zyx 0.3.1

A hyper-fast, fun, quality-of-life focused & genuinely useful LLM toolkit. Inspired by Marvin-AI. Built on LiteLLM, Instructor & Qdrant.

zyx

zyx 0.3.00 - the first 'release' of the library is out!
All CrewAI & other obstructions have been removed, and the library is back to being lightweight.

zyx is a hyper-fast, fun, & ease-of-use focused Python library for using LLMs.
It was created on top of Instructor and LiteLLM, and focuses to provide an abstraction free framework.
The library uses methods such as lazy-loading to provide a single import for all its features. This library is not meant to be used as a production-ready solution, but rather as a tool to quickly & easily experiment with LLMs.

Some of the key features of zyx include:

• Universal Completion Client : A singular function that handles all LiteLLM compatible models, Pydantic structured outputs, tool calling & execution, prompt optimization, streaming & vision support.
• A Large Collection of LLM Powered Functions : This library is inspired by MarvinAI, and it's quick LLM function style framework and has built upon it vastly.
• Easy to Use Memory (Rag) : A Qdrant wrapper, built to support easy store creation, text/document/pydantic model data insertion, universal embedding provider support, LLM completions for RAG & more.
• Multimodel Generations : Supports generations for images, audio & speech transcription.
• Functional / Easy Access Terminal Client : A terminal client built using textual to allow for easy access to zyx features.
• New Experimental Conversational Multi-Agent Framework : Built from the ground up using Instructor, the agentic framework provides a solution towards conversationally state managed agents, with task creation, custom tool use, artifact creation & more.

Getting Started

Installation

pip install zyx

Generating Completions

Open

The primary module of zyx, is the universal .completion() function. This module is an extensive wrapper around the litellm .completion() function, as well as the Instructor library.

The .completion() function is capable of

• Generations with any LiteLLM compatible model
  • Ollama, OpenAI, Anthropic, Groq, Mistral, and more!
• Direct Instructor Pydantc structured outputs
• Tool calling & execution support. (Get a tool interpretation with one function)
  • zyx provides a few prebuilt tools out of the box
  • Can take in a list of Python functions, OpenAI dictionaries, or Pydantic models as tools!
  • Automatic tool execution if a tool is called through the run_tools parameter
• Streaming
• New Vision support
  • Pass in a list of urls
  • Currently uses multi shot prompting if a response model or tools were also passed.
• New Prompt optimization
  • Creates or optimizes a task tuned system prompt using either the COSTAR or TIDD-EC frameworks automatically.

Standard Completion

# Simplest Way to Generate
# Defaults to "gpt-4o-mini" if no model is provided
from zyx import completion

response = completion("Hi, how are you?")

# Returns a standard OpenAI style response object
print(response.choices[0].message.content)

# OUTPUT
Hello! I'm just a program, but I'm here and ready to help you. How can I assist you today?

Instructor Output

import zyx
from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int

# We can pass in a system prompt to change the behavior of the assistant
response = zyx.completion(
    "Create a mock person",
    response_model = Person
)

# Lets print the full response object
print(response)

print(f"Person Name: '{response.name}', Person Age: '{response.age}'")

# OUTPUT
Person(name='John Doe', age=30)
Person Name: 'John Doe', Person Age: '30'

Tool Calling & Execution

# Lets return a tool call
import zyx

# Lets use the prebuilt web search tool!
response = zyx.completion(
    "Who won the 2024 Euro Cup Final?",
    tools = [zyx.tools.web_search],
    run_tools = True # Set to true to execute tool calls
)

print(response.choices[0].message.content)

# OUTPUT
Spain won the 2024 Euro Cup Final, defeating England 2-1. The decisive goal was scored by substitute Mikel 
Oyarzabal in the 86th minute. This victory marked Spain's fourth European championship title. You can find more 
details about the match (https://en.wikipedia.org/wiki/UEFA_Euro_2024_Final).

LLM Powered Functions | Code Generators

Open

LLM Powered Functions | NLP

Open

LLM Powered Functions | Prompt Optimization

Open

LLM Powered Functions | Reasoning (Research Paper Implmenetations)

Open