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pydantic-ai-dependency-injection

# pydantic-ai-dependency-injection This Claude Code skill demonstrates how to implement dependency injection in PydanticAI agents using `RunContext` and the `deps_type` parameter. It provides patterns for defining dependencies as dataclasses or Pydantic models, accessing them within tools and instructions, achieving type safety through generics, and handling cases where no dependencies are needed. Use this skill when building agents that require external resources like database connections, API clients, caching layers, or user context.

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SKILL.md

# PydanticAI Dependency Injection

## Core Pattern

Dependencies flow through `RunContext`:

```python
from dataclasses import dataclass
from pydantic_ai import Agent, RunContext

@dataclass
class Deps:
    db: DatabaseConn
    api_client: HttpClient
    user_id: int

agent = Agent(
    'openai:gpt-4o',
    deps_type=Deps,  # Type for static analysis
)

@agent.tool
async def get_user_balance(ctx: RunContext[Deps]) -> float:
    """Get the current user's account balance."""
    return await ctx.deps.db.get_balance(ctx.deps.user_id)

# At runtime, provide deps
result = await agent.run(
    'What is my balance?',
    deps=Deps(db=db_conn, api_client=client, user_id=123)
)
```

## Defining Dependencies

Use dataclasses or Pydantic models:

```python
from dataclasses import dataclass
from pydantic import BaseModel

# Dataclass (recommended for simplicity)
@dataclass
class Deps:
    db: DatabaseConnection
    cache: CacheClient
    user_context: UserContext

# Pydantic model (if you need validation)
class Deps(BaseModel):
    api_key: str
    endpoint: str
    timeout: int = 30
```

## Accessing Dependencies

In tools and instructions:

```python
@agent.tool
async def query_database(ctx: RunContext[Deps], query: str) -> list[dict]:
    """Run a database query."""
    return await ctx.deps.db.execute(query)

@agent.instructions
async def add_user_context(ctx: RunContext[Deps]) -> str:
    user = await ctx.deps.db.get_user(ctx.deps.user_id)
    return f"User name: {user.name}, Role: {user.role}"

@agent.system_prompt
def add_permissions(ctx: RunContext[Deps]) -> str:
    return f"User has permissions: {ctx.deps.permissions}"
```

## Type Safety

Full type checking with generics:

```python
# Explicit agent type annotation
agent: Agent[Deps, OutputModel] = Agent(
    'openai:gpt-4o',
    deps_type=Deps,
    output_type=OutputModel,
)

# Now these are type-checked:
# - ctx.deps in tools is typed as Deps
# - result.output is typed as OutputModel
# - agent.run() requires deps: Deps
```

## No Dependencies Pattern

When you don't need dependencies:

```python
# Option 1: No deps_type (defaults to NoneType)
agent = Agent('openai:gpt-4o')
result = agent.run_sync('Hello')  # No deps needed

# Option 2: Explicit None for type checker
agent: Agent[None, str] = Agent('openai:gpt-4o')
result = agent.run_sync('Hello', deps=None)

# In tool_plain, no context access
@agent.tool_plain
def simple_calc(a: int, b: int) -> int:
    return a + b
```

## Complete Example

```python
from dataclasses import dataclass
from httpx import AsyncClient
from pydantic import BaseModel
from pydantic_ai import Agent, RunContext

@dataclass
class WeatherDeps:
    client: AsyncClient
    api_key: str

class WeatherReport(BaseModel):
    location: str
    temperature: float
    conditions: str

agent: Agent[WeatherDeps, WeatherReport] = Agent(
    'openai:gpt-4o',
    deps_type=WeatherDeps,
    output_type=WeatherReport,
    instructions='You are a weather assistant.',
)

@agent.tool
async def get_weather(
    ctx: RunContext[WeatherDeps],
    city: str
) -> dict:
    """Fetch weather data for a city."""
    response = await ctx.deps.client.get(
        f'https://api.weather.com/{city}',
        headers={'Authorization': ctx.deps.api_key}
    )
    return response.json()

async def main():
    async with AsyncClient() as client:
        deps = WeatherDeps(client=client, api_key='secret')
        result = await agent.run('Weather in London?', deps=deps)
        print(result.output.temperature)
```

## Override for Testing

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

# Create mock dependencies
mock_deps = Deps(
    db=MockDatabase(),
    api_client=MockClient(),
    user_id=999
)

# Override model and deps for testing
with agent.override(model=TestModel(), deps=mock_deps):
    result = agent.run_sync('Test prompt')
```

## Gates

Run these in order before treating the agent as correct; each step has an objective pass condition.

1. **Deps cover every access** — Collect every `ctx.deps.<attr>` (and nested uses) from tools, `@agent.instructions`, and `@agent.system_prompt`. **Pass:** each `<attr>` exists on `deps_type` (and static checking passes if you use mypy/pyright on `Agent[DepsType, …]`).
2. **Every run that needs deps gets them** — **Pass:** each `agent.run` / `run_sync` path that executes those tools passes `deps=` whose type matches `deps_type` (no `None` unless the agent truly has no deps).
3. **Tests pin deps shape** — **Pass:** tests that use `agent.override` pass a `deps=` value with the same fields/types as production `Deps` (not a partial mock unless tools under test never touch missing fields).

## Best Practices

1. **Keep deps immutable**: Use frozen dataclasses or Pydantic models
2. **Pass connections, not credentials**: Deps should hold initialized clients
3. **Type your agents**: Use `Agent[DepsType, OutputType]` for full type safety
4. **Scope deps appropriately**: Create deps at the start of a request, close after
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