langgraph vs open-webui
Side-by-side comparison of two AI agent tools
langgraphopen-source
Build resilient language agents as graphs.
open-webuifree
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
Metrics
| langgraph | open-webui | |
|---|---|---|
| Stars | 27.8k | 129.1k |
| Star velocity /mo | 2.0k | 2.4k |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 10 |
| Overall score | 0.8044102415616935 | 0.8054298095967891 |
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
- +Multi-provider AI integration supporting both local Ollama models and remote OpenAI-compatible APIs in a single interface
- +Self-hosted deployment with complete offline capability ensuring data privacy and security control
- +Enterprise-grade user management with granular permissions, user groups, and admin controls for organizational deployment
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
- -Requires technical expertise for initial setup and maintenance of Docker/Kubernetes infrastructure
- -Self-hosting demands dedicated server resources and ongoing system administration
- -Limited to local deployment model, lacking the convenience of managed cloud AI services
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
- •Enterprise organizations deploying private AI assistants with strict data governance and user access controls
- •Development teams building local AI workflows with multiple model providers while maintaining code and data privacy
- •Educational institutions providing students and faculty with controlled AI access without external data sharing