PydanticAI

Context #

  • PydanticAI is a Python-based AI framework, created by Pydantic Team applying FastAI-like development experience for building AI Apps.
  • Supports OpenAI, Anthropic, Gemini, Ollama, Groq, and Mistral, and more.
  • Provides integration with Logfire - an observability solution built by Pydantic team.
  • GitHub Repo
  • Multi-agent Apps using PydanticAI covering complexity such as:
    • Single agent workflows
    • Agent delegation — agents using another agent via tools
    • Programmatic agent hand-off — one agent runs, then application code calls another agent
    • Graph based control flow — for the most complex cases, a graph-based state machine can be used to control the execution of multiple agents

Local Setup with Examples #

  • Setup Pydantic AI and install examples
cd genai/pydanticai
pip install 'pydantic-ai[examples]'
pip install 'pydantic-ai[logfire]'
export GEMINI_API_KEY=<TOKEN>
export LOGFIRE_TOKEN=<TOKEN>

from pydantic_ai import Agent
import logfire
logfire.configure()  
logfire.instrument_asyncpg() 

agent = Agent(  
    'gemini-1.5-flash',
    system_prompt='Be concise, reply with one sentence.',  
)

result = agent.run_sync('Where does "hello world" come from?')  
print(result.data)
"""
The first known use of "hello, world" was in a 1974 textbook about the C programming language.
"""

Pros/Cons #

Pros

  • Promising with simplicity and familiar Pydantic model-like coding interface, Type-safety checks, etc.
  • Lightweight framework (not overloaded like LangChain)
  • Use-cases such as RAG, Chat App-like apps
  • Supports Slim installation to avoid additional packages:
'pip/uv install/add pydantic-ai-slim[openai]'

Cons

  • Early days for the framework, still BETA. Framework to expand covering complex use-cases.
  • Graph RAG and others are evolving.

User Experience #

Logfire Console