PydanticAI Common Pitfalls and Debugging

Tool Decorator Errors

Wrong: RunContext in tool_plain

# 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

Fix: Use @agent.tool if you need context:

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

Wrong: Missing RunContext in tool

# 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[...]

Fix: Add RunContext as first parameter:

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

Wrong: RunContext not first

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

Fix: RunContext must always be the first parameter.

Dependency Type Mismatches

Wrong: Missing deps at runtime

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

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

Fix: Always provide deps when deps_type is set:

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

Wrong: Wrong deps type

@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(...))

Output Type Issues

Pydantic validation fails

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

Fix: Increase retries or improve prompt:

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

Complex nested types

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

Fix: Simplify or use intermediate models:

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

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

Async vs Sync Mistakes

Wrong: Calling async in sync context

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

Fix: Use run_sync or make handler async:

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

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

Wrong: Blocking in async tools

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

Fix: Use async I/O:

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

Model Configuration Errors

Missing API key

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

Fix: Set environment variable or use defer_model_check:

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

Invalid model string

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

Fix: Use valid provider:model format.

Streaming Issues

Wrong: Using result before stream completes

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

Wrong: Not iterating stream

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

# Stream was never read - output may be incomplete

Fix: Always consume the stream:

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

Tool Return Issues

Wrong: Returning non-serializable

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

Fix: Return serializable types (str, dict, Pydantic model):

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

Debugging Tips

Enable tracing

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

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

Capture messages

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)

Check model responses

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

Common Error Messages

Error	Cause	Fix
First parameter... RunContext	@agent.tool missing ctx	Add ctx: RunContext[...]
RunContext... only... context	@agent.tool_plain has ctx	Remove ctx or use @agent.tool
Unknown model provider	Invalid model string	Use valid provider:model
ModelAPIError	API auth/quota	Check API key, limits
RetryPromptPart in messages	Validation failed	Check output_type, increase retries