---
name: pydantic-ai-common-pitfalls
description: Avoid common mistakes and debug issues in PydanticAI agents. Use when encountering errors, unexpected behavior, or when reviewing agent implementations.
---
# PydanticAI Common Pitfalls and Debugging
## Tool Decorator Errors
### Wrong: RunContext in tool_plain
```python
# 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:
```python
@agent.tool
async def good_tool(ctx: RunContext[MyDeps]) -> str:
return "works"
```
### Wrong: Missing RunContext in tool
```python
# 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:
```python
@agent.tool
def good_tool(ctx: RunContext[MyDeps], user_id: int) -> str:
return "works"
```
### Wrong: RunContext not first
```python
# 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.
## Valid Patterns (Not Errors)
### Raw Function Tool Registration
The following pattern IS valid and supported by pydantic-ai:
```python
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
)
```
**Why this works:** PydanticAI inspects function signatures. If the first parameter is `RunContext[T]`, it's treated as a context-aware tool. No decorator required.
**Reference:** https://ai.pydantic.dev/agents/#registering-tools-via-the-tools-argument
**Do NOT flag** code that passes functions with `RunContext` signatures to `Agent(tools=[...])`. This is equivalent to using `@agent.tool` and is explicitly documented.
## Dependency Type Mismatches
### Wrong: Missing deps at runtime
```python
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:
```python
result = agent.run_sync('Hello', deps=MyDeps(...))
```
### Wrong: Wrong deps type
```python
@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
```python
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:
```python
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
```python
# May cause schema issues with some models
class Complex(BaseModel):
nested: dict[str, list[tuple[int, str]]]
```
**Fix**: Simplify or use intermediate models:
```python
class Item(BaseModel):
id: int
name: str
class Simple(BaseModel):
items: list[Item]
```
## Async vs Sync Mistakes
### Wrong: Calling async in sync context
```python
# ERROR: Can't await in sync function
def handler():
result = await agent.run('Hello') # SyntaxError!
```
**Fix**: Use run_sync or make handler async:
```python
def handler():
result = agent.run_sync('Hello')
# Or
async def handler():
result = await agent.run('Hello')
```
### Wrong: Blocking in async tools
```python
@agent.tool
async def slow_tool(ctx: RunContext[Deps]) -> str:
time.sleep(5) # WRONG: Blocks event loop!
return "done"
```
**Fix**: Use async I/O:
```python
@agent.tool
async def slow_tool(ctx: RunContext[Deps]) -> str:
await asyncio.sleep(5) # Correct
return "done"
```
## Model Configuration Errors
### Missing API key
```python
# 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:
```python
# For testing
agent = Agent('openai:gpt-4o', defer_model_check=True)
with agent.override(model=TestModel()):
result = agent.run_sync('Hello')
```
### Invalid model string
```python
# 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
```python
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
```python
async with agent.run_stream('Hello') as response:
pass # Never consumed!
# Stream was never read - output may be incomplete
```
**Fix**: Always consume the stream:
```python
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
```python
@agent.tool_plain
def bad_return() -> object:
return CustomObject() # Can't serialize!
```
**Fix**: Return serializable types (str, dict, Pydantic model):
```python
@agent.tool_plain
def good_return() -> dict:
return {"key": "value"}
```
## Debugging Tips
### Gates (ambiguous failures)
When the agent misbehaves but the stack trace or error string is unclear, follow **in order**; do not skip ahead.
1. **Capture evidence** — Re-run with `capture_run_messages()` or, after `run`/`run_sync`, inspect `result.all_messages()` (or print message types in order). **Pass:** You can name the message part type and one line of content that explains the failure (e.g. `RetryPromptPart`, tool return, model text).
2. **Separate model from schema** — If validation or `RetryPromptPart` appears, compare the last model message to your `output_type` fields and types. **Pass:** You identified a concrete mismatch (missing field, wrong type, refusal) before only raising `retries` or changing the model.
3. **Confirm deps on every path** — If tools or deps fail, verify each call site (`run`, `run_sync`, `run_stream`) supplies `deps=...` when `deps_type` is set. **Pass:** A minimal repro with explicit `deps` matches or rules out a deps wiring bug.
### Enable tracing
```python
import logfire
logfire.configure()
logfire.instrument_pydantic_ai()
# Or per-agent
agent = Agent('openai:gpt-4o', instrument=True)
```
### Capture messages
```python
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
```python
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 |