Comparison Preset
Neither framework is a clear fit for an enterprise environment due to significant licensing and maintenance risks. Mastra must be avoided, as its "NOASSERTION" license status presents an unacceptable and undefined legal risk for any commercial use. AutoGen is the only potentially viable option here, backed by a high bus factor of 9/10 and a large number of maintainers, but its CC-BY-4.0 license requires careful legal review due to its attribution clause. Furthermore, its low commit frequency of less than once per week raises concerns about the velocity of future support and bug fixes. A thorough risk assessment is required before adopting either, but Mastra is a non-starter.
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 applications, offering layered components for prototyping, conversational agents, and scalable multi-agent systems. It supports deterministic and dynamic agentic workflows, with extensibility for external services and custom components. Developers can choose between a no-code UI, a Python agent chat framework, or an event-driven core for complex systems.
Mastra is a TypeScript framework designed for rapidly prototyping and confidently shipping AI agents. It integrates with popular web frameworks and supports a wide range of applications from customer service to DevOps automation.
Best For
Building scalable, conversational, and multi-agent AI systems with deterministic or dynamic workflows.
Building and embedding reliable AI agents for diverse applications, from customer support to DevOps.
Avoid If
no data
Project does not involve building or integrating AI agents into an existing application.
Strengths
- +Provides AutoGen Studio, a web-based UI for prototyping agents without writing code.
- +Offers AgentChat, a programming framework for building conversational single and multi-agent applications.
- +Built on Core, an event-driven framework for building scalable multi-agent AI systems.
- +Supports deterministic and dynamic agentic workflows for business processes.
- +Enables research on multi-agent collaboration and distributed agents for multi-language applications.
- +Features an extension mechanism to interface with external services and libraries.
- +Includes built-in extensions for using Model-Context Protocol (MCP) servers and OpenAI Assistant API.
- +Supports running model-generated code in a Docker container via a built-in extension.
- +Facilitates distributed agents via GrpcWorkerAgentRuntime.
- +Is a TypeScript framework.
- +Supports rapid prototyping and confident deployment of AI agents.
- +Provides a quick start with a single command for project creation.
- +Includes an interactive UI (Studio) for project development.
- +Offers broad integration capabilities with popular web frameworks like Next.js, React, and Express.
- +Enables building a wide range of AI agent applications, from customer assistants to DevOps automation.
- +Offers pre-built templates for common use cases.
Weaknesses
- −Requires Python 3.10 or newer, which may limit compatibility with older environments.
- −Specific state management strategies are not detailed in the provided documentation.
- −Lacks explicit information on its licensing model.
- −The documentation provides limited technical details on its architecture, performance characteristics, or underlying LLM integration mechanisms.
- −Framework is explicitly TypeScript; no support for other primary languages like Python is indicated.
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
AutoGen supports conversational and multi-agent applications, requiring state management to maintain context throughout interactions.
no data
Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
Your expertise shapes what we build next.
We build for engineers who make real architectural decisions. If something is missing, inaccurate, or could be more useful — we want to hear it.
FrameworkPicker — The technical decision engine for the agentic AI era.