# ERROR: RunContext not allowed in tool_plain
@agent.tool_plain
async def bad_tool(ctx: RunContext[MyDeps]) -> str:
    return "oops"
# UserError: RunContext annotations can only be used with tools that take context

@agent.tool
async def good_tool(ctx: RunContext[MyDeps]) -> str:
    return "works"

# ERROR: First param must be RunContext
@agent.tool
def bad_tool(user_id: int) -> str:
    return "oops"
# UserError: First parameter of tools that take context must be annotated with RunContext[...]

@agent.tool
def good_tool(ctx: RunContext[MyDeps], user_id: int) -> str:
    return "works"

# ERROR: RunContext must be first parameter
@agent.tool
def bad_tool(user_id: int, ctx: RunContext[MyDeps]) -> str:
    return "oops"

from pydantic_ai import Agent, RunContext

async def search_db(ctx: RunContext[MyDeps], query: str) -> list[dict]:
    """Search the database."""
    return await ctx.deps.db.search(query)

async def get_user(ctx: RunContext[MyDeps], user_id: int) -> dict:
    """Get user by ID."""
    return await ctx.deps.db.get_user(user_id)

# Valid: Pass raw functions to Agent(tools=[...])
agent = Agent(
    'openai:gpt-4o',
    deps_type=MyDeps,
    tools=[search_db, get_user]  # RunContext detected from signature
)

agent = Agent('openai:gpt-4o', deps_type=MyDeps)

# ERROR: deps required but not provided
result = agent.run_sync('Hello')  # Missing deps!

result = agent.run_sync('Hello', deps=MyDeps(...))

@dataclass
class AppDeps:
    db: Database

@dataclass
class WrongDeps:
    api: ApiClient

agent = Agent('openai:gpt-4o', deps_type=AppDeps)

# Type error: WrongDeps != AppDeps
result = agent.run_sync('Hello', deps=WrongDeps(...))

class Response(BaseModel):
    count: int
    items: list[str]

agent = Agent('openai:gpt-4o', output_type=Response)
result = agent.run_sync('List items')
# May fail if LLM returns wrong structure

agent = Agent(
    'openai:gpt-4o',
    output_type=Response,
    retries=3,  # More attempts
    instructions='Return JSON with count (int) and items (list of strings).'
)

# May cause schema issues with some models
class Complex(BaseModel):
    nested: dict[str, list[tuple[int, str]]]

class Item(BaseModel):
    id: int
    name: str

class Simple(BaseModel):
    items: list[Item]

# ERROR: Can't await in sync function
def handler():
    result = await agent.run('Hello')  # SyntaxError!

def handler():
    result = agent.run_sync('Hello')

# Or
async def handler():
    result = await agent.run('Hello')

@agent.tool
async def slow_tool(ctx: RunContext[Deps]) -> str:
    time.sleep(5)  # WRONG: Blocks event loop!
    return "done"

@agent.tool
async def slow_tool(ctx: RunContext[Deps]) -> str:
    await asyncio.sleep(5)  # Correct
    return "done"

# ERROR: OPENAI_API_KEY not set
agent = Agent('openai:gpt-4o')
result = agent.run_sync('Hello')
# ModelAPIError: Authentication failed

# For testing
agent = Agent('openai:gpt-4o', defer_model_check=True)
with agent.override(model=TestModel()):
    result = agent.run_sync('Hello')

# ERROR: Unknown provider
agent = Agent('unknown:model')
# ValueError: Unknown model provider

async with agent.run_stream('Hello') as response:
    # DON'T access .output before streaming completes
    print(response.output)  # May be incomplete!

# Correct: access after context manager
print(response.output)  # Complete result

async with agent.run_stream('Hello') as response:
    pass  # Never consumed!

# Stream was never read - output may be incomplete

async with agent.run_stream('Hello') as response:
    async for chunk in response.stream_output():
        print(chunk, end='')

@agent.tool_plain
def bad_return() -> object:
    return CustomObject()  # Can't serialize!

@agent.tool_plain
def good_return() -> dict:
    return {"key": "value"}

import logfire
logfire.configure()
logfire.instrument_pydantic_ai()

# Or per-agent
agent = Agent('openai:gpt-4o', instrument=True)

from pydantic_ai import capture_run_messages

with capture_run_messages() as messages:
    result = agent.run_sync('Hello')

for msg in messages:
    print(type(msg).__name__, msg)

result = agent.run_sync('Hello')
print(result.all_messages())  # Full message history
print(result.response)  # Last model response
print(result.usage())  # Token usage