Pydantic AI SDK

Pydantic AI is a Python agent framework that brings FastAPI-like ergonomics to GenAI development. Since Pydantic AI uses standard provider SDKs under the hood, Vikat adds enterprise features like governance, semantic caching, MCP tools, observability, etc, on top of your existing agent setup. Endpoint: /pydanticai

Provider Compatibility: This integration only works for AI providers that both Pydantic AI and Vikat support. Currently supported: OpenAI, Anthropic, and Google Gemini.

Setup

Python

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.providers.openai import OpenAIProvider

# Configure provider to use Vikat
provider = OpenAIProvider(
    base_url="http://localhost:8080/pydanticai/v1",  # Point to Vikat
    api_key="dummy-key"  # Keys managed by Vikat, Or add virtual key
)
model = OpenAIChatModel("gpt-4o-mini", provider=provider)

# Create agent with Vikat-routed model
agent = Agent(model, instructions="Be concise and helpful.")

result = agent.run_sync("Hello! How are you?")
print(result.output)

Provider/Model Usage Examples

Your existing Pydantic AI provider switching works unchanged through Vikat:

Python

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.models.anthropic import AnthropicModel
from pydantic_ai.models.google import GoogleModel
from pydantic_ai.providers.openai import OpenAIProvider
from pydantic_ai.providers.anthropic import AnthropicProvider
from pydantic_ai.providers.google import GoogleProvider

base_url = "http://localhost:8080/pydanticai"

# OpenAI models via Pydantic AI
openai_provider = OpenAIProvider(base_url=f"{base_url}/v1")
openai_model = OpenAIChatModel("gpt-4o-mini", provider=openai_provider)
openai_agent = Agent(openai_model)

# Anthropic models via Pydantic AI
# Note: Anthropic SDK adds /v1 internally, so we don't append it here
anthropic_provider = AnthropicProvider(base_url=base_url)
anthropic_model = AnthropicModel("claude-3-haiku-20240307", provider=anthropic_provider)
anthropic_agent = Agent(anthropic_model)

# Google Gemini models via Pydantic AI
google_provider = GoogleProvider(base_url=base_url, api_key="dummy-key")
google_model = GoogleModel("gemini-2.0-flash", provider=google_provider)
google_agent = Agent(google_model)

# All work the same way
openai_result = openai_agent.run_sync("Hello GPT!")
anthropic_result = anthropic_agent.run_sync("Hello Claude!")
gemini_result = google_agent.run_sync("Hello Gemini!")

print(openai_result.output)
print(anthropic_result.output)
print(gemini_result.output)

Tool Calling

Pydantic AI’s powerful tool system works seamlessly through Vikat:

Python

from pydantic_ai import Agent, RunContext, Tool
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.providers.openai import OpenAIProvider
from dataclasses import dataclass

# Configure Vikat
provider = OpenAIProvider(base_url="http://localhost:8080/pydanticai/v1")
model = OpenAIChatModel("gpt-4o-mini", provider=provider)

# Define tools as functions
def get_weather(location: str) -> str:
    """Get the current weather for a location."""
    return f"The weather in {location} is 72°F and sunny."

def calculate(expression: str) -> str:
    """Perform a mathematical calculation."""
    result = eval(expression)  # Use safe evaluation in production
    return f"The result is {result}"

# Create agent with tools
agent = Agent(
    model,
    tools=[get_weather, calculate],
    instructions="You can check weather and do calculations."
)

result = agent.run_sync("What's the weather in Boston?")
print(result.output)

Tools with Dependency Injection

Use RunContext to pass dependencies to your tools:

Python

from pydantic_ai import Agent, RunContext, Tool
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.providers.openai import OpenAIProvider
from dataclasses import dataclass

@dataclass
class UserContext:
    user_id: int
    user_name: str

# Configure Vikat
provider = OpenAIProvider(base_url="http://localhost:8080/pydanticai/v1")
model = OpenAIChatModel("gpt-4o-mini", provider=provider)

def get_user_info(ctx: RunContext[UserContext]) -> str:
    """Get information about the current user."""
    return f"User: {ctx.deps.user_name} (ID: {ctx.deps.user_id})"

agent = Agent(
    model,
    deps_type=UserContext,
    tools=[Tool(get_user_info, takes_ctx=True)],
    instructions="You can look up user information."
)

# Pass dependencies at runtime
deps = UserContext(user_id=123, user_name="Alice")
result = agent.run_sync("What is my user information?", deps=deps)
print(result.output)

Structured Output

Define response types using Pydantic models:

Python

from pydantic import BaseModel, Field
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.providers.openai import OpenAIProvider

# Define structured output type
class CityInfo(BaseModel):
    city: str = Field(description="Name of the city")
    country: str = Field(description="Country where the city is located")
    population: int = Field(description="Approximate population")

# Configure Vikat
provider = OpenAIProvider(base_url="http://localhost:8080/pydanticai/v1")
model = OpenAIChatModel("gpt-4o-mini", provider=provider)

# Agent with typed output
agent = Agent(
    model,
    output_type=CityInfo,
    instructions="Extract city information from user queries."
)

result = agent.run_sync("Tell me about Tokyo, Japan")

# result.output is typed as CityInfo
print(f"City: {result.output.city}")
print(f"Country: {result.output.country}")
print(f"Population: {result.output.population}")

Streaming Responses

Stream responses in real-time for better UX:

Python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.providers.openai import OpenAIProvider

# Configure Vikat
provider = OpenAIProvider(base_url="http://localhost:8080/pydanticai/v1")
model = OpenAIChatModel("gpt-4o-mini", provider=provider)

agent = Agent(model, instructions="Tell engaging stories.")

async def stream_story():
    async with agent.run_stream("Tell me a short story about a robot.") as response:
        async for chunk in response.stream_text():
            print(chunk, end="", flush=True)
    print()  # Newline at end

asyncio.run(stream_story())

Adding Custom Headers

Add Vikat-specific headers for governance and tracking:

Python

from httpx import AsyncClient
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.providers.openai import OpenAIProvider

# Create HTTP client with custom headers
http_client = AsyncClient(
    headers={
        "x-vikat-vk": "your-virtual-key",  # Virtual key for governance
    }
)

# Configure provider with custom client
provider = OpenAIProvider(
    base_url="http://localhost:8080/pydanticai/v1",
    http_client=http_client
)
model = OpenAIChatModel("gpt-4o-mini", provider=provider)

agent = Agent(model)
result = agent.run_sync("Hello!")
print(result.output)

Multi-turn Conversations

Maintain conversation history across multiple turns:

Python

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.providers.openai import OpenAIProvider

# Configure Vikat
provider = OpenAIProvider(base_url="http://localhost:8080/pydanticai/v1")
model = OpenAIChatModel("gpt-4o-mini", provider=provider)

agent = Agent(model, instructions="Remember context from previous messages.")

# First turn
result1 = agent.run_sync("My name is Alice and I live in Paris.")

# Second turn - pass message history to maintain context
result2 = agent.run_sync(
    "What is my name and where do I live?",
    message_history=result1.all_messages()
)

print(result2.output)  # Should mention Alice and Paris

Supported Features

The Pydantic AI integration supports all features available in both the Pydantic AI SDK and Vikat core functionality:

Feature	Supported
Chat Completions	✅
Tool/Function Calling	✅
Structured Output	✅
Streaming	✅
Multi-turn Conversations	✅
Dependency Injection	✅
OpenAI Models	✅
Anthropic Models	✅
Google Gemini Models	✅
Embeddings	✅
Speech/TTS	✅
Transcription	✅

Your existing Pydantic AI agents work seamlessly with Vikat’s enterprise features. 😄

Next Steps

Governance Features - Virtual keys and team management
Semantic Caching - Intelligent response caching
Configuration - Provider setup and API key management

CLI Agents & Editors

SDKs & Frameworks

Identity Providers (SSO)

Content Safety (Guardrails)

Observability

Vector Databases

Pydantic AI SDK

Setup

Provider/Model Usage Examples

Tool Calling

Tools with Dependency Injection

Structured Output

Streaming Responses

Adding Custom Headers

Multi-turn Conversations

Supported Features

Next Steps

​Setup

​Provider/Model Usage Examples

​Tool Calling

​Tools with Dependency Injection

​Structured Output

​Streaming Responses

​Adding Custom Headers

​Multi-turn Conversations

​Supported Features

​Next Steps

Setup

Provider/Model Usage Examples

Tool Calling

Tools with Dependency Injection

Structured Output

Streaming Responses

Adding Custom Headers

Multi-turn Conversations

Supported Features

Next Steps