Pydantic Ai Model Integration

Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.

Provider Model Strings

Format: provider:model-name

python

from pydantic_ai import Agent

# OpenAI
Agent('openai:gpt-4o')
Agent('openai:gpt-4o-mini')
Agent('openai:o1-preview')

# Anthropic
Agent('anthropic:claude-sonnet-4-5')
Agent('anthropic:claude-haiku-4-5')

# Google (API Key)
Agent('google-gla:gemini-2.0-flash')
Agent('google-gla:gemini-2.0-pro')

# Google (Vertex AI)
Agent('google-vertex:gemini-2.0-flash')

# Groq
Agent('groq:llama-3.3-70b-versatile')
Agent('groq:mixtral-8x7b-32768')

# Mistral
Agent('mistral:mistral-large-latest')

# Other providers
Agent('cohere:command-r-plus')
Agent('bedrock:anthropic.claude-3-sonnet')

Model Settings

python

from pydantic_ai import Agent
from pydantic_ai.settings import ModelSettings

agent = Agent(
    'openai:gpt-4o',
    model_settings=ModelSettings(
        temperature=0.7,
        max_tokens=1000,
        top_p=0.9,
        timeout=30.0,  # Request timeout
    )
)

# Override per-run
result = await agent.run(
    'Generate creative text',
    model_settings=ModelSettings(temperature=1.0)
)

Fallback Models

Chain models for resilience:

python

from pydantic_ai.models.fallback import FallbackModel

# Try models in order until one succeeds
fallback = FallbackModel(
    'openai:gpt-4o',
    'anthropic:claude-sonnet-4-5',
    'google-gla:gemini-2.0-flash'
)

agent = Agent(fallback)
result = await agent.run('Hello')

# Custom fallback conditions
from pydantic_ai.exceptions import ModelAPIError

def should_fallback(error: Exception) -> bool:
    """Only fallback on rate limits or server errors."""
    if isinstance(error, ModelAPIError):
        return error.status_code in (429, 500, 502, 503)
    return False

fallback = FallbackModel(
    'openai:gpt-4o',
    'anthropic:claude-sonnet-4-5',
    fallback_on=should_fallback
)

Streaming Responses

python

async def stream_response():
    async with agent.run_stream('Tell me a story') as response:
        # Stream text output
        async for chunk in response.stream_output():
            print(chunk, end='', flush=True)

    # Access final result after streaming
    print(f"\nTokens used: {response.usage().total_tokens}")

Streaming with Structured Output

python

from pydantic import BaseModel

class Story(BaseModel):
    title: str
    content: str
    moral: str

agent = Agent('openai:gpt-4o', output_type=Story)

async with agent.run_stream('Write a fable') as response:
    # For structured output, stream_output yields partial JSON
    async for partial in response.stream_output():
        print(partial)  # Partial Story object as parsed

    # Final validated result
    story = response.output

Dynamic Model Selection

python

import os

# Environment-based selection
model = os.getenv('PYDANTIC_AI_MODEL', 'openai:gpt-4o')
agent = Agent(model)

# Runtime model override
result = await agent.run(
    'Hello',
    model='anthropic:claude-sonnet-4-5'  # Override default
)

# Context manager override
with agent.override(model='google-gla:gemini-2.0-flash'):
    result = agent.run_sync('Hello')

Deferred Model Checking

Delay model validation for testing:

python

# Default: Validates model immediately (checks env vars)
agent = Agent('openai:gpt-4o')

# Deferred: Validates only on first run
agent = Agent('openai:gpt-4o', defer_model_check=True)

# Useful for testing with override
with agent.override(model=TestModel()):
    result = agent.run_sync('Test')  # No OpenAI key needed

Usage Tracking

python

result = await agent.run('Hello')

# Request usage (last request)
usage = result.usage()
print(f"Input tokens: {usage.input_tokens}")
print(f"Output tokens: {usage.output_tokens}")
print(f"Total tokens: {usage.total_tokens}")

# Full run usage (all requests in run)
run_usage = result.run_usage()
print(f"Total requests: {run_usage.requests}")

Usage Limits

python

from pydantic_ai.usage import UsageLimits

# Limit token usage
result = await agent.run(
    'Generate content',
    usage_limits=UsageLimits(
        total_tokens=1000,
        request_tokens=500,
        response_tokens=500,
    )
)

Provider-Specific Features

OpenAI

python

from pydantic_ai.models.openai import OpenAIModel

model = OpenAIModel(
    'gpt-4o',
    api_key='your-key',  # Or use OPENAI_API_KEY env var
    base_url='https://custom-endpoint.com'  # For Azure, proxies
)

Anthropic

python

from pydantic_ai.models.anthropic import AnthropicModel

model = AnthropicModel(
    'claude-sonnet-4-5',
    api_key='your-key'  # Or ANTHROPIC_API_KEY
)

Common Model Patterns

Use Case	Recommendation
General purpose	openai:gpt-4o or anthropic:claude-sonnet-4-5
Fast/cheap	openai:gpt-4o-mini or anthropic:claude-haiku-4-5
Long context	anthropic:claude-sonnet-4-5 (200k) or google-gla:gemini-2.0-flash
Reasoning	openai:o1-preview
Cost-sensitive prod	FallbackModel with fast model first

Check gates before ship

Use these only where they prevent obvious misconfiguration; they do not replace integration tests.

• Fallback chain order: Pass: The first model passed to FallbackModel(...) is the intended primary; each subsequent model is a deliberate fallback (not reversed by mistake).
• Secrets: Pass: Production and shared scripts load API keys from environment variables or a platform secret store; no real keys committed (placeholders only in examples).