LangGraph vs Spring AI Alibaba
Side-by-side comparison of two AI agent tools
LangGraphopen-source
Build resilient language agents as graphs.
S
Spring AI Alibabaopen-source
Agentic AI Framework for Java Developers
Metrics
| LangGraph | Spring AI Alibaba | |
|---|---|---|
| Stars | 42.5k | 11.0k |
| Star velocity /mo | 2.4k | 912.6666666666666 |
| Commits (90d) | 129 | 58 |
| Releases (6m) | 10 | 1 |
| Overall score | 0.7972844109196278 | 0.5550627769899648 |
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
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
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
FAQ
- Which is more popular, LangGraph or Spring AI Alibaba?
- LangGraph has more GitHub stars (42,525 vs 10,952).
- Which is more actively developed, LangGraph or Spring AI Alibaba?
- LangGraph had more commits in the last 90 days (129 vs 58).
- Should I use LangGraph or Spring AI Alibaba?
- 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.