_data_agents(
-      "test-project", mock_creds
-  )
-  assert result["status"] == "ERROR"
-  assert "List failed!" in result["error_details"]
-  mock_requests.get.assert_called_once()
-
-
-@mock.patch.object(data_agent_tool, "requests", autospec=True)
-def test_get_data_agent_info_success(mock_requests):
-  """Tests get_data_agent_info success path."""
-  mock_creds = mock.Mock()
-  mock_creds.token = "fake-token"
-  mock_response = mock.Mock()
-  mock_response.json.return_value = "agent_info"
-  mock_response.raise_for_status.return_value = None
-  mock_requests.get.return_value = mock_response
-  result = data_agent_tool.get_data_agent_info("agent_name", mock_creds)
-  assert result["status"] == "SUCCESS"
-  assert result["response"] == "agent_info"
-  mock_requests.get.assert_called_once()
-
-
-@mock.patch.object(data_agent_tool, "requests", autospec=True)
-def test_get_data_agent_info_exception(mock_requests):
-  """Tests get_data_agent_info exception path."""
-  mock_creds = mock.Mock()
-  mock_creds.token = "fake-token"
-  mock_requests.get.side_effect = Exception("Get failed!")
-  result = data_agent_tool.get_data_agent_info("agent_name", mock_creds)
-  assert result["status"] == "ERROR"
-  assert "Get failed!" in result["error_details"]
-  mock_requests.get.assert_called_once()
-
-
-@mock.patch.object(data_agent_tool, "_get_stream", autospec=True)
-@mock.patch.object(data_agent_tool, "requests", autospec=True)
-@mock.patch.object(data_agent_tool, "get_data_agent_info", autospec=True)
-def test_ask_data_agent_success(
-    mock_get_agent_info, mock_requests, mock_get_stream
-):
-  """Tests ask_data_agent success path."""
-  mock_creds = mock.Mock()
-  mock_creds.token = "fake-token"
-  mock_get_agent_info.return_value = {"status": "SUCCESS", "response": {}}
-  mock_get_stream.return_value = [
-      {"Answer": "response1"},
-      {"Answer": "response2"},
-  ]
-  mock_invocation_context = mock.Mock()
-  mock_invocation_context.session.state = {}
-  mock_context = ToolContext(mock_invocation_context)
-  mock_settings = mock.Mock()
-
-  result = data_agent_tool.ask_data_agent(
-      "projects/p/locations/l/dataAgents/a",
-      "query",
-      credentials=mock_creds,
-      tool_context=mock_context,
-      settings=mock_settings,
-  )
-  assert result["status"] == "SUCCESS"
-  assert result["response"] == [
-      {"Answer": "response1"},
-      {"Answer": "response2"},
-  ]
-  mock_get_agent_info.assert_called_once()
-  mock_get_stream.assert_called_once()
-
-
-@mock.patch.object(data_agent_tool, "_get_stream", autospec=True)
-@mock.patch.object(data_agent_tool, "requests", autospec=True)
-@mock.patch.object(data_agent_tool, "get_data_agent_info", autospec=True)
-def test_ask_data_agent_exception(
-    mock_get_agent_info, mock_requests, mock_get_stream
-):
-  """Tests ask_data_agent exception path."""
-  mock_creds = mock.Mock()
-  mock_creds.token = "fake-token"
-  mock_get_agent_info.return_value = {"status": "SUCCESS", "response": {}}
-  mock_get_stream.side_effect = Exception("Chat failed!")
-  mock_invocation_context = mock.Mock()
-  mock_invocation_context.session.state = {}
-  mock_context = ToolContext(mock_invocation_context)
-  mock_settings = mock.Mock()
-
-  result = data_agent_tool.ask_data_agent(
-      "projects/p/locations/l/dataAgents/a",
-      "query",
-      credentials=mock_creds,
-      tool_context=mock_context,
-      settings=mock_settings,
-  )
-  assert result["status"] == "ERROR"
-  assert "Chat failed!" in result["error_details"]
-  mock_get_stream.assert_called_once()
-
-
-@pytest.mark.parametrize(
-    "case_file_path",
-    [
-        pytest.param("test_data/ask_data_insights_penguins_highest_mass.yaml"),
-    ],
-)
-@mock.patch.object(requests.Session, "post")
-def test_get_stream_from_file(mock_post, case_file_path):
-  """Runs a full integration test for the _get_stream function using data from a specific file."""
-  # 1. Construct the full, absolute path to the data file
-  full_path = pathlib.Path(__file__).parent.parent / "bigquery" / case_file_path
-
-  # 2. Load the test case data from the specified YAML file
-  with open(full_path, "r", encoding="utf-8") as f:
-    case_data = yaml.safe_load(f)
-
-  # 3. Prepare the mock stream and expected output from the loaded data
-  mock_stream_str = case_data["mock_api_stream"]
-  fake_stream_lines = [
-      line.encode("utf-8") for line in mock_stream_str.splitlines()
-  ]
-  # Load the expected output as a list of dictionaries, not a single string
-  expected_final_list = case_data["expected_output"]
-  data_retrieved = {
-      "Data Retrieved": {
-          "headers": ["island", "average_body_mass"],
-          "rows": [
-              ["Biscoe", "4716.017964071853"],
-              ["Dream", "3712.9032258064512"],
-              ["Torgersen", "3706.3725490196075"],
-          ],
-          "summary": "Showing all 3 rows.",
-      }
-  }
-  expected_final_list.insert(-1, data_retrieved)
-
-  # 4. Configure the mock for requests.post
-  mock_response = mock.Mock()
-  mock_response.iter_lines.return_value = fake_stream_lines
-  # Add raise_for_status mock which is called in the updated code
-  mock_response.raise_for_status.return_value = None
-  mock_post.return_value.__enter__.return_value = mock_response
-
-  # 5. Call the function under test
-  result = data_agent_tool._get_stream(  # pylint: disable=protected-access
-      url="fake_url",
-      ca_payload={},
-      headers={},
-      max_query_result_rows=50,
-  )
-
-  # 6. Assert that the final list of dicts matches the expected output
-  assert result == expected_final_list
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Yasir Modak	7e00c52074	Update bug_report.md	2026-01-22 12:40:37 -08:00
Yasir Modak	58204fb421	Merge branch 'main' into ymodak-patch-1	2026-01-21 19:32:46 -08:00
Yasir Modak	565a0e1ecb	Merge branch 'main' into ymodak-patch-1	2026-01-21 14:05:11 -08:00
Yasir Modak	49da199c02	Update .github/ISSUE_TEMPLATE/bug_report.md Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2026-01-20 13:27:59 -08:00
Yasir Modak	88a246f308	Update .github/ISSUE_TEMPLATE/bug_report.md Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2026-01-20 13:27:45 -08:00
Yasir Modak	fd46b7a66b	Update bug_report.md	2026-01-20 13:25:16 -08:00