AutoGen vs Semantic Kernel

Semantic Kernel offers a comprehensive SDK with plugin-based skill composition, built-in memory management, a process framework for stateful workflows, and an agent framework for multi-agent systems. It also includes observability, security filters, and middleware extensibility. Its integration with Microsoft 365, Azure OpenAI, and Power Platform makes it ideal for enterprise copilots. AutoGen focuses exclusively on multi-agent conversations, providing flexible agent role definitions, customizable conversation patterns, and human-in-the-loop support. While AutoGen excels at agent collaboration and research, it lacks Semantic Kernel's memory management, process workflows, and enterprise security features. Both frameworks integrate with various LLMs, but Semantic Kernel's stronger ties to Microsoft ecosystems give it an edge for production deployments.

Both Semantic Kernel and AutoGen are fully open-source and free to use under permissive licenses (MIT). There are no tiered pricing plans, and no premium features are locked behind paywalls. However, both incur costs for LLM API usage (e.g., OpenAI, Azure OpenAI) and any underlying infrastructure. Semantic Kernel's tighter integration with Azure services may lead to higher cloud costs if those services are used extensively, but it also offers cost-saving features like caching and memory management. AutoGen's focus on agent orchestration may require more API calls due to multi-step conversations, potentially increasing variable costs. Neither framework offers vendor lock-in in terms of licensing, but Semantic Kernel has a stronger bias toward Azure.