Cheshire Cat AI

Cheshire Cat AI is an open-source framework for building custom AI agents as microservices¹⁾. It provides a ready-made architecture with plugin system, long-term memory (episodic, declarative, procedural), built-in RAG, multi-LLM support, and an API-first design – all running in a single Docker container.

ai_agent framework microservice plugin rag memory docker open_source

Cheshire Cat AI lets developers build custom AI agents in minutes rather than months. Instead of wiring together LLMs, vector databases, memory systems, and APIs from scratch, the framework provides all of these as a cohesive microservice²⁾. Developers extend functionality through a simple plugin system using hooks, tools, and forms – no complex OOP required. The framework is 100% Dockerized, API-first, and language-model agnostic.

• Plugin Architecture – Extend with hooks (event handlers), tools (custom functions), and forms; one-click install from community registry
• Long-Term Memory – Episodic (chat history), declarative (uploaded documents), and procedural (tools and plugins) memory
• Working Memory – Temporary session storage for state machines and cross-plugin data sharing
• Built-in RAG – Retrieval-augmented generation using Qdrant vector database³⁾
• Multi-Modal RAG – Build knowledge bases from PDFs, text files, markdown, URLs, and more
• LLM Agnostic – Supports OpenAI, Anthropic, Cohere, Hugging Face, Ollama, vLLM, and custom providers
• API-First – REST and WebSocket endpoints with token streaming; community clients in multiple languages
• Multi-Tenancy – Manage multiple chatbots with separate settings, plugins, and LLMs
• Admin Panel – Web-based configuration for LLMs, embedders, plugins, and memory management
• Live Reload – Development-friendly hot reloading of plugins

Plugins are simple Python folders placed in cat/plugins/. A minimal plugin requires:

• plugin.json – Metadata (name, description, version)
• A Python file with hooks, tools, or forms

# Example: Custom hook to filter responses
from cat.mad_hatter.decorators import hook
 
@hook
def agent_fast_reply(fast_reply, cat):
    if len(cat.working_memory.declarative_memories) == 0:
        fast_reply["output"] = "Sorry, I don't know the answer."
    return fast_reply

# Example: Custom tool
from cat.mad_hatter.decorators import tool
 
@tool
def get_weather(location: str, cat) -> str:
    """Get the current weather for a location."""
    return f"Weather in {location}: Sunny, 22C"

All long-term memories support export/import for backup and migration.

# Quick start with Docker
docker run --rm -it -p 1865:80 ghcr.io/cheshire-cat-ai/core:latest
 
# Access points:
# Admin Panel: http://localhost:1865/admin
# API Docs: http://localhost:1865/docs
# WebSocket: ws://localhost:1865/ws

Cheshire Cat integrates with the broader infrastructure as a microservice:⁴⁾

• Reverse Proxies – Caddy, Nginx, Traefik
• Vector Databases – Qdrant (default), extensible
• LLM Runners – Ollama, vLLM for self-hosted models
• Applications – Django, WordPress, custom apps via REST/WebSocket API

• MobileAgent – GUI agent for mobile and desktop automation
• Plandex – AI coding agent with plan/apply workflow
• gptme – Terminal agent with local tools and RAG