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PydanticAI is a Python agent framework from the Pydantic team that brings the same type-safety and validation guarantees as Pydantic to LLM-based applications. It supports structured outputs (validated with Pydantic models), dependency injection for testability, streamed responses, multi-turn conversations, and tool use — across OpenAI, Anthropic, Google Gemini, Groq, Mistral, and Ollama. Use this skill when building production AI agents, chatbots, or LLM pipelines where correctness and testability matter.
When to Use This Skill
Use when building Python AI agents that call tools and return structured data
Use when you need validated, typed LLM outputs (not raw strings)
Use when you want to write unit tests for agent logic without hitting a real LLM
Use when switching between LLM providers without rewriting agent code
Use when the user asks about Agent, @agent.tool, RunContext, ModelRetry, or result_type
How It Works
Step 1: Installation
pip install pydantic-ai
# Install extras for specific providers
pip install 'pydantic-ai[openai]'# OpenAI / Azure OpenAI
pip install 'pydantic-ai[anthropic]'# Anthropic Claude
pip install 'pydantic-ai[gemini]'# Google Gemini
pip install 'pydantic-ai[groq]'# Groq
pip install 'pydantic-ai[vertexai]'# Google Vertex AI
Step 2: A Minimal Agent
from pydantic_ai import Agent
# Simple agent — returns a plain string
agent = Agent(
'anthropic:claude-sonnet-4-6',
system_prompt='You are a helpful assistant. Be concise.',
)
result = agent.run_sync('What is the capital of Japan?')
print(result.data) # "Tokyo"print(result.usage()) # Usage(requests=1, request_tokens=..., response_tokens=...)
Step 3: Structured Output with Pydantic Models
from pydantic import BaseModel
from pydantic_ai import Agent
classMovieReview(BaseModel):
title: str
year: int
rating: float# 0.0 to 10.0
summary: str
recommended: bool
agent = Agent(
'openai:gpt-4o',
result_type=MovieReview,
system_prompt='You are a film critic. Return structured reviews.',
)
result = agent.run_sync('Review Inception (2010)')
review = result.data # Fully typed MovieReview instanceprint(f"{review.title} ({review.year}): {review.rating}/10")
print(f"Recommended: {review.recommended}")
Step 4: Tool Use
Register tools with @agent.tool — the LLM can call them during a run:
from pydantic_ai import Agent, RunContext
from pydantic import BaseModel
import httpx
classWeatherReport(BaseModel):
city: str
temperature_c: float
condition: str
weather_agent = Agent(
'anthropic:claude-sonnet-4-6',
result_type=WeatherReport,
system_prompt='Get current weather for the requested city.',
)
@weather_agent.toolasyncdefget_temperature(ctx: RunContext, city: str) -> dict:
"""Fetch the current temperature for a city from the weather API."""asyncwith httpx.AsyncClient() as client:
r = await client.get(f'https://wttr.in/{city}?format=j1')
data = r.json()
return {
'temp_c': float(data['current_condition'][0]['temp_C']),
'description': data['current_condition'][0]['weatherDesc'][0]['value'],
}
import asyncio
result = asyncio.run(weather_agent.run('What is the weather in Tokyo?'))
print(result.data)
Step 5: Dependency Injection
Inject services (database, HTTP clients, config) into agents for testability:
from dataclasses import dataclass
from pydantic_ai import Agent, RunContext
from pydantic import BaseModel
@dataclassclassDeps:
db: Database
user_id: strclassSupportResponse(BaseModel):
message: str
escalate: bool
support_agent = Agent(
'openai:gpt-4o-mini',
deps_type=Deps,
result_type=SupportResponse,
system_prompt='You are a support agent. Use the tools to help customers.',
)
@support_agent.toolasyncdefget_order_history(ctx: RunContext[Deps]) -> list[dict]:
"""Fetch recent orders for the current user."""returnawait ctx.deps.db.get_orders(ctx.deps.user_id, limit=5)
@support_agent.toolasyncdefcreate_refund(ctx: RunContext[Deps], order_id: str, reason: str) -> dict:
"""Initiate a refund for a specific order."""returnawait ctx.deps.db.create_refund(order_id, reason, ctx.deps.user_id)
# Usageasyncdefhandle_support(user_id: str, message: str):
deps = Deps(db=get_db(), user_id=user_id)
result = await support_agent.run(message, deps=deps)
return result.data
Step 6: Testing with TestModel
Write unit tests without real LLM calls:
from pydantic_ai.models.test import TestModel
deftest_support_agent_escalates():
with support_agent.override(model=TestModel()):
# TestModel returns a minimal valid response matching result_type
result = support_agent.run_sync(
'I want to cancel my account',
deps=Deps(db=FakeDb(), user_id='user-123'),
)
# Test the structure, not the LLM's exact wordsassertisinstance(result.data, SupportResponse)
assertisinstance(result.data.escalate, bool)
FunctionModel for deterministic test responses:
from pydantic_ai.models.function import FunctionModel, ModelContext
defmy_model(messages, info):
return ModelResponse(parts=[TextPart('Always this response')])
with agent.override(model=FunctionModel(my_model)):
result = agent.run_sync('anything')
Step 7: Streaming Responses
import asyncio
from pydantic_ai import Agent
agent = Agent('anthropic:claude-sonnet-4-6')
asyncdefstream_response():
asyncwith agent.run_stream('Write a haiku about Python') as result:
asyncfor chunk in result.stream_text():
print(chunk, end='', flush=True)
print() # newlineprint(f"Total tokens: {result.usage()}")
asyncio.run(stream_response())
Step 8: Multi-Turn Conversations
from pydantic_ai import Agent
from pydantic_ai.messages import ModelMessagesTypeAdapter
agent = Agent('openai:gpt-4o', system_prompt='You are a helpful assistant.')
# First turn
result1 = agent.run_sync('My name is Alice.')
history = result1.all_messages()
# Second turn — passes conversation history
result2 = agent.run_sync('What is my name?', message_history=history)
print(result2.data) # "Your name is Alice."
Examples
Example 1: Code Review Agent
from pydantic import BaseModel, Field
from pydantic_ai import Agent
from typing importLiteralclassCodeReview(BaseModel):
quality: Literal['excellent', 'good', 'needs_work', 'poor']
issues: list[str] = Field(default_factory=list)
suggestions: list[str] = Field(default_factory=list)
approved: bool
code_review_agent = Agent(
'anthropic:claude-sonnet-4-6',
result_type=CodeReview,
system_prompt="""
You are a senior engineer performing code review.
Evaluate code quality, identify issues, and provide actionable suggestions.
Set approved=True only for good or excellent quality code with no security issues.
""",
)
defreview_code(diff: str) -> CodeReview:
result = code_review_agent.run_sync(f"Review this code:\n\n{diff}")
return result.data
Example 2: Agent with Retry Logic
from pydantic_ai import Agent, ModelRetry
from pydantic import BaseModel, field_validator
classStrictJson(BaseModel):
value: int @field_validator('value')defmust_be_positive(cls, v):
if v <= 0:
raise ValueError('value must be positive')
return v
agent = Agent('openai:gpt-4o-mini', result_type=StrictJson)
@agent.result_validatorasyncdefvalidate_result(ctx, result: StrictJson) -> StrictJson:
if result.value > 1000:
raise ModelRetry('Value must be under 1000. Try again with a smaller number.')
return result
Example 3: Multi-Agent Pipeline
from pydantic_ai import Agent
from pydantic import BaseModel
classResearchSummary(BaseModel):
key_points: list[str]
conclusion: strclassBlogPost(BaseModel):
title: str
body: str
meta_description: str
researcher = Agent('openai:gpt-4o', result_type=ResearchSummary)
writer = Agent('anthropic:claude-sonnet-4-6', result_type=BlogPost)
asyncdefresearch_and_write(topic: str) -> BlogPost:
# Stage 1: research
research = await researcher.run(f'Research the topic: {topic}')
# Stage 2: write based on research
post = await writer.run(
f'Write a blog post about: {topic}\n\nResearch:\n' +
'\n'.join(f'- {p}'for p in research.data.key_points) +
f'\n\nConclusion: {research.data.conclusion}'
)
return post.data
Best Practices
✅ Always define result_type with a Pydantic model — avoid returning raw strings in production
✅ Use deps_type with a dataclass for dependency injection — makes agents testable
✅ Use TestModel in unit tests — never hit a real LLM in CI
✅ Add @agent.result_validator for business-logic checks beyond Pydantic validation
✅ Use run_stream for long outputs in user-facing applications to show progressive results
❌ Don't put secrets (API keys) in Agent() arguments — use enprojectnment variables
❌ Don't share a single Agent instance across async tasks if deps differ — create per-request instances or use agent.run() with per-call deps
❌ Don't catch ValidationError broadly — let PydanticAI retry with ModelRetry for recoverable LLM output errors
Security & Safety Notes
Set API keys via enprojectnment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.) — never hardcode them.
Validate all tool inputs before passing to external systems — use Pydantic models or manual checks.
Tools that mutate data (write to DB, send emails, call payment APIs) should require explicit user confirmation before the agent invokes them in production.
Log result.all_messages() for audit trails when agents perform consequential actions.
Set retries= limits on Agent() to prevent runaway loops on persistent validation failures.
Common Pitfalls
Problem:ValidationError on every LLM response — structured output never validates
Solution: Simplify result_type fields. Use Optional and default where appropriate. The model may struggle with overly strict schemas.
Problem: Tool is never called by the LLM
Solution: Write a clear, specific docstring for the tool function — PydanticAI sends the docstring as the tool description to the LLM.
Problem:RunContext dependency is None inside a tool
Solution: Pass deps= when calling agent.run() or agent.run_sync(). Dependencies are not set globally.
Problem:asyncio.run() error when calling agent.run() inside FastAPI
Solution: Use await agent.run() directly in async FastAPI route handlers — don't wrap in asyncio.run().
Related Skills
@langchain-architecture — Alternative Python AI framework (more flexible, less type-safe)
@llm-application-dev-ai-assistant — General LLM application development patterns
@fastapi-templates — Serving PydanticAI agents via FastAPI endpoints
