Comparison Preset
Neither framework is a clear choice for an enterprise deployment without significant due diligence. Mastra is immediately disqualified due to its "NOASSERTION" license, which introduces unacceptable legal and compliance risk. SmolAgents uses a standard Apache-2.0 license and has a high 9/10 bus factor, but it currently has a known CRITICAL vulnerability that must be addressed before adoption. If your team has the resources to audit and mitigate this security risk, SmolAgents is the only viable path forward of the two. A custom state management solution will also need to be scoped and built.
Overview
The bottom line — what this framework is, who it's for, and when to walk away.
Bottom Line Up Front
Mastra is a TypeScript framework designed for rapidly prototyping and deploying AI agents. It integrates with popular web frameworks and supports diverse applications like customer assistants, internal copilots, and data analysis.
SmolAgents is a lightweight Python library designed for building AI agents with minimal code and abstractions. It provides first-class support for `CodeAgent` execution in sandboxed environments and `ToolCallingAgent` for traditional tool use. The framework is highly agnostic, allowing integration with various LLMs, input modalities, and tool sources.
Best For
Rapidly prototyping and deploying AI agents for integration into products or internal workflows.
Quickly building flexible, model/tool/modality-agnostic agents, especially for code-driven task execution.
Avoid If
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Strengths
- +Enables rapid prototyping and confident deployment of AI agents.
- +Offers broad integration with popular JavaScript/TypeScript web frameworks like Next.js, React, and Express.
- +Supports a wide array of AI agent applications, including customer support, data analysis, and DevOps automation.
- +Provides quick project setup with a single command and ready-to-use templates for common use cases.
- +Extremely easy to build and run agents with minimal lines of code.
- +Supports `CodeAgent` for actions written in code, enabling natural composability.
- +Secure code execution is supported via sandboxed environments (Modal, Blaxel, E2B, Docker).
- +Offers `ToolCallingAgent` for standard JSON/text-based tool-calling paradigms.
- +Provides seamless integration with Hugging Face Hub for sharing and loading agents and tools.
- +Model-agnostic, allowing use of any LLM from Hugging Face Inference providers, APIs (OpenAI, Anthropic via LiteLLM), or local models.
- +Modality-agnostic, capable of handling vision, video, and audio inputs.
- +Tool-agnostic, supporting tools from MCP servers, LangChain, or Hugging Face Spaces.
- +Includes CLI tools (`smolagent`, `webagent`) for running agents without boilerplate.
Weaknesses
Project Health
Is this project alive, well-maintained, and safe to bet on long-term?
Bus Factor Score
Maintainers
Open Issues
Fit
Does it support the workflows, patterns, and capabilities your team actually needs?
State Management
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State management for agent execution is primarily handled through the underlying LLM's context window for single interactions or requires custom implementation within the agent's code for persistent or conversational state.
Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
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