Agent Development Kit (ADK) vs Pydantic AI

Side-by-side comparison of two AI agent tools

An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

Pydantic AIopen-source

AI Agent Framework, the Pydantic way

Metrics

Agent Development Kit (ADK)Pydantic AI
Stars21.7k20.3k
Star velocity /mo1.8k711.8181818181818
Commits (90d)1.3k1.4k
Releases (6m)1010
Overall score0.85469770737414040.7924905229931825

Pros

    • +Model-agnostic support for virtually every major LLM provider and cloud platform, offering flexibility in model selection
    • +Built by the Pydantic team with deep integration of proven validation technology used by OpenAI SDK, Google ADK, Anthropic SDK, and other major AI libraries
    • +FastAPI-like developer experience with type hints and validation, providing familiar ergonomics for Python developers

    Cons

      • -Python-only framework, limiting adoption for teams using other programming languages
      • -Relatively new framework compared to established alternatives like LangChain or LlamaIndex
      • -May have a steeper learning curve for developers unfamiliar with Pydantic's validation concepts

      Use Cases

        • •Building production-grade AI agents that need to integrate with multiple LLM providers for redundancy and cost optimization
        • •Developing type-safe AI workflows where data validation and schema enforcement are critical for reliability
        • •Creating AI applications that require seamless switching between different models and providers based on performance or cost requirements

        FAQ

        Which is more popular, Agent Development Kit (ADK) or Pydantic AI?
        Agent Development Kit (ADK) has more GitHub stars (21,688 vs 20,295).
        Which is more actively developed, Agent Development Kit (ADK) or Pydantic AI?
        Pydantic AI had more commits in the last 90 days (1,381 vs 1,322).
        Should I use Agent Development Kit (ADK) or Pydantic AI?
        Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.