Comparisons / Google ADK vs Pydantic AI
Google ADK vs Pydantic AI: Which Agent Framework to Use?
Google's Agent Development Kit (ADK) is an open-source framework for building multi-agent systems. Pydantic AI is a type-safe agent framework built by the Pydantic team. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
Google ADK
18.7k
3.2k
Python
Apache-2.0
2025-04-01
Google/Alphabet
Vertex AI
Yes
Pydantic AI
16.1k
1.9k
Python
MIT
2024-06-21
Pydantic (Samuel Colvin)
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | Google ADK | Pydantic AI |
|---|---|---|
| Agent | `LlmAgent` class with model, instructions, and `sub_agents` list | `Agent()` class with typed `result_type`, system prompt, and `model` parameter |
| Tools | `FunctionTool`, built-in tools (Search, Code Exec), third-party integrations | `@agent.tool` decorator with typed parameters and Pydantic validation |
| Agent Loop | `Runner.run()` with automatic tool dispatch and sub-agent delegation | `agent.run()` handles the tool-call loop internally with typed dispatch |
| Multi-Agent | Hierarchical agent tree with root agent delegating to specialized sub-agents | — |
| Workflows | `SequentialAgent`, `ParallelAgent`, `LoopAgent` workflow primitives | — |
| Session | Session and State service with typed channels and persistence | — |
| Structured Output | — | `result_type=MyModel` enforces Pydantic model on final LLM response |
| Model Switching | — | Swap `model='openai:gpt-4o'` to `model='anthropic:claude-sonnet'` in one line |
| Dependencies | — | `RunContext[DepsType]` injects typed dependencies into tools at runtime |
Google ADK vs Pydantic AI, head to head
Google ADK Google's Agent Development Kit (ADK) is an open-source framework for building multi-agent systems.
Pydantic AI Pydantic AI is a type-safe agent framework built by the Pydantic team.
Both wrap the same underlying agent pattern — an LLM call, a tool dispatch, a loop — in different abstractions. The choice between them is mostly about which mental model and ecosystem fits the team you have, not which one is technically more capable.
Pick Google ADK if
Pick Google ADK if aDK earns its complexity when you need multi-agent orchestration on Google Cloud with Vertex AI deployment. If you're using Gemini and need production-grade agent infrastructure, it's well-designed. For single-agent use cases or non-Google stacks, plain Python keeps things simpler. Google ADK is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Pydantic AI would force you to translate.
Pick Pydantic AI if
Pick Pydantic AI if pydantic AI adds genuine value if you want compile-time type checking across your agent's tools, outputs, and dependencies. If you already use Pydantic in your stack, it fits naturally. But the core agent logic — loop, dispatch, validate — is still ~60 lines of Python you can own entirely. Pydantic AI is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Google ADK would force you to translate.
What both add
Both Google ADK and Pydantic AI pull in a class hierarchy and a dependency tree to wrap what is, at the core, an HTTP POST in a while loop. If your use case is straightforward — one provider, a handful of tools, a single agent — the framework cost may exceed the framework benefit. The lesson below shows the same pattern in ~60 lines without either dependency.
Or build your own in 60 lines
Both Google ADK and Pydantic AI implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.
No framework. No dependencies. No opinions. Just the code.
Build it from scratch →