feat: add ADK agent to be used in Tau2 bench

python/agents/tau2-benchmark-agent/README.md

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+# Using ADK Agent with τ-bench
+
+This guide provides instructions on how to set up and use a custom agent that integrates with the Google ADK (Agent Development Kit) within the τ-bench framework.
+
+## 1. Clone the Repository
+
+First, clone the specific version of the τ-bench repository that is compatible with these modifications.
+
+```bash
+git clone https://github.com/sierra-research/tau2-bench.git
+cd tau2-bench
+git checkout cc97b34
+```
+
+## 2. Setup and Installation
+
+Follow the standard installation instructions to set up the environment for the project.
+
+
+Ensure that you have venv:
+
+```bash
+sudo apt install python3.13-venv
+```
+
+```bash
+python3 -m venv .venv
+source .venv/bin/activate
+
+pip install -e .
+```
+
+You can check tau data:
+
+```bash
+tau2 check-data
+```
+
+After the standard installation, you need to install the Google ADK package.
+
+```bash
+pip install google-adk
+```
+
+tenacity dependecy version may conflict with that of tau2 repo. Upgrading it back in favor of tau2.
+
+```bash
+pip install --upgrade tenacity
+```
+
+**IMPORTANT:** Gemini 3 Pro model makes sending thought signatures mandatory. Tau2 bench relies on litellm for user simulation and non-adk agent simulation. Until https://github.com/BerriAI/litellm/pull/16812 is merged to litellm repository, the PR needs to be applied as shown below:
+
+```bash
+git clone --filter=blob:none --quiet https://github.com/BerriAI/litellm.git /tmp/litellm-pr-16812
+cd /tmp/litellm-pr-16812
+git checkout -q pull/16812/head
+git fetch origin pull/16812/head:pr-16812
+git checkout pr-16812
+pip install .
+cd -
+```
+
+## 3. Add env params
+
+Create `.env` file at root with the following content.
+
+```bash
+GOOGLE_GENAI_USE_VERTEXAI=true
+GOOGLE_CLOUD_PROJECT=your_project_id
+GOOGLE_CLOUD_LOCATION=global
+VERTEXAI_LOCATION=global
+```
+
+## 4. Copy Modified Files
+
+You will need to copy two files into your local `tau2-bench` directory. These files contain the implementation of the ADK agent and its unit tests.
+
+Assuming you have the following files available:
+
+*   `tau2_agent/adk_agent.py`
+*   `tests/test_adk_agent.py`
+
+Copy them to the correct locations within the project:
+
+```bash
+cp ../tau2_agent/adk_agent.py src/tau2/agent/adk_agent.py
+cp ../tests/test_adk_agent.py tests/test_adk_agent.py
+```
+
+## 4.1. Registering the ADK Agent
+
+To enable the `adk_agent` within the `tau2-bench` framework, you need to manually modify the `src/tau2/registry.py` file in your `tau2-bench` repository.
+
+1.  **Add the import statement** for `AdkAgent` at the top of `src/tau2/registry.py`:
+    ```python
+    from tau2.agent.adk_agent import AdkAgent
+    ```
+2.  **Register the agent** within the `try` block where other default components are registered (look for `registry = Registry()`):
+    ```python
+    try:
+        registry = Registry()
+        logger.debug("Registering default components...")
+        # ... existing registrations ...
+        registry.register_agent(AdkAgent, "adk_agent") # Add this line
+        # ... more existing registrations ...
+    ```
+    This allows the `adk_agent` to be selected via command-line arguments (e.g., `--agent adk_agent`).
+
+## 5. Running the ADK Agent
+
+Once the files are in place and dependencies are installed, you can run a simulation using the `adk_agent`.
+
+### Changing baseline ADK agent
+
+You can implement an improved agent and replace `AdkLlmAgent` implementation `_create_agent` returns as shown below. Return type is BaseAgent which accommodates workflow agents as well.
+
+```py
+def _create_agent(name: str, model: Union[str, BaseLlm], instruction: str, tools: List[Tool]) -> BaseAgent:
+    adk_tools = [
+        AdkTool(
+            types.FunctionDeclaration(
+                name=tool.openai_schema['function']['name'],
+                description=tool.openai_schema['function'].get('description', ''),
+                parameters_json_schema=tool.openai_schema['function']['parameters'],
+            )
+        )
+        for tool in tools
+    ]
+    return AdkLlmAgent(
+        model=model,
+        name=name,
+        instruction=instruction,
+        tools=adk_tools,
+        planner=built_in_planner.BuiltInPlanner(
+            thinking_config=types.ThinkingConfig(include_thoughts=True),
+        ),
+    )
+```
+
+### Limited run
+
+Here is an example command to run the agent on an airline domain task:
+
+```bash
+tau2 run --domain airline --agent adk_agent --agent-llm vertex_ai/gemini-3-pro-preview --user-llm vertex_ai/gemini-3-pro-preview --num-trials 1 --num-tasks 1 --user-llm-args '{"temperature": 1, "reasoning_effort": "high"}' --agent-llm-args '{"temperature": 1, "reasoning_effort": "high"}'
+```
+
+Optionally, you can run specific example by using `--task-ids` instead of `--num-tasks`.
+
+**temperature:** When adk_agent is used defaults to 1. The commands in this document sets them explicitly using llm_args for both user and agent models.
+
+**reasoning_level** Only applies to Gemini 3 Pro model. It defaults to high for adk_agent while using this model. Otherwise, it will default to dynamic thinking. Again this document demonsrates setting it explicitly using llm_args.
+
+**NOTE**: It is normal that you will be getting `This model isn't mapped yet` error logs. This is coming from litellm cost calculation workflow used by `--user-llm`. You can suppress is temporarily by swapping `--user-llm vertex_ai/gemini-3-pro-preview` with `--user-llm vertex_ai/gemini-2.5-pro`.
+
+### Viewing trajectories
+
+You can use the following command to view trajectories after following the default options:
+
+```bash
+tau2 view
+```
+
+### Full run
+
+Full run requires dropping the arg `--task-ids`.
+
+```bash
+# Example: Run complete evaluation for all domains
+tau2 run \
+  --domain retail \
+  --agent adk_agent \
+  --agent-llm vertex_ai/gemini-3-pro-preview \
+  --user-llm vertex_ai/gemini-3-pro-preview \
+  --num-trials 4 \
+  --save-to gemini_3_pro_retail \
+  --user-llm-args '{"temperature": 1, "reasoning_effort": "high"}' \
+  --agent-llm-args '{"temperature": 1, "reasoning_effort": "high"}'
+
+
+tau2 run \
+  --domain airline \
+  --agent adk_agent \
+  --agent-llm vertex_ai/gemini-3-pro-preview \
+  --user-llm vertex_ai/gemini-3-pro-preview \
+  --num-trials 4 \
+  --save-to gemini_3_pro_airline \
+  --user-llm-args '{"temperature": 1, "reasoning_effort": "high"}' \
+  --agent-llm-args '{"temperature": 1, "reasoning_effort": "high"}'
+
+
+tau2 run \
+  --domain telecom \
+  --agent adk_agent \
+  --agent-llm vertex_ai/gemini-3-pro-preview \
+  --user-llm vertex_ai/gemini-3-pro-preview \
+  --num-trials 4 \
+  --save-to gemini_3_pro_telecom \
+  --user-llm-args '{"temperature": 1, "reasoning_effort": "high"}' \
+  --agent-llm-args '{"temperature": 1, "reasoning_effort": "high"}'
+```
+
+### Prepare Submission Package
+
+```bash
+tau2 submit prepare data/tau2/simulations/gemini_3_pro_*.json --output ./gemini_3_pro_submission
+```
+
+This command will:
+
+- Verify all trajectory files are valid
+- Check that submission requirements are met
+- Compute performance metrics (Pass^k rates)
+- Prompt for required metadata (model name, organization, contact email) -> you can pass dummy values here as we are not submitting yet.
+- Create a structured submission directory with:
+  - `submission.json`: Metadata and metrics
+  - `trajectories/`: Your trajectory files
+
+## 6. Testing the Agent
+
+To verify that the agent is set up correctly, you can run its unit tests using `pytest`.
+
+```bash
+pytest tests/test_adk_agent.py
+```
+
+To see coverage (optional):
+
+```bash
+pip install pytest-cov
+```
+
+```bash
+pytest --cov=tau2.agent.adk_agent --cov-report=html tests/test_adk_agent.py
+````

python/agents/tau2-benchmark-agent/pyproject.toml

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+[project]
+name = "tau2-benchmark-agent"
+version = "0.1.0"
+description = "ADK Agent for Tau-Bench"
+authors = [{ name = "Google ADK Team", email = "[email protected]" }]
+license = "Apache-2.0"
+readme = "README.md"
+dependencies = [
+    "google-adk==1.19.0",
+    "google-genai==1.51.0",
+    "loguru==0.7.3",
+    "tenacity==9.1.2",
+    "tau2 @ git+https://github.com/sierra-research/tau2-bench.git@cc97b34",
+]
+requires-python = ">=3.10"
+
+[dependency-groups]
+dev = [
+    "pytest>=8.0.0",
+    "pytest-asyncio>=0.23.0",
+]
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["tau2_agent*"]
+
+[tool.pytest.ini_options]
+pythonpath = "."
+asyncio_mode = "auto"