Comparison Preset
Semantic Kernel is the better fit here because of its permissive MIT license, active maintenance, and documented commitment to stability. AutoGen's CC-BY-4.0 license introduces attribution requirements that can complicate commercial use, and its lack of commits for over 130 days presents a significant long-term support risk. Semantic Kernel's frequent updates, high bus factor, and explicit promise of non-breaking changes in version 1.0+ make it a more defensible and maintainable choice. While the two known vulnerabilities require a mitigation plan, this is a manageable risk compared to relying on a framework with a questionable license and inactive development.
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.
Semantic Kernel is a lightweight, open-source development kit for building AI agents and integrating AI models into C#, Python, or Java applications. It acts as efficient middleware, connecting prompts with existing APIs to automate business processes and deliver enterprise-grade solutions.
Best For
Building scalable, distributed multi-agent AI systems and conversational applications with Python or no-code.
Building AI agents, integrating models with existing APIs for enterprise solutions, and process automation.
Avoid If
Your project does not involve AI agents or require multi-agent orchestration.
no data
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.
- +Acts as efficient middleware to easily build AI agents and integrate models into C#, Python, or Java code.
- +Future-proof design allows swapping AI models without rewriting code and easily expanding chat APIs.
- +Flexible, modular, and observable with security features like telemetry, hooks, and filters for enterprise use.
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]).
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
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
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.