langgraph vs quivr
Side-by-side comparison of two AI agent tools
langgraphopen-source
Build resilient language agents as graphs.
quivrfree
Opiniated RAG for integrating GenAI in your apps π§ Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore:
Metrics
| langgraph | quivr | |
|---|---|---|
| Stars | 28.0k | 39.1k |
| Star velocity /mo | 2.5k | 67.5 |
| Commits (90d) | β | β |
| Releases (6m) | 10 | 0 |
| Overall score | 0.8081963872278098 | 0.4264472901167716 |
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
- +LLM-agnostic design supporting multiple providers (OpenAI, Anthropic, Mistral, Gemma) with unified API
- +Extremely simple setup requiring only 5 lines of code to create a working RAG system
- +Flexible file format support with extensible parsers for PDF, TXT, Markdown and custom document types
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
- -Python-only implementation limiting cross-platform development options
- -Requires Python 3.10 or newer, excluding older Python environments
- -Still actively developing core features, indicating potential API instability
Use Cases
- β’Long-running autonomous agents that need to persist through system failures and operate over days or weeks
- β’Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
- β’Stateful agents that must maintain context and memory across multiple sessions and interactions
- β’Integrating document Q&A capabilities into existing Python applications without building RAG from scratch
- β’Building personal knowledge management systems that can query across multiple document formats
- β’Creating AI-powered customer support tools that can answer questions from company documentation