Haystack vs Semantic Kernel
Side-by-side comparison of two AI agent tools
Short answer
- Haystack is growing faster: +318 GitHub stars in the last 30 days vs +165 for Semantic Kernel.
- Pick Haystack for: open-source AI orchestration framework for modular RAG pipelines and agent workflows. Pick Semantic Kernel for: integrate cutting-edge LLM technology quickly and easily into your apps.
From GitHub data refreshed daily.
Haystackopen-source
Open-source AI orchestration framework for modular RAG pipelines and agent workflows
Semantic Kernelopen-source
Integrate cutting-edge LLM technology quickly and easily into your apps
Metrics
| Haystack | Semantic Kernel | |
|---|---|---|
| Stars | 26.6k | 28.6k |
| Star velocity /mo | 317.8421052631579 | 165 |
| Commits (90d) | 768 | 59 |
| Releases (6m) | 10 | 10 |
| Overall score | 0.7901810278193188 | 0.661646916269183 |
Pros
- +Production-ready architecture with robust testing and type safety (Mypy, comprehensive test coverage)
- +Modular pipeline design allows for flexible composition and customization of AI workflows
- +Strong community adoption with 24,000+ GitHub stars and active development by deepset
- +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
- -Learning curve may be steep for developers new to AI orchestration frameworks
- -Complexity might be overkill for simple LLM integration use cases
- -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
- •Building production RAG systems with sophisticated document retrieval and context management
- •Creating AI agent workflows with explicit control over routing and decision-making processes
- •Developing modular AI pipelines that require custom retrieval and context engineering components
- •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, Haystack or Semantic Kernel?
- Semantic Kernel has more GitHub stars (28,620 vs 26,646).
- Which is more actively developed, Haystack or Semantic Kernel?
- Haystack had more commits in the last 90 days (768 vs 59).
- Should I use Haystack 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.