Comparisons / Google ADK vs Smolagents
Google ADK vs Smolagents: Which Agent Framework to Use?
Google ADK vs Smolagents, head to head
Google ADK and Smolagents both let you build an agent, but they sit in different parts of the stack and they assume different things about who's writing the code.
Google's Agent Development Kit (ADK) is an open-source framework for building multi-agent systems.
Smolagents is HuggingFace's minimalist agent library.
Underneath, both wrap the same thing: a model call, a tool dispatch, a loop. The decision is about which abstraction your team wants to think in day to day, and which ecosystem you're willing to inherit along with it. There's an honest, framework-free version of the same pattern in about 60 lines of Python in the lesson at the bottom of this page — useful as a baseline regardless of which framework wins.
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. The tradeoffs in its intro should match how your team already thinks about agents; Smolagents will feel like translation if they don't.
Pick Smolagents if
Pick Smolagents if smolagents lives up to its name — it's genuinely minimal and the code-agent approach is a real innovation that reduces LLM calls by ~30%. If you want a lightweight agent library with HuggingFace ecosystem access, it's excellent. For understanding the fundamentals, the plain version is even simpler. The tradeoffs in its intro should match how your team already thinks about agents; Google ADK will feel like translation if they don't.
By the numbers
By the numbers
Google ADK
18.7k
3.2k
Python
Apache-2.0
2025-04-01
Google/Alphabet
Vertex AI
Yes
Smolagents
26.4k
2.4k
Python
Apache-2.0
2024-12-05
Hugging Face
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | Google ADK | Smolagents |
|---|---|---|
| Agent | `LlmAgent` class with model, instructions, and `sub_agents` list | `CodeAgent` or `ToolCallingAgent` with model and tools list |
| Tools | `FunctionTool`, built-in tools (Search, Code Exec), third-party integrations | `@tool` decorator or `Tool` class with name, description, and callable |
| Agent Loop | `Runner.run()` with automatic tool dispatch and sub-agent delegation | Internal loop: think (LLM reasons), act (code/tool call), observe (result) |
| 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 | — |
| Code Actions | — | `CodeAgent` writes Python code as its action, executed in sandbox |
| Sandbox | — | E2B, Docker, Modal, or Pyodide sandbox for safe code execution |
| Model Support | — | HuggingFace Hub models, OpenAI, Anthropic, local via LiteLLM |
Or build your own in 60 lines
Both Google ADK and Smolagents 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 →