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pydantic-ai-testing

Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.

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Overview

Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.

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Testing PydanticAI Agents

TestModel (Deterministic Testing)

Use TestModel for tests without API calls:

import pytest
from pydantic_ai import Agent
from pydantic_ai.models.test import TestModel

def test_agent_basic():
    agent = Agent('openai:gpt-4o')

    # Override with TestModel for testing
    result = agent.run_sync('Hello', model=TestModel())

    # TestModel generates deterministic output based on output_type
    assert isinstance(result.output, str)

TestModel Configuration

from pydantic_ai.models.test import TestModel

# Custom text output
model = TestModel(custom_output_text='Custom response')
result = agent.run_sync('Hello', model=model)
assert result.output == 'Custom response'

# Custom structured output (for output_type agents)
from pydantic import BaseModel

class Response(BaseModel):
    message: str
    score: int

agent = Agent('openai:gpt-4o', output_type=Response)
model = TestModel(custom_output_args={'message': 'Test', 'score': 42})
result = agent.run_sync('Hello', model=model)
assert result.output.message == 'Test'

# Seed for reproducible random output
model = TestModel(seed=42)

# Force tool calls
model = TestModel(call_tools=['my_tool', 'another_tool'])

Override Context Manager

from pydantic_ai import Agent
from pydantic_ai.models.test import TestModel

agent = Agent('openai:gpt-4o', deps_type=MyDeps)

def test_with_override():
    mock_deps = MyDeps(db=MockDB())

    with agent.override(model=TestModel(), deps=mock_deps):
        # All runs use TestModel and mock_deps
        result = agent.run_sync('Hello')
        assert result.output

FunctionModel (Custom Logic)

For complete control over model responses:

from pydantic_ai import Agent, ModelMessage, ModelResponse, TextPart
from pydantic_ai.models.function import AgentInfo, FunctionModel

def custom_model(
    messages: list[ModelMessage],
    info: AgentInfo
) -> ModelResponse:
    """Custom model that inspects messages and returns response."""
    # Access the last user message
    last_msg = messages[-1]

    # Return custom response
    return ModelResponse(parts=[TextPart('Custom response')])

agent = Agent(FunctionModel(custom_model))
result = agent.run_sync('Hello')

FunctionModel with Tool Calls

from pydantic_ai import ToolCallPart, ModelResponse
from pydantic_ai.models.function import AgentInfo, FunctionModel

def model_with_tools(
    messages: list[ModelMessage],
    info: AgentInfo
) -> ModelResponse:
    # First request: call a tool
    if len(messages) == 1:
        return ModelResponse(parts=[
            ToolCallPart(
                tool_name='get_data',
                args='{"id": 123}'
            )
        ])

    # After tool response: return final result
    return ModelResponse(parts=[TextPart('Done with tool result')])

agent = Agent(FunctionModel(model_with_tools))

@agent.tool_plain
def get_data(id: int) -> str:
    return f"Data for {id}"

result = agent.run_sync('Get data')

VCR Cassettes (Recorded API Calls)

Record and replay real LLM API interactions:

import pytest

@pytest.mark.vcr
def test_with_recorded_response():
    """Uses recorded cassette from tests/cassettes/"""
    agent = Agent('openai:gpt-4o')
    result = agent.run_sync('Hello')
    assert 'hello' in result.output.lower()

# To record/update cassettes:
# uv run pytest --record-mode=rewrite tests/test_file.py

Cassette files are stored in tests/cassettes/ as YAML.

Inline Snapshots

Assert expected outputs with auto-updating snapshots:

from inline_snapshot import snapshot

def test_agent_output():
    result = agent.run_sync('Hello', model=TestModel())

    # First run: creates snapshot
    # Subsequent runs: asserts against it
    assert result.output == snapshot('expected output here')

# Update snapshots:
# uv run pytest --inline-snapshot=fix

Gates: VCR cassettes and inline snapshots

Recording or fixing rewrites files on disk. Follow this sequence; do not skip steps.

Replay pass (no record/fix flags): Run uv run pytest on the target path; all green (or failures are understood and unrelated to the artifact you will refresh).
Scope locked: Identify the cassette under tests/cassettes/ or the snapshot(...) assertion to update; confirm only those files should change.
Record or fix: Run one scoped command: uv run pytest --record-mode=rewrite … or uv run pytest --inline-snapshot=fix … for that path only.
Post-condition: Run the same tests again without record/fix flags; all green. Inspect git diff — only expected .yaml / snapshot changes.

If step 4 fails, revert unintended diffs and fix the test or model before re-recording.

Testing Tools

from pydantic_ai import Agent, RunContext
from pydantic_ai.models.test import TestModel

def test_tool_is_called():
    agent = Agent('openai:gpt-4o')
    tool_called = False

    @agent.tool_plain
    def my_tool(x: int) -> str:
        nonlocal tool_called
        tool_called = True
        return f"Result: {x}"

    # Force TestModel to call the tool
    result = agent.run_sync(
        'Use my_tool',
        model=TestModel(call_tools=['my_tool'])
    )

    assert tool_called

Testing with Dependencies

from dataclasses import dataclass
from unittest.mock import AsyncMock

@dataclass
class Deps:
    api: ApiClient

def test_tool_with_deps():
    # Create mock dependency
    mock_api = AsyncMock()
    mock_api.fetch.return_value = {'data': 'test'}

    agent = Agent('openai:gpt-4o', deps_type=Deps)

    @agent.tool
    async def fetch_data(ctx: RunContext[Deps]) -> dict:
        return await ctx.deps.api.fetch()

    with agent.override(
        model=TestModel(call_tools=['fetch_data']),
        deps=Deps(api=mock_api)
    ):
        result = agent.run_sync('Fetch data')

    mock_api.fetch.assert_called_once()

Capture Messages

Inspect all messages in a run:

from pydantic_ai import Agent, capture_run_messages

agent = Agent('openai:gpt-4o')

with capture_run_messages() as messages:
    result = agent.run_sync('Hello', model=TestModel())

# Inspect captured messages
for msg in messages:
    print(msg)

Testing Patterns Summary

Scenario	Approach
Unit tests without API	`TestModel()`
Custom model logic	`FunctionModel(func)`
Recorded real responses	`@pytest.mark.vcr`
Assert output structure	`inline_snapshot`
Test tools are called	`TestModel(call_tools=[...])`
Mock dependencies	`agent.override(deps=...)`

pytest Configuration

Typical pyproject.toml:

[tool.pytest.ini_options]
testpaths = ["tests"]
asyncio_mode = "auto"  # For async tests

Run tests:

uv run pytest tests/test_agent.py -v
uv run pytest --inline-snapshot=fix  # Update snapshots

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File metadata

name: pydantic-ai-testing
description: Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.

View original text

---
name: pydantic-ai-testing
description: Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.
---

# Testing PydanticAI Agents

## TestModel (Deterministic Testing)

Use `TestModel` for tests without API calls:

```python
import pytest
from pydantic_ai import Agent
from pydantic_ai.models.test import TestModel

def test_agent_basic():
    agent = Agent('openai:gpt-4o')

    # Override with TestModel for testing
    result = agent.run_sync('Hello', model=TestModel())

    # TestModel generates deterministic output based on output_type
    assert isinstance(result.output, str)
```

## TestModel Configuration

```python
from pydantic_ai.models.test import TestModel

# Custom text output
model = TestModel(custom_output_text='Custom response')
result = agent.run_sync('Hello', model=model)
assert result.output == 'Custom response'

# Custom structured output (for output_type agents)
from pydantic import BaseModel

class Response(BaseModel):
    message: str
    score: int

agent = Agent('openai:gpt-4o', output_type=Response)
model = TestModel(custom_output_args={'message': 'Test', 'score': 42})
result = agent.run_sync('Hello', model=model)
assert result.output.message == 'Test'

# Seed for reproducible random output
model = TestModel(seed=42)

# Force tool calls
model = TestModel(call_tools=['my_tool', 'another_tool'])
```

## Override Context Manager

```python
from pydantic_ai import Agent
from pydantic_ai.models.test import TestModel

agent = Agent('openai:gpt-4o', deps_type=MyDeps)

def test_with_override():
    mock_deps = MyDeps(db=MockDB())

    with agent.override(model=TestModel(), deps=mock_deps):
        # All runs use TestModel and mock_deps
        result = agent.run_sync('Hello')
        assert result.output
```

## FunctionModel (Custom Logic)

For complete control over model responses:

```python
from pydantic_ai import Agent, ModelMessage, ModelResponse, TextPart
from pydantic_ai.models.function import AgentInfo, FunctionModel

def custom_model(
    messages: list[ModelMessage],
    info: AgentInfo
) -> ModelResponse:
    """Custom model that inspects messages and returns response."""
    # Access the last user message
    last_msg = messages[-1]

    # Return custom response
    return ModelResponse(parts=[TextPart('Custom response')])

agent = Agent(FunctionModel(custom_model))
result = agent.run_sync('Hello')
```

### FunctionModel with Tool Calls

```python
from pydantic_ai import ToolCallPart, ModelResponse
from pydantic_ai.models.function import AgentInfo, FunctionModel

def model_with_tools(
    messages: list[ModelMessage],
    info: AgentInfo
) -> ModelResponse:
    # First request: call a tool
    if len(messages) == 1:
        return ModelResponse(parts=[
            ToolCallPart(
                tool_name='get_data',
                args='{"id": 123}'
            )
        ])

    # After tool response: return final result
    return ModelResponse(parts=[TextPart('Done with tool result')])

agent = Agent(FunctionModel(model_with_tools))

@agent.tool_plain
def get_data(id: int) -> str:
    return f"Data for {id}"

result = agent.run_sync('Get data')
```

## VCR Cassettes (Recorded API Calls)

Record and replay real LLM API interactions:

```python
import pytest

@pytest.mark.vcr
def test_with_recorded_response():
    """Uses recorded cassette from tests/cassettes/"""
    agent = Agent('openai:gpt-4o')
    result = agent.run_sync('Hello')
    assert 'hello' in result.output.lower()

# To record/update cassettes:
# uv run pytest --record-mode=rewrite tests/test_file.py
```

Cassette files are stored in `tests/cassettes/` as YAML.

## Inline Snapshots

Assert expected outputs with auto-updating snapshots:

```python
from inline_snapshot import snapshot

def test_agent_output():
    result = agent.run_sync('Hello', model=TestModel())

    # First run: creates snapshot
    # Subsequent runs: asserts against it
    assert result.output == snapshot('expected output here')

# Update snapshots:
# uv run pytest --inline-snapshot=fix
```

## Gates: VCR cassettes and inline snapshots

Recording or fixing rewrites files on disk. Follow this sequence; do not skip steps.

1. **Replay pass (no record/fix flags):** Run `uv run pytest` on the target path; **all green** (or failures are understood and unrelated to the artifact you will refresh).
2. **Scope locked:** Identify the cassette under `tests/cassettes/` or the `snapshot(...)` assertion to update; confirm **only** those files should change.
3. **Record or fix:** Run **one** scoped command: `uv run pytest --record-mode=rewrite …` **or** `uv run pytest --inline-snapshot=fix …` for that path only.
4. **Post-condition:** Run the same tests again **without** record/fix flags; **all green**. Inspect `git diff` — only expected `.yaml` / snapshot changes.

If step 4 fails, revert unintended diffs and fix the test or model before re-recording.

## Testing Tools

```python
from pydantic_ai import Agent, RunContext
from pydantic_ai.models.test import TestModel

def test_tool_is_called():
    agent = Agent('openai:gpt-4o')
    tool_called = False

    @agent.tool_plain
    def my_tool(x: int) -> str:
        nonlocal tool_called
        tool_called = True
        return f"Result: {x}"

    # Force TestModel to call the tool
    result = agent.run_sync(
        'Use my_tool',
        model=TestModel(call_tools=['my_tool'])
    )

    assert tool_called
```

## Testing with Dependencies

```python
from dataclasses import dataclass
from unittest.mock import AsyncMock

@dataclass
class Deps:
    api: ApiClient

def test_tool_with_deps():
    # Create mock dependency
    mock_api = AsyncMock()
    mock_api.fetch.return_value = {'data': 'test'}

    agent = Agent('openai:gpt-4o', deps_type=Deps)

    @agent.tool
    async def fetch_data(ctx: RunContext[Deps]) -> dict:
        return await ctx.deps.api.fetch()

    with agent.override(
        model=TestModel(call_tools=['fetch_data']),
        deps=Deps(api=mock_api)
    ):
        result = agent.run_sync('Fetch data')

    mock_api.fetch.assert_called_once()
```

## Capture Messages

Inspect all messages in a run:

```python
from pydantic_ai import Agent, capture_run_messages

agent = Agent('openai:gpt-4o')

with capture_run_messages() as messages:
    result = agent.run_sync('Hello', model=TestModel())

# Inspect captured messages
for msg in messages:
    print(msg)
```

## Testing Patterns Summary

| Scenario | Approach |
|----------|----------|
| Unit tests without API | `TestModel()` |
| Custom model logic | `FunctionModel(func)` |
| Recorded real responses | `@pytest.mark.vcr` |
| Assert output structure | `inline_snapshot` |
| Test tools are called | `TestModel(call_tools=[...])` |
| Mock dependencies | `agent.override(deps=...)` |

## pytest Configuration

Typical `pyproject.toml`:

```toml
[tool.pytest.ini_options]
testpaths = ["tests"]
asyncio_mode = "auto"  # For async tests
```

Run tests:
```bash
uv run pytest tests/test_agent.py -v
uv run pytest --inline-snapshot=fix  # Update snapshots
```

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Source repository: existential-birds/beagle
License: Apache-2.0
Version: 1.0.0
Last GitHub push: Aug 10, 2026
Registry updated: Sep 7, 2026
Instruction path: plugins/beagle-ai/skills/pydantic-ai-testing/SKILL.md @ d1a74899fbfe

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      "repository": "https://github.com/existential-birds/beagle/tree/main/plugins/beagle-ai/skills/pydantic-ai-testing",
      "install": "npx skills add existential-birds/beagle --skill pydantic-ai-testing",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "shell or command execution, network or browser access",
      "documentation": "Usable metadata, review docs",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Test manually in an isolated workspace and compare against safer alternatives."
    },
    "best_for": [
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      "agent-skill"
    ],
    "known_risks": [
      "Quality score needs review",
      "GitHub adoption: 80 GitHub stars",
      "Stars/forks activity: 80 stars, 8 forks; issue activity unavailable in current metadata"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 76,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Quality score needs review",
      "GitHub adoption: 80 GitHub stars",
      "Stars/forks activity: 80 stars, 8 forks; issue activity unavailable in current metadata"
    ]
  },
  "safety_gate": {
    "tier": "experimental",
    "label": "Experimental",
    "auto_install_policy": "review",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": false,
    "recommended_action": "Test manually in an isolated workspace and compare against safer alternatives."
  },
  "quality": {
    "score": 63,
    "label": "Promising"
  },
  "supply": {
    "track": "Coding and developer agents",
    "scenario": "Coding agents",
    "maintenance": "3mo since push",
    "risk": "Needs review"
  },
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  "do_not_use_when": [
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    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution",
    "Quality score needs review",
    "GitHub adoption: 80 GitHub stars",
    "Stars/forks activity: 80 stars, 8 forks; issue activity unavailable in current metadata",
    "Production credentials, payments, or irreversible account changes without explicit human review"
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  "agent_contract": {
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    "recommended_action": "Test manually in an isolated workspace and compare against safer alternatives.",
    "install_policy": "review",
    "minimum_review_before_use": [
      "Trust: 72/100 Strong shortlist",
      "Audit: 76/100 Needs review",
      "Safety: 48/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "existential-birds-pydantic-ai-testing (pydantic-ai-testing)",
      "install_command": "npx skills add existential-birds/beagle --skill pydantic-ai-testing",
      "risk_summary": "Needs review; Experimental; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
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    "method": "POST",
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      "task": "Use pydantic-ai-testing in an agent workflow",
      "agent": "codex",
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      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
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  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/existential-birds-pydantic-ai-testing",
    "api": "https://www.openagentskill.com/api/agent/skills/existential-birds-pydantic-ai-testing",
    "audit": "https://www.openagentskill.com/skills/existential-birds-pydantic-ai-testing/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=existential-birds-pydantic-ai-testing&task=Use%20pydantic-ai-testing%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20pydantic-ai-testing%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20pydantic-ai-testing%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/existential-birds-pydantic-ai-testing/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/existential-birds-pydantic-ai-testing"
  }
}

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Creator: existential-birds
Source: existential-birds/beagle
Indexed by: OpenAgentSkill community index

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Overview

Testing PydanticAI Agents

TestModel (Deterministic Testing)

TestModel Configuration

Override Context Manager

FunctionModel (Custom Logic)

FunctionModel with Tool Calls

VCR Cassettes (Recorded API Calls)

Inline Snapshots

Gates: VCR cassettes and inline snapshots

Testing Tools

Testing with Dependencies

Capture Messages

Testing Patterns Summary

pytest Configuration

Use with my agent

Price & running costs

Start with one small task

Source & usage notes

Agent access

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