# Cheshire Cat AI Core: An AI Agent Microservice Framework

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Cheshire Cat AI Core is an open-source framework designed for building custom AI agents as microservices. It offers an API-first approach, enabling easy integration of conversational layers into applications with WebSocket chat and a customizable REST API. Key features include built-in RAG with Qdrant, extensibility via plugins, function calling, and full Dockerization for straightforward deployment.

GitHub: https://github.com/cheshire-cat-ai/core
OSRepos URL: https://osrepos.com/repo/cheshire-cat-ai-core

## Summary

Cheshire Cat AI Core is an open-source framework designed for building custom AI agents as microservices. It offers an API-first approach, enabling easy integration of conversational layers into applications with WebSocket chat and a customizable REST API. Key features include built-in RAG with Qdrant, extensibility via plugins, function calling, and full Dockerization for straightforward deployment.

## Topics

- AI Agent
- Microservice
- Python
- LLM
- Chatbot
- Framework
- Docker
- RAG

## Repository Information

Last analyzed by OSRepos: Sun Feb 15 2026 00:00:47 GMT+0000 (Western European Standard Time)
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## Content

## Introduction

Cheshire Cat AI Core is a robust, API-first framework for developing custom AI agents as microservices. Written in Python, it provides a comprehensive toolkit for integrating conversational AI capabilities into any application. The framework supports WebSocket for chat interactions and offers a customizable REST API for agent management, making it highly versatile for various use cases.

Key features include built-in Retrieval Augmented Generation (RAG) with Qdrant, a powerful plugin system for extensibility, and support for event callbacks, function calling (tools), and conversational forms. It also boasts an easy-to-use admin panel, multi-user support with granular permissions, and compatibility with any identity provider and language model via Langchain. The entire system is 100% Dockerized for effortless deployment.

## Installation

Getting Cheshire Cat AI Core up and running on your machine is straightforward, requiring only [Docker](https://docs.docker.com/get-docker/ "Docker") to be installed.

To quickly start the latest version, use the following command:

bash
docker run --rm -it -p 1865:80 ghcr.io/cheshire-cat-ai/core:latest


Once running, you can interact with the Cheshire Cat:

*   Chat with the Cheshire Cat on [localhost:1865/admin](http://localhost:1865/admin "Admin Panel")
*   Explore the REST API on [localhost:1865/docs](http://localhost:1865/docs "REST API Docs")

For a more persistent setup with Docker Compose and volumes, refer to the [official documentation](https://cheshire-cat-ai.github.io/docs/quickstart/installation-configuration/ "Installation and Configuration").

## Examples

Cheshire Cat AI Core provides powerful mechanisms for customizing agent behavior, including hooks, tools, and conversational forms.

### Hooks (Events)

Hooks are an event system that allows for fine-grained control over your assistant's behavior at various stages of a conversation.

python
from cat.mad_hatter.decorators import hook

@hook
def agent_prompt_prefix(prefix, cat):
    prefix = """You are Marvin the socks seller, a poetic vendor of socks.\nYou are an expert in socks, and you reply with exactly one rhyme.\n"""
    return prefix


### Tools (Function Calling)

Inspired by Langchain, tools enable your AI agent to perform specific actions or retrieve information by calling external functions.

python
from cat.mad_hatter.decorators import tool

@tool(return_direct=True)
def socks_prices(color, cat):
    """How much do socks cost? Input is the sock color."""
    prices = {
        "black": 5,
        "white": 10,
        "pink": 50,
    }

    price = prices.get(color, 0)
    return f"{price} bucks, meeeow!" 


### Conversational Forms

Conversational forms allow you to guide goal-oriented conversations, collecting specific data from the user in a structured manner.

python
from pydantic import BaseModel
from cat.experimental.form import form, CatForm

# data structure to fill up
class PizzaOrder(BaseModel):
    pizza_type: str
    phone: int

# forms let you control goal oriented conversations
@form
class PizzaForm(CatForm):
    description = "Pizza Order"
    model_class = PizzaOrder
    start_examples = [
        "order a pizza!",
        "I want pizza"
    ]
    stop_examples = [
        "stop pizza order",
        "not hungry anymore",
    ]
    ask_confirm = True

    def submit(self, form_data):
        
        # do the actual order here!

        # return to convo
        return {
            "output": f"Pizza order on its way: {form_data}"
        }


## Why Use Cheshire Cat AI Core?

Cheshire Cat AI Core stands out as an excellent choice for building AI agents due to several compelling reasons:

*   **API-First Design:** Easily integrate conversational layers into existing applications with its robust WebSocket and REST APIs.
*   **Extensibility:** Customize and extend functionality through a powerful plugin system, hooks, and function calling capabilities.
*   **Built-in RAG:** Leverage integrated Retrieval Augmented Generation with Qdrant for enhanced knowledge retrieval and context awareness.
*   **LLM Agnostic:** Supports any language model via Langchain, offering flexibility in choosing your preferred AI backend.
*   **Simplified Deployment:** Being 100% Dockerized, it ensures consistent and easy deployment across different environments.
*   **Active Community:** Benefit from an active [Discord community](https://discord.gg/bHX5sNFCYU "Discord Server") and comprehensive [documentation](https://cheshire-cat-ai.github.io/docs/ "Official Documentation").

## Links

Explore more about Cheshire Cat AI Core through these official resources:

*   [Official Documentation](https://cheshire-cat-ai.github.io/docs/ "Official Documentation")
*   [Discord Server](https://discord.gg/bHX5sNFCYU "Discord Server")
*   [Website](https://cheshirecat.ai/ "Cheshire Cat AI Website")
*   [Tutorial - Write your first plugin](https://cheshirecat.ai/write-your-first-plugin/ "Plugin Tutorial")