A comprehensive testing framework for PydanticAI agents featuring deterministic models, API recording, and automated snapshots.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install pydantic-ai-testing
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install pydantic-ai-testing using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is PydanticAI Testing Skill?
The pydantic-ai-testing skill provides a robust suite of tools designed to ensure AI agents behave predictably and reliably. By utilizing Openclaw Skills for testing, developers can move away from flakey LLM calls and toward a deterministic CI/CD pipeline using specialized test models and recorded interactions.
This skill bridges the gap between the non-deterministic nature of LLMs and the strict requirements of software engineering. It enables developers to mock complex agentic behaviors, verify tool execution, and maintain a high standard of code quality through integrated pytest support and snapshot testing.
PydanticAI Testing Skill Use Cases
- Creating unit tests for AI agents without incurring LLM API costs or latency.
- Mocking specific tool call sequences to verify business logic and dependency injection.
- Recording real LLM interactions via VCR cassettes for regression testing and debugging.
- Using inline snapshots to automatically update and assert against complex agent output structures.
- Verifying that agents correctly handle dependencies and mock database connections.
How PydanticAI Testing Skill Works
- The developer configures a TestModel or FunctionModel to override the standard LLM provider during test execution.
- For recorded testing, the @pytest.mark.vcr decorator captures real API requests and saves them to local YAML cassettes.
- The agent.override context manager injects mock dependencies and testing models into the agent's execution lifecycle.
- During execution, the framework captures all ModelMessages and responses for post-run inspection and assertion.
- Assertions are validated against inline snapshots or manually defined values to ensure the agent's logic remains intact across code changes.
PydanticAI Testing Skill Setup
To get started with these Openclaw Skills for testing, ensure you have pytest and pydantic-ai installed. Add the following configuration to your pyproject.toml:
[tool.pytest.ini_options]
testpaths = ["tests"]
asyncio_mode = "auto"
Run your test suite using the following CLI commands:
# Run all tests
uv run pytest tests/test_agent.py -v
# Record or rewrite VCR cassettes
uv run pytest --record-mode=rewrite tests/
# Update inline snapshots
uv run pytest --inline-snapshot=fix
PydanticAI Testing Skill Data Schema & Taxonomy
The skill organizes testing artifacts and metadata through a structured filesystem approach:
| Data Type | Storage Location | Format | Description |
|---|---|---|---|
| VCR Cassettes | tests/cassettes/*.yaml |
YAML | Recorded HTTP interactions with LLM providers. |
| Inline Snapshots | Inline in .py files |
String/Python | Expected outputs stored directly in the source code. |
| Test Logs | CLI Output | Text | Verbose logs of message parts and tool calls. |
| Dependency Mocks | In-memory | Python Objects | Mocked services injected via agent.override. |
PydanticAI Testing Skill Advanced Features
- Multi-agent support via recursive model overriding in nested agent calls.
- Deterministic random output generation using seed values in TestModel for reproducible failures.
- Force-call specific tools to test edge cases in tool execution logic.
- Complete model logic customization using FunctionModel for complex state-based responses.
- Message capture utilities to inspect the full conversation history of a run for audit or compliance testing.
SKILL.md
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