Pydantic AI is a Python agent framework designed to help you quickly, confidently, and painlessly build production grade applications and workflows with Generative AI.

Why use Pydantic AI

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('anthropic:claude-sonnet-4-0',# Register static instructions using a keyword argument to the agent.# For more complex dynamically-generated instructions, see the example below.instructions='Be concise, reply with one sentence.',)# Run the agent synchronously, conducting a conversation with the LLM.result=agent.run_sync('Where does "hello world" come from?')print(result.output)"""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# instructions 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 output returned by the agent.classSupportOutput(BaseModel):support_advice:str"""Advice returned to the customer"""# (14)!block_card:bool"""Whether to block the customer's card"""risk:int=Field(ge=0,le=10)"""Risk level of query"""# The docstrings of fields on a Pydantic model are passed to the LLM,# so that it has all the context needed to generate a value.# 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 output they return.# In this case, the support agent has type `Agent[SupportDependencies, SupportOutput]`.support_agent=Agent('openai:gpt-4o',deps_type=SupportDependencies,# The response from the agent will, be guaranteed to be a SupportOutput,# if validation fails the agent is prompted to try again.output_type=SupportOutput,instructions=('You are a support agent in our bank, give the ''customer support and judge the risk level of their query.'    ),)# Dynamic instructions 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.instructionsasyncdefadd_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}"# The `tool` decorator 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 an output.result=awaitsupport_agent.run('What is my balance?',deps=deps)# The `result.output` will be validated with Pydantic to guarantee it is a `SupportOutput`. Since the agent is generic,# it'll also be typed as a `SupportOutput` to aid with static type checking.print(result.output)"""    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.output)"""    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