langgraph vs private-gpt
Side-by-side comparison of two AI agent tools
langgraphopen-source
Build resilient language agents as graphs.
private-gptopen-source
Interact with your documents using the power of GPT, 100% privately, no data leaks
Metrics
| langgraph | private-gpt | |
|---|---|---|
| Stars | 28.0k | 57.2k |
| Star velocity /mo | 2.5k | -30 |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 0 |
| Overall score | 0.8081963872278098 | 0.28879155410393564 |
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 data privacy with 100% local processing and no external data transmission
- +Production-ready with comprehensive API following OpenAI standards and streaming support
- +Flexible architecture offering both high-level RAG pipeline and low-level API for custom implementations
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 significant local compute resources to run LLMs effectively
- -Setup complexity may be challenging for non-technical users
- -Limited to documents that can be processed and stored locally
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 document analysis for regulated industries requiring complete data privacy
- •Offline research and document querying in environments without internet connectivity
- •Building custom AI applications with contextual document understanding without cloud dependencies