Why use PydanticAI

In Beta!

Hello World Example

frompydantic_aiimportAgent# Define a very simple agent including the model to use, you can also set the model when running the agent.agent=Agent('gemini-1.5-flash',# Register a static system prompt using a keyword argument to the agent.# For more complex dynamically-generated system prompts, see the example below.system_prompt='Be concise, reply with one sentence.',)# Run the agent synchronously, conducting a conversation with the LLM.# Here the exchange should be very short: PydanticAI will send the system prompt and the user query to the LLM,# the model will return a text response. See below for a more complex run.result=agent.run_sync('Where does "hello world" come from?')print(result.data)"""The first known use of "hello, world" was in a 1974 textbook about the C programming language."""

Tools & Dependency Injection Example

fromdataclassesimportdataclassfrompydanticimportBaseModel,Fieldfrompydantic_aiimportAgent,RunContextfrombank_databaseimportDatabaseConn# SupportDependencies is used to pass data, connections, and logic into the model that will be needed when running# system prompt and tool functions. Dependency injection provides a type-safe way to customise the behavior of your agents.@dataclassclassSupportDependencies:customer_id:intdb:DatabaseConn# This pydantic model defines the structure of the result returned by the agent.classSupportResult(BaseModel):support_advice:str=Field(description='Advice returned to the customer')block_card:bool=Field(description="Whether to block the customer's card")risk:int=Field(description='Risk level of query',ge=0,le=10)# This agent will act as first-tier support in a bank.# Agents are generic in the type of dependencies they accept and the type of result they return.# In this case, the support agent has type `Agent[SupportDependencies, SupportResult]`.support_agent=Agent('openai:gpt-4o',deps_type=SupportDependencies,# The response from the agent will, be guaranteed to be a SupportResult,# if validation fails the agent is prompted to try again.result_type=SupportResult,system_prompt=('You are a support agent in our bank, give the ''customer support and judge the risk level of their query.'    ),)# Dynamic system prompts can make use of dependency injection.# Dependencies are carried via the `RunContext` argument, which is parameterized with the `deps_type` from above.# If the type annotation here is wrong, static type checkers will catch it.@support_agent.system_promptasyncdefadd_customer_name(ctx:RunContext[SupportDependencies])->str:customer_name=awaitctx.deps.db.customer_name(id=ctx.deps.customer_id)returnf"The customer's name is{customer_name!r}"# `tool` let you register functions which the LLM may call while responding to a user.# Again, dependencies are carried via `RunContext`, any other arguments become the tool schema passed to the LLM.# Pydantic is used to validate these arguments, and errors are passed back to the LLM so it can retry.@support_agent.toolasyncdefcustomer_balance(ctx:RunContext[SupportDependencies],include_pending:bool)->float:"""Returns the customer's current account balance."""# The docstring of a tool is also passed to the LLM as the description of the tool.# Parameter descriptions are extracted from the docstring and added to the parameter schema sent to the LLM.balance=awaitctx.deps.db.customer_balance(id=ctx.deps.customer_id,include_pending=include_pending,    )returnbalance...# In a real use case, you'd add more tools and a longer system promptasyncdefmain():deps=SupportDependencies(customer_id=123,db=DatabaseConn())# Run the agent asynchronously, conducting a conversation with the LLM until a final response is reached.# Even in this fairly simple case, the agent will exchange multiple messages with the LLM as tools are called to retrieve a result.result=awaitsupport_agent.run('What is my balance?',deps=deps)# The result will be validated with Pydantic to guarantee it is a `SupportResult`, since the agent is generic,# it'll also be typed as a `SupportResult` to aid with static type checking.print(result.data)"""    support_advice='Hello John, your current account balance, including pending transactions, is $123.45.' block_card=False risk=1    """result=awaitsupport_agent.run('I just lost my card!',deps=deps)print(result.data)"""    support_advice="I'm sorry to hear that, John. We are temporarily blocking your card to prevent unauthorized transactions." block_card=True risk=8    """

Next Steps