feat(samples): add hello_doctor health assessment agent

contributing/samples/gepa/experiment.py

@@ -43,7 +43,6 @@
 from tau_bench.types import EnvRunResult
 from tau_bench.types import RunConfig
 import tau_bench_agent as tau_bench_agent_lib
-
 import utils
 
 

contributing/samples/gepa/run_experiment.py

@@ -25,7 +25,6 @@
 from absl import flags
 import experiment
 from google.genai import types
-
 import utils
 
 _OUTPUT_DIR = flags.DEFINE_string(

contributing/samples/hello_doctor/README.md

@@ -0,0 +1,52 @@
+# hello_doctor
+
+A health assessment agent sample that demonstrates safe, educational health conversations with structured intake questions and risk assessment tools.
+
+## Features
+
+- **Structured health intake**: Asks six key questions (age, smoking, alcohol, medical conditions/medications, allergies, lifestyle) before providing any advice
+- **Session state tracking**: Uses `log_health_answer` tool to build a longitudinal picture of user responses
+- **Risk assessment**: Uses `summarize_risk_profile` tool to provide non-diagnostic risk summaries
+- **Strong safety disclaimers**: Always emphasizes that it is not a medical professional and directs users to licensed healthcare providers
+- **Few-shot examples**: Includes examples for handling mild symptoms, concerning symptoms, and supplement questions
+
+## Safety
+
+**Important**: This agent is for **educational purposes only**. It:
+- Does NOT diagnose, treat, or prescribe
+- Does NOT replace professional medical advice
+- Always directs users to licensed healthcare professionals for any real health concerns
+- Emphasizes emergency care for serious symptoms
+
+## Files
+
+- `agent.py`: Defines the `root_agent` with safety instructions, tools, and few-shot examples
+- `main.py`: CLI demo script showing how to run the agent programmatically
+
+## Running
+
+### Via CLI script
+
+```bash
+# Make sure you have GOOGLE_GENAI_API_KEY set in .env
+python contributing/samples/hello_doctor/main.py
+```
+
+### Via ADK Web UI
+
+```bash
+adk web contributing/samples
+```
+
+Then select `hello_doctor` from the app dropdown in the web UI at `http://127.0.0.1:8000`.
+
+## Configuration
+
+Create a `.env` file in the project root with:
+
+```env
+GOOGLE_GENAI_API_KEY=your_api_key_here
+```
+
+Or configure Vertex AI credentials if using the Vertex AI backend.
+

contributing/samples/hello_doctor/__init__.py

@@ -0,0 +1,15 @@
+# Copyright 2025 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/hello_doctor/agent.py

@@ -0,0 +1,173 @@
+# Copyright 2025 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 google.adk import Agent
+from google.adk.tools.tool_context import ToolContext
+
+
+def log_health_answer(
+    question: str, answer: str, tool_context: ToolContext
+) -> str:
+  """Log a structured health answer into session state.
+
+  The model can call this tool after it asks a question such as
+  "What is your age?" or "How often do you exercise?" to build up a
+  longitudinal picture of the user over the conversation.
+  """
+  state = tool_context.state
+  answers = state.get("health_answers", [])
+  answers.append({"question": question, "answer": answer})
+  state["health_answers"] = answers
+  return "Logged."
+
+
+def summarize_risk_profile(tool_context: ToolContext) -> str:
+  """Return a simple textual summary of the collected answers.
+
+  This is intentionally simplistic and non-diagnostic, but gives the
+  model a place to anchor a longitudinal summary. The LLM can call
+  this near the end of an assessment and include the returned text in
+  its final response.
+  """
+  answers = tool_context.state.get("health_answers", [])
+  if not answers:
+    return (
+        "No structured health answers have been logged yet. Ask more "
+        "questions first, then call this tool again."
+    )
+
+  # Very lightweight heuristic: count how many answers mention words
+  # like 'chest pain', 'shortness of breath', or 'bleeding'.
+  concerning_keywords = (
+      "chest pain",
+      "shortness of breath",
+      "fainting",
+      "vision loss",
+      "severe bleeding",
+      "suicidal",
+  )
+  has_concerning = False
+  for answer in answers:
+    text = str(answer.get("answer", "")).lower()
+    if any(keyword in text for keyword in concerning_keywords):
+      has_concerning = True
+      break
+
+  risk_level = "low-to-moderate"
+  if has_concerning:
+    risk_level = "potentially serious – urgent evaluation recommended"
+
+  return (
+      "Based on the logged answers, this appears to be a "
+      f"{risk_level} situation. This is only a rough heuristic, not a "
+      "diagnosis. A licensed healthcare professional must make any "
+      "real assessment."
+  )
+
+
+root_agent = Agent(
+    model="gemini-2.5-flash",
+    name="ai_doctor_agent",
+    description=(
+        "A simple AI doctor-style assistant for educational purposes. "
+        "It can explain basic medical concepts and always reminds users "
+        "to consult a licensed healthcare professional."
+    ),
+    instruction="""
+You are AI Doctor, a friendly educational assistant that answers
+high-level health and wellness questions.
+
+Important safety rules:
+- You are NOT a medical professional and cannot diagnose, treat,
+  or prescribe.
+- You MUST clearly remind the user to talk to a licensed healthcare
+  professional for any diagnosis, treatment, or emergency.
+- If the user describes any urgent or severe symptoms (for example
+  chest pain, trouble breathing, signs of stroke, suicidal thoughts),
+  you must tell them to seek emergency medical care immediately.
+- Keep your explanations simple, balanced, and non-alarming.
+
+You have access to two tools to help you reason over the conversation:
+- log_health_answer(question: str, answer: str): Call this after each
+  important question you ask the user so that their answer is stored
+  in the session state as structured data.
+- summarize_risk_profile(): Call this near the end of the assessment
+  to get a brief, non-diagnostic summary string based on everything
+  that has been logged so far. You should quote or paraphrase that
+  string in your final answer, along with your own explanation.
+
+For every new symptom message from the user:
+- You MUST ask at least six focused follow-up questions (one at a
+  time) before giving any advice or summary. In most conversations,
+  the questions should cover:
+  1) age,
+  2) smoking or tobacco use,
+  3) alcohol use,
+  4) major medical conditions and current medications,
+  5) allergies to medications or other substances,
+  6) basic lifestyle factors (diet, exercise, sleep).
+- After the user answers a question, you MUST call log_health_answer
+  with the question you asked and the user's answer.
+- Only after you have asked and logged at least six follow-up
+  questions should you call summarize_risk_profile and then provide
+  your final summary and suggestions.
+
+Even when these tools suggest that the situation looks low risk, you
+must still make it clear that only a licensed healthcare professional
+can diagnose or treat medical conditions.
+
+Example 1: Mild symptom, low risk
+User: "I am having a mild headache today."
+Assistant:
+- Acknowledge the symptom with empathy.
+- Ask a few brief follow-up questions (for example about sleep, hydration,
+  screen time, or stress) and log the answers using log_health_answer.
+- Offer simple, common self-care ideas such as rest, hydration, or a cool
+  compress, without naming specific prescription medications.
+- Clearly state that you are an AI system, not a medical professional, and
+  that if the headache is severe, persistent, or accompanied by red-flag
+  symptoms like fever, neck stiffness, vision changes, or confusion, the
+  user should seek care from a licensed healthcare professional.
+
+Example 2: Concerning symptom, high risk
+User: "I'm 55, I smoke, and I get chest pain when I walk up stairs."
+Assistant:
+- Log important details (age, smoking status, chest pain triggers) with
+  log_health_answer.
+- Call summarize_risk_profile before giving your final answer and use its
+  output as part of your explanation.
+- Explain that chest pain with exertion can sometimes be a sign of a
+  serious heart problem, without offering a diagnosis.
+- Strongly recommend urgent in-person evaluation by a licensed clinician
+  or emergency services, depending on how severe or new the symptoms are.
+- Emphasize again that you are an AI assistant, not a doctor.
+
+Example 3: Asking about supplements
+User: "What supplements should I take to boost my immunity?"
+Assistant:
+- Ask a couple of follow-up questions about general health, medications,
+  allergies, and any chronic conditions, and log the answers.
+- Provide high-level information about commonly discussed supplements
+  (such as vitamin D or vitamin C) but avoid specific doses or brands.
+- Remind the user to review any supplement plans with their doctor or
+  pharmacist, especially if they take prescription medications or have
+  chronic health conditions.
+- Clearly state that your suggestions are general wellness information
+  and not personalized medical advice.
+""",
+    tools=[
+        log_health_answer,
+        summarize_risk_profile,
+    ],
+)

contributing/samples/hello_doctor/main.py

@@ -0,0 +1,89 @@
+# Copyright 2025 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 asyncio
+import time
+
+import agent
+from dotenv import load_dotenv
+from google.adk import Runner
+from google.adk.artifacts.in_memory_artifact_service import InMemoryArtifactService
+from google.adk.cli.utils import logs
+from google.adk.sessions.in_memory_session_service import InMemorySessionService
+from google.adk.sessions.session import Session
+from google.genai import types
+
+load_dotenv(override=True)
+logs.log_to_tmp_folder()
+
+
+async def main():
+  app_name = "hello_doctor"
+  user_id = "user1"
+
+  session_service = InMemorySessionService()
+  artifact_service = InMemoryArtifactService()
+
+  runner = Runner(
+      app_name=app_name,
+      agent=agent.root_agent,
+      artifact_service=artifact_service,
+      session_service=session_service,
+  )
+
+  session = await session_service.create_session(
+      app_name=app_name, user_id=user_id
+  )
+
+  async def run_prompt(session: Session, new_message: str):
+    content = types.Content(
+        role="user", parts=[types.Part.from_text(text=new_message)]
+    )
+    print("** User says:", content.model_dump(exclude_none=True))
+    async for event in runner.run_async(
+        user_id=user_id,
+        session_id=session.id,
+        new_message=content,
+    ):
+      if event.content.parts and event.content.parts[0].text:
+        print(f"** {event.author}: {event.content.parts[0].text}")
+
+  start_time = time.time()
+  print("Start time:", start_time)
+  print("------------------------------------")
+
+  await run_prompt(
+      session,
+      (
+          "I'd like you to perform a high-level health assessment. Ask me "
+          "structured questions about my age, lifestyle, symptoms, and "
+          "medical history one by one. At the end, provide: "
+          "1) a concise longitudinal summary of my situation, "
+          "2) general wellness suggestions including over-the-counter "
+          "supplements that are commonly considered safe for most adults, "
+          "3) clear guidance on which licensed medical professionals I "
+          "should talk to and which medical tests I could ask them about. "
+          "You must clearly state that you are not a doctor and that your "
+          "advice is not a diagnosis or a substitute for professional care."
+      ),
+  )
+
+  end_time = time.time()
+  print("------------------------------------")
+  print("End time:", end_time)
+  print("Total time:", end_time - start_time)
+
+
+if __name__ == "__main__":
+  asyncio.run(main())

src/google/adk/evaluation/eval_config.py

@@ -172,7 +172,20 @@ def get_evaluation_criteria_or_default(
   if eval_config_file_path and os.path.exists(eval_config_file_path):
     with open(eval_config_file_path, "r", encoding="utf-8") as f:
       content = f.read()
-      return EvalConfig.model_validate_json(content)
+      if not content or not content.strip():
+        logger.warning(
+            f"Config file {eval_config_file_path} exists but is empty. "
+            "Using default criteria."
+        )
+        return _DEFAULT_EVAL_CONFIG
+      try:
+        return EvalConfig.model_validate_json(content)
+      except (ValidationError, ValueError) as e:
+        logger.warning(
+            f"Failed to parse config file {eval_config_file_path}: {e}. "
+            "Using default criteria."
+        )
+        return _DEFAULT_EVAL_CONFIG
 
   logger.info(
       "No config file supplied or file not found. Using default criteria."

tests/unittests/evaluation/test_eval_config.py

@@ -47,6 +47,16 @@ def test_get_evaluation_criteria_or_default_returns_default_if_file_not_found(
   )
 
 
+def test_get_evaluation_criteria_or_default_returns_default_if_file_is_empty(
+    mocker,
+):
+  mocker.patch("os.path.exists", return_value=True)
+  mocker.patch("builtins.open", mocker.mock_open(read_data=""))
+  assert (
+      get_evaluation_criteria_or_default("dummy_path") == _DEFAULT_EVAL_CONFIG
+  )
+
+
 def test_get_eval_metrics_from_config():
   rubric_1 = Rubric(
       rubric_id="test-rubric",