FrameworkPicker
AutoGen
Mastra

Comparison Preset

VerdictAutoGen vs Mastra · For Enterprises

Neither framework is a suitable choice for an enterprise environment due to significant licensing and maintenance risks. Mastra's `NOASSERTION` license is a critical blocker, as it provides no legal permission to use, modify, or distribute the software. Conversely, AutoGen appears unmaintained, with its last commit being 138 days ago and a commit frequency of less than once per week. This lack of activity presents a major long-term support and security risk that is unacceptable for enterprise use. Both options fail to meet the baseline requirements for stability and legal compliance.

Overview

The bottom line — what this framework is, who it's for, and when to walk away.

Bottom Line Up Front

AutoGen is a Python framework for building AI agents and multi-agent applications, offering components from a no-code UI for prototyping to an event-driven core for scalable, distributed systems. It supports various agentic workflows and integrations via extensions.

Mastra is a TypeScript framework for building AI agents and applications, abstracting away LLM complexities. It provides a structured approach for defining agents and tools, integrating with various LLM providers. Developers can quickly set up projects, leveraging templates and a Studio UI for management.

Best For

Building scalable, distributed multi-agent AI systems and conversational applications with Python or no-code.

Building AI agents and applications, embedding in products, customer support, and internal copilots.

Avoid If

Your project does not involve AI agents or require multi-agent orchestration.

Avoid if you are not building AI agents or prefer to avoid TypeScript.

Strengths

  • +Supports building conversational single and multi-agent applications.
  • +Provides an event-driven core for scalable, distributed multi-agent AI systems.
  • +Offers multiple abstraction levels, including a no-code UI (AutoGen Studio), Python scripting (AgentChat), and a foundational core.
  • +Facilitates deterministic and dynamic agentic workflows for business processes or research.
  • +Includes extensions for external services, such as OpenAI models, Model-Context Protocol (MCP) servers, and Docker-based code execution.
  • +Supports distributed agents through runtimes like GrpcWorkerAgentRuntime.
  • +TypeScript-first framework for robust type checking and developer experience.
  • +Structured agent and tool definition via dedicated classes and functions (Agent, createTool).
  • +Integrated model router supports multiple LLM providers and thousands of models through a consistent API.
  • +Automated environment variable handling for LLM API keys simplifies setup.
  • +Includes an interactive UI, Mastra Studio, for building, testing, and managing agents and workflows.
  • +Provides quickstart commands, framework integration guides, and pre-built templates for common use cases.
  • +Tools enforce input and output schemas using Zod for data validation.

Weaknesses

  • −Requires Python 3.10 or newer, which might restrict compatibility for projects on older Python versions.
  • −The framework's modularity, while powerful, may present a learning curve for understanding its various components (Core, AgentChat, Studio, Extensions).
  • −Focus on multi-agent systems could introduce overhead and complexity for simpler, single-agent automation tasks.
  • −External service integrations, such as OpenAI, require specific extension installations (e.g., autogen-ext[openai]).
  • −Requires a Node.js and TypeScript development environment.
  • −Tools must be defined with 'createTool()' and specific properties; plain objects silently fail execution.
  • −Specific 'provider/model' string format must be followed for LLM models.
  • −Abstracts LLM provider SDKs, potentially limiting direct access to provider-specific advanced features.

Project Health

Is this project alive, well-maintained, and safe to bet on long-term?

Bus Factor Score

9 / 10
9 / 10

Maintainers

100
100

Open Issues

995
506

Fit

Does it support the workflows, patterns, and capabilities your team actually needs?

State Management

Agents manage their state through conversational context and event-driven interactions, facilitating communication and collaboration within multi-agent systems.

no data

Cost & Licensing

What does it actually cost? License type, pricing model, and hidden fees.

License

CC-BY-4.0
NOASSERTION
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Perspective

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