Exploring AI Agent Frameworks: PydanticAI
Frameworks that facilitate the development of agentic AI applications are becoming of increasing interest in the field of AI software development...and with good cause, considering the cacophony of industry leaders shouting from the rooftops how agentic AI is going to be the technology of 2025 (including the executive team here at Expected X).
Among these frameworks, the relative newcomer, PydanticAI, stands out due to its unique features and capabilities for addressing some of the more idiosyncratic elements of software development when LLMs are involved. While we’ve seen countless agentic AI frameworks build up hype only to fizzle and fade, we’re really crossing our fingers that PydanticAI builds a strong community and solicits enterprise adoption.
What is PydanticAI?
PydanticAI is a Python-based framework designed specifically for creating production-grade applications powered by LLMs. Now, this might sound like nothing special on the surface, but the focus on features for creating applications that can be used at enterprise scale in mission-critical environments is a huge deal.
PydanticAI leverages the capabilities of Pydantic—one of the most widely-used data validation libraries in the Python ecosystem and a library already used as a dependency in most existing agentic AI frameworks—to ensure that outputs from various AI models conform to expected data structures. This model-agnostic framework allows developers to quickly iterate and evaluate different LLMs in their agent mesh, including OpenAI, Anthropic, Mistral, and Gemini without being locked into a specific stack.
Let’s look closer at some of these features:
Model-Agnostic Support: Like we just stated, developers can easily switch between different LLMs, enhancing flexibility in application development. What we’d like to see here is, in addition to being model-agnostic, PydanticAI being prompt agnostic — a feature “implemented” (a term we use loosely given how this actually works behind the scenes) with DSPy.
Type-Safe Development: The integration with Pydantic ensures that all inputs/outputs are validated against predefined schemas, significantly reducing errors in production environments. This makes intra-agent communication much easier given the non-deterministic nature of working with LLM outputs.
Structured Response Validation: Along with type-safe development, this feature guarantees that the output data produced by AI models adheres to expected formats, which is crucial for applications requiring high data integrity.
Dependency Injection System: PydanticAI includes a novel dependency injection system that simplifies application logic and reduces boilerplate code. This might be one of the most useful features of PydanticAI and conforms with best practices familiar to most software developers.
Logfire Integration: Pydantic’s observability platform. This allows for robust debugging and monitoring capabilities, important for maintaining performance in real-time applications.
What Makes PydanticAI Effective?
For the remainder of this post, let’s take a closer look at what has us excited about PydanticAI’s design philosophy:
Type-Safe Validation: One of the standout features of PydanticAI is its type-safe validation mechanism. By verifying that all responses conform to expected structures, developers can trust the integrity of their data. This is particularly important in industries such as finance and healthcare, where data accuracy is critical. In contrast, from our experience, frameworks like LangGraph may focus more on dynamic workflows but lack the stringent validation features that PydanticAI offers.
Comprehensive Development Lifecycle Support: The framework supports the entire application development lifecycle—from initial design through deployment—providing tools such as dependency injection and Logfire integration for monitoring and debugging. This holistic approach contrasts with other frameworks that may only focus on specific aspects of development or require additional tools for lifecycle management.
Scalability and Modularity: PydanticAI is designed with scalability in mind which is a big plus for enterprise clients. Its modular architecture allows developers to integrate new agents into existing workflows without disrupting current processes. This capability ensures that applications can evolve alongside user needs and technological advancements, making it a solid choice for growing projects.
Native Python Support: Python is the defacto standard in backend programming today—let’s face it. By allowing developers to write application logic using plain Python, PydanticAI minimizes the learning curve associated with domain-specific languages found in some other frameworks. This accessibility should enable a broader range of developers to leverage its capabilities effectively with a shorter learning curve…and as an additional plus, since Pydantic has also become the defacto standard in data validation, many Python developers are already familiar with its design philosophy and API.
The Expected X Take…
Well, let’s face it: we’ve been here before when talking about agentic AI frameworks. Expected X began with LangChain/LangGraph as its go-to framework, and while it’s still fairly effective, other frameworks have come along that we’ve enjoyed working with as well. However, most of the applications we’ve built with agentic AI are small PoCs for startups and small businesses, so the promise of production-grade capabilities in PydanticAI is something we are keenly interested in exploring and, if the results are positive, adopting into our own development stack. Hopefully, more to come…!
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