Star Growth
Overview
Cheshire Cat AI is a comprehensive framework for building custom AI agents as microservices. It provides an API-first approach that makes it easy to add conversational capabilities to any application through WebSocket chat and REST API endpoints. The framework comes with built-in RAG (Retrieval Augmented Generation) capabilities using Qdrant vector database, allowing agents to work with custom knowledge bases. Its plugin architecture enables extensibility through Python-based plugins that can implement hooks for event handling and tools for function calling. The system supports any language model via LangChain integration and includes multiuser functionality with granular permissions that can integrate with existing identity providers. Cheshire Cat AI is fully containerized with Docker, making deployment straightforward. It features an intuitive admin panel for managing agents and includes conversational forms for structured interactions. The framework is designed for developers who need to rapidly prototype and deploy AI agents with enterprise-ready features like user management, custom tools, and event-driven architecture.
Deep Analysis
vs LangChain/LlamaIndex: opinionated, ready-to-deploy conversational AI microservice with built-in admin panel, plugin system, and Qdrant RAG — not a framework but a complete product
⚡ Capabilities
- • API-first conversational AI framework as microservice
- • Built-in RAG with Qdrant vector database
- • Extensible plugin system
- • Event callbacks and function calling (tools)
- • Conversational forms for structured data collection
- • Admin panel with user management
- • Multi-user support with granular permissions
- • Any LLM via LangChain integration
🔗 Integrations
✓ Best For
- ✓ Building custom AI assistants as embeddable microservices
- ✓ Teams needing plugin-extensible conversational AI with admin panel
✗ Not Ideal For
- ✗ Projects requiring permissive (MIT/Apache) licensing
- ✗ Simple chatbot needs without plugin/RAG requirements
Languages
Deployment
⚠ Known Limitations
- ⚠ Version 2 in development; v1 only gets bug fixes
- ⚠ GPL3 license restricts proprietary derivative works
- ⚠ Pull requests require prior issue discussion
- ⚠ Qdrant dependency for vector storage
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
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
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