Comparisons / Mastra vs Smolagents
Mastra vs Smolagents: Which Agent Framework to Use?
Mastra vs Smolagents, head to head
Mastra 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.
Mastra is a TypeScript-first framework for building AI agents, from the team behind Gatsby.
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 Mastra if
Pick Mastra if mastra is the best option for TypeScript teams that want a batteries-included agent framework without leaving the Node.js ecosystem. The workflow engine and Studio are genuinely productive. For simple agents or Python teams, the plain approach avoids an unnecessary dependency. 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; Mastra will feel like translation if they don't.
By the numbers
By the numbers
Mastra
22.7k
1.8k
TypeScript
Apache-2.0
2024-08-06
Mastra AI
Spark Capital, Y Combinator
Series A ($22M, Apr 2026 — $35M total)
244.0k
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 | Mastra | Smolagents |
|---|---|---|
| Agent | `new Agent({ model, instructions, tools })` with automatic tool dispatch | `CodeAgent` or `ToolCallingAgent` with model and tools list |
| Tools | `createTool({ name, schema, execute })` with Zod validation | `@tool` decorator or `Tool` class with name, description, and callable |
| Workflows | `Workflow` class with `.step()`, `.then()`, `.branch()` for orchestration | — |
| RAG | Built-in document syncing, chunking, embedding, and vector search | — |
| Memory | Short-term thread memory + long-term vector memory across sessions | — |
| Studio | Mastra Studio: local GUI for testing agents, viewing traces, debugging | — |
| Code Actions | — | `CodeAgent` writes Python code as its action, executed in sandbox |
| Sandbox | — | E2B, Docker, Modal, or Pyodide sandbox for safe code execution |
| Agent Loop | — | Internal loop: think (LLM reasons), act (code/tool call), observe (result) |
| Model Support | — | HuggingFace Hub models, OpenAI, Anthropic, local via LiteLLM |
Or build your own in 60 lines
Both Mastra 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 →