langgraph vs llmflows
Side-by-side comparison of two AI agent tools
langgraphopen-source
Build resilient language agents as graphs.
llmflowsopen-source
LLMFlows - Simple, Explicit and Transparent LLM Apps
Metrics
| langgraph | llmflows | |
|---|---|---|
| Stars | 28.0k | 708 |
| Star velocity /mo | 2.5k | 7.5 |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 0 |
| Overall score | 0.8081963872278098 | 0.34439655184814355 |
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
- +Complete transparency with no hidden prompts or LLM calls, making debugging and monitoring straightforward
- +Minimalistic design with clear abstractions that don't compromise on flexibility or capabilities
- +Explicit API design that promotes clean, readable code and easy maintenance of complex LLM workflows
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
- -Relatively small community with 707 GitHub stars, which may limit community support and resources
- -Minimalistic approach might require more manual setup compared to more feature-rich frameworks
- -Limited built-in integrations compared to larger LLM frameworks, requiring more custom implementation
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
- •Building transparent chatbots where every LLM interaction needs to be traceable and debuggable
- •Creating question-answering systems that combine multiple LLMs with vector stores for document retrieval
- •Developing AI agents with complex multi-step workflows that require explicit control over each LLM call