Comparisons / Agno vs Smolagents
Agno vs Smolagents: Which Agent Framework to Use?
Agno (formerly Phidata) is a lightweight Python framework for building agents. Smolagents is HuggingFace's minimalist agent library. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
Agno
39.2k
5.2k
Python
Apache-2.0
2022-05-04
Agno (formerly Phidata)
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 | Agno | Smolagents |
|---|---|---|
| Agent | `Agent(model=OpenAIChat(), instructions=[...])` class with `run()` method | `CodeAgent` or `ToolCallingAgent` with model and tools list |
| Tools | Function tools via `@tool` decorator or built-in toolkits (web search, SQL, etc.) | `@tool` decorator or `Tool` class with name, description, and callable |
| Agent Loop | `Agent.run()` handles tool dispatch internally, configurable via `show_tool_calls` | Internal loop: think (LLM reasons), act (code/tool call), observe (result) |
| Memory / Knowledge | Knowledge bases (PDF, URL, vector DB) injected via `knowledge` param + built-in memory | — |
| Multi-Agent (Teams) | `Team` class with `agents` list, `mode` (sequential, parallel, coordinate), and shared memory | — |
| Storage | `SqlAgentStorage`, `PostgresAgentStorage` for persisting sessions and state | — |
| 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 |
Agno vs Smolagents, head to head
Agno Agno (formerly Phidata) is a lightweight Python framework for building agents.
Smolagents Smolagents is HuggingFace's minimalist agent library.
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 Agno if
Pick Agno if agno adds value when you want a batteries-included agent with minimal boilerplate — especially for multi-modal agents or team orchestration. But each of its abstractions maps to a small piece of plain Python. If your agent is straightforward, writing it directly gives you full control with zero framework overhead. Agno is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Smolagents would force you to translate.
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. Smolagents is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Agno would force you to translate.
What both add
Both Agno and Smolagents 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 Agno 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 →