Comparison Preset
SmolAgents is the only viable choice here due to its clear Apache-2.0 license. Mastra's `NOASSERTION` license status presents an unacceptable and unquantifiable legal risk for any enterprise. While SmolAgents has a known critical vulnerability that must be immediately addressed, this is a manageable technical risk compared to Mastra's foundational legal uncertainty. SmolAgents also has a high bus factor score of 9/10 and features for secure sandboxed execution, which are important for long-term stability and security.
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 building and deploying AI agents and applications. It provides a comprehensive UI, Mastra Studio, for managing agents and workflows. The framework integrates with popular web frameworks and supports a wide array of LLM providers and models.
SmolAgents is an open-source Python library designed to easily build and run agents with minimal code. It supports both code-execution and tool-calling agents, is model and tool-agnostic, and integrates with Hugging Face Hub. Secure code execution and modality-agnostic capabilities extend its utility.
Best For
Building reliable, scalable AI agents and applications across various domains.
Rapidly building, running, and sharing LLM agents, especially those executing Python code securely.
Avoid If
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Executing untrusted `CodeAgent` prompts without configuring a sandboxed environment.
Strengths
- +Facilitates rapid prototyping and confident shipping of AI agents.
- +Offers a comprehensive UI, Mastra Studio, for building, testing, and managing agents and workflows.
- +Provides access to over 3000 models from numerous LLM providers via its model router.
- +Supports integration with popular web frameworks like Next.js, React, Astro, Express, SvelteKit, and Hono.
- +Includes templates for various specific AI applications, from customer support to data analysis.
- +Provides a simple API for agent development, with agent logic fitting in approximately 1000 lines of code.
- +Offers first-class support for `CodeAgent`s that write and execute Python code, enabling natural composability.
- +Supports secure code execution in sandboxed environments via Modal, Blaxel, E2B, or Docker.
- +Includes `ToolCallingAgent` support for traditional JSON/text-based tool invocation paradigms.
- +Integrates seamlessly with Hugging Face Hub for sharing and loading agents and tools as Gradio Spaces.
- +Is model-agnostic, supporting Hugging Face Inference providers, OpenAI, Anthropic (via LiteLLM), and local models (Transformers, Ollama).
- +Is modality-agnostic, capable of handling vision, video, and audio inputs for broader application types.
- +Is tool-agnostic, allowing integration with tools from MCP servers, LangChain, or other Hugging Face Spaces.
- +Includes command-line utilities (smolagent, webagent) for quick agent execution without boilerplate code.
Weaknesses
- −Potential security risks exist if `CodeAgent` is used to execute untrusted code without configuring a sandboxed environment.
- −Minimalistic abstractions may require more direct coding or boilerplate for highly customized or complex agent workflows.
- −No explicit mention of built-in memory management or long-term conversational state management is provided.
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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Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
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