Merge branch 'main' into agent-evaluator-support-custom-metric

Author: ftnext

.github/workflows/analyze-releases-for-adk-docs-updates.yml

@@ -24,7 +24,7 @@ jobs:
           python-version: '3.11'
 
       - name: Load adk-bot SSH Private Key
-        uses: webfactory/ssh-agent@v0.9.0
+        uses: webfactory/ssh-agent@v0.9.1
         with:
           ssh-private-key: ${{ secrets.ADK_BOT_SSH_PRIVATE_KEY }}
 

.github/workflows/mypy-new-errors.yml

@@ -15,17 +15,17 @@ jobs:
         python-version: ['3.10', '3.11', '3.12', '3.13',]
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
 
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v5
+        uses: astral-sh/setup-uv@v7
 
       - name: Generate Baseline (Main)
         run: |

.github/workflows/mypy.yml

@@ -14,13 +14,13 @@ jobs:
         python-version: ['3.10', '3.11', '3.12', '3.13',]
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v1
+        uses: astral-sh/setup-uv@v7
 
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 

.github/workflows/python-unit-tests.yml

@@ -37,7 +37,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
 
     - name: Install the latest version of uv
-      uses: astral-sh/setup-uv@v6
+      uses: astral-sh/setup-uv@v7
 
     - name: Install dependencies
       run: |

contributing/samples/bigquery/README.md

@@ -119,6 +119,39 @@ type.
 
 1. Set `CREDENTIALS_TYPE=AuthCredentialTypes.OAUTH2` in `agent.py` and run the agent
 
+### With Agent Engine and Gemini Enterprise
+
+This mode is useful when you deploy the agent to Vertex AI Agent Engine and
+want to make it available in Gemini Enterprise, allowing the agent to access
+BigQuery on behalf of the end-user. This setup uses OAuth 2.0 managed by
+Gemini Enterprise.
+
+1. Create an Authorization resource in Gemini Enterprise by following the guide at
+[Register and manage ADK agents hosted on Vertex AI Agent Engine](https://docs.cloud.google.com/gemini/enterprise/docs/register-and-manage-an-adk-agent) to:
+  * Create OAuth 2.0 credentials in your Google Cloud project.
+  * Create an Authorization resource in Gemini Enterprise, linking it to your
+    OAuth 2.0 credentials. When creating this resource, you will define a
+    unique identifier (`AUTH_ID`).
+
+2. Prepare the sample agent for consuming the access token provided by Gemini
+Enterprise and deploy to Vertex AI Agent Engine.
+  * Set `CREDENTIALS_TYPE=AuthCredentialTypes.HTTP` in `agent.py`. This
+configures the agent to use access tokens provided by Gemini Enterprise and
+provided by Agent Engine via the tool context.
+  * Replace `AUTH_ID` in `agent.py` with your authorization resource identifier
+    from step 1.
+  * [Deploy your agent to Vertex AI Agent Engine](https://google.github.io/adk-docs/deploy/agent-engine/).
+
+3. [Register your deployed agent with Gemini Enterprise](https://docs.cloud.google.com/gemini/enterprise/docs/register-and-manage-an-adk-agent#register-an-adk-agent), attaching the
+Authorization resource `AUTH_ID`. When this agent is invoked through Gemini
+Enterprise, an access token obtained using these OAuth credentials will be
+passed to the agent and made available in the ADK `tool_context` under the key
+`AUTH_ID`, which `agent.py` is configured to use.
+
+Once registered, users interacting with your agent via Gemini Enterprise will
+go through an OAuth consent flow, and Agent Engine will provide the agent with
+the necessary access tokens to call BigQuery APIs on their behalf.
+
 ## Sample prompts
 
 * which weather datasets exist in bigquery public data?

contributing/samples/bigquery/agent.py

@@ -56,6 +56,17 @@
   # https://cloud.google.com/iam/docs/service-account-creds#user-managed-keys
   creds, _ = google.auth.load_credentials_from_file("service_account_key.json")
   credentials_config = BigQueryCredentialsConfig(credentials=creds)
+elif CREDENTIALS_TYPE == AuthCredentialTypes.HTTP:
+  # Initialize the tools to use the externally provided access token. One such
+  # use case is creating an authorization resource `AUTH_ID` in Gemini
+  # Enterprise and using it to register an ADK agent deployed to Vertex AI
+  # Agent Engine with Gemini Enterprise. See for more details:
+  # https://docs.cloud.google.com/gemini/enterprise/docs/register-and-manage-an-adk-agent.
+  # This access token will be passed to the agent via the tool context, with
+  # the key `AUTH_ID`.
+  credentials_config = BigQueryCredentialsConfig(
+      external_access_token_key="AUTH_ID"
+  )
 else:
   # Initialize the tools to use the application default credentials.
   # https://cloud.google.com/docs/authentication/provide-credentials-adc

contributing/samples/gepa/adk_agent.py

@@ -159,7 +159,7 @@ def _adk_agent(
 
 
 class _UserAgent(base_agent.BaseAgent):
-  """An agent that wraps the provided environment and simulates an user."""
+  """An agent that wraps the provided environment and simulates a user."""
 
   env: Env
 

contributing/samples/gepa/tau_bench_agent.py

@@ -103,7 +103,7 @@ def solve(
       max_num_steps: The maximum number of steps to run the agent.
 
     Returns:
-      The result of the solve.
+      The result of the solve function.
 
     Raises:
       - ValueError: If the LLM inference failed.

contributing/samples/skills_agent/__init__.py

@@ -0,0 +1,15 @@
+# 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

contributing/samples/skills_agent/agent.py

@@ -0,0 +1,54 @@
+# 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.
+
+"""Example agent demonstrating the use of SkillToolset."""
+
+import inspect
+
+from google.adk import Agent
+from google.adk.skills import models
+from google.adk.tools import skill_toolset
+
+greeting_skill = models.Skill(
+    frontmatter=models.Frontmatter(
+        name="greeting-skill",
+        description=(
+            "A friendly greeting skill that can say hello to a specific person."
+        ),
+    ),
+    instructions=(
+        "Step 1: Read the 'references/hello_world.txt' file to understand how"
+        " to greet the user. Step 2: Return a greeting based on the reference."
+    ),
+    resources=models.Resources(
+        references={
+            "hello_world.txt": "Hello! 👋👋👋 So glad to have you here! ✨✨✨",
+            "example.md": "This is an example reference.",
+        },
+    ),
+)
+
+my_skill_toolset = skill_toolset.SkillToolset(skills=[greeting_skill])
+
+root_agent = Agent(
+    model="gemini-2.5-flash",
+    name="skill_user_agent",
+    description="An agent that can use specialized skills.",
+    instruction=(
+        "You are a helpful assistant that can leverage skills to perform tasks."
+    ),
+    tools=[
+        my_skill_toolset,
+    ],
+)