Pytest helpers for evaluating agents built with Google ADK. The package provides:

• an auto-registered AgentEvaluator pytest fixture that saves ADK eval result JSON files under each test's tmp_path;
• TOML evalset support, including multi-line prompts;
• external prompt templates for repeated evalset text, rendered with string.Template by default or optionally with Jinja2;
• a pytest-adk-eval-schema CLI for generating fill-in evalset templates;
• helpers for resuming an exported ADK session with an in-memory Runner.

pip install pytest-adk

For development and tests, install the dev extra:

pip install "pytest-adk[dev]"

AgentEvaluator is a pytest fixture, auto-registered via the pytest11 entry point — installing pytest-adk makes it available with no import and no conftest.py. Just request it as a test argument:

import pytest


@pytest.mark.asyncio
async def test_home_automation(AgentEvaluator):
    await AgentEvaluator.evaluate(
        agent_module='home_automation_agent',
        eval_dataset_file_path_or_dir=(
            'tests/integration/fixture/home_automation_agent/'
            'simple_test.test.json'
        ),
    )

The fixture binds the eval results directory to pytest's tmp_path, so you no longer pass results_dir yourself. Result JSON files are written under tmp_path/test_app/.adk/eval_history/.

After the run, pytest's terminal summary prints an ADK eval results section listing, for every test that used the fixture, the eval_history directory where its results were saved — shown regardless of whether the test passed or failed, so you can always find them:

=================== ADK eval results ===================
tests/test_home_automation.py::test_home_automation
  /tmp/pytest-of-you/pytest-0/test_home_automation0/test_app/.adk/eval_history

AgentEvaluator.evaluate discovers and loads evalset files in two formats:

• *.test.json — the schema used by google-adk's AgentEvaluator.
• *.test.toml — the same EvalSet schema, written in TOML.

How eval_dataset_file_path_or_dir is interpreted depends on whether it points at a directory or a single file:

• Directory: only files matching the *.test.json / *.test.toml naming convention are discovered, recursively. The .test. infix is required, so sibling files such as test_config.json (eval metrics) and the *.evalset_result.json files written by this helper are naturally excluded — no special-casing needed. A plain data.json without .test. is not picked up.
• Single file: any .json or .toml file is accepted, since pointing at a file is an explicit choice. If the path does not contain .test., a logging.warning is emitted (under the pytest_adk.evaluation logger) noting that it falls outside the naming convention, and the file is loaded anyway. The loader is chosen by extension: .toml → TOML, otherwise JSON.

TOML is handy when a user prompt spans multiple lines: TOML multi-line strings ("""...""") keep newlines readable, instead of JSON's \n-escaped one-liners. Like JSON, TOML is parsed with the standard library (tomllib, Python 3.11+; on Python 3.10 the tomli backport is installed automatically as a dependency).

A *.test.toml evalset follows the same EvalSet schema as JSON:

eval_set_id = "home_automation"

[[eval_cases]]
eval_id = "turn_on_living_room"

[[eval_cases.conversation]]
invocation_id = "inv-1"

[eval_cases.conversation.user_content]
role = "user"
parts = [ { text = """
Please turn on the living room light.
Then confirm it is on.
""" } ]

[eval_cases.conversation.final_response]
role = "model"
parts = [ { text = "The living room light is now on." } ]

Notes:

• TOML evalsets support the current EvalSet schema only; the legacy data format and a separate initial_session file (both JSON-only in google-adk) are not handled. Express the initial session inside the EvalSet instead.
• The companion test_config.json (eval metrics / criteria) is unchanged; only the evalset data file gains TOML support.

When several eval cases share the same (often long) prompt, you can keep the prompt in a separate file and reference it from a text field. If the entire value of a text field is a <prompt:...> marker, AgentEvaluator.evaluate reads the referenced file, substitutes its variables, and replaces the marker with the rendered prompt before the evalset reaches the evaluator.

Marker syntax:

<prompt:FILENAME [KEY=VALUE ...]>

Given prompt.txt:

Please turn on the ${ROOM} light.
Then confirm it is ${STATE}.

an evalset can reference it like this:

[eval_cases.conversation.user_content]
role = "user"
parts = [ { text = "<prompt:prompt.txt ROOM=living STATE=on>" } ]

After expansion the agent sees the fully rendered prompt. This works for both *.test.toml and *.test.json evalsets, and applies to both user_content and final_response text parts.

Details:

• Variables use string.Template syntax by default: ${VAR} (or $VAR).
• FILENAME is resolved relative to the evalset file's directory.
• The marker must be the whole text value (leading/trailing whitespace is ignored); markers embedded inside other text are not expanded.
• KEY=VALUE pairs are space-separated, so values cannot contain spaces.
• It is an error if the prompt file is missing, a KEY=VALUE pair is malformed, or the prompt references a variable that the marker does not provide.

By default the prompt file is rendered with string.Template (${VAR}). To use Jinja2 ({{ VAR }}) syntax instead, install the optional extra and opt in via the pytest_adk_prompt_template_engine ini option in pyproject.toml:

pip install "pytest-adk[jinja]"

[tool.pytest.ini_options]
pytest_adk_prompt_template_engine = "jinja"

With the Jinja engine selected, the same prompt.txt would be written as:

Please turn on the {{ ROOM }} light.
Then confirm it is {{ STATE }}.

The marker syntax (<prompt:FILENAME KEY=VALUE ...>) is unchanged; only the placeholder syntax inside the prompt file differs. Referencing a variable that the marker does not provide is an error (Jinja runs with StrictUndefined).

Use pytest-adk-eval-schema to generate a minimal EvalSet file with REPLACE_ME placeholders:

pytest-adk-eval-schema -o tests/evals/example.test.toml

TOML is the default output format. JSON is also available:

pytest-adk-eval-schema --format json

The command refuses to overwrite an existing file unless you pass --force. The same generator is available from Python:

from pytest_adk import eval_set_template

template = eval_set_template("toml")

load_session_from_json reads a session exported by ADK from either a file path or a raw JSON string. runner_from_exported_session restores that session into an in-memory ADK Runner, copying the exported state and replaying events via the session service.

from pathlib import Path

from google.genai import types
from pytest_adk import runner_from_exported_session
from your_agent.agent import root_agent


async def test_resume_exported_session():
    runner, session = await runner_from_exported_session(
        root_agent,
        Path("tests/fixtures/roll_die.session.json"),
    )

    events = runner.run_async(
        user_id=session.user_id,
        session_id=session.id,
        new_message=types.Content(
            role="user",
            parts=[types.Part(text="What numbers did I get?")],
        ),
    )
    async for _ in events:
        pass

You can override app_name, user_id, or session_id when restoring, and you can pass custom artifact, memory, or credential services. If you do not provide services, in-memory ADK services are used.