pydantic--pydantic-ai/tests/AGENTS.md

Testing Guidelines

Testing philosophy

VCR + public-API tests are the default. We test through the public API the way a user would (Agent(...), agent.run(...)) against real provider responses recorded as cassettes — provider APIs are the ultimate judge of whether the code is correct when run as intended, and that user-facing correctness is what we care about, not behavior in isolated units.

Unit tests still earn their place — for internal behavior that is definitory and worth pinning against drift. That includes behavior you can't reach or reliably trigger through the public API (pre-request guards, defensive branches no real model produces), but also behavior a VCR test wouldn't actually protect: our cassette matchers aren't always sensitive to the request body, so a changed internal payload can still match an existing recording and pass green — a unit test asserting the internal shape directly is what catches that regression. Each unit test should still say why it isn't (or can't be) a VCR test.

Recording cassettes needs provider API keys and isn't trivial, so contributors routinely under-test the real behavior — writing the VCR test a contributor couldn't is core maintainer work.

Test File Structure

from __future__ import annotations

import pytest
from inline_snapshot import snapshot

from pydantic_ai import Agent
from pydantic_ai.models import Model
# ... other imports

pytestmark = [pytest.mark.anyio, pytest.mark.vcr]


# fixtures/helpers immediately before their test
@pytest.fixture
def my_helper():
    ...


@pytest.mark.parametrize('model', ['openai', 'anthropic', 'google'], indirect=True)
@pytest.mark.parametrize('stream', [False, True])
async def test_feature(model: Model, stream: bool):
    ...

Parametrization with Expectations

For cartesian product tests, use a dict to map parameter combinations to expected results:

from vcr.cassette import Cassette

from pydantic_ai.models import Model

# expectation can be a dataclass, for more complex cases
EXPECTATIONS: dict[tuple[str, bool], str] = {
    ('openai', False): 'expected output for openai non-streaming',
    ('openai', True): 'expected output for openai streaming',
    ('anthropic', False): 'expected output for anthropic non-streaming',
    ('anthropic', True): 'expected output for anthropic streaming',
}


@pytest.mark.parametrize('model', ['openai', 'anthropic'], indirect=True)
@pytest.mark.parametrize('stream', [False, True])
async def test_feature(model: Model, stream: bool, request: pytest.FixtureRequest, vcr: Cassette):
    """What the test is asserting.

    Use the `request` fixture to access test parameter values.

    Use the `vcr` to make assertions about the HTTP requests if needed.
    Another creative way of, for instance, asserting headers, is to use a patched httpx client fixture.
    This spares us the overhead of parsing cassette fields, so it is to be preferred whenever optimal.
    """
    model_name = request.node.callspec.params['model']
    expected = EXPECTATIONS[(model_name, stream)]

    agent = Agent(model)
    if stream:
        async with agent.run_stream('hello') as result:
            output = await result.get_output()
    else:
        result = await agent.run('hello')
        output = result.output

    assert output == expected

Use the EXPECTATIONS dict only for a pure cartesian output lookup keyed by (model, stream). For feature-centric files where cases are heterogeneous — different inputs, expectations, and xfails per case, all run through one minimal comprehensive test — use a @dataclass Case with sensible defaults plus per-case overrides:

from dataclasses import dataclass, field

import pytest
from inline_snapshot import snapshot

from pydantic_ai import Agent
from pydantic_ai.messages import ModelMessage


@dataclass(frozen=True)
class Case:
    id: str
    model: str
    prompt: str = 'hello'
    instructions: str | None = None
    expected_messages: list[ModelMessage] = field(default_factory=list[ModelMessage])
    marks: tuple[pytest.MarkDecorator, ...] = ()


CASES = [
    Case(
        id='openai',
        model='openai:gpt-5',
        expected_messages=snapshot([...]),
    ),
    Case(
        id='anthropic',
        model='anthropic:claude-sonnet-4-5',
        instructions='be terse',
        expected_messages=snapshot([...]),
        marks=(pytest.mark.skipif(not anthropic_available(), reason='anthropic not installed'),),
    ),
]


@pytest.mark.parametrize('case', [pytest.param(c, id=c.id, marks=c.marks) for c in CASES])
async def test_feature(case: Case):
    agent = Agent(case.model, instructions=case.instructions)
    result = await agent.run(case.prompt)
    assert result.all_messages() == case.expected_messages

Each case carries its own snapshot (not the central test body), so a reviewer can read the cases top to bottom and check that every expectation is realistic.

VCR Workflow

Record cassettes with --record-mode=rewrite, verify playback without the flag, and review diffs. For detailed workflows see .claude/skills/testing-skill/SKILL.md.

Key Fixtures

From conftest.py

Model requests

• allow_model_requests - bypasses the default ALLOW_MODEL_REQUESTS = False

The model fixture (use with indirect=True)

The model fixture takes a string param (e.g. 'openai', 'anthropic', 'google') and returns a configured Model instance, using session-scoped API key fixtures that default to 'mock-api-key' (real keys loaded from env when recording). See tests/conftest.py for the full list of supported param values.

@pytest.mark.parametrize('model', ['openai', 'anthropic'], indirect=True)
async def test_something(model: Model):
    ...

Environment management

• env - TestEnv instance for temporary env var changes

  def test_missing_key(env: TestEnv):
      env.remove('OPENAI_API_KEY')
      with pytest.raises(UserError):
          ...
  

Binary content (session-scoped)

• assets_path - Path to tests/assets/
• image_content - BinaryImage (kiwi.jpg)
• audio_content - BinaryContent (marcelo.mp3)
• video_content - BinaryContent (small_video.mp4)
• document_content - BinaryContent (dummy.pdf)
• text_document_content - BinaryContent (dummy.txt)

SSRF protection for URL downloads

• disable_ssrf_protection_for_vcr - required for VCR tests that download URL content (ImageUrl, AudioUrl, DocumentUrl, VideoUrl with force_download=True)
• An autouse guard raises a RuntimeError if a VCR test triggers SSRF validation without this fixture

Assertion Helpers

From conftest.py

• IsNow(tz=timezone.utc) - datetime within 10 seconds of now
• IsStr() - any string, supports regex=r'...'
• IsDatetime() - any datetime
• IsBytes() - any bytes
• IsInt() - any int
• IsFloat() - any float
• IsList() - any list
• IsInstance(SomeClass) - instance of class

Additional helpers

• IsSameStr() - asserts same string value across multiple uses in one assertion

  assert events == [
      {'id': (msg_id := IsSameStr())},
      {'id': msg_id},  # must match first
  ]
  

Best Practices

• Test through public APIs, not private methods (prefixed with _) or helpers — validates actual user-facing behavior and prevents brittle tests tied to implementation details
• Prefer feature-centric parametrized test files (e.g. test_multimodal_tool_returns.py) over appending to monolithic test_<provider>.py files — the legacy per-provider files are large and hard for agents to navigate; new features should get their own test file with a Case class and parametrized providers
• Use snapshot() for complex structured outputs (objects, message sequences, API responses, nested dicts) — catches unexpected changes more reliably than field-by-field assertions; use IsStr and similar matchers for variable values
• Assert the core aspect of the change being introduced — use whatever means necessary: patching clients to inspect request payloads, tapping into pydantic-ai internals, snapshot comparisons. Snapshots are valuable for catching structural drift in objects and message arrays, but only use result.all_messages() or output assertions when the structure demonstrates behavior you care about keeping consistent
• Test both positive and negative cases for optional capabilities (model features, server features, streaming) — ensures features work when supported AND fail gracefully when absent
• Ensure test assertions match test names and docstrings — tests without proper assertions or that verify opposite behavior create false positives
• Test MCP against real tests.mcp_server instance, not mocks — extend test server with helper tools to expose runtime context (instructions, client info, session state)
• Remove stale test docstrings, comments, and historical provider bug notes when behavior changes
• Prefer instructions= over system_prompt= when the test doesn't specifically need the system-prompt code path — instructions= is the canonical entry point for non-system-prompt-specific behavior (cacheable prefix, persona priming, format guidance), and reserving system_prompt= for tests that exercise the system-prompt machinery keeps intent legible
• Never reference line numbers in test docstrings or comments (lines 872-873, L42, line 100) — they go stale on the next edit to the referenced file. Describe the condition or behavior instead
• When testing prompt caching, assert prefix stability, not just a cache hit — cache_read_tokens > 0 only proves that some prefix was reused, not that the prefix you intended stayed stable as history grows; pin it by asserting the cacheable region (serialized leading blocks up to the breakpoint) is byte-identical across consecutive requests and/or that the cache read covers the full prior prefix, since a per-request injection or serialization that moves with history length silently busts the cache with no error

Directory Structure

tests/
├── conftest.py              # shared fixtures
├── json_body_serializer.py  # custom VCR serializer
├── assets/                  # binary test files
├── cassettes/               # VCR recordings for root tests
├── models/
│   ├── conftest.py          # model-specific fixtures (if needed)
│   ├── cassettes/           # VCR recordings per test file
│   │   ├── test_openai/
│   │   ├── test_anthropic/
│   │   └── ...
│   └── test_*.py
├── providers/
│   └── test_*.py            # provider initialization tests (unit)
└── test_*.py                # feature tests (prefer VCR + parametrize)