Comparison Preset
LangGraph is the more defensible choice due to risk management. While Semantic Kernel is positioned for enterprise use with v1.0+ stability promises, it currently has a known CRITICAL vulnerability, which presents an unacceptable security risk for most organizations. LangGraph's single known vulnerability is only of MODERATE severity. Both frameworks have strong bus factor scores (8/10 and 9/10) and permissive MIT licenses, making the security posture the key differentiator. The low-level nature of LangGraph requires more engineering effort, but this is a manageable resourcing challenge compared to the inherent risk of a critical vulnerability.
Overview
The bottom line — what this framework is, who it's for, and when to walk away.
Bottom Line Up Front
LangGraph is a low-level Python orchestration framework for building complex, stateful, and durable AI agents. It provides core benefits like persistence, human-in-the-loop capabilities, and comprehensive memory. While very powerful, it requires familiarity with agent components and offers less abstraction than other frameworks.
Semantic Kernel is an open-source development kit for building AI agents and integrating current AI models into C#, Python, or Java applications. It functions as middleware, translating AI model requests into existing API calls. This enables rapid delivery of enterprise-grade AI solutions, automating business processes effectively.
Best For
Building low-level, long-running, stateful AI agents requiring durable execution and human oversight.
Building enterprise AI agents, integrating AI models, automating business processes, and extending existing APIs.
Avoid If
You are new to agents or need a higher-level abstraction for simpler LLM application development.
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Strengths
- +Provides durable execution, allowing agents to persist through failures and resume operations.
- +Supports human-in-the-loop interaction, enabling inspection and modification of agent state at any point.
- +Offers comprehensive memory management for both short-term working memory and long-term cross-session memory.
- +Integrates with LangSmith for deep visibility, tracing, debugging, and production deployment of agent systems.
- +Acts as a low-level orchestration runtime, offering fine-grained control over agent workflow and state transitions.
- +Lightweight and open-source development kit for AI agents.
- +Integrates the latest AI models into C#, Python, or Java codebases.
- +Efficient middleware for rapid delivery of enterprise-grade solutions.
- +Flexible, modular, and observable architecture.
- +Includes security-enhancing capabilities like telemetry, hooks, and filters.
- +Version 1.0+ support across C#, Python, and Java ensures reliability and commitment to non-breaking changes.
- +Easily expands existing chat-based APIs to support additional modalities like voice and video.
- +Designed to be future-proof, allowing easy model swapping without codebase rewrites.
- +Combines prompts with existing APIs to perform actions.
- +Uses OpenAPI specifications, enabling sharing extensions with pro or low-code developers.
Weaknesses
- −It is a very low-level framework, requiring engineers to manage prompts and architecture explicitly.
- −Does not abstract prompts or architecture, demanding familiarity with agent components like models and tools.
- −Has a higher learning curve for those just starting with agents or seeking a more abstract development experience.
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
LangGraph manages state through a graph-based structure allowing for persistence, human intervention, and comprehensive short-term/long-term memory.
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Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
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