FastAgency vs Semantic Kernel
Side-by-side comparison of two AI agent tools
Short answer
- FastAgency has had no commit in 9 months; Semantic Kernel is actively maintained (59 commits in the last 90 days).
- Semantic Kernel is growing faster: +165 GitHub stars in the last 30 days vs +3 for FastAgency.
- Pick FastAgency for: the fastest way to bring multi-agent workflows to production. Pick Semantic Kernel for: integrate cutting-edge LLM technology quickly and easily into your apps.
From GitHub data refreshed daily.
FastAgencyopen-source
The fastest way to bring multi-agent workflows to production.
Semantic Kernelopen-source
Integrate cutting-edge LLM technology quickly and easily into your apps
Metrics
| FastAgency | Semantic Kernel | |
|---|---|---|
| Stars | 548 | 28.6k |
| Star velocity /mo | 2.526315789473684 | 165 |
| Commits (90d) | 0 | 59 |
| Releases (6m) | 0 | 10 |
| Downloads (30d, npm + PyPI) | 396 | 287.7K |
| Overall score | 0.16848510206411044 | 0.661646916269183 |
Pros
- +Unified interface for deploying AG2 workflows to production with minimal code changes
- +Supports both web chat applications and REST API services from the same codebase
- +Built-in scaling capabilities with distributed architecture and message broker coordination
- +Model-agnostic design supports multiple LLM providers including OpenAI, Azure OpenAI, Hugging Face, and local models
- +Enterprise-ready with built-in observability, security features, and stable APIs for production deployments
- +Multi-language support (Python, .NET, Java) with comprehensive agent orchestration and multi-agent system capabilities
Cons
- -Dependent on AG2 framework, limiting flexibility to other agent frameworks
- -Relatively small community with 532 GitHub stars compared to major frameworks
- -Limited documentation available in the provided materials for advanced features
- -Requires significant programming knowledge and understanding of AI agent concepts
- -Complex setup and configuration for advanced multi-agent workflows
- -Learning curve for mastering the framework's extensive feature set and architectural patterns
Use Cases
- •Deploying AG2 multi-agent chatbots as web applications for customer service or support
- •Creating REST API services that expose agent workflows for integration with existing systems
- •Building scalable distributed agent systems that coordinate across multiple servers or datacenters
- •Building enterprise chatbots and conversational AI applications with reliable LLM integration
- •Creating complex multi-agent systems where specialized AI agents collaborate on business processes
- •Developing AI applications that need flexibility to switch between different LLM providers and deployment environments
FAQ
- Which is more popular, FastAgency or Semantic Kernel?
- Semantic Kernel has more GitHub stars (28,620 vs 548).
- Which is more actively developed, FastAgency or Semantic Kernel?
- Semantic Kernel had more commits in the last 90 days (59 vs 0).
- Should I use FastAgency or Semantic Kernel?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.