Cheshire Cat AI vs RAGapp
Side-by-side comparison of two AI agent tools
Short answer
- RAGapp has had no commit in 23 months; Cheshire Cat AI is actively maintained (13 commits in the last 90 days).
- Cheshire Cat AI is growing faster: +14 GitHub stars in the last 30 days vs +6 for RAGapp.
- Pick Cheshire Cat AI for: aI agent microservice. Pick RAGapp for: the easiest way to use Agentic RAG in any enterprise.
From GitHub data refreshed daily.
Cheshire Cat AIopen-source
AI agent microservice
RAGappopen-source
The easiest way to use Agentic RAG in any enterprise
Metrics
| Cheshire Cat AI | RAGapp | |
|---|---|---|
| Stars | 3.1k | 4.4k |
| Star velocity /mo | 14.36842105263158 | 5.842105263157895 |
| Commits (90d) | 13 | 0 |
| Releases (6m) | 5 | 0 |
| Downloads (30d, npm + PyPI) | 210 | — |
| Overall score | 0.3830458573070783 | 0.1851795233490897 |
Pros
- +Complete microservice architecture with WebSocket and REST API support makes integration seamless
- +Built-in RAG with Qdrant vector database provides out-of-the-box knowledge management capabilities
- +Extensive plugin system with hooks and tools allows deep customization of agent behavior
- +Zero-config Docker deployment with comprehensive UI stack (admin, chat, API) included out of the box
- +Enterprise-grade architecture supporting both cloud and on-premises models with built-in vector database integration
- +Production-ready with pre-built Docker Compose templates for common scenarios like Ollama + Qdrant deployment
Cons
- -Requires Docker knowledge and infrastructure for deployment and management
- -Python-only plugin development may limit accessibility for teams using other languages
- -Complexity of features may create a steep learning curve for simple chatbot use cases
- -No built-in authentication layer - requires external API gateway or proxy for user management
- -Limited customization of UI components compared to building a custom solution
- -Authorization features are still in development for access control based on user tokens
Use Cases
- •Adding conversational AI capabilities to existing web applications through API integration
- •Building knowledge-aware customer support bots that can query internal documentation
- •Creating specialized AI agents with custom tools and workflows for business process automation
- •Enterprise document search systems where teams need to query internal knowledge bases with natural language
- •Customer support automation where agents need instant access to product documentation and policies
- •Research and development environments where scientists need to search through technical papers and reports
FAQ
- Which is more popular, Cheshire Cat AI or RAGapp?
- RAGapp has more GitHub stars (4,447 vs 3,094).
- Which is more actively developed, Cheshire Cat AI or RAGapp?
- Cheshire Cat AI had more commits in the last 90 days (13 vs 0).
- Should I use Cheshire Cat AI or RAGapp?
- 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.