What is Cheshire Cat AI?
Cheshire Cat AI provides a production-ready framework designed for building sophisticated AI agents. This platform allows developers to train their agents using various document types, including PDFs, text files, markdown, JSON, and web pages, enabling personalized knowledge bases.
Built with a microservice-first approach, the framework facilitates easy integration into existing software architectures. It is fully dockerized for straightforward deployment alongside components like reverse proxies, vector databases, and LLM runners. Users can choose between commercial or open Large Language Models (LLMs) and embedders. The platform supports extension through a plugin system, allowing developers to add custom functionalities like hooks, tools (function calling), and forms to manage complex, goal-oriented conversations. An admin panel is included for managing installations, plugins, memory, configurations, and users.
Features
- AI Agent Framework: Build and deploy production-ready AI agents.
- Document Training: Train agents by uploading PDF, TXT, Markdown, JSON files, and web pages.
- External Integration: Easily connect agents to external APIs and applications.
- Model Flexibility: Supports commercial or open LLMs and embedders.
- Dockerized Deployment: 100% dockerized with live reload for easy integration and development.
- Extensible Plugin System: Install community plugins or create custom ones to add hooks, tools, and forms.
- Smart Dialogues: Implement cutting-edge conversational skills with hooks, tools (function calling), and forms.
- Developer Focused: Designed with developer experience in mind, including live reload and extensive APIs.
- Admin Panel: Manage installations, plugins, memory, configurations, and chat interactions.
- API Access: Offers extensive HTTP and WebSocket APIs for microservice integration.
Use Cases
- Developing custom AI agents for specific tasks.
- Building chatbots trained on proprietary documents.
- Integrating conversational AI layers into existing software.
- Creating agents capable of interacting with external tools and APIs.
- Automating complex information gathering through conversational forms.
- Customizing AI agent behavior and responses using hooks.
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