langgraph vs LlamaFactory
Side-by-side comparison of two AI agent tools
langgraphopen-source
Build resilient language agents as graphs.
LlamaFactoryopen-source
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
Metrics
| langgraph | LlamaFactory | |
|---|---|---|
| Stars | 28.0k | 69.3k |
| Star velocity /mo | 2.5k | 1.1k |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 1 |
| Overall score | 0.8081963872278098 | 0.7336586989754887 |
Pros
- +Durable execution ensures agents automatically resume from exactly where they left off after failures or interruptions
- +Comprehensive memory system with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
- +Seamless human-in-the-loop capabilities allow for inspection and modification of agent state at any point during execution
- +Supports unified fine-tuning of 100+ different LLMs and VLMs with consistent interface
- +Proven industry adoption by major companies like Amazon, NVIDIA, and Aliyun
- +Multiple deployment options including Docker, cloud platforms, and easy PyPI installation
Cons
- -Low-level framework requires more technical expertise and setup compared to high-level agent builders
- -Graph-based agent design paradigm may have a steeper learning curve for developers new to agent orchestration
- -Production deployment complexity may be overkill for simple chatbot or single-turn use cases
- -Learning curve may be steep due to supporting numerous model architectures and configurations
- -Fine-tuning operations require significant computational resources and GPU memory
Use Cases
- •Long-running autonomous agents that need to persist through system failures and operate over days or weeks
- •Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
- •Stateful agents that must maintain context and memory across multiple sessions and interactions
- •Domain-specific fine-tuning of language models for specialized applications like legal or medical text
- •Customizing vision-language models for specific visual understanding tasks
- •Enterprise deployment of tailored AI models with proprietary data while maintaining model performance