core
Production ready AI agent framework
Stars: 2304
The Cheshire Cat is a framework for building custom AIs on top of any language model. It provides an API-first approach, making it easy to add a conversational layer to your application. The Cat remembers conversations and documents, and uses them in conversation. It is extensible via plugins, and supports event callbacks, function calling, and conversational forms. The Cat is easy to use, with an admin panel that allows you to chat with the AI, visualize memory and plugins, and adjust settings. It is also production-ready, 100% dockerized, and supports any language model.
README:
The Cheshire Cat is a framework to build custom AIs on top of any language model. If you have ever used systems like WordPress or Django to build web apps, imagine the Cat as a similar tool, but specific for AI.
To make Cheshire Cat run on your machine, you just need docker
installed:
docker run --rm -it -p 1865:80 ghcr.io/cheshire-cat-ai/core:latest
- Chat with the Cheshire Cat on localhost:1865/admin.
- You can also interact via REST API and try out the endpoints on localhost:1865/docs
As a first thing, the Cat will ask you to configure your favourite language model. It can be done directly via the interface in the Settings page (top right in the admin).
Enjoy the Cat!
Follow instructions on how to run it with docker compose and volumes.
from cat.mad_hatter.decorators import tool, hook
# hooks are an event system to get finegraned control over your assistant
@hook
def agent_prompt_prefix(prefix, cat):
prefix = """You are Marvin the socks seller, a poetic vendor of socks.
You are an expert in socks, and you reply with exactly one rhyme.
"""
return prefix
# langchain inspired tools (function calling)
@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!"
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}"
}
- Official Documentation
- Discord Server
- Website
- YouTube tutorial - How to install
- Tutorial - Write your first plugin
- โก๏ธ API first, so you get a microservice to easily add a conversational layer to your app
- ๐ Remembers conversations and documents and uses them in conversation
- ๐ Extensible via plugins (public plugin registry + private plugins allowed)
- ๐ Event callbacks, function calling (tools), conversational forms
- ๐ Easy to use admin panel (chat, visualize memory and plugins, adjust settings)
- ๐ Supports any language model (works with OpenAI, Google, Ollama, HuggingFace, custom services)
- ๐ Production ready - 100% dockerized
- ๐ฉโ๐งโ๐ฆ Active Discord community and easy to understand docs
We are committed to openness, privacy and creativity, we want to bring AI to the long tail. If you want to know more about our vision and values, read the Code of Ethics.
Detailed roadmap is here.
Send your pull request to the develop
branch. Here is a full guide to contributing.
Join our community on Discord and give the project a star โญ! Thanks again!๐
"Would you tell me, please, which way I ought to go from here?"
"That depends a good deal on where you want to get to," said the Cat.
"I don't much care where--" said Alice.
"Then it doesn't matter which way you go," said the Cat.
(Alice's Adventures in Wonderland - Lewis Carroll)
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