PydanticAI

PydanticAI is the framework for engineers who are tired of string-ly typed agent orchestration. While other tools like CrewAI treat agents as role-playing characters, PydanticAI treats them as software components: strictly typed, testable, and predictable. It is built on the philosophy that if you can't validate the input and output, you shouldn't be shipping it to production.

The framework itself is free and open-source, but its secret weapon is the tight integration with Pydantic Logfire for observability. Pricing for Logfire is aggressive: the free tier covers 10 million traces/spans per month. To put that in perspective, if your agent generates 50 spans per run (a complex RAG workflow), you can run 200,000 invocations a month for $0. Once you cross that, it’s a flat $2 per million spans. Compared to the “call us” pricing of enterprise observability tools or the per-seat taxing of other agent platforms, this is laughably cheap.

Technically, PydanticAI shines by making the "implicit" explicit. You define agent dependencies (like database connections) using standard Python typing, which means your IDE actually helps you. The pydantic-graph library, included for stateful workflows, is a direct competitor to LangGraph. It avoids the “magic dictionary” state management of LangChain in favor of typed dataclasses. If you modify state, the type checker knows about it.

However, this strictness is also its main friction point. You cannot just throw a prompt at it and hope for the best. You must define Pydantic models for your structured responses. For a quick hackathon prototype, this feels like homework. Additionally, while the Monty sandbox for safe code execution is promising (sub-microsecond startup!), it only supports a subset of Python, meaning your agent might crash if it tries to import unsupported libraries.

Skip PydanticAI if you are building a simple chatbot or if you prefer the “drag-and-drop” mental model of flow-based builders. Use it if you are a Python team already using FastAPI and you want to build agents that don't break whenever an LLM decides to return a slightly different JSON format.

Pricing

The framework is MIT-licensed and free. The only potential cost is the Pydantic Logfire observability platform, which is optional but highly recommended. The free tier is massive: 10 million spans/month. For a heavy workload (e.g., 5,000 multi-step agent runs/day), you likely won't pay a dime. If you scale to 50M spans/month, your bill would be ~$80. This is orders of magnitude cheaper than Datadog or LangSmith for similar volume. There are no per-agent or per-seat fees.

Technical Verdict

The Developer Experience (DX) is exceptional for modern Python users. It relies heavily on modern Python features (type hints, generics, async). Latency overhead is negligible. Documentation is pragmatic, focusing on patterns rather than marketing fluff. pydantic-graph is verbose but clearer than LangGraph's state dicts. Expect a steeper learning curve if you aren't comfortable with Pydantic validators or dependency injection patterns.

Quick Start

from pydantic_ai import Agent
 
agent = Agent(
    'openai:gpt-4o',
    system_prompt='You are a helpful assistant.'
)
 
result = agent.run_sync('What is the capital of France?')
print(result.data)  # Paris

Watch Out

• The Monty code sandbox is experimental and supports only a subset of Python—don't expect pandas/numpy to work out of the box.
• Streaming structured responses requires careful schema design; complex nested validations can break the stream.
• It does not have built-in "ready-to-use" tools like Google Search or Wikipedia wrappers; you have to write the function interfaces yourself.