LangGraph vs Semantic Kernel
Agent-Assisted
A framework for building stateful, multi-actor applications with LLMs using directed graphs and agent orchestration.
Agent-Assisted
A Microsoft framework for building AI applications that orchestrates interactions between large language models and native code through semantic functions and plugins.
Summary
- LangGraph scores 3.9/5 and Semantic Kernel scores 3.7/5 on the Dark Factory Score, a difference of 0.2.
- The widest gap is Self-Correction: LangGraph 4.0, Semantic Kernel 2.5.
- LangGraph scores higher on autonomy and self-correction.
- Semantic Kernel scores higher on code quality and integration.
- They score the same on project complexity.
- Both are listed as open source.
- Both are listed under Agent Frameworks and Orchestration.
Dimension by dimension
| Dimension | Weight | LangGraph | Semantic Kernel |
|---|---|---|---|
| Autonomy | 28% | 4.2 | 3.5 |
| Code Quality | 20% | 3.5 | 4.0 |
| Integration | 17% | 3.8 | 4.5 |
| Self-Correction | 15% | 4.0 | 2.5 |
| Project Complexity | 20% | 4.0 | 4.0 |
| Dark Factory Score | 3.9 | 3.7 |
Why LangGraph scores 3.9
LangGraph excels at orchestrating complex, stateful multi-agent workflows through its directed graph architecture, earning high autonomy scores for its ability to manage sophisticated task execution patterns with multiple LLM actors. The framework's agent orchestration capabilities and built-in state management enable robust self-correction mechanisms, allowing workflows to handle failures and iterate on solutions effectively. Its integration with the broader LangChain ecosystem provides solid CI/CD and deployment support, though it's primarily focused on orchestration rather than direct system integration. The tool shows strong performance across different project scales, particularly excelling at distributed systems and multi-service applications where its graph-based orchestration shines. While it may be overkill for simple scripts, LangGraph becomes increasingly valuable as project complexity grows, with its stateful architecture and multi-actor coordination being essential for enterprise-scale AI applications. Code quality is solid but varies depending on the underlying LLM implementations, with the framework providing good structure but relying on external models for actual code generation.
Scored by Claude AI — last evaluated February 27, 2026
Why Semantic Kernel scores 3.7
Semantic Kernel is a sophisticated orchestration framework that excels at integrating AI capabilities into applications through its plugin architecture and semantic functions. It provides strong code quality features with comprehensive documentation, TypeScript support, and production-ready patterns for building AI applications. The framework's Microsoft backing ensures enterprise-grade reliability and extensive integration capabilities with Azure services, CI/CD pipelines, and existing codebases. Its modular design allows developers to compose complex AI workflows and manage multi-step interactions between LLMs and native code effectively.
Scored by Claude AI — last evaluated February 27, 2026
More comparisons
Generated from Dark Factory Score data. How the score works. Comparing three or more tools at once is part of Pro.