tomevault-io PydanticAI Dependency Injection
Core Pattern
Dependencies flow through RunContext:
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:
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:
@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:
# 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:
# 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
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
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')
Best Practices
- Keep deps immutable: Use frozen dataclasses or Pydantic models
- Pass connections, not credentials: Deps should hold initialized clients
- Type your agents: Use
Agent[DepsType, OutputType]for full type safety - Scope deps appropriately: Create deps at the start of a request, close after
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