from pydantic_ai import Agent

# 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-4-6',
    # 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.
"""

from pydantic_ai import Agent
from pydantic_ai.capabilities import Thinking, WebSearch

agent = Agent(
    'anthropic:claude-sonnet-4-6',
    instructions='Be concise, reply with one sentence.',
    capabilities=[Thinking(), WebSearch()],
)

result = agent.run_sync('What was the mass of the largest meteorite found this year?')
print(result.output)

from dataclasses import dataclass

from pydantic import BaseModel, Field
from pydantic_ai import Agent, RunContext

from bank_database import DatabaseConn


# 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.
@dataclass
class SupportDependencies:
    customer_id: int
    db: DatabaseConn


# This Pydantic model defines the structure of the output returned by the agent.
class SupportOutput(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 output they return.
# In this case, the support agent has type `Agent[SupportDependencies, SupportOutput]`.
support_agent = Agent(
    'openai:gpt-5.2',
    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.instructions
async def add_customer_name(ctx: RunContext[SupportDependencies]) -> str:
    customer_name = await ctx.deps.db.customer_name(id=ctx.deps.customer_id)
    return f"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.tool
async def customer_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 = await ctx.deps.db.customer_balance(
        id=ctx.deps.customer_id,
        include_pending=include_pending,
    )
    return balance


...  # In a real use case, you'd add more tools and a longer system prompt


async def main():
    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 = await support_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 = await support_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
    """